- a() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.QuadraticSyntheticDivision
-
Get a as in the remainder (b * (x + u) + a).
- A(int, double) - Method in interface com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction.Partials
-
Compute an.
- a() - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
-
Get a.
- A() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LSProblem
-
Get the homogeneous part, the coefficient matrix, of the linear system.
- A() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearConstraints
-
Get the constraint coefficients.
- A - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
This is either [A] or
[ A]
[-C]
- A(int) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPDualProblem
-
Get Ai.
- A(int) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPPrimalProblem
-
Get Ai.
- A(int) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Get Ai.
- A() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
\[
A = [A_1, A_2, ...
- A() - Method in interface com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Get the coefficients, A, of the greater-than-or-equal-to constraints A * x ≥ b.
- A() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- A() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Get the coefficients of the inequality constraints: A as in \(Ax \geq b\).
- a - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- A() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
- A() - Method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Get the state transition probabilities.
- A - Variable in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
the design matrix, the regressors, including the intercept if any; each column corresponds to one regressor
- A - Variable in class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159.RandomMatrix
-
a random matrix constructed
- a0() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the constant term.
- AbelianGroup<G> - Interface in com.numericalmethod.suanshu.mathstructure
-
An Abelian group is a group with a binary additive operation (+), satisfying the group axioms:
closure
associativity
existence of additive identity
existence of additive opposite
commutativity of addition
- abs(double[]) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the absolute values.
- absoluteError(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Compute the absolute difference between x1 and x0.
- AbsoluteError - Class in com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod
-
This penalty function sums up the absolute error penalties.
- AbsoluteError(EqualityConstraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.AbsoluteError
-
Construct an absolute error penalty function from a collection of equality constraints.
- AbsoluteError(EqualityConstraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.AbsoluteError
-
Construct an absolute error penalty function from a collection of equality constraints.
- AbsoluteError(EqualityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.AbsoluteError
-
Construct an absolute error penalty function from a collection of equality constraints.
- AbsoluteTolerance - Class in com.numericalmethod.suanshu.algorithm.iterative.tolerance
-
The stopping criteria is that the norm of the residual r is equal to
or smaller than the specified tolerance, that is,
||r||2 ≤ tolerance
- AbsoluteTolerance() - Constructor for class com.numericalmethod.suanshu.algorithm.iterative.tolerance.AbsoluteTolerance
-
- AbsoluteTolerance(double) - Constructor for class com.numericalmethod.suanshu.algorithm.iterative.tolerance.AbsoluteTolerance
-
Construct an instance with specified tolerance.
- acos(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Inverse of cosine.
- ActiveList - Interface in com.numericalmethod.suanshu.algorithm.bb
-
This interface defines the node popping strategy used in a branch-and-bound algorithm, e.g., depth-first-search, best-first-search.
- add(BBNode) - Method in interface com.numericalmethod.suanshu.algorithm.bb.ActiveList
-
Add a node to the active list.
- add(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- add(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
Dynamically add points to the step function.
- add(Interval<T>) - Method in class com.numericalmethod.suanshu.interval.Intervals
-
Add an interval to the set.
- add(Interval<T>...) - Method in class com.numericalmethod.suanshu.interval.Intervals
-
Add intervals to the set.
- add(G) - Method in interface com.numericalmethod.suanshu.mathstructure.AbelianGroup
-
+ : G × G → G
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- add(Matrix) - Method in interface com.numericalmethod.suanshu.matrix.doubles.MatrixRing
-
this + that
- add(DenseData) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
-
Add up the elements in this and that, element-by-element.
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Compute the sum of two diagonal matrices.
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- add(MatrixAccess, MatrixAccess) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
A1 + A2
- add(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.ParallelMatrixMathOperation
-
- add(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- add(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- add(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
- add(ComplexMatrix) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- add(GenericMatrix<F>) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
-
- add(RealMatrix) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- add(Complex) - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- add(double) - Method in class com.numericalmethod.suanshu.number.Counter
-
Add a number to the counter.
- add(double...) - Method in class com.numericalmethod.suanshu.number.Counter
-
Add numbers to the counter.
- add(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
-
- add(double[], double) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Adds a double value to each element in an array.
- add(double[], double[]) - Method in interface com.numericalmethod.suanshu.number.doublearray.DoubleArrayOperation
-
Add two double arrays.
- add(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.ParallelDoubleArrayOperation
-
- add(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.SimpleDoubleArrayOperation
-
- add(Real) - Method in class com.numericalmethod.suanshu.number.Real
-
- add(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- add(double) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- add(Vector, Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- add(Vector, double) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- add(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- add(double) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- add(Vector) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
\(this + that\)
- add(double) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Add a constant to all entries in this vector.
- addColAt(int, Object) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Add a column at i.
- addColAt(int) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Add a column at i.
- addColumn(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
Column addition:
A[, j1] = A[, j1] + c * A[, j2]
- addData(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.interpolation.LinearInterpolator
-
- addData(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable
-
- addData(OrderedPairs) - Method in interface com.numericalmethod.suanshu.analysis.interpolation.OnlineInterpolator
-
Add more points for interpolation.
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.Covariance
-
Update the covariance statistic with more data.
- addData(double[][]) - Method in class com.numericalmethod.suanshu.stats.descriptive.Covariance
-
Update the covariance statistic with more data.
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Quantile
-
- addData(double...) - Method in interface com.numericalmethod.suanshu.stats.descriptive.Statistic
-
Recompute the statistic with more data, incrementally if possible.
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.SynchronizedStatistic
-
- addIntercept - Variable in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
true iff to add an intercept term to the linear regression
- addIterate(S) - Method in class com.numericalmethod.suanshu.algorithm.iterative.monitor.CountMonitor
-
- addIterate(S) - Method in class com.numericalmethod.suanshu.algorithm.iterative.monitor.IteratesMonitor
-
- addIterate(S) - Method in interface com.numericalmethod.suanshu.algorithm.iterative.monitor.IterationMonitor
-
Record a new iteration state.
- addIterate(S) - Method in class com.numericalmethod.suanshu.algorithm.iterative.monitor.NullMonitor
-
- addIterate(Vector) - Method in class com.numericalmethod.suanshu.algorithm.iterative.monitor.VectorMonitor
-
- AdditiveModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess
-
The additive model of a time series is an additive composite of the trend, seasonality and irregular random components.
- AdditiveModel(double[], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.AdditiveModel
-
Construct a univariate time series by adding up the components.
- AdditiveModel(double[], double[], RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.AdditiveModel
-
Construct a univariate time series by adding up the components.
- addRow(double, double[]) - Method in class com.numericalmethod.suanshu.datastructure.MathTable
-
Add a row to the table.
- addRow(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
Row addition:
A[i1, ] = A[i1, ] + c * A[i2, ]
- addRowAt(int, Object) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Add a row at i.
- addRowAt(int) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Add a row at i.
- addRows(double[][]) - Method in class com.numericalmethod.suanshu.datastructure.MathTable
-
Add rows by a double[][].
- ADFAsymptoticDistribution - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
This class computes the asymptotic distribution of the augmented Dickey-Fuller (ADF) test statistics.
- ADFAsymptoticDistribution(AugmentedDickeyFuller.TrendType, int, int, long) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution
-
Construct the asymptotic distribution for the augmented Dickey-Fuller test statistics.
- ADFAsymptoticDistribution(AugmentedDickeyFuller.TrendType) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution
-
Construct the asymptotic distribution for the augmented Dickey-Fuller test statistics.
- ADFAsymptoticDistribution1 - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
- ADFAsymptoticDistribution1(ADFAsymptoticDistribution1.Type) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution1
-
Deprecated.
Construct the asymptotic distribution for the Augmented Dickey Fuller test statistics.
- ADFAsymptoticDistribution1(int, int, ADFAsymptoticDistribution1.Type, long) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution1
-
Deprecated.
Construct the asymptotic distribution for the Augmented Dickey Fuller test statistics.
- ADFAsymptoticDistribution1.Type - Enum in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
Deprecated.
the types of Dickey-Fuller tests available
- ADFDistribution - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
- ADFFiniteSampleDistribution - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
This class computes the finite sample distribution of the augmented Dickey-Fuller (ADF) test statistics.
- ADFFiniteSampleDistribution(int, AugmentedDickeyFuller.TrendType, boolean, int, int, int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
Construct the finite sample distribution for the augmented Dickey-Fuller test statistics.
- ADFFiniteSampleDistribution(int, AugmentedDickeyFuller.TrendType, boolean, int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
Construct the finite sample distribution for the augmented Dickey-Fuller test statistics.
- ADFFiniteSampleDistribution(int, AugmentedDickeyFuller.TrendType) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
Construct the finite sample distribution for the augmented Dickey-Fuller test statistics.
- ADFFiniteSampleDistribution(int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
Construct the finite sample distribution for the augmented Dickey-Fuller test statistics.
- Aeq() - Method in interface com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Get the coefficients, Aeq, of the equality constraints Aeq * x ≥ beq.
- Aeq() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- Aeq() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Get the coefficients of the equality constraints: Aeq as in \(A_{eq}x = b_{eq}\).
- Aeq() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
- AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
-
- AIC(Vector, Vector, Vector, double, double, int) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.ExponentialDistribution
-
AIC = 2 * #param - 2 * log-likelihood
- AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gamma
-
- AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gaussian
-
- AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
-
- AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Poisson
-
- AIC - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
-
- AIC - Variable in class com.numericalmethod.suanshu.stats.regression.linear.logistic.Logistic
-
the AIC
- AIC - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.InformationCriteria
-
Akaike information criterion
- AIC() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFitting
-
Compute the AIC of model fitting.
- AIC() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Compute the AIC, a model selection criterion.
- AICC() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFitting
-
Compute the AICC of model fitting.
- AICC() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Compute the AICC, a model selection criterion.
- ak - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.QuasiNewton.QuasiNewtonImpl
-
the increment in the search direction
- algebraicMultiplicity() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.EigenProperty
-
Get the multiplicity of the eigenvalue (a root) of the characteristic
polynomial.
- AllIntegers() - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.AllIntegers
-
- alpha(Vector, Vector, Vector) - Method in interface com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation
-
Get the percentage increment along the minimizer increment direction.
- alpha(Vector, Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- alpha(Vector, Vector, Vector, Vector) - Method in interface com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPASVariation
-
Get the percentage increment along the minimizer increment direction.
- alpha(Vector, Vector, Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPASVariation1
-
Get the percentage increment along the minimizer increment direction.
- alpha() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Get the set of adjusting coefficients, by columns.
- alpha - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaDistribution.Lambda
-
α: the shape parameter
- alpha() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the ARCH coefficients.
- angle(H) - Method in interface com.numericalmethod.suanshu.mathstructure.HilbertSpace
-
∠ : H × H → F
Inner product formalizes the geometrical notions such as the length of a vector and the angle between two vectors.
- angle(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- angle(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- angle(Vector, Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- angle(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- angle(Vector) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Measure the angle, \(\theta\), between this and that.
- AR(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Get the i-th AR coefficient; AR(0) = 1.
- AR() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Get the AR coefficients, excluding the initial 1.
- AR - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
the AR coefficients
- AR(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the i-th AR coefficient; AR(0) = 1.
- AR() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the AR coefficients, excluding the initial 1.
- AR2 - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
-
diagnostic measure: the adjusted R-squared
- AreMatrices - Class in com.numericalmethod.suanshu.matrix.doubles
-
These are the boolean operators that take two or more matrices or vectors and compare their properties.
- arg() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Get the θ of the complex number in polar representation.
- ARIMAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima
-
This class represents a multivariate ARIMA model.
- ARIMAModel(Vector, Matrix[], int, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAModel
-
Construct a multivariate ARIMA model.
- ARIMAModel(Vector, Matrix[], int, Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAModel
-
Construct a multivariate ARIMA model with unit variance.
- ARIMAModel(Matrix[], int, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAModel
-
Construct a zero-intercept (mu) multivariate ARIMA model.
- ARIMAModel(Matrix[], int, Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAModel
-
Construct a zero-intercept (mu) multivariate ARIMA model with unit variance.
- ARIMAModel(ARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAModel
-
Copy constructor.
- ARIMAModel(ARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAModel
-
Cast a univariate ARIMA model to a multivariate model.
- ARIMAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
-
This class represents an ARIMA model.
- ARIMAModel(double, double[], int, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a univariate ARIMA model.
- ARIMAModel(double, double[], int, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a univariate ARIMA model with unit variance.
- ARIMAModel(double[], int, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a zero-intercept (mu) univariate ARIMA model.
- ARIMAModel(double[], int, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a zero-intercept (mu) univariate ARIMA model with unit variance.
- ARIMAModel(ARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
-
Copy constructor.
- ARIMASim - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima
-
This class generates simulations of a multivariate ARIMA model.
- ARIMASim(int, ARIMAModel, long) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMASim
-
Simulate a multivariate ARIMA process.
- ARIMASim(int, ARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMASim
-
Simulate a multivariate ARIMA process.
- ARIMASim - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
-
This class simulates the ARIMA models.
- ARIMASim(int, ARIMAModel, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMASim
-
Simulate an ARIMA model.
- ARIMASim(int, ARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMASim
-
Simulate an ARIMA model.
- ARIMAXModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima
-
This class represents a multivariate ARIMAX (ARIMA model with eXogenous inputs) model.
- ARIMAXModel(Vector, Matrix[], int, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Construct a multivariate ARIMAX (ARIMA model with eXogenous inputs) model.
- ARIMAXModel(Vector, Matrix[], int, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Construct a multivariate ARIMAX model with unit variance.
- ARIMAXModel(Matrix[], int, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Construct a zero-intercept (mu) multivariate ARIMAX model.
- ARIMAXModel(Matrix[], int, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Construct a zero-intercept (mu) multivariate ARIMAX model with unit variance.
- ARIMAXModel(ARIMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Copy constructor.
- ARIMAXModel(ARIMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Cast a univariate ARIMAX model to a multivariate model.
- ARIMAXModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
-
This class represents a univariate ARIMAX (ARIMA model with eXogenous inputs) model.
- ARIMAXModel(double, double[], int, double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Construct a univariate ARIMAX (ARIMA model with eXogenous inputs) model.
- ARIMAXModel(double, double[], int, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Construct a univariate ARIMAX model with unit variance.
- ARIMAXModel(double[], int, double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Construct a zero-intercept (mu) univariate ARIMAX model.
- ARIMAXModel(double[], int, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Construct a zero-intercept (mu) univariate ARIMAX model with unit variance.
- ARIMAXModel(ARIMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Copy constructor.
- ARMAFitting - Interface in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
This interface represents a fitting method for estimating φ, θ, μ and σ^2 in an ARMA model.
- armaMean(Matrix, Matrix) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAModel
-
Compute the multivariate ARMA conditional mean.
- armaMean(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Compute the univariate ARMA conditional mean.
- armaMeanNoIntercept(Matrix, Matrix) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAModel
-
Compute the zero-intercept (mu) multivariate ARMA conditional mean.
- armaMeanNoIntercept(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Compute the zero-intercept (mu) univariate ARMA conditional mean.
- ARMAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a multivariate ARMA model.
- ARMAModel(Vector, Matrix[], Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAModel
-
Construct a multivariate ARMA model.
- ARMAModel(Vector, Matrix[], Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAModel
-
Construct a multivariate ARMA model with unit variance.
- ARMAModel(Matrix[], Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAModel
-
Construct a zero-intercept (mu) multivariate ARMA model.
- ARMAModel(Matrix[], Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAModel
-
Construct a zero-intercept (mu) multivariate ARMA model with unit variance.
- ARMAModel(ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAModel
-
Copy constructor.
- ARMAModel(ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAModel
-
Cast a univariate ARMA model to a multivariate model.
- ARMAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
This class represents a univariate ARMA model.
- ARMAModel(double, double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Construct a univariate ARMA model.
- ARMAModel(double, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Construct a univariate ARMA model with unit variance.
- ARMAModel(double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Construct a zero-intercept (mu) univariate ARMA model.
- ARMAModel(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Construct a zero-intercept (mu) univariate ARMA model with unit variance.
- ARMAModel(ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Copy constructor.
- armaxMean(Matrix, Matrix, Vector) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAXModel
-
Compute the multivariate ARMAX conditional mean.
- armaxMean(double[], double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Compute the univariate ARMAX conditional mean.
- armaxMeanNoIntercept(Matrix, Matrix, Vector) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAXModel
-
Compute the zero-intercept (mu) multivariate ARMAX conditional mean.
- armaxMeanNoIntercept(double[], double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Compute the zero-intercept (mu) univariate ARMAX conditional mean.
- ARMAXModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a multivariate ARMAX (ARMA model with eXogenous inputs) model.
- ARMAXModel(Vector, Matrix[], Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAXModel
-
Construct a multivariate ARMAX (ARMA model with eXogenous inputs) model.
- ARMAXModel(Vector, Matrix[], Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAXModel
-
Construct a multivariate ARMAX model with unit variance.
- ARMAXModel(Matrix[], Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAXModel
-
Construct a zero-intercept (mu) multivariate ARMAX model.
- ARMAXModel(Matrix[], Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAXModel
-
Construct a zero-intercept (mu) multivariate ARMAX model with unit variance.
- ARMAXModel(ARMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAXModel
-
Copy constructor.
- ARMAXModel(ARMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARMAXModel
-
Cast a univariate ARMAX model to a multivariate model.
- ARMAXModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
This class represents a univariate ARMAX (ARMA model with eXogenous inputs) model.
- ARMAXModel(double, double[], double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Construct a univariate ARMAX (ARMA model with eXogenous inputs) model.
- ARMAXModel(double, double[], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Construct a univariate ARMAX model with unit variance.
- ARMAXModel(double[], double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Construct a zero-intercept (mu) univariate ARMAX model.
- ARMAXModel(double[], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Construct a zero-intercept (mu) univariate ARMAX model with unit variance.
- ARMAXModel(ARMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Copy constructor.
- ARModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a VAR model.
- ARModel(Vector, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARModel
-
Construct a VAR model.
- ARModel(Vector, Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARModel
-
Construct a VAR model with unit variance.
- ARModel(Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARModel
-
Construct a zero-intercept (mu) VAR model.
- ARModel(Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARModel
-
Construct a zero-intercept (mu) VAR model with unit variance.
- ARModel(ARModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARModel
-
Copy constructor.
- ARModel(ARModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.ARModel
-
Cast a univariate AR model to a multivariate model.
- ARModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
This class represents an AR model.
- ARModel(double, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a univariate AR model.
- ARModel(double, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a univariate AR model with unit variance.
- ARModel(double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a zero-intercept (mu) univariate AR model.
- ARModel(double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a zero-intercept (mu) univariate AR model with unit variance.
- ARModel(ARModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Copy constructor.
- ARTIFICIAL - Static variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- ARTIFICIAL_COST - Static variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- AS159 - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson
-
Algorithm AS 159 accepts a table shape (the number of rows and columns), and two vectors, the lists of row and column sums.
- AS159(int[], int[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159
-
Construct a random table generator according to row and column totals.
- AS159(int[], int[], RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159
-
Construct a random table generator according to row and column totals.
- AS159.RandomMatrix - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson
-
a random matrix generated by AS159 and its probability
- asArray() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
-
Cast this data structure as a double[].
- asin(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Inverse of sine.
- assertArgument(boolean, String, Object...) - Static method in class com.numericalmethod.suanshu.misc.SuanShuUtils
-
Check if an argument condition is satisfied.
- assertOrThrow(RuntimeException) - Static method in class com.numericalmethod.suanshu.misc.SuanShuUtils
-
This is a wrapper method that throws a RuntimeException if it is not null.
- asymptoticCDF(double) - Static method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
the asymptotic distribution of the KolmogorovDistribution distribution
- asymptoticCDF(double, double) - Static method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
the asymptotic distribution of the one-sided Kolmogorov distribution
- atan(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Inverse of tangent.
- AugmentedDickeyFuller - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
The Augmented Dickey Fuller test tests whether a one-time differencing (d = 1) will make the time series stationary.
- AugmentedDickeyFuller(double[], AugmentedDickeyFuller.TrendType, int, ADFDistribution) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
-
Perform the Augmented Dickey-Fuller test statistics to test for the existence of uniroot.
- AugmentedDickeyFuller(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
-
Perform the Augmented Dickey-Fuller test statistics to test for the existence of uniroot.
- AugmentedDickeyFuller.TrendType - Enum in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
the three versions of augmented Dickey-Fuller (ADF) test
- AutoCorrelation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
Compute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that
EXt = 0.
- AutoCorrelation(ARIMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.AutoCorrelation
-
Compute the auto-correlation function of a vector ARMA model.
- AutoCorrelation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample
-
This computes the sample Auto-Correlation Function (ACF) for a univariate data set.
- AutoCorrelation(TimeSeries, AutoCovariance.Type) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCorrelation
-
- AutoCorrelation(TimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCorrelation
-
- AutoCorrelation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that
EXt = 0.
- AutoCorrelation(ARIMAModel, double, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
-
Compute the auto-correlation function of an ARMA model.
- AutoCorrelationFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate
-
This class represents an auto-correlation function for a multi-dimensional time series {Xt}.
- AutoCorrelationFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.AutoCorrelationFunction
-
- AutoCorrelationFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate
-
This class represents an auto-correlation function for a univariate time series {xt},
- AutoCorrelationFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCorrelationFunction
-
- AutoCovariance - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
Compute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that
EXt = 0.
- AutoCovariance(ARIMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.AutoCovariance
-
Compute the auto-covariance function of a vector ARMA model.
- AutoCovariance - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample
-
This computes the sample Auto-Covariance Function (ACVF) for a univariate data set.
- AutoCovariance(TimeSeries, AutoCovariance.Type) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCovariance
-
- AutoCovariance(TimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCovariance
-
- AutoCovariance - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
Compute the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model, assuming that
EXt = 0.
- AutoCovariance(ARIMAModel, double, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
-
Compute the auto-covariance function of an ARMA model.
- AutoCovariance.Type - Enum in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample
-
- AutoCovarianceFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate
-
This class represents an auto-covariance function for a multi-dimensional time series {Xt},
K(i, j) = E((Xi - μi) * (Xj - μj)')
- AutoCovarianceFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.AutoCovarianceFunction
-
- AutoCovarianceFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate
-
This class represents an auto-covariance function for a univariate time series {xt},
- AutoCovarianceFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCovarianceFunction
-
- autoEpsilon(double...) - Static method in class com.numericalmethod.suanshu.misc.SuanShuUtils
-
Guess a reasonable precision parameter.
- autoEpsilon(double[]...) - Static method in class com.numericalmethod.suanshu.misc.SuanShuUtils
-
Guess a reasonable precision parameter.
- autoEpsilon(MatrixTable) - Static method in class com.numericalmethod.suanshu.misc.SuanShuUtils
-
Guess a reasonable precision parameter.
- b() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.QuadraticSyntheticDivision
-
Get b as in the remainder (b * (x + u) + a).
- B(int, double) - Method in interface com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction.Partials
-
Compute bn.
- b() - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
-
Get b.
- B() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.diagonalization.BiDiagonalization
-
Get B, which is the square upper part of U.t().multiply(A).multiply(V).
- b() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LSProblem
-
Get the non-homogeneous part, the right-hand side vector, of the linear
system.
- B() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.Pow
-
Get the double precision matrix.
- b() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearConstraints
-
Get the constraint values.
- b() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPDualProblem
-
Get b.
- b() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Get b.
- b() - Method in interface com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Get the values, b, of the greater-than-or-equal-to constraints A * x ≥ b.
- b() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- B - Static variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- b() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Get the values of the inequality constraints: b as in \(Ax \geq b\).
- b() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
- B() - Method in class com.numericalmethod.suanshu.stats.hmm.rabiner.HiddenMarkovModel
-
Get the conditional probabilities of the observation symbols:
rows correspond to state; columns corresponds symbols.
- B(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
-
Get the Brownian motion value at time t.
- backSearch(Matrix, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.Hessenberg
-
Find H22 such that H22 is the largest unreduced Hessenberg sub-matrix.
- backward(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep
-
Perform a backward sweep.
- Backward - Class in com.numericalmethod.suanshu.stats.regression.linear.modelselection
-
To construct a GLM getModel for a set of observations using the backward selection method,
we first assume that all getFactors are included in the getModel.
- Backward(GLMProblem, double) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.modelselection.Backward
-
Construct automatically a GLM getModel using the backward selection method.
- BackwardSubstitution - Class in com.numericalmethod.suanshu.matrix.doubles.linearsystem
-
Backward substitution solves a matrix equation in the form Ux = b
by an iterative process for an upper triangular matrix U.
- BackwardSubstitution() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.linearsystem.BackwardSubstitution
-
- BanachSpace<B,F extends Field<F> & java.lang.Comparable<F>> - Interface in com.numericalmethod.suanshu.mathstructure
-
A Banach space, B, is a complete normed vector space such that
every Cauchy sequence (with respect to the metric d(x, y) = |x − y|) in B has a limit in B.
- Bartlett - Class in com.numericalmethod.suanshu.stats.test.variance
-
Bartlett's test is used to test if k samples are from populations with equal variances, hence homoscedasticity.
- Bartlett(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.Bartlett
-
Perform the Bartlett test to test if the samples are from populations with equal variances.
- base() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.Pow
-
Get the radix or base of the coefficient.
- basis() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel
-
Get the kernel basis.
- Basis - Class in com.numericalmethod.suanshu.vector.doubles.dense.operation
-
A basis is a set of linearly independent vectors spanning a vector space.
- Basis(int, int) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.operation.Basis
-
Construct a vector that corresponds to the i-th dimension in Rn.
- basisAndFreeVars() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel
-
Get the kernel basis and the associated free variables for each basis/column.
- BBNode - Interface in com.numericalmethod.suanshu.algorithm.bb
-
A branch-and-bound algorithm maintains a tree of nodes to keep track of the search paths and the pruned paths.
- begin() - Method in class com.numericalmethod.suanshu.interval.Interval
-
Get the beginning of this interval.
- BEGINNING_OF_TIME - Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
This represents a time before all (representable) times.
- BEGINNING_OF_TIME_LONG - Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
This represents a time before all (representable) times, in long representation.
- beq() - Method in interface com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Get the values, beq, of the equality constraints Aeq * x ≥ beq.
- beq() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- beq() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Get the values of the equality constraints: beq as in \(A_{eq}x = b_{eq}\).
- beq() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
- Best1Bin - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim
-
The Best-1-Bin rule is the same as the
Rand-1-Bin rule,
except that it always pick the best candidate in the population to be the base.
- Best1Bin(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Best1Bin
-
Construct an instance of Best1Bin.
- Best1Bin(double, double) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Best1Bin
-
Construct an instance of Best1Bin.
- Best2Bin - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim
-
The Best-1-Bin rule always picks the best chromosome as the base.
- Best2Bin(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Best2Bin
-
Construct an instance of Best2Bin.
- Best2Bin(double, double) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Best2Bin
-
Construct an instance of Best2Bin.
- Best2Bin.DeBest2BinCell - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim
-
- Beta - Class in com.numericalmethod.suanshu.analysis.function.special.beta
-
The beta function defined as:
\[
B(x,y) = \frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}= \int_0^1t^{x-1}(1-t)^{y-1}\,dt, x > 0, y > 0
\]
The R equivalent function is
beta.
- Beta() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.beta.Beta
-
- beta() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Get the set of cointegrating factors, by columns.
- beta(int) - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Get the r-th cointegrating factor, counting from 1.
- beta - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaDistribution.Lambda
-
β: the shape parameter
- Beta - Class in com.numericalmethod.suanshu.stats.regression.linear
-
Beta coefficients are the outcomes of fitting a linear regression model.
- Beta(Vector, Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.Beta
-
Construct an instance of Beta.
- Beta - Class in com.numericalmethod.suanshu.stats.regression.linear.glm
-
This class represents the estimates of the beta in a Generalized Linear Model.
- Beta(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.Beta
-
Construct an instance of Beta.
- beta - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
-
the GLM coefficients β^ statistics
- Beta - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.quasi
-
This class represents the estimates of the beta in a quasi Generalized Linear Model,
i.e., a GLM with a quasi-family.
- beta - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
-
the GLM coefficients β^ statistics
- Beta - Class in com.numericalmethod.suanshu.stats.regression.linear.logistic
-
Beta coefficient estimates, β^, of a logistic regression model.
- beta - Variable in class com.numericalmethod.suanshu.stats.regression.linear.logistic.Logistic
-
the β^ statistics
- Beta - Class in com.numericalmethod.suanshu.stats.regression.linear.ols
-
Beta coefficient estimates, β^, of an Ordinary Least Square linear regression model.
- Beta(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.ols.Beta
-
Construct an instance of Beta.
- beta - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression
-
the \(\hat{\beta}\) statistics
- beta() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the GARCH coefficients.
- BetaDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The beta distribution is the posterior distribution of the parameter p of a binomial distribution
after observing α − 1 independent events with probability p and
β − 1 with probability 1 - p,
if the prior distribution of p is uniform.
- BetaDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
Construct a Beta distribution.
- BetaDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Beta distribution to model the observations.
- BetaDistribution(BetaDistribution.Lambda[], boolean, boolean, double, int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaDistribution
-
Construct a Beta distribution for each state in the HMM model.
- BetaDistribution(BetaDistribution.Lambda[], int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaDistribution
-
Construct a Beta distribution for each state in the HMM model.
- BetaDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
the Beta distribution parameters
- betaHat - Variable in class com.numericalmethod.suanshu.stats.regression.linear.Beta
-
the coefficient estimates, β^
- betaHat() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.Fitting
-
Get the estimates of β, β^, as in
- betaHat() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
-
- BetaRegularized - Class in com.numericalmethod.suanshu.analysis.function.special.beta
-
The Regularized Incomplete Beta function is defined as:
\[
I_x(p,q) = \frac{B(x;\,p,q)}{B(p,q)} = \frac{1}{B(p,q)} \int_0^x t^{p-1}\,(1-t)^{q-1}\,dt, p > 0, q > 0
\]
The R equivalent function is
pbeta.
- BetaRegularized(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.beta.BetaRegularized
-
Construct an instance of Ix(p,q) with the parameters p and q.
- BetaRegularizedInverse - Class in com.numericalmethod.suanshu.analysis.function.special.beta
-
The inverse of the Regularized Incomplete Beta function is defined at:
\[
x = I^{-1}_{(p,q)}(u), 0 \le u \le 1
\]
The R equivalent function is
qbeta.
- BetaRegularizedInverse(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.beta.BetaRegularizedInverse
-
Construct an instance of \(I^{-1}_{(p,q)}(u)\) with parameters p and p.
- BFGS - Class in com.numericalmethod.suanshu.optimization.unconstrained.quasinewton
-
The Broyden-Fletcher-Goldfarb-Shanno method is a quasi-Newton method
to solve unconstrained nonlinear optimization problems.
- BFGS(boolean, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.BFGS
-
Construct a multivariate minimizer using the BFGS method.
- BFGS.BFGSImpl - Class in com.numericalmethod.suanshu.optimization.unconstrained.quasinewton
-
an implementation of the BFGS algorithm
- BFGSImpl(C2OptimProblem) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.BFGS.BFGSImpl
-
- BIC - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.InformationCriteria
-
Bayesian information criterion
- BiconjugateGradientSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Biconjugate Gradient method (BiCG) is useful for solving non-symmetric
n-by-n linear systems.
- BiconjugateGradientSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
Construct a Biconjugate Gradient (BiCG) solver.
- BiconjugateGradientSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
Construct a Biconjugate Gradient (BiCG) solver.
- BiconjugateGradientStabilizedSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Biconjugate Gradient Stabilized (BiCGSTAB) method is useful for solving
non-symmetric n-by-n linear systems.
- BiconjugateGradientStabilizedSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
Construct a Biconjugate Gradient Stabilized solver (BiCGSTAB) .
- BiconjugateGradientStabilizedSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
Construct a Biconjugate Gradient Stabilized solver (BiCGSTAB) .
- BiDiagonalization - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.diagonalization
-
Given a tall (m x n) matrix A, where m ≥ n,
we find orthogonal matrices U and V such that U' * A * V = B.
- BiDiagonalization(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.diagonalization.BiDiagonalization
-
Run the Householder bi-diagonalization for a tall matrix.
- BidiagonalMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal
-
A bi-diagonal matrix is either upper or lower diagonal.
- BidiagonalMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
Construct a bi-diagonal matrix from a 2D double[][] array.
- BidiagonalMatrix(int, BidiagonalMatrix.BidiagonalMatrixType) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
Construct a 0 bi-diagonal matrix of dimension dim * dim.
- BidiagonalMatrix(BidiagonalMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
Copy constructor.
- BidiagonalMatrix.BidiagonalMatrixType - Enum in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal
-
the types of bi-diagonal matrices available
- BigDecimalUtils - Class in com.numericalmethod.suanshu.number.big
-
These are the utility functions to manipulate BigDecimal.
- bigDecimalValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
Convert the number to BigDecimal.
- BigIntegerUtils - Class in com.numericalmethod.suanshu.number.big
-
These are the utility functions to manipulate BigInteger.
- bigN - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
the big N for which n > bigN we use the asymptotic distribution
- bigN - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
the big N for which n > bigN we use the asymptotic distribution
- bigN - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
the big N for which n > bigN we use the asymptotic distribution
- bin(MultinomialRvg) - Static method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Pick the first non-empty bin.
- BinaryRelation - Class in com.numericalmethod.suanshu.analysis.function.tuple
-
A binary relation on a set A is a collection of ordered pairs of elements in A.
- BinaryRelation(double[], double[]) - Constructor for class com.numericalmethod.suanshu.analysis.function.tuple.BinaryRelation
-
Construct a binary relation from {(x,y)}.
- Binomial - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
The Binomial distribution for the error distribution in a GLM model.
- Binomial() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
-
Construct an instance of Binomial.
- Binomial(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
-
Construct an instance of Binomial with an overriding link function.
- Binomial - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family
-
The quasi Binomial family of GLM.
- Binomial() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Binomial
-
Construct an instance of Binomial.
- Binomial(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Binomial
-
Construct an instance of Binomial with an overriding link function.
- BinomialDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments,
each of which yields success with probability p.
- BinomialDistribution(int, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
Construct a Binomial distribution.
- BinomialDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Binomial distribution to model the observations.
- BinomialDistribution(BinomialDistribution.Lambda[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialDistribution
-
Construct a Binomial distribution for each state in the HMM model.
- BinomialDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
the Binomial distribution parameters
- BinomialRng - Class in com.numericalmethod.suanshu.stats.random.univariate
-
This random number generator samples from the binomial distribution.
- BinomialRng(int, double) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.BinomialRng
-
Construct a random number generator to sample from the binomial distribution.
- BivariateRealFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1
-
A bivariate real function takes two real arguments and outputs one real value.
- BivariateRealFunction() - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction
-
- BootstrapEstimator - Class in com.numericalmethod.suanshu.stats.sampling.resampling.bootstrap
-
This class estimates the statistic for a sample using a bootstrap method.
- BootstrapEstimator(Resampling, StatisticFactory, int) - Constructor for class com.numericalmethod.suanshu.stats.sampling.resampling.bootstrap.BootstrapEstimator
-
Constructs a bootstrap estimator.
- BootstrapEstimator(Resampling, StatisticFactory, int, boolean) - Constructor for class com.numericalmethod.suanshu.stats.sampling.resampling.bootstrap.BootstrapEstimator
-
Constructs a bootstrap estimator.
- BorderedHessian - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
-
A bordered Hessian matrix consists of the Hessian of a multivariate function f,
and the gradient of a multivariate function g.
- BorderedHessian(RealScalarFunction, RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.BorderedHessian
-
Construct the bordered Hessian matrix for multivariate functions f and g at point x.
- Bound(int, double, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.BoxConstraints.Bound
-
Construct a bound constraint for a variable.
- BoxConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.constraint.linear
-
This represents the lower and upper bounds for a variable.
- BoxConstraints(int, BoxConstraints.Bound...) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.BoxConstraints
-
Construct a set of bound constraints.
- BoxConstraints.Bound - Class in com.numericalmethod.suanshu.optimization.constrained.constraint.linear
-
a bound constraint for a variable
- BoxMuller - Class in com.numericalmethod.suanshu.stats.random.univariate.normal
-
The Box–Muller transform (by George Edward Pelham Box and Mervin Edgar Muller 1958)
is a pseudo-random number sampling method for generating pairs of independent standard normally distributed
(zero expectation, unit variance) random numbers,
given a source of uniformly distributed random numbers.
- BoxMuller(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.normal.BoxMuller
-
Construct a random number generator to sample from the standard Normal distribution.
- BoxMuller() - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.normal.BoxMuller
-
Construct a random number generator to sample from the standard Normal distribution.
- BoxPierce - Class in com.numericalmethod.suanshu.stats.test.timeseries.portmanteau
-
The Box–Pierce test (named for George E.
- BoxPierce(double[], int, int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.BoxPierce
-
Compute the Box–Pierce test statistic for examining the null hypothesis of independence in a given time series.
- BracketSearch - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
This class provides implementation support for those univariate optimization algorithms that are based on bracketing.
- BracketSearch(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch
-
Construct a univariate minimizer using a bracket search method.
- BracketSearch.Solution - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
- BranchAndBound - Class in com.numericalmethod.suanshu.algorithm.bb
-
Branch-and-Bound (BB or B&B) is a general algorithm for finding optimal solutions of various optimization problems,
especially in discrete and combinatorial optimization.
- BranchAndBound(ActiveList, BBNode) - Constructor for class com.numericalmethod.suanshu.algorithm.bb.BranchAndBound
-
Solve a minimization problem using a branch-and-bound algorithm.
- BranchAndBound(BBNode) - Constructor for class com.numericalmethod.suanshu.algorithm.bb.BranchAndBound
-
Solve a minimization problem using a branch-and-bound algorithm using depth-first search.
- branching() - Method in interface com.numericalmethod.suanshu.algorithm.bb.BBNode
-
Get the children of this node by using the branching operation.
- branching() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPNode
-
Get the children of this node by using the branching operation.
- Brent - Class in com.numericalmethod.suanshu.analysis.uniroot
-
Brent's root-finding algorithm combines super-linear convergence with reliability of bisection.
- Brent(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.uniroot.Brent
-
Construct an instance of Brent's root finding algorithm.
- Brent - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
Brent's algorithm is the preferred method for finding the minimum of a univariate function.
- Brent(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Brent
-
Construct a univariate minimizer using Brent's algorithm.
- Brent.Solution - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
This is the solution to a Brent's univariate optimization.
- BreuschPagan - Class in com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity
-
The Breusch–Pagan test is used to test for heteroskedasticity in a linear regression model.
- BreuschPagan(Residuals, boolean) - Constructor for class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.BreuschPagan
-
Perform the Breusch-Pagan test to test for heteroskedasticity in a linear regression model.
- BrownForsythe - Class in com.numericalmethod.suanshu.stats.test.variance
-
The Brown–Forsythe test is a statistical test for the equality of group variances based on performing an ANOVA on a transformation of the response variable.
- BrownForsythe(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
-
Perform the Brown-Forsythe test to test for equal variances of the samples.
- Brownian - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian
-
A multivariate Brownian motion is a stochastic process with the following properties.
- Brownian(int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.Brownian
-
Construct a multi-dimensional Brownian motion.
- Brownian(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.Brownian
-
Construct a multi-dimensional Brownian motion with μ and σ.
- Brownian - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian
-
A Brownian motion is a stochastic process with the following properties.
- Brownian(double, double) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.Brownian
-
Construct a univariate Brownian motion.
- Brownian() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.Brownian
-
Construct a univariate standard Brownian motion.
- BruteForceIPMinimizer - Class in com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce
-
This implementation solves an integral constrained minimization problem by brute force search for all possible integer combinations.
- BruteForceIPMinimizer(BruteForceIPMinimizer.ConstrainedMinimizerFactory<? extends ConstrainedMinimizer<ConstrainedOptimProblem, IterativeMinimizer<Vector>>>) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPMinimizer
-
Construct a brute force minimizer to solve integral constrained minimization problems.
- BruteForceIPMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPMinimizer
-
Construct a brute force minimizer to solve integral constrained minimization problems.
- BruteForceIPMinimizer.ConstrainedMinimizerFactory<U extends ConstrainedMinimizer<ConstrainedOptimProblem,IterativeMinimizer<Vector>>> - Interface in com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce
-
This factory constructs a new instance of ConstrainedMinimizer to solve a real valued minimization problem.
- BruteForceIPMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce
-
This is the solution to an integral constrained minimization using the brute-force search.
- BruteForceIPProblem - Class in com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce
-
This implementation is an integral constrained minimization problem that has enumerable integral domains.
- BruteForceIPProblem(RealScalarFunction, EqualityConstraints, LessThanConstraints, BruteForceIPProblem.IntegerDomain[], double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPProblem
-
Construct an integral constrained minimization problem with explicit integral domains.
- BruteForceIPProblem(RealScalarFunction, BruteForceIPProblem.IntegerDomain[], double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPProblem
-
Construct an integral constrained minimization problem with explicit integral domains.
- BruteForceIPProblem.IntegerDomain - Class in com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce
-
This specifies the integral domain for an integral variable,
i.e., the integer values the variable can take.
- Bt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
This is a FiltrationFunction that returns B(t),
the Brownian motion value at the t-th time point.
- Bt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Bt
-
- Bt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
-
Get the entire Brownian path.
- BuildInitials - Interface in com.numericalmethod.suanshu.optimization.initialization
-
Some optimization algorithms, e.g., Nelder–Mead, Differential-Evolution, require a set of initial points to work with.
- c() - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
-
Get c.
- C() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPDualProblem
-
Get C.
- C() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPPrimalProblem
-
Get C.
- c(int) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Get ci.
- c() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- c() - Method in interface com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Get the objective function.
- c() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- c() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
- C0() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLM
-
Get the covariance matrix of x0.
- C0() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Get the variance of x0.
- C1 - Interface in com.numericalmethod.suanshu.analysis.differentiation.differentiability
-
A function, f, is said to be of class C1 if the derivative f' exists.
- C2 - Interface in com.numericalmethod.suanshu.analysis.differentiation.differentiability
-
A function, f, is said to be of class C2 if the first and second derivatives, f' and f'', exist.
- C2OptimProblem - Interface in com.numericalmethod.suanshu.optimization.problem
-
This is an optimization problem of a real valued function that is twice differentiable.
- C2OptimProblemImpl - Class in com.numericalmethod.suanshu.optimization.problem
-
This is an optimization problem of a real valued function: \(\max_x f(x)\).
- C2OptimProblemImpl(RealScalarFunction, RealVectorFunction, RntoMatrix) - Constructor for class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
Construct an optimization problem with an objective function.
- C2OptimProblemImpl(RealScalarFunction, RealVectorFunction) - Constructor for class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
Construct an optimization problem with an objective function.
- C2OptimProblemImpl(RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
Construct an optimization problem with an objective function.
- C2OptimProblemImpl(C2OptimProblemImpl) - Constructor for class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
Copy Ctor.
- CanonicalLPProblem1 - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem
-
This is a linear programming problem in the 1st canonical form (following the convention in the reference):
min c'x
s.t.
- CanonicalLPProblem1(Vector, Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.CanonicalLPProblem1
-
Construct a linear programming problem in the canonical form.
- CanonicalLPProblem1(Vector, LinearGreaterThanConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.CanonicalLPProblem1
-
Construct a linear programming problem in the canonical form.
- CanonicalLPProblem1(CanonicalLPProblem2) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.CanonicalLPProblem1
-
Convert a linear programming problem from the 2nd canonical form to the 1st canonical form.
- CanonicalLPProblem2 - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem
-
This is a linear programming problem in the 2nd canonical form (following the convention in the wiki):
min c'x
s.t.
- CanonicalLPProblem2(Vector, Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.CanonicalLPProblem2
-
Construct a linear programming problem in the canonical form.
- CanonicalLPProblem2(Vector, LinearLessThanConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.CanonicalLPProblem2
-
Construct a linear programming problem in the canonical form.
- CanonicalLPProblem2(CanonicalLPProblem1) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.CanonicalLPProblem2
-
Convert a linear programming problem from the 1st canonical form to the 2nd canonical form.
- CauchyPolynomial - Class in com.numericalmethod.suanshu.analysis.function.polynomial
-
The Cauchy's polynomial of a polynomial takes this form:
- CauchyPolynomial(Polynomial) - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.CauchyPolynomial
-
- cbind(Vector...) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Combine an array of vectors by columns.
- cbind(List<Vector>) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Combine a list of vectors by columns.
- cbind(Matrix...) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Combine an array of matrices by columns.
- ccdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- ccdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
The complementary cumulative distribution function.
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
Get the cumulative probability F(x) = Pr(X ≤ x).
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- cdf(double) - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
Get the cumulative probability F(x) = Pr(X ≤ x).
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- centralMoment(int) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
Get the value of the k-th central moment.
- CentralPath - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing
-
A central path is a solution to both the primal and dual problems of a semi-definite programming problem.
- CentralPath(Matrix, Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.CentralPath
-
Construct a central path.
- ChangeOfVariable - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
Change of variable can easy the computation of some integrals, such as improper integrals.
- ChangeOfVariable(SubstitutionRule, Integrator) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.ChangeOfVariable
-
Construct an integrator that uses change of variable to do integration.
- CharacteristicPolynomial - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
-
The characteristic polynomial of a square matrix is the function
- CharacteristicPolynomial(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.CharacteristicPolynomial
-
Construct the characteristic polynomial for a square matrix.
- checkInputs() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Check whether this LMProblem instance is valid.
- checkInputs() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticProblem
-
- Cheng1978 - Class in com.numericalmethod.suanshu.stats.random.univariate.beta
-
Cheng, 1978, is a new rejection method for generating beta variates.
- Cheng1978(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.beta.Cheng1978
-
Construct a random number generator to sample from the beta distribution.
- Cheng1978(double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.beta.Cheng1978
-
Construct a random number generator to sample from the beta distribution.
- ChiSquare4Independence - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson
-
Pearson's chi-square test of independence assesses whether paired observations on two variables,
expressed in a contingency table, are independent of each other.
- ChiSquare4Independence(Matrix, int, ChiSquare4Independence.Type) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquare4Independence
-
Assess whether the two random variable in the contingency table is independent.
- ChiSquare4Independence(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquare4Independence
-
Assess whether the two random variable in the contingency table is independent.
- ChiSquare4Independence.Type - Enum in com.numericalmethod.suanshu.stats.test.distribution.pearson
-
the distribution used for the test
- ChiSquareDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The Chi-square distribution is the distribution of
the sum of the squares of a set of statistically independent standard Gaussian random variables.
- ChiSquareDistribution(double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
Construct a Chi-Square distribution.
- Cholesky - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.triangle
-
Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into
A = L * Lt, where L is a lower triangular matrix.
- Cholesky(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Cholesky
-
Run the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.
- Cholesky(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Cholesky
-
Run the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.
- CholeskyWang2006 - Class in com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite
-
Cholesky decomposition works only for a positive definite matrix.
- CholeskyWang2006(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite.CholeskyWang2006
-
Construct the Cholesky decomposition of a matrix.
- Chromosome - Interface in com.numericalmethod.suanshu.optimization.geneticalgorithm
-
A chromosome is a representation of a solution to an optimization problem.
- clear() - Method in interface com.numericalmethod.suanshu.algorithm.bb.ActiveList
-
Removes all of the elements from this collection.
- Cloglog - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the complementary log-log link function:
- Cloglog() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Cloglog
-
- CointegrationMLE - Class in com.numericalmethod.suanshu.stats.cointegration
-
Two or more time series are cointegrated if they each share a common type of stochastic drift,
that is, to a limited degree they share a certain type of behavior in terms of their long-term fluctuations,
but they do not necessarily move together and may be otherwise unrelated.
- CointegrationMLE(SimpleMultiVariateTimeSeries, boolean, int, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Perform the Johansen MLE procedure on a multivariate time series.
- CointegrationMLE(SimpleMultiVariateTimeSeries, boolean, int) - Constructor for class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Perform the Johansen MLE procedure on a multivariate time series, using the EIGEN test.
- CointegrationMLE(SimpleMultiVariateTimeSeries, boolean) - Constructor for class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Perform the Johansen MLE procedure on a multivariate time series,
using the EIGEN test, with the number of lags = 2.
- collection2DoubleArray(Collection<? extends Number>) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert a collection of numbers to a double array.
- collection2IntArray(Collection<Integer>) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert a collection of Integers to an int array.
- collection2LongArray(Collection<Long>) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert a collection of Longs to a long array.
- colSums(MatrixTable) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixUtils
-
Get the column sums.
- columns(Matrix, int[]) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Construct a sub-matrix from the columns of a matrix.
- columns(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Construct a sub-matrix from the columns of a matrix.
- com.numericalmethod.suanshu - package com.numericalmethod.suanshu
-
- com.numericalmethod.suanshu.algorithm - package com.numericalmethod.suanshu.algorithm
-
- com.numericalmethod.suanshu.algorithm.bb - package com.numericalmethod.suanshu.algorithm.bb
-
- com.numericalmethod.suanshu.algorithm.iterative - package com.numericalmethod.suanshu.algorithm.iterative
-
- com.numericalmethod.suanshu.algorithm.iterative.monitor - package com.numericalmethod.suanshu.algorithm.iterative.monitor
-
- com.numericalmethod.suanshu.algorithm.iterative.tolerance - package com.numericalmethod.suanshu.algorithm.iterative.tolerance
-
- com.numericalmethod.suanshu.analysis.differentiation - package com.numericalmethod.suanshu.analysis.differentiation
-
- com.numericalmethod.suanshu.analysis.differentiation.differentiability - package com.numericalmethod.suanshu.analysis.differentiation.differentiability
-
- com.numericalmethod.suanshu.analysis.differentiation.multivariate - package com.numericalmethod.suanshu.analysis.differentiation.multivariate
-
- com.numericalmethod.suanshu.analysis.differentiation.univariate - package com.numericalmethod.suanshu.analysis.differentiation.univariate
-
- com.numericalmethod.suanshu.analysis.function - package com.numericalmethod.suanshu.analysis.function
-
- com.numericalmethod.suanshu.analysis.function.matrix - package com.numericalmethod.suanshu.analysis.function.matrix
-
- com.numericalmethod.suanshu.analysis.function.polynomial - package com.numericalmethod.suanshu.analysis.function.polynomial
-
- com.numericalmethod.suanshu.analysis.function.polynomial.root - package com.numericalmethod.suanshu.analysis.function.polynomial.root
-
- com.numericalmethod.suanshu.analysis.function.polynomial.root.jenkinstraub - package com.numericalmethod.suanshu.analysis.function.polynomial.root.jenkinstraub
-
- com.numericalmethod.suanshu.analysis.function.rn2r1 - package com.numericalmethod.suanshu.analysis.function.rn2r1
-
- com.numericalmethod.suanshu.analysis.function.rn2r1.univariate - package com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
-
- com.numericalmethod.suanshu.analysis.function.rn2rm - package com.numericalmethod.suanshu.analysis.function.rn2rm
-
- com.numericalmethod.suanshu.analysis.function.special.beta - package com.numericalmethod.suanshu.analysis.function.special.beta
-
- com.numericalmethod.suanshu.analysis.function.special.gamma - package com.numericalmethod.suanshu.analysis.function.special.gamma
-
- com.numericalmethod.suanshu.analysis.function.special.gaussian - package com.numericalmethod.suanshu.analysis.function.special.gaussian
-
- com.numericalmethod.suanshu.analysis.function.tuple - package com.numericalmethod.suanshu.analysis.function.tuple
-
- com.numericalmethod.suanshu.analysis.integration.univariate - package com.numericalmethod.suanshu.analysis.integration.univariate
-
- com.numericalmethod.suanshu.analysis.integration.univariate.riemann - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
- com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
- com.numericalmethod.suanshu.analysis.interpolation - package com.numericalmethod.suanshu.analysis.interpolation
-
- com.numericalmethod.suanshu.analysis.sequence - package com.numericalmethod.suanshu.analysis.sequence
-
- com.numericalmethod.suanshu.analysis.uniroot - package com.numericalmethod.suanshu.analysis.uniroot
-
- com.numericalmethod.suanshu.datastructure - package com.numericalmethod.suanshu.datastructure
-
- com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles - package com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles
-
- com.numericalmethod.suanshu.interval - package com.numericalmethod.suanshu.interval
-
- com.numericalmethod.suanshu.mathstructure - package com.numericalmethod.suanshu.mathstructure
-
- com.numericalmethod.suanshu.matrix - package com.numericalmethod.suanshu.matrix
-
- com.numericalmethod.suanshu.matrix.doubles - package com.numericalmethod.suanshu.matrix.doubles
-
- com.numericalmethod.suanshu.matrix.doubles.factorization.diagonalization - package com.numericalmethod.suanshu.matrix.doubles.factorization.diagonalization
-
- com.numericalmethod.suanshu.matrix.doubles.factorization.eigen - package com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
-
- com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr - package com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr
-
- com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination - package com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination
-
- com.numericalmethod.suanshu.matrix.doubles.factorization.qr - package com.numericalmethod.suanshu.matrix.doubles.factorization.qr
-
- com.numericalmethod.suanshu.matrix.doubles.factorization.svd - package com.numericalmethod.suanshu.matrix.doubles.factorization.svd
-
- com.numericalmethod.suanshu.matrix.doubles.factorization.triangle - package com.numericalmethod.suanshu.matrix.doubles.factorization.triangle
-
- com.numericalmethod.suanshu.matrix.doubles.linearsystem - package com.numericalmethod.suanshu.matrix.doubles.linearsystem
-
- com.numericalmethod.suanshu.matrix.doubles.matrixtype - package com.numericalmethod.suanshu.matrix.doubles.matrixtype
-
- com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense
-
- com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal
-
- com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle
-
- com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation
-
- com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
-
- com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative
-
- com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
- com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
-
- com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
-
- com.numericalmethod.suanshu.matrix.doubles.operation - package com.numericalmethod.suanshu.matrix.doubles.operation
-
- com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite - package com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite
-
- com.numericalmethod.suanshu.matrix.generic - package com.numericalmethod.suanshu.matrix.generic
-
- com.numericalmethod.suanshu.matrix.generic.matrixtype - package com.numericalmethod.suanshu.matrix.generic.matrixtype
-
- com.numericalmethod.suanshu.misc - package com.numericalmethod.suanshu.misc
-
- com.numericalmethod.suanshu.number - package com.numericalmethod.suanshu.number
-
- com.numericalmethod.suanshu.number.big - package com.numericalmethod.suanshu.number.big
-
- com.numericalmethod.suanshu.number.complex - package com.numericalmethod.suanshu.number.complex
-
- com.numericalmethod.suanshu.number.doublearray - package com.numericalmethod.suanshu.number.doublearray
-
- com.numericalmethod.suanshu.optimization - package com.numericalmethod.suanshu.optimization
-
- com.numericalmethod.suanshu.optimization.constrained - package com.numericalmethod.suanshu.optimization.constrained
-
- com.numericalmethod.suanshu.optimization.constrained.constraint - package com.numericalmethod.suanshu.optimization.constrained.constraint
-
- com.numericalmethod.suanshu.optimization.constrained.constraint.general - package com.numericalmethod.suanshu.optimization.constrained.constraint.general
-
- com.numericalmethod.suanshu.optimization.constrained.constraint.linear - package com.numericalmethod.suanshu.optimization.constrained.constraint.linear
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.activeset - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.activeset
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver
-
- com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.problem - package com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.problem
-
- com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod - package com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod
-
- com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset - package com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset
-
- com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint - package com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint
-
- com.numericalmethod.suanshu.optimization.constrained.integer - package com.numericalmethod.suanshu.optimization.constrained.integer
-
- com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce - package com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce
-
- com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb - package com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb
-
- com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane - package com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane
-
- com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem - package com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem
-
- com.numericalmethod.suanshu.optimization.constrained.problem - package com.numericalmethod.suanshu.optimization.constrained.problem
-
- com.numericalmethod.suanshu.optimization.geneticalgorithm - package com.numericalmethod.suanshu.optimization.geneticalgorithm
-
- com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim - package com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim
-
- com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained - package com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained
-
- com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local - package com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local
-
- com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid - package com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid
-
- com.numericalmethod.suanshu.optimization.initialization - package com.numericalmethod.suanshu.optimization.initialization
-
- com.numericalmethod.suanshu.optimization.minmax - package com.numericalmethod.suanshu.optimization.minmax
-
- com.numericalmethod.suanshu.optimization.problem - package com.numericalmethod.suanshu.optimization.problem
-
- com.numericalmethod.suanshu.optimization.unconstrained - package com.numericalmethod.suanshu.optimization.unconstrained
-
- com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection - package com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection
-
- com.numericalmethod.suanshu.optimization.unconstrained.linesearch - package com.numericalmethod.suanshu.optimization.unconstrained.linesearch
-
- com.numericalmethod.suanshu.optimization.unconstrained.quasinewton - package com.numericalmethod.suanshu.optimization.unconstrained.quasinewton
-
- com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent - package com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent
-
- com.numericalmethod.suanshu.optimization.univariate - package com.numericalmethod.suanshu.optimization.univariate
-
- com.numericalmethod.suanshu.optimization.univariate.bracketsearch - package com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
- com.numericalmethod.suanshu.parallel - package com.numericalmethod.suanshu.parallel
-
- com.numericalmethod.suanshu.stats.cointegration - package com.numericalmethod.suanshu.stats.cointegration
-
- com.numericalmethod.suanshu.stats.descriptive - package com.numericalmethod.suanshu.stats.descriptive
-
- com.numericalmethod.suanshu.stats.descriptive.moment - package com.numericalmethod.suanshu.stats.descriptive.moment
-
- com.numericalmethod.suanshu.stats.descriptive.rank - package com.numericalmethod.suanshu.stats.descriptive.rank
-
- com.numericalmethod.suanshu.stats.distribution - package com.numericalmethod.suanshu.stats.distribution
-
- com.numericalmethod.suanshu.stats.distribution.univariate - package com.numericalmethod.suanshu.stats.distribution.univariate
-
- com.numericalmethod.suanshu.stats.dlm.multivariate - package com.numericalmethod.suanshu.stats.dlm.multivariate
-
- com.numericalmethod.suanshu.stats.dlm.univariate - package com.numericalmethod.suanshu.stats.dlm.univariate
-
- com.numericalmethod.suanshu.stats.factoranalysis - package com.numericalmethod.suanshu.stats.factoranalysis
-
- com.numericalmethod.suanshu.stats.hmm - package com.numericalmethod.suanshu.stats.hmm
-
- com.numericalmethod.suanshu.stats.hmm.mixture - package com.numericalmethod.suanshu.stats.hmm.mixture
-
- com.numericalmethod.suanshu.stats.hmm.mixture.distribution - package com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
- com.numericalmethod.suanshu.stats.hmm.rabiner - package com.numericalmethod.suanshu.stats.hmm.rabiner
-
- com.numericalmethod.suanshu.stats.markovchain - package com.numericalmethod.suanshu.stats.markovchain
-
- com.numericalmethod.suanshu.stats.pca - package com.numericalmethod.suanshu.stats.pca
-
- com.numericalmethod.suanshu.stats.random - package com.numericalmethod.suanshu.stats.random
-
- com.numericalmethod.suanshu.stats.random.concurrent - package com.numericalmethod.suanshu.stats.random.concurrent
-
- com.numericalmethod.suanshu.stats.random.multivariate - package com.numericalmethod.suanshu.stats.random.multivariate
-
- com.numericalmethod.suanshu.stats.random.univariate - package com.numericalmethod.suanshu.stats.random.univariate
-
- com.numericalmethod.suanshu.stats.random.univariate.beta - package com.numericalmethod.suanshu.stats.random.univariate.beta
-
- com.numericalmethod.suanshu.stats.random.univariate.exp - package com.numericalmethod.suanshu.stats.random.univariate.exp
-
- com.numericalmethod.suanshu.stats.random.univariate.gamma - package com.numericalmethod.suanshu.stats.random.univariate.gamma
-
- com.numericalmethod.suanshu.stats.random.univariate.normal - package com.numericalmethod.suanshu.stats.random.univariate.normal
-
- com.numericalmethod.suanshu.stats.random.univariate.poisson - package com.numericalmethod.suanshu.stats.random.univariate.poisson
-
- com.numericalmethod.suanshu.stats.random.univariate.uniform - package com.numericalmethod.suanshu.stats.random.univariate.uniform
-
- com.numericalmethod.suanshu.stats.random.univariate.uniform.linear - package com.numericalmethod.suanshu.stats.random.univariate.uniform.linear
-
- com.numericalmethod.suanshu.stats.regression.linear - package com.numericalmethod.suanshu.stats.regression.linear
-
- com.numericalmethod.suanshu.stats.regression.linear.glm - package com.numericalmethod.suanshu.stats.regression.linear.glm
-
- com.numericalmethod.suanshu.stats.regression.linear.glm.distribution - package com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
- com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link - package com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
- com.numericalmethod.suanshu.stats.regression.linear.glm.quasi - package com.numericalmethod.suanshu.stats.regression.linear.glm.quasi
-
- com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family - package com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family
-
- com.numericalmethod.suanshu.stats.regression.linear.logistic - package com.numericalmethod.suanshu.stats.regression.linear.logistic
-
- com.numericalmethod.suanshu.stats.regression.linear.modelselection - package com.numericalmethod.suanshu.stats.regression.linear.modelselection
-
- com.numericalmethod.suanshu.stats.regression.linear.ols - package com.numericalmethod.suanshu.stats.regression.linear.ols
-
- com.numericalmethod.suanshu.stats.regression.panel - package com.numericalmethod.suanshu.stats.regression.panel
-
- com.numericalmethod.suanshu.stats.sampling.discrete - package com.numericalmethod.suanshu.stats.sampling.discrete
-
- com.numericalmethod.suanshu.stats.sampling.resampling - package com.numericalmethod.suanshu.stats.sampling.resampling
-
- com.numericalmethod.suanshu.stats.sampling.resampling.bootstrap - package com.numericalmethod.suanshu.stats.sampling.resampling.bootstrap
-
- com.numericalmethod.suanshu.stats.stochasticprocess - package com.numericalmethod.suanshu.stats.stochasticprocess
-
- com.numericalmethod.suanshu.stats.stochasticprocess.multivariate - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate
-
- com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian
-
- com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde
-
- com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
- com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
- com.numericalmethod.suanshu.stats.stochasticprocess.timepoints - package com.numericalmethod.suanshu.stats.stochasticprocess.timepoints
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients
-
- com.numericalmethod.suanshu.stats.test - package com.numericalmethod.suanshu.stats.test
-
- com.numericalmethod.suanshu.stats.test.distribution.kolmogorov - package com.numericalmethod.suanshu.stats.test.distribution.kolmogorov
-
- com.numericalmethod.suanshu.stats.test.distribution.normality - package com.numericalmethod.suanshu.stats.test.distribution.normality
-
- com.numericalmethod.suanshu.stats.test.distribution.pearson - package com.numericalmethod.suanshu.stats.test.distribution.pearson
-
- com.numericalmethod.suanshu.stats.test.mean - package com.numericalmethod.suanshu.stats.test.mean
-
- com.numericalmethod.suanshu.stats.test.rank - package com.numericalmethod.suanshu.stats.test.rank
-
- com.numericalmethod.suanshu.stats.test.rank.wilcoxon - package com.numericalmethod.suanshu.stats.test.rank.wilcoxon
-
- com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity - package com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity
-
- com.numericalmethod.suanshu.stats.test.timeseries.adf - package com.numericalmethod.suanshu.stats.test.timeseries.adf
-
- com.numericalmethod.suanshu.stats.test.timeseries.portmanteau - package com.numericalmethod.suanshu.stats.test.timeseries.portmanteau
-
- com.numericalmethod.suanshu.stats.test.variance - package com.numericalmethod.suanshu.stats.test.variance
-
- com.numericalmethod.suanshu.stats.timeseries - package com.numericalmethod.suanshu.stats.timeseries
-
- com.numericalmethod.suanshu.stats.timeseries.linear.multivariate - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate
-
- com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima
-
- com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess
-
- com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
- com.numericalmethod.suanshu.stats.timeseries.linear.univariate - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate
-
- com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
-
- com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample
-
- com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess
-
- com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
- com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch
-
- com.numericalmethod.suanshu.stats.timeseries.multivariate - package com.numericalmethod.suanshu.stats.timeseries.multivariate
-
- com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime - package com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime
-
- com.numericalmethod.suanshu.stats.timeseries.univariate - package com.numericalmethod.suanshu.stats.timeseries.univariate
-
- com.numericalmethod.suanshu.stats.timeseries.univariate.realtime - package com.numericalmethod.suanshu.stats.timeseries.univariate.realtime
-
- com.numericalmethod.suanshu.time - package com.numericalmethod.suanshu.time
-
- com.numericalmethod.suanshu.vector.doubles - package com.numericalmethod.suanshu.vector.doubles
-
- com.numericalmethod.suanshu.vector.doubles.dense - package com.numericalmethod.suanshu.vector.doubles.dense
-
- com.numericalmethod.suanshu.vector.doubles.dense.operation - package com.numericalmethod.suanshu.vector.doubles.dense.operation
-
- com.numericalmethod.suanshu.vector.doubles.operation - package com.numericalmethod.suanshu.vector.doubles.operation
-
- Combination<T> - Class in com.numericalmethod.suanshu.algorithm
-
A combination is a way of selecting several things out of a larger group, where (unlike permutations) order does not matter.
- Combination(T[]...) - Constructor for class com.numericalmethod.suanshu.algorithm.Combination
-
Construct an Iterable of all combinations of arrays, taking one element from each array.
- combination(int, int) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
Compute the combination function or binomial coefficient.
- combination(int, int) - Static method in class com.numericalmethod.suanshu.number.big.BigIntegerUtils
-
Compute the combination function or the binomial coefficient.
- compare(SparseEntry, SparseEntry) - Method in enum com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseEntry.TopLeftFirstComparator
-
- compare(BigDecimal, BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compare two BigDecimals up to a precision.
- compare(Number, double) - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- compare(double, double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Compares two doubles up to a precision.
- compare(Number, double) - Method in interface com.numericalmethod.suanshu.number.NumberUtils.Comparable
-
Compare this and that numbers up to a precision.
- compare(Number, Number, double) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
-
Compare two numbers.
- compareTo(Real) - Method in class com.numericalmethod.suanshu.number.Real
-
- compareTo(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
- Complex - Class in com.numericalmethod.suanshu.number.complex
-
A complex number is a number consisting of a real number part and an imaginary number part.
- Complex(double, double) - Constructor for class com.numericalmethod.suanshu.number.complex.Complex
-
Construct a complex number from the real and imaginary parts.
- Complex(double) - Constructor for class com.numericalmethod.suanshu.number.complex.Complex
-
Construct a complex number from a real number.
- ComplexMatrix - Class in com.numericalmethod.suanshu.matrix.generic.matrixtype
-
- ComplexMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- ComplexMatrix(Complex[][]) - Constructor for class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- ComplexMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- CompositeDoubleArrayOperation - Class in com.numericalmethod.suanshu.number.doublearray
-
It is desirable to have multiple implementations and switch between them for, e.g., performance reason.
- CompositeDoubleArrayOperation(CompositeDoubleArrayOperation.ImplementationChooser) - Constructor for class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
-
Construct a CompositeDoubleArrayOperation by supplying the multiplexing criterion and the multiple DoubleArrayOperations.
- CompositeDoubleArrayOperation(int, DoubleArrayOperation, DoubleArrayOperation) - Constructor for class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
-
Construct a CompositeDoubleArrayOperation that chooses an implementation by array length.
- CompositeDoubleArrayOperation.ImplementationChooser - Interface in com.numericalmethod.suanshu.number.doublearray
-
Specify which implementation to use.
- CompositeLinearCongruentialGenerator - Class in com.numericalmethod.suanshu.stats.random.univariate.uniform.linear
-
A composite generator combines a number of simple
LinearCongruentialGenerator, such as
Lehmer,
to form one longer period generator by first summing values and then taking modulus.
- CompositeLinearCongruentialGenerator(LinearCongruentialGenerator[]) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
Construct a linear congruential generator from some simpler and shorter modulus generators.
- concat(double[]...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Concatenate an array of arrays into one array.
- concat(LinearConstraints...) - Static method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearConstraints
-
Concatenate collections of linear constraints into one collection.
- concat(Vector...) - Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.CreateVector
-
Concatenate an array of vectors into one vector.
- ConcurrentCachedGenerator<T> - Class in com.numericalmethod.suanshu.stats.random.concurrent
-
A generic wrapper that makes an underlying item generator thread-safe by
caching generated items in a concurrently-accessible list.
- ConcurrentCachedGenerator(ConcurrentCachedGenerator.Generator<T>, int) - Constructor for class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedGenerator
-
Creates a new instance which wraps the given item generator and uses a
cache of the specified size.
- ConcurrentCachedGenerator.Generator<T> - Interface in com.numericalmethod.suanshu.stats.random.concurrent
-
Defines a generic generator of type T.
- ConcurrentCachedRLG - Class in com.numericalmethod.suanshu.stats.random.concurrent
-
This is a fast thread-safe wrapper for random long generators.
- ConcurrentCachedRLG(RandomLongGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedRLG
-
Constructs a new instance which wraps the given random long generator and
uses a cache of the specified size.
- ConcurrentCachedRLG(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedRLG
-
Construct a new instance which wraps the given random long generator and
uses a cache which has 8 entries per available core.
- ConcurrentCachedRNG - Class in com.numericalmethod.suanshu.stats.random.concurrent
-
This is a fast thread-safe wrapper for random number generators.
- ConcurrentCachedRNG(RandomNumberGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedRNG
-
Constructs a new instance which wraps the given random number generator
and uses a cache of the specified size.
- ConcurrentCachedRNG(RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedRNG
-
Construct a new instance which wraps the given random number generator and
uses a cache which has 8 entries per available core.
- ConcurrentCachedRVG - Class in com.numericalmethod.suanshu.stats.random.concurrent
-
This is a fast thread-safe wrapper for random vector generators.
- ConcurrentCachedRVG(RandomVectorGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedRVG
-
Constructs a new instance which wraps the given random vector generator
and uses a cache of the specified size.
- ConcurrentCachedRVG(RandomVectorGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedRVG
-
Constructs a new instance which wraps the given random vector generator
and uses a cache which has 8 entries per available core.
- conditionalForEach(boolean, Iterable<T>, IterationBody<T>) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
-
Calls
forEach
only if
conditionToParallelize is
true.
- conditionalForLoop(boolean, int, int, int, LoopBody) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
-
Runs a parallel for-loop only if conditionToParallelize is true.
- conditionalForLoop(boolean, int, int, LoopBody) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
-
- ConditionalSumOfSquares - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
This class does fitting for an ARIMA model by minimizing the conditional sum of squares (CSS).
- ConditionalSumOfSquares(TimeSeries, int, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Fit an ARIMA model for the observations.
- confidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
Compute the confidence interval.
- confidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
Compute the confidence interval.
- CongruentMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
Given a matrix A and an invertible matrix P, we create the congruent matrix B s.t.,
B = P'AP
- CongruentMatrix(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.CongruentMatrix
-
Construct the congruent matrix B = P'AP.
- conjugate() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Get the conjugate of the complex number, namely, (a - bi).
- ConjugateGradient - Class in com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection
-
A conjugate direction optimization method is performed by using
sequential line search along directions that bear a strict mathematical relationship to one another.
- ConjugateGradient(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.ConjugateGradient
-
Construct a multivariate minimizer using the Conjugate-Gradient method.
- ConjugateGradientNormalErrorSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
For an under-determined system of linear equations, Ax = b, or
when the coefficient matrix A is non-symmetric and nonsingular,
the normal equation matrix AAt is symmetric and
positive definite, and hence CG is applicable.
- ConjugateGradientNormalErrorSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
Construct a Conjugate Gradient Normal Error (CGNE) solver.
- ConjugateGradientNormalErrorSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
Construct a Conjugate Gradient Normal Error (CGNE) solver.
- ConjugateGradientNormalResidualSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
For an under-determined system of linear equations, Ax = b, or
when the coefficient matrix A is non-symmetric and nonsingular,
the normal equation matrix AAt is symmetric and
positive definite, and hence CG is applicable.
- ConjugateGradientNormalResidualSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
Construct a Conjugate Gradient Normal Residual method (CGNR) solver.
- ConjugateGradientNormalResidualSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
Construct a Conjugate Gradient Normal Residual method (CGNR) solver.
- ConjugateGradientSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Conjugate Gradient method (CG) is useful for solving a symmetric n-by-n
linear system.
- ConjugateGradientSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
Construct a Conjugate Gradient (CG) solver.
- ConjugateGradientSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
Construct a Conjugate Gradient (CG) solver.
- ConjugateGradientSquaredSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Conjugate Gradient Squared method (CGS) is useful for solving
a non-symmetric n-by-n linear system.
- ConjugateGradientSquaredSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
Construct a Conjugate Gradient Squared (CGS) solver.
- ConjugateGradientSquaredSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
Construct a Conjugate Gradient Squared (CGS) solver.
- Constant - Class in com.numericalmethod.suanshu
-
This class lists the global parameters and constants in this SuanShu library.
- ConstantDrift - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
The class represents a constant drift function.
- ConstantDrift(Vector) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantDrift
-
Construct a constant drift function.
- ConstantSigma1 - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
The class represents a constant diffusion coefficient function.
- ConstantSigma1(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
-
Construct a constant diffusion coefficient function.
- ConstantSigma2 - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
- ConstantSigma2(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
-
Deprecated.
Construct a constant diffusion coefficient function.
- ConstrainedCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell
-
- ConstrainedCellFactory - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained
-
This defines a Differential Evolution operator that takes in account constraints.
- ConstrainedCellFactory(DEOptimCellFactory) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
-
Construct an instance of a ConstrainedCellFactory that define the constrained Differential Evolution operators.
- ConstrainedCellFactory.ConstrainedCell - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained
-
A ConstrainedCell is a chromosome for a constrained optimization problem.
- ConstrainedMinimizer<P extends ConstrainedOptimProblem,S extends MinimizationSolution<?>> - Interface in com.numericalmethod.suanshu.optimization.constrained
-
- ConstrainedOptimProblem - Interface in com.numericalmethod.suanshu.optimization.constrained.problem
-
A constrained optimization problem takes this form.
- ConstrainedOptimProblemImpl1 - Class in com.numericalmethod.suanshu.optimization.constrained.problem
-
This implements a constrained optimization problem for a function f subject to equality and less-than-or-equal-to constraints.
- ConstrainedOptimProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.problem.ConstrainedOptimProblemImpl1
-
Construct a constrained optimization problem.
- ConstrainedOptimProblemImpl1(ConstrainedOptimProblemImpl1) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.problem.ConstrainedOptimProblemImpl1
-
Copy constructor.
- Constraints - Interface in com.numericalmethod.suanshu.optimization.constrained.constraint
-
A set of constraints for a (real-valued) optimization problem is a set of functions.
- constraints - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.MultiplierPenalty
-
the constraint/cost functions
- ConstraintsUtils - Class in com.numericalmethod.suanshu.optimization.constrained.constraint
-
These are the utility functions for manipulating
Constraints.
- ConstraintsUtils() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.ConstraintsUtils
-
- Construction - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde
-
This interface defines how to construct a realization for a stochastic process.
- Construction - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde
-
This interface defines how a realization of a stochastic process is constructed.
- ContinuedFraction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
-
A continued fraction representation of a number has this form:
\[
z = b_0 + \cfrac{a_1}{b_1 + \cfrac{a_2}{b_2 + \cfrac{a_3}{b_3 + \cfrac{a_4}{b_4 + \ddots\,}}}}
\]
ai and bi can be functions of x, which in turn makes z a function of x.
- ContinuedFraction(ContinuedFraction.Partials, double, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction
-
Construct a continued fraction.
- ContinuedFraction(ContinuedFraction.Partials, int, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction
-
Construct a continued fraction.
- ContinuedFraction(ContinuedFraction.Partials) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction
-
Construct a continued fraction.
- ContinuedFraction.MaxIterationsExceededException - Exception in com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
-
RuntimeException thrown when the continued fraction fails to converge for a given epsilon before a certain number of iterations.
- ContinuedFraction.Partials - Interface in com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
-
This interface defines a continued fraction in terms of
the partial numerators an, and the partial denominators bn.
- ConvergenceFailure - Exception in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative
-
This exception is thrown by
IterativeLinearSystemSolver#solve(com.numericalmethod.suanshu.matrix.doubles.linearsystem.LSProblem)
when the iterative algorithm detects a breakdown or fails to converge.
- ConvergenceFailure(ConvergenceFailure.Reason) - Constructor for exception com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
-
Construct an exception with reason.
- ConvergenceFailure(ConvergenceFailure.Reason, String) - Constructor for exception com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
-
Construct an exception with reason and error message.
- ConvergenceFailure.Reason - Enum in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative
-
the reasons for the convergence failure
- cookDistances - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Diagnostics
-
Cook distance
- Coordinates - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
-
The location of a matrix entry is specified by a 2D coordinates (i,
j), where i and j are the row-index and column-index of the
entry respectively.
- Coordinates(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.Coordinates
-
Construct a matrix coordinate specifying an entry location.
- coordinates - Variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseEntry
-
the coordinates of this entry
- copyAndReplace(Matrix, int, int, int, int, Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
- correlation() - Method in class com.numericalmethod.suanshu.stats.descriptive.Covariance
-
Get the correlation.
- CorrelationMatrix - Class in com.numericalmethod.suanshu.stats.descriptive
-
The correlation matrix of n random variables X1, ..., Xn is the n × n matrix whose i,j entry is corr(Xi, Xj),
the correlation between X1 and Xn.
- CorrelationMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.CorrelationMatrix
-
Construct a correlation matrix from a covariance matrix.
- cos(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Cosine of a complex number.
- cosh(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Hyperbolic cosine of a complex number.
- COST - Static variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- count(double) - Method in class com.numericalmethod.suanshu.number.Counter
-
Get the count, i.e., the number of occurrences, of a particular number.
- Counter - Class in com.numericalmethod.suanshu.number
-
A counter keeps track of the number of occurrences of numbers.
- Counter() - Constructor for class com.numericalmethod.suanshu.number.Counter
-
Construct a counter with no rounding.
- Counter(int) - Constructor for class com.numericalmethod.suanshu.number.Counter
-
Construct a counter.
- CountMonitor<S> - Class in com.numericalmethod.suanshu.algorithm.iterative.monitor
-
This
IterationMonitor counts the number of iterates generated, hence
the number of iterations.
- CountMonitor() - Constructor for class com.numericalmethod.suanshu.algorithm.iterative.monitor.CountMonitor
-
- Courant - Class in com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod
-
This penalty function sums up the squared error penalties.
- Courant(EqualityConstraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Courant
-
Construct a Courant penalty function from a collection of equality constraints.
- Courant(EqualityConstraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Courant
-
Construct a Courant penalty function from a collection of equality constraints.
- Courant(EqualityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Courant
-
Construct a Courant penalty function from a collection of equality constraints.
- Covariance - Class in com.numericalmethod.suanshu.stats.descriptive
-
Covariance is a measure of how much two variables change together.
- Covariance() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.Covariance
-
Construct an empty Covariance calculator.
- Covariance(double[][]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.Covariance
-
Construct a Covariance calculator, initialized with two samples.
- Covariance(Covariance) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.Covariance
-
Copy constructor.
- covariance - Variable in class com.numericalmethod.suanshu.stats.regression.linear.Beta
-
the covariance matrix of the coefficient estimates, β^
- covariance(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.InnovationAlgorithmImpl
-
Get the covariance matrix for prediction errors at time t for X^t+1.
- covariance() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFitting
-
Get the asymptotic covariance matrix of the estimators.
- covariance() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Compute the asymptotic covariance matrix for the estimated parameters, φ and θ.
- CovarianceMatrix - Class in com.numericalmethod.suanshu.stats.descriptive
-
This class computes the Covariance matrix of a matrix,
where the (i, j) entry is the covariance of the i-th column and j-th column of the matrix.
- CovarianceMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.CovarianceMatrix
-
Construct the covariance matrix of a matrix.
- Cr - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
the crossover probability
- CreateMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
These are the utility functions to create a new matrix/vector from existing ones.
- CreateVector - Class in com.numericalmethod.suanshu.vector.doubles.dense.operation
-
These are the utility functions that create new instances of vectors from existing ones.
- crossover(Chromosome) - Method in interface com.numericalmethod.suanshu.optimization.geneticalgorithm.Chromosome
-
Construct a Chromosome by crossing over a pair of chromosomes.
- crossover(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell
-
- crossover(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell
-
- crossover(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell
-
- crossover(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell
-
Crossover by taking the midpoint.
- CSRSparseMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
-
The Compressed Sparse Row (CSR) format for sparse matrix has this
representation: (value, col_ind, row_ptr).
- CSRSparseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Construct a sparse matrix in CSR format.
- CSRSparseMatrix(int, int, int[], int[], double[]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Construct a sparse matrix in CSR format.
- CSRSparseMatrix(int, int, List<SparseEntry>) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Construct a sparse matrix in CSR format by a list of non-zero entries.
- CSRSparseMatrix(CSRSparseMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Copy constructor.
- Ctor2x2(double, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
Same as new GivensMatrix(2, 1, 2, c, s).
- CtorFromRho(int, int, int, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
Construct a Givens matrix from ρ.
- CtorToRotateColumns(int, int, int, double, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
Construct a Givens matrix such that [a b] * G = [* 0].
- CtorToRotateRows(int, int, int, double, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
Construct a Givens matrix such that G * [a b]t = [* 0]t.
- CtorToZeroOutEntry(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
Construct a Givens matrix such that G * A has 0 in the [i,j] entry.
- CtorToZeroOutEntryByTranspose(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
Construct a Givens matrix such that Gt * A has 0 in the [i,j] entry.
- CubicRoot - Class in com.numericalmethod.suanshu.analysis.function.polynomial.root
-
This is a cubic equation solver.
- CubicRoot() - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.root.CubicRoot
-
- cumsum(double[]) - Static method in class com.numericalmethod.suanshu.misc.R
-
Get the cumulative sums of the elements in an array.
- cumsum(int[]) - Static method in class com.numericalmethod.suanshu.misc.R
-
Get the cumulative sums of the elements in an array.
- cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
-
- cumulant(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.ExponentialDistribution
-
The cumulant function of the exponential distribution.
- cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gamma
-
- cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gaussian
-
- cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
-
- cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Poisson
-
- CumulativeNormalHastings - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
Hastings algorithm is faster but less accurate way to compute the cumulative standard Normal.
- CumulativeNormalHastings() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalHastings
-
- CumulativeNormalInverse - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
The inverse of the cumulative standard Normal distribution function is defined as:
\[
N^{-1}(u)
/]
This implementation uses the Beasley-Springer-Moro algorithm.
- CumulativeNormalInverse() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalInverse
-
- CumulativeNormalMarsaglia - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
Marsaglia is about 3 times slower but is more accurate to compute the cumulative standard Normal.
- CumulativeNormalMarsaglia() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalMarsaglia
-
- cumulativeProportionVar() - Method in interface com.numericalmethod.suanshu.stats.pca.PCA
-
Get the cumulative proportion of overall variance explained by the principal components
- cut(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane.GomoryMixedCut.MyCutter
-
- cut(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane.GomoryPureCut.MyCutter
-
- cut(SimplexTable) - Method in interface com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane.SimplexCuttingPlane.CutterFactory.Cutter
-
Cut a simplex table.
- D() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.EigenDecomposition
-
Get the diagonal matrix D as in Q * D * Q' = A.
- D() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.GloubKahanSVD
-
- D() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVD
-
- D() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVDDecomposition
-
Get the D matrix as in SVD decomposition.
- D() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LDL
-
Get D the the diagonal matrix in the LDL decomposition.
- D() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Get the diagonal matrix D in the LDL decomposition.
- d - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.Brownian
-
the dimension of this Brownian motion
- d - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization.Iterator
-
the dimension of the Brownian motion
- d() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Get the order of integration.
- d() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the order of integration.
- DAgostino - Class in com.numericalmethod.suanshu.stats.test.distribution.normality
-
D'Agostino's K2 test is a goodness-of-fit measure of departure from normality.
- DAgostino(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
-
Perform D'Agostino's test to test for the departure from normality.
- dampedBFGSHessianUpdate(Matrix, Vector, Vector) - Static method in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.BFGS
-
Damped BFGS Hessian update.
- DateTimeGenericTimeSeries<V> - Class in com.numericalmethod.suanshu.stats.timeseries
-
This is a generic time series where time is indexed by DateTime and value can be any type.
- DateTimeGenericTimeSeries(DateTime[], V[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.DateTimeGenericTimeSeries
-
Construct a time series.
- DateTimeGenericTimeSeries.Entry<V> - Class in com.numericalmethod.suanshu.stats.timeseries
-
This is the TimeSeries.Entry for a DateTime -indexed time series.
- DateTimeTimeSeries - Class in com.numericalmethod.suanshu.stats.timeseries.univariate
-
This is a time series has its double values indexed by DateTime.
- DateTimeTimeSeries(DateTime[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.univariate.DateTimeTimeSeries
-
Construct a time series from DateTime and double.
- DateTimeTimeSeries(ArrayList<DateTime>, ArrayList<Double>) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.univariate.DateTimeTimeSeries
-
Construct a time series from DateTime and double.
- dB(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization.Iterator
-
- dB(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
-
Get the Brownian increment at the t-th time grid point.
- dB(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.Realization.Iterator
-
- db(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Milstein
-
- DBeta - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
This is the first order derivative function of the
Beta function w.r.t
x, \({\partial \over \partial x} \mathrm{B}(x, y)\).
- DBeta() - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBeta
-
- DBetaRegularized - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
This is the first order derivative function of the Regularized Incomplete Beta function,
BetaRegularized, w.r.t the upper limit,
x.
- DBetaRegularized(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBetaRegularized
-
Construct the derivative function of the Regularized Incomplete Beta function,
BetaRegularized.
- dBt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
-
Get all the Brownian increments.
- DeBest2BinCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Best2Bin.DeBest2BinCell
-
- deepCopy() - Method in interface com.numericalmethod.suanshu.DeepCopyable
-
The implementation returns an instance created from this by the copy
constructor of the class, or just this if the instance itself is
immutable.
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
Make a deep copy of the underlying matrix.
- deepCopy() - Method in interface com.numericalmethod.suanshu.matrix.doubles.Matrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
Return
this as this
Matrix is immutable.
- deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
Return this as the reference is immutable.
- deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
-
- deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtWt
-
- deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
- deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt
-
- deepCopy() - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- deepCopy() - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- deepCopy() - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
- DeepCopyable - Interface in com.numericalmethod.suanshu
-
This interface provides a way to do polymorphic copying.
- DEFAULT_NLAGS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.Invertibility
-
the default number of lags
- DEFAULT_NUMBER_OF_LAGS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.LinearRepresentation
-
the default number of lags
- DEFAULT_NUMBER_OF_LAGS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
-
the default number of lags
- DEFAULT_PENALTY_FUNCTION_FACTORY - Static variable in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
the default penalty function factory
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
The algorithm recomputes the residual as b - Axi once
per this number of iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
The algorithm recomputes the residual as b - Axi once per this number of iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
The algorithm recomputes the residual as b - Axi once per this number of iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
The algorithm recomputes the residual as b - Axi once per this number of iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
The algorithm recomputes the residual as b - Axi once per this number of iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
The algorithm recomputes the residual as b - Axi once per this number of iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
-
The algorithm recomputes the residual as b - Axi once per this number of iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
The algorithm recomputes the residual as b - Axi once per this number of iterations
- DEFAULT_TOLERANCE - Static variable in class com.numericalmethod.suanshu.algorithm.iterative.tolerance.AbsoluteTolerance
-
default tolerance
- DEFAULT_TOLERANCE - Static variable in class com.numericalmethod.suanshu.algorithm.iterative.tolerance.RelativeTolerance
-
default tolerance
- DefaultDeflationCriterion(double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.Hessenberg.DefaultDeflationCriterion
-
Construct the default deflation criterion.
- DefaultDeflationCriterion() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.Hessenberg.DefaultDeflationCriterion
-
Construct the default deflation criterion.
- DefaultSimplex - Class in com.numericalmethod.suanshu.optimization.initialization
-
A simplex optimization algorithm, e.g., Nelder–Mead, requires an initial simplex to start the search.
- DefaultSimplex(double) - Constructor for class com.numericalmethod.suanshu.optimization.initialization.DefaultSimplex
-
Construct a simplex builder.
- DefaultSimplex() - Constructor for class com.numericalmethod.suanshu.optimization.initialization.DefaultSimplex
-
Construct a simplex builder.
- deflationCriterion - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.Hessenberg
-
- degree() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Get the degree of this polynomial.
- deleteCol(int) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Delete column i.
- deleteRow(int) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Delete row i.
- delta - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- DenseData - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense
-
This implementation of the storage of a dense matrix stores the data of a 2D matrix as an 1D array.
- DenseData(double[], DoubleArrayOperation) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
-
Construct a storage, and specify the implementations of the element-wise operations.
- DenseData(double[]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
-
Construct a storage.
- DenseData(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
-
Construct a storage and initialize all data content to 0.
- DenseMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense
-
This class implements the standard, dense, double based matrix representation.
- DenseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
Construct a 0 matrix of dimension nRows * nCols.
- DenseMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
Construct a matrix from a 2D double[][] array.
- DenseMatrix(double[], int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
Construct a matrix from a 1D double[].
- DenseMatrix(Vector) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
Construct a column matrix from a vector.
- DenseMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
Convert any matrix to the standard matrix representation.
- DenseMatrix(DenseMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
Copy constructor performing a deep copy.
- DenseVector - Class in com.numericalmethod.suanshu.vector.doubles.dense
-
This class implements the standard, dense, double based vector representation.
- DenseVector(int) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
Construct a vector.
- DenseVector(int, double) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
Construct a vector, initialized by repeating a value.
- DenseVector(double...) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
Construct a vector, initialized by a double[].
- DenseVector(int[]) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
Construct a vector, initialized by a int[].
- DenseVector(Matrix) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
Construct a vector from a column or row matrix.
- DenseVector(Vector) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
Cast any vector to a DenseVector.
- DenseVector(DenseVector) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
Copy constructor.
- Densifiable - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense
-
This interface specifies whether a matrix implementation can be efficiently converted to the standard dense matrix representation.
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
This is the probability mass function.
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
This is the probability mass function for the discrete sample.
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- density(double) - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
The density function, which, if exists, is the derivative of F.
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- density(int, double) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.HiddenMarkovModel
-
Get the probability density of making an observation in a particular state.
- density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- DEOptim - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim
-
Differential Evolution (DE) is a global optimization method.
- DEOptim(DEOptim.NewCellFactory, boolean, RandomLongGenerator, double, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptim
-
Construct a DEOptim to solve unconstrained minimization problems.
- DEOptim(double, double, boolean, double, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptim
-
Construct a DEOptim to solve unconstrained minimization problems.
- DEOptim.NewCellFactory - Interface in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim
-
This factory constructs a new DEOptimCellFactory for each minimization problem.
- DEOptim.Solution - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim
-
This is the solution to a minimization problem using DEOptim.
- DeOptimCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell
-
- DEOptimCellFactory - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim
-
A DEOptimCellFactory produces DeOptimCells.
- DEOptimCellFactory(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Construct an instance of a DEOptimCellFactory.
- DEOptimCellFactory(DEOptimCellFactory) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Copy constructor.
- DEOptimCellFactory.DeOptimCell - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim
-
A DeOptimCell is a chromosome for a real valued function (an optimization problem) and a candidate solution.
- DeRand1BinCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell
-
- DErf - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
This is the first order derivative function of the Error function,
Erf.
- DErf() - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DErf
-
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Cloglog
-
Derivative of the link function, i.e., g'(x).
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Identity
-
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Inverse
-
Derivative of the link function, i.e., g'(x).
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.InverseSquared
-
- derivative(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkFunction
-
Derivative of the link function, i.e., g'(x).
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Log
-
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Logit
-
Derivative of the link function, i.e., g'(x).
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Probit
-
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Sqrt
-
Derivative of the link function, i.e., g'(x).
- det() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.HilbertMatrix
-
The determinant of a Hilbert matrix is the reciprocal of an integer.
- det(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixMeasure
-
Compute the determinant of a matrix.
- deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
-
- deviance(double, double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.ExponentialDistribution
-
Deviance D(y;μ^) measures the goodness-of-fit of a model, which is defined as
the difference between the maximum log likelihood achievable and that achieved by the model.
- deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Family
-
Deviance D(y;μ^) measures the goodness-of-fit of a model, which is defined as
the difference between the maximum log likelihood achievable and that achieved by the model.
- deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gamma
-
- deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gaussian
-
- deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
-
- deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Poisson
-
- deviance - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.Residuals
-
deviance
- deviance - Variable in class com.numericalmethod.suanshu.stats.regression.linear.logistic.Residuals
-
the residual deviance
- devianceResiduals - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.Residuals
-
deviances residuals
- devianceResiduals - Variable in class com.numericalmethod.suanshu.stats.regression.linear.logistic.Residuals
-
the residuals, ε
- deviances() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.Residuals
-
- deviances - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.Residuals
-
deviances of observations
- deviances() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.Residuals
-
- df(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference
-
Compute the finite difference for f at x with an increment h for the n-th order using either forward, backward, or central difference.
- df - Variable in class com.numericalmethod.suanshu.stats.test.mean.T
-
degree of freedom
- df - Variable in class com.numericalmethod.suanshu.stats.test.variance.Bartlett
-
the degree of freedom
- df1 - Variable in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
-
degree of freedoms
- df1 - Variable in class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
-
degree of freedoms
- df1 - Variable in class com.numericalmethod.suanshu.stats.test.variance.F
-
the degree of freedoms
- df1 - Variable in class com.numericalmethod.suanshu.stats.test.variance.Levene
-
the degree of freedoms
- df2 - Variable in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
-
degree of freedoms
- df2 - Variable in class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
-
degree of freedoms
- df2 - Variable in class com.numericalmethod.suanshu.stats.test.variance.F
-
the degree of freedoms
- df2 - Variable in class com.numericalmethod.suanshu.stats.test.variance.Levene
-
the degree of freedoms
- Dfdx - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
The first derivative is a measure of how a function changes as its input changes.
- Dfdx(UnivariateRealFunction, Dfdx.Method) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx
-
Construct the first order derivative function of a univariate function f.
- Dfdx(UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx
-
Construct, using the central finite difference, the first order derivative function of a univariate function f.
- Dfdx.Method - Enum in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
the methods available to compute the numerical derivative
- DFFITS - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Diagnostics
-
DFFITS, Welsch and Kuh Measure
- DFP - Class in com.numericalmethod.suanshu.optimization.unconstrained.quasinewton
-
The Davidon-Fletcher-Powell method is a quasi-Newton method
to solve unconstrained nonlinear optimization problems.
- DFP(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.DFP
-
Construct a multivariate minimizer using the DFP method.
- DGamma - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
This is the first order derivative function of the Gamma function, \({d \mathrm{\Gamma}(x) \over dx}\).
- DGamma() - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGamma
-
- DGaussian - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
This is the first order derivative function of a
Gaussian function, \({d \mathrm{\phi}(x) \over dx}\).
- DGaussian(Gaussian) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGaussian
-
Construct the derivative function of a Gaussian function.
- Dhat() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Get the modified diagonal matrix which is positive definite.
- Diagnostics - Class in com.numericalmethod.suanshu.stats.regression.linear.ols
-
This class collects some diagnostics measures for the goodness of fit for an Ordinary Least Square linear regression model.
- diagnostics - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression
-
the diagnostic measures of this linear regression
- diagonal(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a square matrix is a diagonal matrix, up to a precision.
- diagonal(Matrix) - Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.CreateVector
-
Get the diagonal of a matrix as a vector.
- DiagonalMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal
-
A diagonal matrix has non-zero entries only on the main diagonal.
- DiagonalMatrix(double[]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Construct a diagonal matrix from a double[].
- DiagonalMatrix(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Construct a 0 diagonal matrix of dimension dim * dim.
- DiagonalMatrix(DiagonalMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Copy constructor.
- diagonalMatrix(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Get the diagonal of a matrix.
- diff(double[], int, int) - Static method in class com.numericalmethod.suanshu.misc.R
-
Get the lagged and iterated differences.
- diff(double[]) - Static method in class com.numericalmethod.suanshu.misc.R
-
Get the first differences of an array.
- diff(double[][], int, int) - Static method in class com.numericalmethod.suanshu.misc.R
-
Get the lagged and iterated differences of vectors.
- diff(double[][]) - Static method in class com.numericalmethod.suanshu.misc.R
-
Get the first differences of an array of vectors.
- diff(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
Construct an instance of GenericTimeTimeSeries by taking the first difference d times.
- diff(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
Construct an instance of SimpleMultiVariateTimeSeries by taking the first difference d times.
- diff(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
Construct an instance of GenericTimeTimeSeries by taking the first difference d times.
- diff(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
-
Construct an instance of SimpleTimeSeries by taking the first difference d times.
- Diffusion - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
This represents the diffusion term, σ, of an SDE.
- Diffusion - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients
-
This class represents the diffusion term, σ, of a univariate SDE.
- Digamma - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The digamma function is defined as the logarithmic derivative of the gamma function.
- Digamma() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.Digamma
-
- dim() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
-
Get the dimension of the process.
- dimension() - Method in interface com.numericalmethod.suanshu.optimization.constrained.constraint.Constraints
-
Get the number of variables.
- dimension() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.general.GeneralConstraints
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearConstraints
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.problem.QPProblem
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.IPProblemImpl1
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPNode
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.constrained.problem.ConstrainedOptimProblemImpl1
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.constrained.problem.NonNegativityConstraintOptimProblem
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
- dimension() - Method in interface com.numericalmethod.suanshu.optimization.problem.OptimProblem
-
Get the number of variables.
- dimension() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Get the dimension of the system, i.e., the dimension of the state vector.
- dimension() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.ObservationEquation
-
Get the dimension of observation yt.
- dimension() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.StateEquation
-
Get the dimension of state xt.
- dimension() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.ObservationEquation
-
Get the dimension of observation yt.
- dimension() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Get the dimension of state xt.
- dimension() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk.MultiVariateRealization
-
- dimension() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Get the dimension of multivariate time series.
- dimension() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the dimension of multivariate time series.
- dimension() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
- dimension() - Method in interface com.numericalmethod.suanshu.stats.timeseries.multivariate.MultiVariateTimeSeries
-
Get the dimension of the multivariate time series.
- dimension() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
- DimensionCheck - Class in com.numericalmethod.suanshu.datastructure
-
These are the utility functions for checking table dimension.
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.FiniteDifference
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.GradientFunction
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.HessianFunction
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.JacobianFunction
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
- dimensionOfDomain() - Method in interface com.numericalmethod.suanshu.analysis.function.Function
-
Get the number of variables the function has.
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.Projection
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.QuadraticFunction
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.UnivariateRealFunction
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.MultiplierPenalty
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.SumOfPenalties
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.ZERO
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.FiniteDifference
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.GradientFunction
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.HessianFunction
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.JacobianFunction
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
- dimensionOfRange() - Method in interface com.numericalmethod.suanshu.analysis.function.Function
-
Get the dimension of the range space of the function.
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.Projection
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.QuadraticFunction
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.UnivariateRealFunction
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyFunction
-
- DiscreteSampling<X> - Class in com.numericalmethod.suanshu.stats.sampling.discrete
-
This class samples from a discrete probability distribution.
- DiscreteSampling(Iterable<X>, ProbabilityMassFunction<X>) - Constructor for class com.numericalmethod.suanshu.stats.sampling.discrete.DiscreteSampling
-
- discretization - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- DiscretizedSDE - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
This interface represents the discretized version of a multivariate SDE.
- DiscretizedSDE - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
This interface represents the discretized version of a univariate SDE.
- dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
-
- dispersion(Vector, Vector, int) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.ExponentialDistribution
-
Different distribution models have different ways to compute dispersion, φ.
- dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gamma
-
- dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gaussian
-
- dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
-
- dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Poisson
-
- divide(F) - Method in interface com.numericalmethod.suanshu.mathstructure.Field
-
/ : F × F → F
That is the same as
this.multiply(that.inverse())
- divide(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- divide(Complex) - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Compute the quotient of this complex number divided by another complex number.
- divide(Real) - Method in class com.numericalmethod.suanshu.number.Real
-
- divide(Real, int) - Method in class com.numericalmethod.suanshu.number.Real
-
/ : R × R → R
Divide this number by another one.
- divide(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- divide(Vector, Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- divide(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- divide(Vector) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Divide this by that, entry-by-entry.
- dk - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.QuasiNewton.QuasiNewtonImpl
-
the line search direction at the k-th iteration
- DLM - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification.
- DLM(Vector, Matrix, ObservationEquation, StateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.DLM
-
Construct a (multivariate) controlled dynamic linear model.
- DLM(DLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.DLM
-
Copy constructor.
- DLM - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification.
- DLM(double, double, ObservationEquation, StateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Construct a univariate controlled dynamic linear model.
- DLM(DLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Copy constructor.
- DLMSeries - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
This is a simulator for a multivariate controlled dynamic linear model process.
- DLMSeries(int, DLM, MultiVariateTimeSeries, NormalRvg) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.DLMSeries
-
Simulate a multivariate controlled dynamic linear model process.
- DLMSeries(int, DLM, MultiVariateTimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.DLMSeries
-
Simulate a multivariate controlled dynamic linear model process.
- DLMSeries(int, DLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.DLMSeries
-
Simulate a multivariate controlled dynamic linear model process.
- DLMSeries - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
This is a simulator for a multivariate controlled dynamic linear model process.
- DLMSeries(int, DLM, double[], NormalRng) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
Simulate a univariate controlled dynamic linear model process.
- DLMSeries(int, DLM, double[]) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
Simulate a univariate controlled dynamic linear model process.
- DLMSeries(int, DLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
Simulate a univariate controlled dynamic linear model process.
- DLMSeries.Entry - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
This is the TimeSeries.Entry for a multivariate DLM time series.
- DLMSeries.Entry - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
This is the TimeSeries.Entry for a univariate DLM time series.
- DLMSim - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
This is a simulator for a multivariate controlled dynamic linear model process.
- DLMSim(DLM, NormalRvg) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.DLMSim
-
Simulate a multivariate controlled dynamic linear model process.
- DLMSim - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
This is a simulator for a univariate controlled dynamic linear model process.
- DLMSim(DLM, NormalRng) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSim
-
Simulate a univariate controlled dynamic linear model process.
- DLMSim.Innovation - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
a simulated innovation
- DLMSim.Innovation - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
a simulated innovation
- dof() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FAEstimator
-
Get the degree of freedom in the factor analysis model.
- DOKSparseMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
-
The Dictionary Of Key (DOK) format for sparse matrix uses the coordinates of
non-zero entries in the matrix as keys.
- DOKSparseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
Construct a sparse matrix in DOK format.
- DOKSparseMatrix(int, int, int[], int[], double[]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
Construct a sparse matrix in DOK format.
- DOKSparseMatrix(int, int, List<SparseEntry>) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
Construct a sparse matrix in DOK format by a list of non-zero entries.
- DOKSparseMatrix(DOKSparseMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
Copy constructor.
- domain - Variable in class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
the integer values the variable can take
- Doolittle - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.triangle
-
Doolittle algorithm is an LU decomposition of a square matrix.
- Doolittle(Matrix, boolean, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Doolittle
-
Run the Doolittle algorithm on a square matrix for LU decomposition.
- Doolittle(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Doolittle
-
Run the Doolittle algorithm on a square matrix for LU decomposition.
- dotProduct(long[], long[]) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
\(x_1 \cdot x_2\)
This operation is called inner product when used in the context of vector space.
- dotProduct(double[], double[]) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
\(x_1 \cdot x_2\)
This operation is called inner product when used in the context of vector space.
- doubleArray2intArray(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert a double array to an int array, rounding down if necessary.
- doubleArray2List(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert a double array to a list.
- DoubleArrayMath - Class in com.numericalmethod.suanshu.number.doublearray
-
These are the math functions that operate on double[].
- DoubleArrayOperation - Interface in com.numericalmethod.suanshu.number.doublearray
-
It is possible to provide different implementations for different platforms, hardware, etc.
- DoubleExponential - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This transformation speeds up the convergence of the Trapezoidal Rule exponentially.
- DoubleExponential(UnivariateRealFunction, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential
-
Construct a DoubleExponential substitution rule by trying to automatically determine the substitution rule.
- DoubleExponential4HalfRealLine - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This transformation is good for the region \((0, +\infty)\).
- DoubleExponential4HalfRealLine(UnivariateRealFunction, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential4HalfRealLine
-
Construct a DoubleExponential4HalfRealLine substitution rule.
- DoubleExponential4RealLine - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This transformation is good for the region \((-\infty, +\infty)\).
- DoubleExponential4RealLine(UnivariateRealFunction, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential4RealLine
-
Construct a DoubleExponential4RealLine substitution rule.
- DoubleUtils - Class in com.numericalmethod.suanshu.number
-
These are the utility functions to manipulate double and int.
- DoubleUtils.RoundingScheme - Enum in com.numericalmethod.suanshu.number
-
the schemes available to round a number.
- doubleValue() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
Construct a
DenseMatrix equivalent of this Complex matrix (rounded if necessary).
- doubleValue() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
Construct a
DenseMatrix equivalent of this Real matrix (rounded if necessary).
- doubleValue() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- doubleValue() - Method in class com.numericalmethod.suanshu.number.Real
-
- doubleValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
- DPolynomial - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
This is the first order derivative function of a
Polynomial, which, again, is a polynomial.
- DPolynomial(Polynomial) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DPolynomial
-
Construct the derivative function of a
Polynomial, which, again, is a polynomial.
- Drift - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
This represents the drift term, μ, of an SDE.
- Drift - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients
-
This class represents the drift term, μ, of a univariate SDE.
- drop(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
Construct an instance of GenericTimeTimeSeries by dropping the leading nItems entries,
those most backward in time entries.
- drop(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
Construct an instance of SimpleMultiVariateTimeSeries by dropping the leading nItems entries.
- drop(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
Construct an instance of GenericTimeTimeSeries by dropping the leading nItems entries.
- drop(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
-
Construct an instance of SimpleTimeSeries by dropping the leading nItems entries.
- dt - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
-
the time differential
- dt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
-
Get the current time differential.
- dt(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
-
Get the t-th time increment.
- dt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
-
Get all the time increments.
- dt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Get the current time differential.
- du() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDB
-
- du() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDt
-
- du() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integrator
-
Get an array of the measure values.
- DuplicatedAbscissae - Exception in com.numericalmethod.suanshu.analysis.interpolation
-
This exception is thrown when a function has two same x-abscissae, hence ill-defined.
- DuplicatedAbscissae(String) - Constructor for exception com.numericalmethod.suanshu.analysis.interpolation.DuplicatedAbscissae
-
Construct a DuplicatedAbscissae runtime exception.
- dWt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
-
Get the increment of the driving Brownian motion during the time differential.
- dWt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Get the increment of the driving Brownian motion during the time differential.
- dx() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential
-
- dx() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.Exponential
-
- dx() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.InvertingVariable
-
- dx() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
-
- dx() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
-
- dx() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.StandardInterval
-
- dx() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.SubstitutionRule
-
the first order derivative of the transformation: x'(t) = dx(t)/dt
- dXt(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.Brownian
-
- dXt(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.DiscretizedSDE
-
This is the SDE specification of a stochastic process.
- dXt(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Euler
-
This is the SDE specification of a stochastic process.
- dXt(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.Brownian
-
- dXt(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.DiscretizedSDE
-
This is the SDE specification of the stochastic process.
- dXt(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Euler
-
This is the SDE specification of the stochastic process.
- dXt(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Milstein
-
This is the SDE specification of the stochastic process.
- Eigen - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
-
Given a square matrix A, an eigenvalue λ and its associated eigenvector v are defined by Av = λv.
- Eigen(Matrix, Eigen.Method, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
-
Compute the eigenvalues and eigenvectors for a square matrix.
- Eigen(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
-
Compute the eigenvalues and eigenvectors for a square matrix.
- eigen() - Method in class com.numericalmethod.suanshu.stats.pca.PCAbyEigen
-
Get the eigenvalue decomposition of the correlation (or covariance) matrix.
- Eigen.Method - Enum in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
-
the methods available to compute eigenvalues and eigenvectors
- eigenbasis() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.EigenProperty
-
Get the eigenvectors.
- EigenDecomposition - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
-
Let A be a square, diagonalizable N × N matrix with N linearly independent eigenvectors.
- EigenDecomposition(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.EigenDecomposition
-
Run the eigen decomposition on a square matrix.
- EigenDecomposition(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.EigenDecomposition
-
Run the eigen decomposition on a square matrix.
- EigenProperty - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
-
Eigen.Property is a read-only structure that contains the information
about a particular eigenvalue,
such as its multiplicity and eigenvectors.
- eigenvalue() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.EigenProperty
-
Get the eigenvalue.
- eigenVector() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.EigenProperty
-
Get an eigenvector.
- ElementaryFunction - Class in com.numericalmethod.suanshu.number.complex
-
This class contains some elementary functions for complex number,
Complex.
- ElementaryFunction() - Constructor for class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
- ElementaryOperation - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
There are three elementary row operations which are equivalent to left multiplying an elementary matrix.
- ElementaryOperation(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
Construct a transformation matrix of elementary operations.
- ElementaryOperation(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
Construct a transformation matrix of elementary operations.
- ElementaryOperation(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
Transform A by elementary operations.
- EmpiricalDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
An empirical cumulative probability distribution function
is a cumulative probability distribution function that
assigns probability 1/n at each of the n numbers in a sample.
- EmpiricalDistribution(double[], Quantile.QuantileType) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
Construct an empirical distribution from a sample.
- EmpiricalDistribution(double[]) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- end() - Method in class com.numericalmethod.suanshu.interval.Interval
-
Get the end of this interval.
- ENDING_OF_TIME - Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
This represents a time after all (representable) times.
- ENDING_OF_TIME_LONG - Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
This represents a time after all (representable) times, in long representation.
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- entropy() - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
Get the entropy of this distribution.
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- Entry(T, Vector) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.MultiVariateTimeSeries.Entry
-
Construct an instance of TimeSeries.Entry.
- Entry(double, Vector) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.MultiVariateRealization.Entry
-
Construct an instance of TimeSeries.Entry.
- Entry(int, Vector) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.MultiVariateTimeSeries.Entry
-
Construct an instance of Entry.
- Entry(double, double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.Realization.Entry
-
Construct an instance of TimeSeries.Entry.
- Entry(int, double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.TimeSeries.Entry
-
Construct an instance of TimeSeries.Entry.
- Entry(T, double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.univariate.TimeSeries.Entry
-
Construct an instance of Entry.
- EPSILON - Static variable in class com.numericalmethod.suanshu.Constant
-
the default epsilon used in this library
- epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.GloubKahanSVD
-
- epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVD
-
- epsilon - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- epsilon - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver
-
- epsilon() - Method in interface com.numericalmethod.suanshu.optimization.constrained.integer.IPProblem
-
Get the threshold to check whether a variable is an integer.
- epsilon() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.IPProblemImpl1
-
- epsilon() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
- epsilon - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- epsilon - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent
-
a precision parameter: when a number |x| ≤ ε, it is considered 0
- epsilon - Variable in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch
-
the convergence tolerance
- epsilon - Variable in class com.numericalmethod.suanshu.optimization.univariate.GridSearch
-
a precision parameter: when a number |x| ≤ ε, it is considered 0
- equal(Matrix, Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.AreMatrices
-
Check the equality of two matrices up to a precision.
- equal(Vector, Vector, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.AreMatrices
-
Check if two vectors are equal, i.e., v1 - v2 is a zero vector, up to a precision.
- equal(double, double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if two doubles are close enough, hence equal.
- equal(double[], double[], double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if two double arrays are close enough, hence equal, entry-by-entry.
- equal(double[][], double[][], double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if two 2D arrays, double[][], are close enough, hence equal, entry-by-entry.
- equal(int[], int[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if two int arrays, int[], are equal, entry-by-entry.
- equal(Number, Number, double) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
-
Check the equality of two Numbers, up to a precision.
- equal(Vector, Vector, double) - Static method in class com.numericalmethod.suanshu.vector.doubles.IsVector
-
Check the equality of two vectors up to a precision.
- EqualityConstraints - Interface in com.numericalmethod.suanshu.optimization.constrained.constraint
-
The domain of an optimization problem may be restricted by equality constraints.
- EqualityConstraints(Vector, SymmetricMatrix, SymmetricMatrix[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
-
Construct the equality constraints for a dual SDP problem, \(\sum_{i=1}^{p}y_i\mathbf{A_i}+\textbf{S} = \textbf{C}, \textbf{S} \succeq \textbf{0}\).
- EqualityConstraints(Vector, Matrix[], Vector[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
-
Construct the equality constraints for a dual SOCP problem, \(\max_y \mathbf{b'y} \textrm{ s.t.,} \\ \mathbf{\hat{A}_i'y + s_i = \hat{c}_i} \\ s_i \in K_i, i = 1, 2, ..., q\).
- equals(Object) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- equals(Object) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
- equals(Object) - Method in class com.numericalmethod.suanshu.interval.Interval
-
- equals(Object) - Method in class com.numericalmethod.suanshu.interval.Intervals
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.Coordinates
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseEntry
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- equals(BigDecimal, BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Check if two BigDecimals are equal up to a precision.
- equals(Object) - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- equals(Object) - Method in class com.numericalmethod.suanshu.number.Real
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.DateTimeGenericTimeSeries.Entry
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.MultiVariateTimeSeries.Entry
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.TimeSeries.Entry
-
- equals(Object) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- equals(Object) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- Erf - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
The Error function is defined as:
\[
\operatorname{erf}(x) = \frac{2}{\sqrt{\pi}}\int_{0}^x e^{-t^2} dt
\]
- Erf() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.Erf
-
- Erfc - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
This complementary Error function is defined as:
\[
\operatorname{erfc}(x)
= 1-\operatorname{erf}(x)
= \frac{2}{\sqrt{\pi}} \int_x^{\infty} e^{-t^2}\,dt
\]
- Erfc() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.Erfc
-
- ErfInverse - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
The inverse of the Error function is defined as:
\[
\operatorname{erf}^{-1}(x)
\]
- ErfInverse() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.ErfInverse
-
- error(T) - Method in interface com.numericalmethod.suanshu.optimization.minmax.MinMaxProblem
-
e(x, ω) is the error function, or the minmax objective, for a given ω.
- EST - Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
EST
- estimate - Variable in class com.numericalmethod.suanshu.stats.test.variance.F
-
the estimate of the ratio of two variances
- Euler - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde
-
The Euler method is a first-order numerical procedure for integrating stochastic differential equations (SDEs) with a given initial value.
- Euler(SDE, TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.Euler
-
Simulate an SDE using the Euler scheme at time points specified.
- Euler(SDE, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.Euler
-
Simulate an SDE using the Euler scheme at even time points, [0, 1, ......, T].
- Euler - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
The Euler scheme is the first order approximation of an SDE.
- Euler(SDE) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Euler
-
Discretize a multivariate SDE using the Euler scheme.
- Euler - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde
-
The Euler method is a first-order numerical procedure for integrating stochastic differential equations (SDEs) with a given initial value.
- Euler(SDE, TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Euler
-
Simulate an SDE using the Euler scheme at time points specified.
- Euler(SDE, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Euler
-
Simulate an SDE using the Euler scheme at even time points, [0, 1, ......, T].
- Euler - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
The Euler scheme is the first order approximation of an SDE.
- Euler(SDE) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Euler
-
Discretize a univariate SDE using the Euler scheme.
- EULER_MASCHERONI - Static variable in class com.numericalmethod.suanshu.Constant
-
the Euler–Mascheroni constant
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.FiniteDifference
-
Evaluate numerically the partial derivative of f at point x.
- evaluate(Vector, double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.FiniteDifference
-
Evaluate numerically the partial derivative of f at point x with step size h.
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.GradientFunction
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.HessianFunction
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.JacobianFunction
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
Evaluate the function f at x, where x is from the domain.
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
- evaluate(Vector, double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
Evaluate numerically the derivative of f at point x, f'(x), with step size h.
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBeta
-
Evaluate \({\partial \over \partial x} \mathrm{B}(x, y)\).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBetaRegularized
-
Evaluate \({\partial \over \partial x} \mathrm{B_x}(p, q)\).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DErf
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGamma
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGaussian
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference
-
Evaluate numerically the derivative of f at point x, f'(x), with step size h.
- evaluate(D) - Method in interface com.numericalmethod.suanshu.analysis.function.Function
-
Evaluate the function f at x, where x is from the domain.
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toConstantMatrix
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
-
Evaluate f(x) = A.
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
-
Evaluate f(x1, x2) = A.
- evaluate(Number) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Evaluate this polynomial at x.
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Evaluate this polynomial at x.
- evaluate(Complex) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Evaluate this polynomial at x.
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction
-
Evaluate y = f(x1,x2).
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.Projection
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.QuadraticFunction
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.RealScalarSubFunction
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.RealScalarSubFunction
-
Evaluate the function f at x.
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction
-
Evaluate y = f(x).
- evaluate(BigDecimal) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction
-
Evaluate z.
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.UnivariateRealFunction
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.UnivariateRealFunction
-
Evaluate y = f(x).
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2rm.RealVectorSubFunction
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.beta.Beta
-
Evaluate B(x,y).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.beta.BetaRegularized
-
Evaluate Ix(p,q).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.beta.BetaRegularizedInverse
-
Evaluate \(I^{-1}_{(p,q)}(u)\).
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.beta.LogBeta
-
Compute log(B(x,y)).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.Digamma
-
- evaluate(double) - Method in interface com.numericalmethod.suanshu.analysis.function.special.gamma.Gamma
-
Evaluate \(\Gamma(z) = \int_0^\infty e^{-t} t^{z-1} dt\).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaGergoNemes
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLanczos
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLanczosQuick
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLowerIncomplete
-
Evaluate \(\gamma(s,x)\).
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaRegularizedP
-
Evaluate P(s,x).
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaRegularizedPInverse
-
Evaluate x = P-1(s,u).
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaRegularizedQ
-
Evaluate Q(s,x).
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaUpperIncomplete
-
Compute Γ(s,x).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.LogGamma
-
Evaluate the log of the Gamma function in the positive real domain.
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.Trigamma
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalHastings
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalInverse
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalMarsaglia
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Erf
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Erfc
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.ErfInverse
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
-
- evaluate(double) - Method in interface com.numericalmethod.suanshu.analysis.function.special.gaussian.StandardCumulativeNormal
-
Evaluate \(F(x;\,\mu,\sigma^2)\).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.interpolation.LinearInterpolator
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable
-
- evaluate(double) - Method in interface com.numericalmethod.suanshu.analysis.sequence.Summation.Term
-
Evaluate the term for an index.
- evaluate(Constraints, Vector) - Static method in class com.numericalmethod.suanshu.optimization.constrained.constraint.ConstraintsUtils
-
Evaluate the constraints.
- evaluate(Matrix) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.Hp
-
Compute \(H_p(U) = \frac{1}{2}[PUP^{-1}]+P^{-*}U^*P^*\).
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.AbsoluteError
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Courant
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Fletcher
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.SumOfPenalties
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.ZERO
-
- evaluate(Realization[]) - Method in interface com.numericalmethod.suanshu.stats.cointegration.JohansenAsymptoticDistribution.F
-
F(B).
- evaluate(X) - Method in interface com.numericalmethod.suanshu.stats.distribution.ProbabilityMassFunction
-
Compute the probability mass for a discrete realization x.
- evaluate(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantDrift
-
- evaluate(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
-
- evaluate(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.Diffusion
-
σ(dt, Xt, Zt, ...)
- evaluate(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.Sigma
-
- evaluate(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ZeroDrift
-
- evaluate(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtAdaptedRealFunction
-
Evaluate this function, f, at time t.
- evaluate(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtAdaptedVectorFunction
-
Evaluate this function, f, at time t.
- evaluate(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Bt
-
- evaluate(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.FiltrationFunction
-
Compute the value at the t-th time point, f(T[t]).
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.FiltrationFunction
-
- evaluate(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtAdaptedFunction
-
Evaluate this function, f, at time t.
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.XtAdaptedFunction
-
Evaluate this function, f, based on only the current value of the stochastic process.
- evaluate(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.XtAdaptedFunction
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.AutoCorrelation
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.AutoCorrelation
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.AutoCovariance
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.AutoCovariance
-
Get the i-th auto-covariance matrix.
- evaluate(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCorrelation
-
Compute the auto-correlation for lag k.
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCorrelation
-
- evaluate(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCovariance
-
Compute the auto-covariance for lag k.
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCovariance
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.PartialAutoCorrelation
-
- evaluate(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.PartialAutoCorrelation
-
Compute the partial auto-correlation for lag k.
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
-
Get the i-th auto-correlation.
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
-
Get the i-th auto-covariance.
- EvaluationException(String) - Constructor for exception com.numericalmethod.suanshu.analysis.function.Function.EvaluationException
-
Constructs an EvaluationException with the specified detail message.
- EvenlySpacedGrid - Class in com.numericalmethod.suanshu.stats.stochasticprocess.timepoints
-
This class represents an evenly spaced/discretized time grid for a stochastic process.
- EvenlySpacedGrid(double, double, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.EvenlySpacedGrid
-
Construct an evenly spaced/discretized time grid.
- execute(Runnable) - Method in class com.numericalmethod.suanshu.parallel.Mutex
-
The runnable is executed under synchronization of this
Mutex instance.
- executeAll(List<? extends Callable<T>>) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
-
Executes a list of Callable tasks, and returns a list of results
in the same sequential order as tasks.
- executeAll(Callable<T>...) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
-
Executes an arbitrary number of Callable tasks, and returns a
list of results in the same order.
- executeAny(List<? extends Callable<T>>) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
-
Executes a list of tasks in parallel, and returns the result from the
earliest successfully completed tasks (without throwing an exception).
- executeAny(Callable<T>...) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
-
Executes a list of tasks in parallel, and returns the result from the
earliest successfully completed tasks (without throwing an exception).
- exp(double) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute ex.
- exp(double, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute ex.
- exp(BigDecimal) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute ex.
- exp(BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute ex.
- exp(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Exponential of a complex number.
- exp(double[]) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the exponentials of values.
- Expectation - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
This class computes the expectation of the following class of integrals.
- Expectation(Integrator, double, double, int, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Expectation
-
Compute the expectation for the integral of a stochastic process.
- Expectation - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde
-
This class computes the expectation of a stochastic integral.
- Expectation(Construction, double, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Expectation
-
Compute an expectation of a stochastic integral.
- Expectation(SDE, double, double, int, double, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Expectation
-
Compute an expectation of a stochastic integral.
- exponent() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
Get the exponent.
- Exponential - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This transformation is good for when the lower limit is finite, the upper limit is infinite, and the integrand falls off exponentially.
- Exponential(double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.Exponential
-
Construct an Exponential substitution rule.
- ExponentialDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The exponential distribution describes the times between events in a Poisson process,
a process in which events occur continuously and independently at a constant average rate.
- ExponentialDistribution() - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
Construct an instance of the standard exponential distribution, where the rate/lambda is 1.
- ExponentialDistribution(double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
Construct an exponential distribution.
- ExponentialDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Exponential distribution to model the observations.
- ExponentialDistribution(Double[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.ExponentialDistribution
-
Construct an Exponential distribution for each state in the HMM model.
- ExponentialDistribution - Interface in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
This interface represents a probability distribution from the exponential family.
- f - Variable in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
the original, unrestricted function
- f() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- f() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- f() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.problem.QPProblem
-
- f - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- f() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.IPProblemImpl1
-
- f() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPNode
-
- f() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
- f() - Method in class com.numericalmethod.suanshu.optimization.constrained.problem.ConstrainedOptimProblemImpl1
-
- f() - Method in class com.numericalmethod.suanshu.optimization.constrained.problem.NonNegativityConstraintOptimProblem
-
- F - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
the scaling factor
- f() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
Get the objective function.
- f - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- f() - Method in class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
- f() - Method in interface com.numericalmethod.suanshu.optimization.problem.OptimProblem
-
Get the objective function.
- f - Variable in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch.Solution
-
- F(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.ObservationEquation
-
Get F(t), the coefficient matrix of xt.
- F(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.ObservationEquation
-
Get F(t), the coefficient of xt.
- f - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
-
diagnostic measure: F statistics
mean of regression / mean squared error =
sum((y_i_hat-y_mean)^2) / mean squared error
[(TSS-RSS)/n] / [RSS/(m-n)]
y_i_hat are the fitted values of the regression.
- f - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integrator
-
the integrand
- F - Class in com.numericalmethod.suanshu.stats.test.variance
-
The FDistribution-test tests whether two normal populations have the same variance.
- F(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.F
-
Perform the FDistribution test to test for equal variance of two normal populations.
- F(double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.F
-
Perform the FDistribution test to test for equal variance of two normal populations.
- F - Variable in class com.numericalmethod.suanshu.stats.test.variance.F
-
the associated FDistribution distribution
- F_sum_BtDt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
a function of this integral
/1
I = | (B)(dt)
/0
- F_sum_BtDt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.F_sum_BtDt
-
- F_sum_tBtDt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
a function of this integral
/1
| (t - 0.5) * (B) (dt)
/0
- F_sum_tBtDt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.F_sum_tBtDt
-
- FactorAnalysis - Class in com.numericalmethod.suanshu.stats.factoranalysis
-
Factor analysis is a statistical method used to describe variability among
observed variables in terms of a potentially lower number of unobserved variables called factors.
- FactorAnalysis(Matrix, int, FactorAnalysis.ScoringRule, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.factoranalysis.FactorAnalysis
-
Perform factor analysis on the data set with a user defined scoring rule
and a user defined covariance (or correlation) matrix.
- FactorAnalysis(Matrix, int, FactorAnalysis.ScoringRule) - Constructor for class com.numericalmethod.suanshu.stats.factoranalysis.FactorAnalysis
-
Perform factor analysis on the data set with a user defined scoring rule.
- FactorAnalysis(Matrix, int) - Constructor for class com.numericalmethod.suanshu.stats.factoranalysis.FactorAnalysis
-
Perform factor analysis on the data set,
using Bartlett's weighted least-squares scores, and sample correlation matrix.
- FactorAnalysis.ScoringRule - Enum in com.numericalmethod.suanshu.stats.factoranalysis
-
These are the different ways to compute the factor analysis scores.
- factorial(int) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
n!
- factorial(int) - Static method in class com.numericalmethod.suanshu.number.big.BigIntegerUtils
-
Compute the n factorial.
- factory - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- factoryCtor - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- FAEstimator - Class in com.numericalmethod.suanshu.stats.factoranalysis
-
These are the estimators
(estimated psi, loading matrix, scores, degrees of freedom, test statistics, p-value, etc.)
from the factor analysis MLE optimization.
- Family - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
Family is a description of the error distribution and link function to be used in the GLM model.
- Family(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Family
-
Construct an instance of Family.
- family - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMProblem
-
the exponential family distribution for the mean
- FastKroneckerProduct - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
This is a fast and memory-saving implementation of computing the Kronecker product.
- FastKroneckerProduct(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
Construct a Kronecker product for read-only.
- FDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The F distribution is the distribution of the ratio of two independent chi-squared variates.
- FDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
Construct an F distribution.
- fdx(UnivariateRealFunction) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.ChangeOfVariable
-
Get the integrand in the "transformed" integral, g(t) = f(x(t)) * x'(t).
- FerrisMangasarianWrightPhase1 - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex
-
The phase 1 procedure finds a feasible table from an infeasible one by pivoting the simplex table of a related problem.
- FerrisMangasarianWrightPhase1(SimplexTable) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightPhase1
-
Construct the phase 1 algorithm for an infeasible table corresponding to a non-standard linear programming problem, e.g., b ≥ 0.
- FerrisMangasarianWrightPhase2 - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver
-
This implementation solves a canonical linear programming problem that does not need preprocessing its simplex table.
- FerrisMangasarianWrightPhase2(SimplexPivoting) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
-
Construct an LP solver to solve canonical LP problems using the Phase 2 algorithm in Ferris, Mangasarian & Wright.
- FerrisMangasarianWrightPhase2() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
-
Construct an LP solver to solve canonical LP problems using the Phase 2 algorithm in Ferris, Mangasarian & Wright.
- FerrisMangasarianWrightScheme2 - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex
-
The scheme 2 procedure removes equalities and free variables.
- FerrisMangasarianWrightScheme2(SimplexTable) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightScheme2
-
Construct the scheme 2 algorithm for a table with equalities and free variables.
- Fibonacci - Class in com.numericalmethod.suanshu.analysis.sequence
-
A Fibonacci sequence starts with 0 and 1 as the first two numbers.
- Fibonacci(int) - Constructor for class com.numericalmethod.suanshu.analysis.sequence.Fibonacci
-
Construct a Fibonacci sequence.
- Fibonacci - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
The Fibonacci search is a dichotomous search where a bracketing interval is sub-divided by the Fibonacci ratio.
- Fibonacci(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Fibonacci
-
Construct a univariate minimizer using the Fibonacci method.
- Fibonacci.Solution - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
This is the solution to a Fibonacci's univariate optimization.
- Field<F> - Interface in com.numericalmethod.suanshu.mathstructure
-
As an algebraic structure, every field is a ring, but not every ring is a field.
- Field.InverseNonExistent - Exception in com.numericalmethod.suanshu.mathstructure
-
This is the exception thrown when the inverse of a field element does not exist.
- Filter - Interface in com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles
-
A filter, for signal processing, takes (real) input signal and transforms it to (real) output signal.
- filtering(MultiVariateTimeSeries, MultiVariateTimeSeries) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Filter the observations.
- filtering(MultiVariateTimeSeries) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Filter the observations without control variable.
- filtering(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Filter the observations.
- filtering(double[]) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Filter the observations without control variable.
- Filtration - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
This class represents the filtration information known at the end of time.
- Filtration(TimeSeries<Double, ? extends TimeSeries.Entry<Double>>) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
-
Construct a Filtration from a Brownian path.
- FiltrationFunction - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
This class represents a function of time and a (fixed) Brownian path.
- FiltrationFunction() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.FiltrationFunction
-
- FiniteDifference - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
-
A partial derivative of a multivariate function is the derivative with respect to one of the variables with the others held constant.
- FiniteDifference(RealScalarFunction, int[]) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.FiniteDifference
-
Construct the partial derivative of a multi-variable function.
- FiniteDifference - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
A finite difference (divided by a small increment) is an approximation of the derivative of a function.
- FiniteDifference(UnivariateRealFunction, int, FiniteDifference.Type) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference
-
Construct an approximate derivative function for f using finite difference.
- FiniteDifference.Type - Enum in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
the types of finite difference available
- FirstOrder - Class in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent
-
This implements the steepest descent line search using the first order expansion of the Taylor's series.
- FirstOrder(FirstOrder.Method, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.FirstOrder
-
Construct a multivariate minimizer using the First-Order method.
- FirstOrder(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.FirstOrder
-
Construct a multivariate minimizer using the First-Order method.
- FirstOrder.Method - Enum in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent
-
the methods available to do line search
- FisherExactDistribution - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson
-
Fisher's exact test is a statistical significance test used in the analysis of contingency tables where nextSample sizes are small.
- FisherExactDistribution(int[], int[], int, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.FisherExactDistribution
-
Construct the distribution for the Fisher's exact test.
- FisherExactDistribution(int[], int[], int) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.FisherExactDistribution
-
Construct the distribution for the Fisher's exact test.
- fit(GLMProblem, Vector) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.Fitting
-
Fit a Generalized Linear Model.
- fit(GLMProblem, Vector) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
-
- fitness() - Method in interface com.numericalmethod.suanshu.optimization.geneticalgorithm.Chromosome
-
This is the fitness to determine how good this chromosome is.
- fitness() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
- fitted - Variable in class com.numericalmethod.suanshu.stats.regression.linear.Residuals
-
the fitted values, y^
- Fitting - Interface in com.numericalmethod.suanshu.stats.regression.linear.glm
-
This interface represents a fitting method for estimating β in a Generalized Linear Model (GLM).
- fixing - Variable in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
the restrictions or fixed values
- Fletcher - Class in com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod
-
This penalty function sums up the squared costs penalties.
- Fletcher(LessThanConstraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Fletcher
-
Construct a Fletcher penalty function from a collection of inequality constraints.
- Fletcher(LessThanConstraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Fletcher
-
Construct a Fletcher penalty function from a collection of inequality constraints.
- Fletcher(LessThanConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Fletcher
-
Construct a Fletcher penalty function from a collection of inequality constraints.
- Fletcher - Class in com.numericalmethod.suanshu.optimization.unconstrained.linesearch
-
This is Fletcher's inexact line search method.
- Fletcher(double, double, double, double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.linesearch.Fletcher
-
Construct a line search minimizer using the Fletcher method.
- Fletcher() - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.linesearch.Fletcher
-
Construct a line search minimizer using the Fletcher method with the default control parameters.
- FletcherReeves - Class in com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection
-
The Fletcher-Reeves method is a variant of the Conjugate-Gradient method.
- FletcherReeves(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.FletcherReeves
-
Construct a multivariate minimizer using the Fletcher-Reeves method.
- FlexibleTable - Class in com.numericalmethod.suanshu.datastructure
-
This is a 2D table that can shrink or grow by row or by column.
- FlexibleTable(Object[], Object[], double[][]) - Constructor for class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Construct a flexible table that can shrink or grow.
- FlexibleTable(Object[], Object[]) - Constructor for class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Construct a table by row and column labels, initializing the content to 0.
- FlexibleTable(int, int) - Constructor for class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Construct a table using default labeling.
- FlexibleTable(FlexibleTable) - Constructor for class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Copy constructor.
- floatValue() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- floatValue() - Method in class com.numericalmethod.suanshu.number.Real
-
- floatValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
- Fmin(double, int) - Static method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Compute the F critical value.
- fmin - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- fmin - Variable in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch.Solution
-
the best minimum found so far
- fminLast - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- fnext - Variable in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch.Solution
-
the next guess of the minimum
- foreach(double[], UnivariateRealFunction) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Apply a function f to each element in an array.
- forEach(Iterable<T>, IterationBody<T>) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
-
Runs a "foreach" loop in parallel.
- foreach(Vector, UnivariateRealFunction) - Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.CreateVector
-
Construct a new vector in which each entry is the result of applying a
function to the corresponding entry of a vector.
- forLoop(int, int, int, LoopBody) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
-
Runs a for-loop in parallel.
- forLoop(int, int, LoopBody) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
-
- forward(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep
-
Perform a forward sweep.
- Forward - Class in com.numericalmethod.suanshu.stats.regression.linear.modelselection
-
To construct a GLM getModel for a set of observations using the forward selection method,
we iteratively add a significant factor to the getModel, one at a time.
- Forward(GLMProblem, double) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.modelselection.Forward
-
Construct automatically a GLM getModel using the forward selection method.
- ForwardSubstitution - Class in com.numericalmethod.suanshu.matrix.doubles.linearsystem
-
Forward substitution solves a matrix equation in the form Lx = b
by an iterative process for a lower triangular matrix L.
- ForwardSubstitution() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.linearsystem.ForwardSubstitution
-
- foundPositiveDefiniteHessian - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- Frobenius(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixMeasure
-
Compute the Frobenius norm, i.e., the sqrt of the sum of squares of all elements of a matrix.
- fromPolar(double, double) - Static method in class com.numericalmethod.suanshu.number.complex.Complex
-
Factory method to construct a complex number from the polar form: (r, θ).
- fromVarx(VARXModel) - Static method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
Construct a transitory VECM(p) from a VARX(p).
- Ft - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
This represents the concept 'Filtration', the information available at time t.
- Ft() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
-
Construct an empty filtration (no information).
- Ft(Ft) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
-
Copy constructor.
- FT - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.FiltrationFunction
-
the filtration, containing all histories
- ft() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.FiltrationFunction
-
Compute all values at all time points.
- ft() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDB
-
- ft() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDt
-
- ft() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integrator
-
Get an array of function values.
- Ft - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
This represents the concept 'Filtration', the information available at time t.
- Ft() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Construct an empty filtration (no information).
- Ft(Ft) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Copy constructor.
- FtAdaptedFunction - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
This represents a Ft-adapted function that depends on X(t), B(t), or even on the whole past path of B(s), s ≤ t.
- FtAdaptedRealFunction - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
This represents a real-valued Ft-adapted function that depends on X(t), B(t), or even on the whole past path of B(s), s ≤ t.
- FtAdaptedVectorFunction - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
This represents a vector-valued Ft-adapted function that depends on X(t), B(t), or even on the whole past path of B(s), s ≤ t.
- FtWt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
This is a filtration implementation that includes the path-dependent information,
e.g., Wt.
- FtWt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtWt
-
Construct an empty filtration (no information).
- FtWt(FtWt) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtWt
-
Copy constructor.
- FtWt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
This is a filtration implementation that includes the path-dependent information,
e.g., Wt.
- FtWt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt
-
Construct an empty filtration (no information).
- FtWt(FtWt) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt
-
Copy constructor.
- Function<D,R> - Interface in com.numericalmethod.suanshu.analysis.function
-
The mathematical concept of a function expresses the idea that one quantity (the argument of the function, also known as the input) completely determines another quantity (the value, or output).
- Function.EvaluationException - Exception in com.numericalmethod.suanshu.analysis.function
-
This is the RuntimeException thrown when it fails to evaluate an expression.
- FunctionOps - Class in com.numericalmethod.suanshu.analysis.function
-
These are some commonly used mathematical functions.
- FunctionOps() - Constructor for class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
- fx() - Method in exception com.numericalmethod.suanshu.analysis.uniroot.NoRootFoundException
-
Get f(x).
- g() - Method in interface com.numericalmethod.suanshu.analysis.differentiation.differentiability.C1
-
Get the gradient function, g, of a real valued function f.
- g() - Method in class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
- G(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.StateEquation
-
Get G(t), the coefficient matrix of xt - 1.
- G(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Get G(t), the coefficient of xt - 1.
- Gamma - Interface in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Gamma function is an extension of the factorial function to real and complex numbers, with its argument shifted down by 1.
- gamma - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- gamma(int[]) - Method in class com.numericalmethod.suanshu.stats.hmm.rabiner.HmmGamma
-
Get the (T * N) γ matrix,
where the (t, i)-th entry is γt(i).
- Gamma - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
The Gamma distribution for the error distribution in a GLM model.
- Gamma() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gamma
-
Construct an instance of Gamma.
- Gamma(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gamma
-
Construct an instance of Gamma with an overriding link function.
- Gamma - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family
-
The quasi Gamma family of GLM.
- Gamma() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Gamma
-
Create an instance of Gamma.
- Gamma(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Gamma
-
Create an instance of Gamma with an overriding link function.
- gamma(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the AR coefficient on the i-th lagged differences.
- GammaDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
This gamma distribution, when k is an integer, is the distribution of
the sum of k independent exponentially distributed random variables,
each of which has a mean of θ (which is equivalent to a rate parameter of θ−1).
- GammaDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
Construct a Gamma distribution.
- GammaDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Gamma distribution to model the observations.
- GammaDistribution(GammaDistribution.Lambda[], boolean, boolean, double, int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaDistribution
-
Construct a Gamma distribution for each state in the HMM model.
- GammaDistribution(GammaDistribution.Lambda[], int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaDistribution
-
Construct a Gamma distribution for each state in the HMM model.
- GammaDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
the Gamma distribution parameters
- GammaGergoNemes - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Gergo Nemes' algorithm is very simple and quick to compute the Gamma function, if accuracy is not critical.
- GammaGergoNemes() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaGergoNemes
-
- GammaLanczos - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
Lanczos approximation provides a way to compute the Gamma function such that the accuracy can be made arbitrarily precise.
- GammaLanczos() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLanczos
-
Construct an instance of a Gamma function, computed using the Lanczos approximation.
- GammaLanczos(double, int, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLanczos
-
Construct an instance of a Gamma function, computed using the Lanczos approximation.
- GammaLanczosQuick - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
Lanczos approximation, computations are done in double.
- GammaLanczosQuick() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLanczosQuick
-
Construct an instance of a Gamma function, computed using the Lanczos approximation.
- GammaLanczosQuick(double, int, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLanczosQuick
-
Construct an instance of a Gamma function, computed using the Lanczos approximation.
- GammaLowerIncomplete - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Lower Incomplete Gamma function is defined as:
\[
\gamma(s,x) = \int_0^x t^{s-1}\,e^{-t}\,{\rm d}t = P(s,x)\Gamma(s)
\]
P(s,x) is the Regularized Incomplete Gamma P function.
- GammaLowerIncomplete() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLowerIncomplete
-
- GammaRegularizedP - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Regularized Incomplete Gamma P function is defined as:
\[
P(s,x) = \frac{\gamma(s,x)}{\Gamma(s)} = 1 - Q(s,x), s \geq 0, x \geq 0
\]
The R equivalent function is
pgamma.
- GammaRegularizedP() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaRegularizedP
-
- GammaRegularizedPInverse - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The inverse of the Regularized Incomplete Gamma P function is defined as:
\[
x = P^{-1}(s,u), 0 \geq u \geq 1
\]
When s > 1, we use the asymptotic inversion method.
- GammaRegularizedPInverse() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaRegularizedPInverse
-
- GammaRegularizedQ - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Regularized Incomplete Gamma Q function is defined as:
\[
Q(s,x)=\frac{\Gamma(s,x)}{\Gamma(s)}=1-P(s,x), s \geq 0, x \geq 0
\]
The algorithm used for computing the regularized incomplete Gamma Q function depends on the values of s and x.
- GammaRegularizedQ() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaRegularizedQ
-
- GammaUpperIncomplete - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Upper Incomplete Gamma function is defined as:
\[
\Gamma(s,x) = \int_x^{\infty} t^{s-1}\,e^{-t}\,{\rm d}t = Q(s,x) \times \Gamma(s)
\]
The integrand has the same form as the Gamma function, but the lower limit of the integration is a variable.
- GammaUpperIncomplete() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaUpperIncomplete
-
- GARCH - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch
-
This class does fitting for the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) getModel.
- GARCH(TimeSeries, int, int, int, GARCH.GRADIENT) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCH
-
Fit the GARCH(p, q) getModel to the time series.
- GARCH(TimeSeries, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCH
-
Fit the GARCH(p, q) getModel to the time series.
- GARCH.GRADIENT - Enum in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch
-
the gradient information used to guild the optimization search
- GARCHModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch
-
This class represents a GARCH specification.
- GARCHModel(double, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Construct a GARCH model.
- GARCHModel(GARCHModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Copy constructor.
- GARCHSim - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch
-
This class simulates the GARCH models.
- GARCHSim(int, GARCHModel, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
Simulate an GARCH model.
- GARCHSim(int, GARCHModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
Simulate an GARCH model.
- Gaussian - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
The Gaussian function is defined as:
\[
f(x) = a e^{- { \frac{(x-b)^2 }{ 2 c^2} } }
\]
- Gaussian(double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
-
Construct an instance of the Gaussian function.
- Gaussian() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
-
Construct an instance of the standard Gaussian function: \(f(x) = e^{-{\frac{(x)^2}{2}}}\)
- Gaussian - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
The Gaussian distribution for the error distribution in a GLM model.
- Gaussian() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gaussian
-
Construct an instance of Gaussian.
- Gaussian(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gaussian
-
Construct an instance of Gaussian with an overriding link function.
- Gaussian - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family
-
The quasi Gaussian family of GLM.
- Gaussian() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Gaussian
-
Create an instance of Gaussian.
- Gaussian(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Gaussian
-
Create an instance of Gaussian with an overriding link function.
- GaussianElimination - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination
-
The Gaussian elimination performs elementary row operations to reduce a matrix to the row echelon form.
- GaussianElimination(Matrix, boolean, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Run the Gaussian elimination algorithm.
- GaussianElimination(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Run the Gaussian elimination algorithm with partial pivoting.
- GaussianElimination4SquareMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination
-
- GaussianElimination4SquareMatrix(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
-
Run the Gaussian elimination algorithm on a square matrix.
- GaussianElimination4SquareMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
-
Run the Gaussian elimination algorithm on a square matrix.
- GaussJordanElimination - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination
-
Gauss-Jordan elimination performs elementary row operations to reduce a matrix to the reduced row echelon form.
- GaussJordanElimination(Matrix, boolean, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
-
Run the Gauss-Jordan elimination algorithm.
- GaussJordanElimination(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
-
Run the Gauss-Jordan elimination algorithm.
- GaussNewton - Class in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent
-
The Gauss-Newton method is a steepest descent method to minimize a real vector function in the form:
/[
f(x) = [f_1(x), f_2(x), ..., f_m(x)]'
/]
The objective function is
/[
F(x) = f' %*% f
]/
- GaussNewton(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.GaussNewton
-
Construct a multivariate minimizer using the Gauss-Newton method.
- GaussNewton.MySteepestDescent - Class in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent
-
- GaussNewton.MySteepestDescent.GaussNewtonImpl - Class in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent
-
an implementation of the Gauss-Newton algorithm.
- GaussNewtonImpl(C2OptimProblem, RntoMatrix) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.GaussNewton.MySteepestDescent.GaussNewtonImpl
-
- GaussSeidelSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
-
Similar to the
Jacobi method, the Gauss-Seidel method (GS)
solves each equation in sequential order.
- GaussSeidelSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
-
Construct a Gauss-Seidel (GS) solver.
- GeneralConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.constraint.general
-
The real-valued constraints define the domain (feasible regions) for a real-valued objective function in a
constrained optimization problem.
- GeneralConstraints(Collection<RealScalarFunction>) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.general.GeneralConstraints
-
Construct an instance of constraints from a collection of real-valued functions.
- GeneralConstraints(RealScalarFunction...) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.general.GeneralConstraints
-
Construct an instance of constraints from an array of real-valued functions.
- GeneralEqualityConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.constraint.general
-
This is the collection of equality constraints for an optimization problem.
- GeneralEqualityConstraints(Collection<RealScalarFunction>) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.general.GeneralEqualityConstraints
-
Construct an instance of equality constraints from a collection of real-valued functions.
- GeneralEqualityConstraints(RealScalarFunction...) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.general.GeneralEqualityConstraints
-
Construct an instance of equality constraints from an array of real-valued functions.
- GeneralGreaterThanConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.constraint.general
-
This is the collection of greater-than-or-equal-to constraints for an optimization problem.
- GeneralGreaterThanConstraints(Collection<RealScalarFunction>) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.general.GeneralGreaterThanConstraints
-
Construct an instance of greater-than-or-equal-to inequality constraints from a collection of real-valued functions.
- GeneralGreaterThanConstraints(RealScalarFunction...) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.general.GeneralGreaterThanConstraints
-
Construct an instance of greater-than-or-equal-to inequality constraints from an array of real-valued functions.
- GeneralizedConjugateResidualSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Generalized Conjugate Residual method (GCR) is useful for solving
a non-symmetric n-by-n linear system.
- GeneralizedConjugateResidualSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
Construct a GCR solver with restarts.
- GeneralizedConjugateResidualSolver(int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
Construct a GCR solver with restarts.
- GeneralizedConjugateResidualSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
Construct a full GCR solver.
- GeneralizedLinearModel - Class in com.numericalmethod.suanshu.stats.regression.linear.glm
-
The Generalized Linear Model (GLM) is a flexible generalization of ordinary least squares regression.
- GeneralizedLinearModel(GLMProblem, Fitting) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
-
Construct a GeneralizedLinearModel instance.
- GeneralizedLinearModel(GLMProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
-
Solve a generalized linear problem using the Iterative Re-weighted Least Squares algorithm.
- GeneralizedLinearModelQuasiFamily - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.quasi
-
GLM for the quasi-families.
- GeneralizedLinearModelQuasiFamily(QuasiGlmProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
-
Construct a GeneralizedLinearModelQuasiFamily instance.
- GeneralizedMinimalResidualSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Generalized Minimal Residual method (GMRES) is useful for solving
a non-symmetric n-by-n linear system.
- GeneralizedMinimalResidualSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
Construct a GMRES solver with restarts.
- GeneralizedMinimalResidualSolver(int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
Construct a GMRES solver with restarts.
- GeneralizedMinimalResidualSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
Construct a full GMRES solver.
- GeneralLessThanConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.constraint.general
-
This is the collection of less-than or equal-to constraints for an optimization problem.
- GeneralLessThanConstraints(Collection<RealScalarFunction>) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.general.GeneralLessThanConstraints
-
Construct an instance of less-than or equal-to inequality constraints from a collection of real-valued functions.
- GeneralLessThanConstraints(RealScalarFunction...) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.general.GeneralLessThanConstraints
-
Construct an instance of less-than or equal-to inequality constraints from an array of real-valued functions.
- generator - Variable in class com.numericalmethod.suanshu.matrix.doubles.operation.Householder.Context
-
the defining vector which is perpendicular to the Householder hyperplane
- generator() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.Householder
-
Get the Householder generating vector.
- GenericMatrix<F extends Field<F>> - Class in com.numericalmethod.suanshu.matrix.generic.matrixtype
-
This is a generic matrix over a
Field.
- GenericMatrix(int, int, F) - Constructor for class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
-
Construct a matrix over a field.
- GenericMatrix(F[][]) - Constructor for class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
-
Construct a matrix over a field.
- GenericTimeTimeSeries<T extends java.lang.Comparable<? super T>> - Class in com.numericalmethod.suanshu.stats.timeseries.multivariate
-
This is a multivariate time series indexed by some notion of time.
- GenericTimeTimeSeries(T[], Vector[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
Construct a multivariate time series from timestamps and vectors.
- GenericTimeTimeSeries(T[], double[][]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
Construct a multivariate time series from timestamps and vectors.
- GenericTimeTimeSeries(T[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
Construct a multivariate time series from timestamps and vectors.
- GenericTimeTimeSeries<T extends java.lang.Comparable<? super T>> - Class in com.numericalmethod.suanshu.stats.timeseries.univariate
-
This is a univariate time series indexed by some notion of time.
- GenericTimeTimeSeries(T[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
Construct a univariate time series from timestamps and values.
- GeneticAlgorithm - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm
-
A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution.
- GeneticAlgorithm(boolean, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
Construct an instance of this implementation of genetic algorithm.
- GeometricBrownian - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
A Geometric Brownian motion (GBM) (occasionally, exponential Brownian motion) is
a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion.
- GeometricBrownian(double, double) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.GeometricBrownian
-
Construct a Geometric Brownian motion.
- geometricMultiplicity() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.EigenProperty
-
Get the dimension of the vector space spanned by the eigenvectors.
- get(int, int) - Method in class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable
-
Get the value of a table entry.
- get(int) - Method in class com.numericalmethod.suanshu.analysis.sequence.Fibonacci
-
- get(int) - Method in interface com.numericalmethod.suanshu.analysis.sequence.Sequence
-
Get the i-th entry in the sequence, counting from 1.
- get(int, int) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
- get(double, int) - Method in class com.numericalmethod.suanshu.datastructure.MathTable
-
Get a particular table entry at [i,j].
- get(double, String) - Method in class com.numericalmethod.suanshu.datastructure.MathTable
-
Get a particular table entry at [i, "header"].
- get(int) - Method in class com.numericalmethod.suanshu.datastructure.MathTable.Row
-
Get the value in the row by column index.
- get(String) - Method in class com.numericalmethod.suanshu.datastructure.MathTable.Row
-
Get the value in the row by column name.
- get(int) - Method in class com.numericalmethod.suanshu.interval.Intervals
-
Get the i-th interval.
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- get(int, int) - Method in interface com.numericalmethod.suanshu.matrix.doubles.MatrixAccess
-
Get the matrix entry at [i,j].
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- get(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
- get(int, int) - Method in interface com.numericalmethod.suanshu.matrix.generic.MatrixAccess
-
Get the matrix entry at [i,j].
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- get() - Method in class com.numericalmethod.suanshu.parallel.Reference
-
- get(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.AutoCorrelationFunction
-
Get the auto-correlation matrix for Xi and Xj.
- get(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.AutoCovarianceFunction
-
Get the auto-covariance matrix for Xi and Xj.
- get(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCorrelationFunction
-
Get the auto-correlation for xi and xj.
- get(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCovarianceFunction
-
Get the auto-covariance for xi and xj.
- get(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
Get the i-th value.
- get(int) - Method in interface com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.MultiVariateTimeSeries
-
Get the value at time t (random access).
- get(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
- get(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
Get the i-th value.
- get(T) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
Get the value at time t.
- get(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
-
- get(int) - Method in interface com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.TimeSeries
-
Get the value at time t.
- get(int) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- get(int) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- get(int) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Get the value at position i.
- getActiveConstraints(Vector, double) - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearConstraints
-
Get the active constraint.
- getActiveRows(Vector, double) - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearConstraints
-
Get the active constraint indices.
- getAll() - Method in class com.numericalmethod.suanshu.analysis.sequence.Fibonacci
-
- getAll() - Method in interface com.numericalmethod.suanshu.analysis.sequence.Sequence
-
Get a copy of the whole (finite) sequence in double[].
- getAllParts(Vector, Map<Integer, Double>) - Static method in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
Combine the variable and fixed values to form an input to the original function.
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FAEstimator
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBera
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.Lilliefors
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilk
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquare4Independence
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
Get a description of the alternative hypothesis.
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.KruskalWallis
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.VanDerWaerden
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.BoxPierce
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.Bartlett
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.Levene
-
- getArma() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAModel
-
Get the ARMA specification of this ARIMA model, essentially ignoring the differencing.
- getArma() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
-
Get the ARMA specification of this ARIMA model, essentially ignoring the differencing.
- getArmax() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Get the ARMAX specification of this ARIMAX model, essentially ignoring the differencing.
- getArmax() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the ARMAX specification of this ARIMAX model, essentially ignoring the differencing.
- getAuxiliaryOLSRegression(Vector, Residuals) - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.White
-
the auxiliary regression
- getAuxiliaryRegression() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.BreuschPagan
-
- getAuxiliaryRegression() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Glejser
-
- getAuxiliaryRegression() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.HarveyGodfrey
-
- getAuxiliaryRegression() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.White
-
- getBase() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Best1Bin
-
- getBase() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Pick a base chromosome from the population.
- getBasis(int) - Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.Basis
-
Get the full set of the standard basis vectors.
- getBasis(int, int) - Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.Basis
-
Get a subset of the standard basis vectors.
- getBasis() - Method in class com.numericalmethod.suanshu.vector.doubles.operation.VectorSpace
-
Get the orthogonal basis.
- getBCol(int) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Get the table entry at [i, B].
- getBest(int) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
Get the i-th best chromosome.
- getBeta() - Method in class com.numericalmethod.suanshu.stats.regression.panel.PanelRegressionResult
-
- getBounds() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.BoxConstraints
-
Get a deep copy of the bounds.
- getCharacteristicPolynomial() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.CharacteristicPolynomial
-
Get the characteristic polynomial.
- getChild(int) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
Produce a child chromosome.
- getChild(int) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptim.Solution
-
- getCoefficient(int) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Get an-i, the coefficient of xn-i.
- getCoefficients() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Get a copy of the polynomial coefficients.
- getColLabel(int) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Get the label for column i.
- getColLabel(int) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- getColumn(int) - Method in interface com.numericalmethod.suanshu.matrix.doubles.Matrix
-
Get the specified column in the matrix as a vector.
- getColumn(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- getColumn(int, int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
Get a sub-column of the j-th column, from beginRow row to endRow row, inclusively.
- getColumn(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
Get a column.
- getColumn(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
- getComplement() - Method in class com.numericalmethod.suanshu.vector.doubles.operation.VectorSpace
-
Get the basis of the orthogonal complement.
- getComplexRoots(List<? extends Number>) - Static method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.PolyRoot
-
Get a copy of only the
Complex but not real roots of a polynomial.
- getConcurrency() - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
-
Gets the number of concurrent threads.
- getConstraints() - Method in interface com.numericalmethod.suanshu.optimization.constrained.constraint.Constraints
-
Get the list of constraint functions.
- getConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.general.GeneralConstraints
-
Get the constraints.
- getConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearConstraints
-
- getConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
-
- getConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
-
- getContext(Vector) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.Householder
-
Generate the context information from a generating vector x.
- getCostRow(int) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Get the table entry at [COST, j].
- getCount() - Method in class com.numericalmethod.suanshu.algorithm.iterative.monitor.CountMonitor
-
Get the number of iterations.
- getCutter(ILPProblem) - Method in interface com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane.SimplexCuttingPlane.CutterFactory
-
Construct a new Cutter for a MILP problem.
- getDate(int, int, int, DateTimeZone) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Construct a DateTime instance with year, month, day, and time zone.
- getDateTime(String) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Construct a DateTime instance from a string which ends in TimeZone specification.
- getDateTime(String, DateFormat, DateTimeZone) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Construct a DateTime instance from a string with no TimeZone specified.
- getDirection(Vector) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.Powell.PowellImpl
-
- getDirection(Vector) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.Zangwill.ZangwillImpl
-
- getDirection(Vector) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.QuasiNewton.QuasiNewtonImpl
-
- getDirection(Vector) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.GaussNewton.MySteepestDescent.GaussNewtonImpl
-
- getDirection(Vector) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.NewtonRaphson.NewtonRaphsonImpl
-
- getDirection(Vector) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.SteepestDescentImpl
-
Get the next search direction.
- getDistribution() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.HiddenMarkovModel
-
Get the distribution in the hidden Markov model.
- getDistribution(AugmentedDickeyFuller.TrendType, int, int) - Static method in class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFDistribution
-
the factory to construct various ADF distributions
- getDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaDistribution
-
- getDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialDistribution
-
- getDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.ExponentialDistribution
-
- getDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaDistribution
-
- getDistributions() - Method in interface com.numericalmethod.suanshu.stats.hmm.mixture.distribution.HMMDistribution
-
Get the distributions (possibly differently parameterized) for all states.
- getDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalDistribution
-
- getDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalDistribution
-
- getDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.PoissonDistribution
-
- getEigenvalue(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
-
Get the i-th eigenvalue.
- getEigenvalues() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.CharacteristicPolynomial
-
- getEigenvalues() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
-
- getEigenvalues() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.QRAlgorithm
-
Get all the eigenvalues.
- getEigenvalues() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Spectrum
-
Get all the eigenvalues.
- getEigenvalues() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Get the set of real eigenvalues.
- getEigenVector(Vector, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.InverseIteration
-
Get an eigenvector from an initial guess.
- getEigenVector() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.InverseIteration
-
Get an eigenvector.
- getEntrytList() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- getEntrytList() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- getEntrytList() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- getEntrytList() - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseMatrix
-
Export the non-zero values in the matrix as a list of
SparseEntrys.
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.problem.QPProblem
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.IPProblemImpl1
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPNode
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
- getEqualityConstraints() - Method in interface com.numericalmethod.suanshu.optimization.constrained.problem.ConstrainedOptimProblem
-
Get the equality constraints, hi(x) = 0
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.problem.ConstrainedOptimProblemImpl1
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.problem.NonNegativityConstraintOptimProblem
-
- getEstimators(int) - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FactorAnalysis
-
Get the estimators (estimated psi, loading matrix, degree of
freedom, test statistics, p-value, etc) obtained from the factor analysis,
given the maximum number of iterations.
- getEstimators(Vector, int) - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FactorAnalysis
-
Get the estimators (estimated psi, loading matrix, degree of
freedom, test statistics, p-value, etc) obtained from the factor analysis,
given the initial psi and the maximum number of iterations.
- getExceptions() - Method in exception com.numericalmethod.suanshu.parallel.MultipleExecutionException
-
Get all exceptions encountered during execution.
- getExpectedContingencyTable(int[], int[]) - Static method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquare4Independence
-
Assume the null hypothesis of independence, we compute the expected frequency of each category.
- getFeasibleInitialPoint(LinearEqualityConstraints) - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearGreaterThanConstraints
-
Given a collection of linear greater-than-or-equal-to constraints as well as a collection of equality constraints,
find a feasible initial point that satisfy the constraints.
- getFeasibleInitialPoint() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearGreaterThanConstraints
-
Given a collection of linear greater-than-or-equal-to constraints,
find a feasible initial point that satisfy the constraints.
- getFeasibleInitialPoint(LinearEqualityConstraints) - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearLessThanConstraints
-
Given a collection of linear less-than-or-equal-to constraints as well as a collection of equality constraints,
find a feasible initial point that satisfy the constraints.
- getFeasibleInitialPoint() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearLessThanConstraints
-
Given a collection of linear less-than-or-equal-to constraints,
find a feasible initial point that satisfy the constraints.
- getFirstNonIntegralIndices(double[]) - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.IPProblemImpl1
-
Get the index of the first integral variable whose value is not an integer, violating the integral constraints.
- getFittedARMA() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFitting
-
Get the ARMA coefficients, φ.
- getFittedARMA() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
- getFittedArModel() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARFitting
-
- getFittedState(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Get the posterior expected state.
- getFittedState(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the posterior expected state.
- getFittedStates() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Get the posterior expected states.
- getFittedStates() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the posterior expected states.
- getFittedStateVariance(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Get the posterior expected state variance.
- getFittedStateVariance(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the posterior expected state variance.
- getFractional(BigDecimal) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Get the fractional part of a number.
- getFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.SDE
-
Get an empty filtration for the process.
- getFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.SDE
-
Get an empty filtration for the process.
- getGreaterThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.CanonicalLPProblem1
-
Get the greater-than-or-equal-to constraints of the linear programming problem.
- getGreaterThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Get the set of linear greater-than-or-equal-to constraints.
- getHeaders() - Method in class com.numericalmethod.suanshu.datastructure.MathTable
-
Get the column names.
- getHomogeneousSoln() - Method in interface com.numericalmethod.suanshu.matrix.doubles.linearsystem.LinearSystemSolver.Solution
-
Get the basis of the homogeneous solution for the linear system,
Ax = b.
- getIncrement(Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.Powell.PowellImpl
-
- getIncrement(Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.Zangwill.ZangwillImpl
-
- getIncrement(Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.QuasiNewton.QuasiNewtonImpl
-
- getIncrement(Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.SteepestDescentImpl
-
Get the increment fraction, αk.
- getIndexFromColLabel(Object) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Translate a column label to a column index.
- getIndexFromRowLabel(Object) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Translate a row label to a row index.
- getIndices() - Method in class com.numericalmethod.suanshu.datastructure.MathTable
-
Get a copy of the row indices.
- getInitialGuess() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LSProblem
-
Gets the initial guess of the solution for the problem.
- getInitialHessian(Vector, Vector) - Method in interface com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation
-
Get the initial Hessian matrix.
- getInitialHessian(Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- getInitialHessian(Vector, Vector, Vector) - Method in interface com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPASVariation
-
Get the initial Hessian matrix.
- getInitialHessian(Vector, Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPASVariation1
-
- getInitials(Vector...) - Method in interface com.numericalmethod.suanshu.optimization.initialization.BuildInitials
-
Generate a set of initial points for optimization from the fewer than required points.
- getInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.initialization.DefaultSimplex
-
Build a simplex of N+1 vertices from an initial point, where N is the dimension of the initial points.
- getInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.initialization.UniformDistributionOverBox1
-
- getInitials() - Method in class com.numericalmethod.suanshu.optimization.initialization.UniformDistributionOverBox1
-
Generate a set of initial points for optimization.
- getInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.initialization.UniformDistributionOverBox2
-
- getInitials() - Method in class com.numericalmethod.suanshu.optimization.initialization.UniformDistributionOverBox2
-
Generate a set of initial points for optimization.
- getInstance() - Static method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
-
- getIntegerIndices() - Method in interface com.numericalmethod.suanshu.optimization.constrained.integer.IPProblem
-
Get the indices of the integral variables.
- getIntegerIndices() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.IPProblemImpl1
-
- getIntegerIndices() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
- getIntegralConstraint(int) - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPProblem
-
Get the integral domain of a particular integral variable.
- getIterates() - Method in class com.numericalmethod.suanshu.algorithm.iterative.monitor.IteratesMonitor
-
Get a list of all iterative states.
- getKalmanGain(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Get the Kalman gain.
- getKalmanGain(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the Kalman gain.
- getLastDayOfMonth(DateTime) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Get the last day of the month which contain a time.
- getLastMillisecondOfDay(DateTime) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Get the last millisecond the day which contain a time.
- getLeftPreconditioner() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LSProblem
-
Gets the left preconditioner.
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.problem.QPProblem
-
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.IPProblemImpl1
-
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPNode
-
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
- getLessThanConstraints() - Method in interface com.numericalmethod.suanshu.optimization.constrained.problem.ConstrainedOptimProblem
-
Get the less-than-or-equal-to constraints, gi(x) ≤ 0
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.problem.ConstrainedOptimProblemImpl1
-
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.problem.NonNegativityConstraintOptimProblem
-
- getLinearSpan(double...) - Method in class com.numericalmethod.suanshu.vector.doubles.operation.VectorSpace
-
- getLowerBounds() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.BoxConstraints
-
Split the equality constraints and get the greater-than-the-lower-bounds part.
- getMaxIteration() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LSProblem
-
Gets the specified maximum number of iterations.
- getMaxIterations() - Method in exception com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction.MaxIterationsExceededException
-
Get the maximum number of iterations.
- getMaxIterations() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.IterativeIntegrator
-
Get the maximum number of iterations for this iterative procedure.
- getMaxIterations() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.NewtonCotes
-
- getMaxIterations() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Simpson
-
- getMinEigenValue(Matrix, double) - Static method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing
-
Get the minimum of all the eigen values of a matrix.
- getModel() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCH
-
Get the fitted GARCH getModel.
- getMStepParams(double[], Matrix, Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaDistribution
-
- getMStepParams(double[], Matrix, Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialDistribution
-
- getMStepParams(double[], Matrix, Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.ExponentialDistribution
-
- getMStepParams(double[], Matrix, Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaDistribution
-
- getMStepParams(double[], Matrix, Object[]) - Method in interface com.numericalmethod.suanshu.stats.hmm.mixture.distribution.HMMDistribution
-
Maximize, for each state, the log-likelihood of the distribution with respect to the observations and current estimators.
- getMStepParams(double[], Matrix, Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalDistribution
-
- getMStepParams(double[], Matrix, Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalDistribution
-
- getMStepParams(double[], Matrix, Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.PoissonDistribution
-
- getNewFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.Brownian
-
- getNewFt() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.DiscretizedSDE
-
Get an empty filtration for the process.
- getNewFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Euler
-
- getNewFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.Brownian
-
- getNewFt() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.DiscretizedSDE
-
Get an empty filtration for the process.
- getNewFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Euler
-
- getNewFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Milstein
-
- getNewPool(int) - Static method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
Allocate space for a population pool.
- getNextGeneration(List<Chromosome>, List<Chromosome>) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
Populate the next generation using the parent and children chromosome pools.
- getNextGeneration(List<Chromosome>, List<Chromosome>) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptim.Solution
-
- getNonIntegralIndices(double[]) - Method in interface com.numericalmethod.suanshu.optimization.constrained.integer.IPProblem
-
Check which elements in x do not satisfy the integral constraints.
- getNonIntegralIndices(double[]) - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.IPProblemImpl1
-
- getNonIntegralIndices(double[]) - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
- getNormalization() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Get the normalized version of this polynomial so the leading coefficient is 1.
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FAEstimator
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBera
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.Lilliefors
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilk
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquare4Independence
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
Get a description of the null hypothesis.
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.KruskalWallis
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.VanDerWaerden
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.BoxPierce
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.Bartlett
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.Levene
-
- getNumberOfPeriodsBetween(DateTime, DateTime, Period) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Return the number of periods between two times, rounding up.
- getObsDimension() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLM
-
Get the dimension of the observations.
- getObsDimension() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Get the dimension of observations.
- getObservation() - Method in class com.numericalmethod.suanshu.stats.hmm.HmmInnovation
-
Get the observation associated with the hidden state.
- getObservationModel() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLM
-
Get the observation model.
- getObservationModel() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Get the observation model.
- getObservations() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLMSeries
-
Get the observations.
- getObservations() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
Get the observations.
- getObservations(HmmInnovation[], int) - Static method in class com.numericalmethod.suanshu.stats.markovchain.MCUtils
-
Get all observations that occur in a particular state.
- getOmega() - Method in interface com.numericalmethod.suanshu.optimization.minmax.MinMaxProblem
-
Get the list of omegas, the domain.
- getOne() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
Pick a chromosome for mutation/crossover.
- getOne() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Pick a random chromosome from the population.
- getOperation(double[], double[]) - Method in interface com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation.ImplementationChooser
-
Get an implementation based on the inputs.
- getOrthogonalVector() - Method in class com.numericalmethod.suanshu.vector.doubles.operation.Projection
-
Get the orthogonal vector which is equal to v minus the projection of v on {wi}.
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaDistribution
-
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialDistribution
-
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.ExponentialDistribution
-
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaDistribution
-
- getParams() - Method in interface com.numericalmethod.suanshu.stats.hmm.mixture.distribution.HMMDistribution
-
Get the parameters, for each state, of the distribution.
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalDistribution
-
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalDistribution
-
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.PoissonDistribution
-
- getParticularSolution(Vector) - Method in interface com.numericalmethod.suanshu.matrix.doubles.linearsystem.LinearSystemSolver.Solution
-
Get a particular solution for the linear system.
- getPenaltyFunction(ConstrainedOptimProblem) - Method in interface com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyMethodMinimizer.PenaltyFunctionFactory
-
Get an instance of the penalty function.
- getPivot(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
-
- getPivot(SimplexTable) - Method in interface com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting
-
Get the next pivot.
- getPopulation() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Get the current generation.
- getPrecision() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.ChangeOfVariable
-
- getPrecision() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Integrator
-
Get the convergence threshold.
- getPrecision() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.NewtonCotes
-
- getPrecision() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Riemann
-
- getPrecision() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Romberg
-
- getPrecision() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Simpson
-
- getPredictedObservation(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Get the prior observation prediction.
- getPredictedObservation(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the prior observation prediction.
- getPredictedObservations() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Get the prior observation predictions.
- getPredictedObservations() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the prior observation predictions.
- getPredictedObservationVariance(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Get the prior observation prediction variance.
- getPredictedObservationVariance(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the prior observation prediction variance.
- getPredictedState(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Get the prior expected state.
- getPredictedState(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the prior expected state.
- getPredictedStates() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Get the prior expected states.
- getPredictedStates() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the prior expected states.
- getPredictedStateVariance(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Get the prior expected state variance.
- getPredictedStateVariance(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the prior expected state variance.
- getProblemSize() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Get the number of variables in the problem or the cost/objective function.
- getProjectionLength(int) - Method in class com.numericalmethod.suanshu.vector.doubles.operation.Projection
-
Get the length of v projected on each dimension {wi}.
- getProjectionVector(int) - Method in class com.numericalmethod.suanshu.vector.doubles.operation.Projection
-
Get the i-th projected vector of v on {wi}.
- getProperty(Number) - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
-
- getProperty(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
-
- getRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaDistribution
-
- getRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialDistribution
-
- getRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.ExponentialDistribution
-
- getRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaDistribution
-
- getRandomNumberGenerators() - Method in interface com.numericalmethod.suanshu.stats.hmm.mixture.distribution.HMMDistribution
-
Get the random number generators corresponding to the distributions (possibly differently parameterized) for all states.
- getRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalDistribution
-
- getRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalDistribution
-
- getRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.PoissonDistribution
-
- getRealEigenvalues() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
-
Get all real eigenvalues.
- getRealRoots(List<? extends Number>) - Static method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.PolyRoot
-
Get a copy of only the real roots of a polynomial.
- getReason() - Method in exception com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
-
Get the reason for the convergence failure.
- getReducedLinearEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearEqualityConstraints
-
Get the collection of linearly independent linear constraints.
- getResample() - Method in class com.numericalmethod.suanshu.stats.sampling.resampling.bootstrap.NonParametricBootstrap
-
- getResample() - Method in interface com.numericalmethod.suanshu.stats.sampling.resampling.Resampling
-
Get a resample from the original sample
- getResiduals() - Method in class com.numericalmethod.suanshu.stats.regression.panel.PanelRegressionResult
-
- getResultantTableau() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
- getResultantTableau() - Method in interface com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPSimplexMinimizer
-
Get the solution simplex table as a result of solving a linear programming problem.
- getResultantTableau() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
- getResults() - Method in exception com.numericalmethod.suanshu.parallel.MultipleExecutionException
-
Get the results obtained so far.
- getRightPreconditioner() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LSProblem
-
Gets the right preconditioner.
- getRow(int) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
- getRow(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- getRow(int) - Method in interface com.numericalmethod.suanshu.matrix.doubles.Matrix
-
Get the specified row in the matrix as a vector.
- getRow(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- getRow(int, int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
Get a sub-row of the i-th row, from beginCol column to endCol column, inclusively.
- getRow(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
Get a row.
- getRow(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
- getRow(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
- getRowLabel(int) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Get the label for row i.
- getRowLabel(int) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- getRowOnOrAfter(double) - Method in class com.numericalmethod.suanshu.datastructure.MathTable
-
Get the row corresponding to a row index.
- getRowOnOrBefore(double) - Method in class com.numericalmethod.suanshu.datastructure.MathTable
-
Get the row corresponding to a row index.
- getRowsOnOrAfter(double) - Method in class com.numericalmethod.suanshu.datastructure.MathTable
-
Get the rows having the row index value equal to or just bigger than i.
- getRowsOnOrBefore(double) - Method in class com.numericalmethod.suanshu.datastructure.MathTable
-
Get the rows having the row index value equal to or just smaller than i.
- getSample(double) - Method in class com.numericalmethod.suanshu.stats.sampling.discrete.DiscreteSampling
-
Get a sample from the probability distribution.
- getSimpleCell(RealScalarFunction, Vector) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Best2Bin
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
-
Override this method to put in whatever constraints in the minimization problem.
- getSimpleCell(RealScalarFunction, Vector) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Rand1Bin
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local.LocalSearchCellFactory
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory
-
Construct an instance of a SimpleCell.
- getSingularValues() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.GloubKahanSVD
-
- getSingularValues() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVD
-
- getSingularValues() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVDDecomposition
-
Get the normalized, hence positive, singular values.
- getSolutionToOriginalProblem(Vector) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.problem.QPProblemOnlyEqualityConstraints
-
Back out the solution for the original (constrained) problem, if the modified (unconstrained) problem can be solved.
- getSpanningCoefficients(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.operation.VectorSpace
-
Find a linear combination of the basis that best approximates a vector in the least square sense.
- getState() - Method in class com.numericalmethod.suanshu.stats.hmm.HmmInnovation
-
Get the hidden state.
- getStateCounts(int[]) - Static method in class com.numericalmethod.suanshu.stats.markovchain.MCUtils
-
Count the numbers of occurrences of states.
- getStateDimension() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLM
-
Get the dimension of states.
- getStateDimension() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Get the dimension of states.
- getStateModel() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLM
-
Get the state model.
- getStateModel() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Get the state model.
- getStates() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLMSeries
-
Get the states.
- getStates() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
Get the states.
- getStationaryProbabilities(Matrix) - Static method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Get the stationary state probabilities of a Markov chain
that is irreducible, aperiodic and strongly connected (positive recurrent).
- getStatistic() - Method in interface com.numericalmethod.suanshu.stats.descriptive.StatisticFactory
-
- getStats(CointegrationMLE) - Method in class com.numericalmethod.suanshu.stats.cointegration.JohansenTest
-
Get the set of likelihood ratio test statistics for testing H(r) in H(r+1).
- getTable() - Method in class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable
-
Get a copy of the Neville table.
- getTime() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLMSeries.Entry
-
- getTime() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries.Entry
-
- getTime() - Method in class com.numericalmethod.suanshu.stats.timeseries.DateTimeGenericTimeSeries.Entry
-
- getTime() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.MultiVariateTimeSeries.Entry
-
- getTime() - Method in interface com.numericalmethod.suanshu.stats.timeseries.TimeSeries.Entry
-
Get the timestamp.
- getTime() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.TimeSeries.Entry
-
- getTolerance() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LSProblem
-
- getTransitionCounts(int[]) - Static method in class com.numericalmethod.suanshu.stats.markovchain.MCUtils
-
Count the numbers of times the state goes from one state to another.
- getType() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
Get the bi-diagonal matrix type.
- getUpperBounds() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.BoxConstraints
-
Split the equality constraints and get the less-than-the-upper-bounds part.
- getValue() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLMSeries.Entry
-
- getValue() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries.Entry
-
- getValue() - Method in class com.numericalmethod.suanshu.stats.timeseries.DateTimeGenericTimeSeries.Entry
-
- getValue() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.MultiVariateTimeSeries.Entry
-
- getValue() - Method in interface com.numericalmethod.suanshu.stats.timeseries.TimeSeries.Entry
-
Get the entry value.
- getValue() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.TimeSeries.Entry
-
- getVariablePart(double[]) - Method in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
Given an input to the original function, this extracts the variable parts (excluding the fixed values).
- getViterbiStates(double[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.HmmViterbi
-
Generate the most likely sequence of states by using Viterbi algorithm (global decoding),
given the observations and the parameters of the underlying hidden Markov model.
- getWhiteNoise(int) - Static method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMASim
-
- getWhole(BigDecimal) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Get the integral part of a number (discarding the fractional part).
- getX2() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.White
-
- GivensMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype
-
Givens rotation is a rotation in the plane spanned by two coordinates axes.
- GivensMatrix(int, int, int, double, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
Construct a Givens matrix in the form
\[
G(i,j,c,s) = \begin{bmatrix}
1 & ...
- GivensMatrix(GivensMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
Copy constructor.
- gk - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.QuasiNewton.QuasiNewtonImpl
-
the gradient at the k-th iteration
- Glejser - Class in com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity
-
The Glejser test is used to test for heteroskedasticity in a linear regression model.
- Glejser(Residuals) - Constructor for class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Glejser
-
Perform the Glejser test to test for heteroskedasticity in a linear regression model.
- GLMProblem - Class in com.numericalmethod.suanshu.stats.regression.linear.glm
-
This class represents a Generalized Linear regression problem.
- GLMProblem(Vector, Matrix, boolean, Family) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMProblem
-
Construct a GLM problem.
- GLMProblem(LMProblem, Family) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMProblem
-
Construct a GLM problem from a linear regression problem.
- GlobalSearchByLocalMinimizer - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local
-
This minimizer is a global optimization method.
- GlobalSearchByLocalMinimizer(LocalSearchCellFactory.MinimizerFactory, boolean, RandomLongGenerator, double, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local.GlobalSearchByLocalMinimizer
-
Construct a GlobalSearchByLocalMinimizer to solve unconstrained minimization problems.
- GlobalSearchByLocalMinimizer(boolean, RandomLongGenerator, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local.GlobalSearchByLocalMinimizer
-
Construct a GlobalSearchByLocalMinimizer to solve unconstrained minimization problems.
- GlobalSearchByLocalMinimizer(boolean) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local.GlobalSearchByLocalMinimizer
-
Construct a GlobalSearchByLocalMinimizer to solve unconstrained minimization problems.
- GloubKahanSVD - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.svd
-
Gloub-Kahan algorithm does the SVD decomposition of a tall matrix in two stages.
- GloubKahanSVD(Matrix, boolean, boolean, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.GloubKahanSVD
-
Run the Gloub-Kahan SVD decomposition on a tall matrix.
- GMT - Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
GMT
- Golden - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
This is the golden section univariate minimization algorithm.
- Golden(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Golden
-
Construct a univariate minimizer using the Golden method.
- Golden.Solution - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
This is the solution to a Golden section univariate optimization.
- GOLDEN_RATIO - Static variable in class com.numericalmethod.suanshu.Constant
-
the Golden ratio
- GoldfeldQuandtTrotter - Class in com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite
-
Goldfeld, Quandt and Trotter propose the following way
to coerce a non-positive definite Hessian matrix to become symmetric, positive definite.
- GoldfeldQuandtTrotter(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite.GoldfeldQuandtTrotter
-
Construct a symmetric, positive definite matrix using the Goldfeld-Quandt-Trotter algorithm.
- GomoryMixedCut - Class in com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane
-
This cutting-plane implementation uses Gomory's mixed cut method.
- GomoryMixedCut() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane.GomoryMixedCut
-
Construct a Gomory mixed cutting-plane minimizer to solve an MILP problem.
- GomoryMixedCut.MyCutter - Class in com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane
-
This is Gomory's mixed cut.
- GomoryPureCut - Class in com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane
-
This cutting-plane implementation uses Gomory's pure cut method for pure integer programming,
in which all variables are integral.
- GomoryPureCut() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane.GomoryPureCut
-
Construct a Gomory pure cutting-plane minimizer to solve pure ILP problems,
in which all variables are integral.
- GomoryPureCut.MyCutter - Class in com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane
-
This is Gomory's pure cut.
- Gradient - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
-
The gradient of a scalar field is a vector field which points in the direction of the greatest rate of increase of the scalar field,
and of which the magnitude is the greatest rate of change.
- Gradient(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.Gradient
-
Construct the gradient vector for a multivariate function f at point x.
- gradient(T) - Method in interface com.numericalmethod.suanshu.optimization.minmax.MinMaxProblem
-
g(x, ω) = ∇|e(x, ω)| is the gradient function of the absolute error, |e(x, ω)|, for a given ω.
- GradientFunction - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
-
The gradient function, g(x), evaluates the gradient of a real scalar function f at a point x.
- GradientFunction(RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.GradientFunction
-
Construct the gradient function of a real scalar function f.
- GramSchmidt - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.qr
-
The Gram–Schmidt process is a method for orthogonalizing a set of vectors in an inner product space.
- GramSchmidt(Matrix, boolean, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.GramSchmidt
-
Run the Gram-Schmidt process to orthogonalize a matrix.
- GramSchmidt(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.GramSchmidt
-
Run the Gram-Schmidt process to orthogonalize a matrix.
- GreaterThanConstraints - Interface in com.numericalmethod.suanshu.optimization.constrained.constraint
-
The domain of an optimization problem may be restricted by greater-than or equal-to constraints.
- GridSearch - Class in com.numericalmethod.suanshu.optimization.univariate
-
This performs a grid search to find the minimum of a univariate function.
- GridSearch(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.GridSearch
-
Construct a univariate minimizer using the grid search method.
- GridSearch.Solution - Class in com.numericalmethod.suanshu.optimization.univariate
-
This is the solution to the GridSearch.
- H() - Method in interface com.numericalmethod.suanshu.analysis.differentiation.differentiability.C2
-
Get the Hessian matrix function, H, of a real valued function f.
- h() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.IterativeIntegrator
-
Get the discretization size for the current iteration.
- h() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.NewtonCotes
-
- h() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Simpson
-
- H() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.HessenbergDecomposition
-
Get the H matrix.
- H() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.Householder
-
Get the Householder matrix H = I - 2 * v * v'.
- H() - Method in class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
- H(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.StateEquation
-
Get H(t), the covariance matrix of ut.
- H(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Get H(t), the variance of ut.
- Hadi - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Diagnostics
-
Hadi's influence measure
- Halley - Class in com.numericalmethod.suanshu.analysis.uniroot
-
Halley's method is an iterative root finding method for a univariate function
with a continuous second derivative, i.e., a C2 function.
- Halley(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.uniroot.Halley
-
Construct an instance of Halley's root finding algorithm.
- HarveyGodfrey - Class in com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity
-
The Harvey-Godfrey test is used to test for heteroskedasticity in a linear regression model.
- HarveyGodfrey(Residuals) - Constructor for class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.HarveyGodfrey
-
Perform the Harvey-Godfrey test to test for heteroskedasticity in a linear regression model.
- hasDuplicate(double[], double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if a double array contains any duplicates.
- hashCode() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- hashCode() - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
- hashCode() - Method in class com.numericalmethod.suanshu.interval.Interval
-
- hashCode() - Method in class com.numericalmethod.suanshu.interval.Intervals
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.Coordinates
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseEntry
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
-
- hashCode() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- hashCode() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- hashCode() - Method in class com.numericalmethod.suanshu.number.Real
-
- hashCode() - Method in class com.numericalmethod.suanshu.stats.timeseries.DateTimeGenericTimeSeries.Entry
-
- hashCode() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
- hashCode() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.MultiVariateTimeSeries.Entry
-
- hashCode() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
- hashCode() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
- hashCode() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
-
- hashCode() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.TimeSeries.Entry
-
- hashCode() - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- hashCode() - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- hasNext() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector.Iterator
-
- hasNext() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization.Iterator
-
- hasNext() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.Realization.Iterator
-
- hasZero(double[], double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if a double array has any 0.
- Hessenberg - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr
-
An upper Hessenberg matrix is a square matrix which has zero entries below the first sub-diagonal.
- Hessenberg(Hessenberg.DeflationCriterion) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.Hessenberg
-
Construct a Hessenberg utility class.
- Hessenberg() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.Hessenberg
-
Construct a Hessenberg utility class with the default deflation criterion.
- Hessenberg.DefaultDeflationCriterion - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr
-
The default deflation criterion is to use eq.
- Hessenberg.Deflation - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr
-
This class encapsulates the indices for the upper left hand corner and lower right hand corner of H22 as a result of the deflation procedure.
- Hessenberg.DeflationCriterion - Interface in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr
-
Deflation of an upper Hessenberg matrix splits it into multiple smaller upper Hessenberg matrices
when the sub-diagonal entries are sufficiently small.
- HessenbergDecomposition - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr
-
Given a square matrix A, we find Q such that Q' * A * Q = H where H is a Hessenberg matrix.
- HessenbergDecomposition(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.HessenbergDecomposition
-
Run the Hessenberg decomposition for a square matrix.
- Hessian - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
-
The Hessian matrix is the square matrix of the second-order partial derivatives of a multivariate function.
- Hessian(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.Hessian
-
Construct the Hessian matrix for a multivariate function f at point x.
- Hessian() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.QuadraticFunction
-
- HessianFunction - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
-
The Hessian function, H(x), evaluates the Hessian of a real scalar function f at a point x.
- HessianFunction(RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.HessianFunction
-
Construct the Hessian function of a real scalar function f.
- hHat - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
-
projection matrix H-hat
- HiddenMarkovModel - Class in com.numericalmethod.suanshu.stats.hmm
-
In a (discrete) hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible.
- HiddenMarkovModel(Vector, Matrix, RandomNumberGenerator[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel
-
Construct a hidden Markov model.
- HiddenMarkovModel(Matrix, RandomNumberGenerator[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel
-
Construct a hidden Markov model using the stationary probabilities of the initial states.
- HiddenMarkovModel(HiddenMarkovModel) - Constructor for class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel
-
Copy constructor.
- HiddenMarkovModel - Class in com.numericalmethod.suanshu.stats.hmm.mixture
-
This is the mixture hidden Markov model (HMM).
- HiddenMarkovModel(Vector, Matrix, HMMDistribution) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.HiddenMarkovModel
-
Construct a mixture hidden Markov model.
- HiddenMarkovModel(HiddenMarkovModel) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.HiddenMarkovModel
-
Copy constructor.
- HiddenMarkovModel - Class in com.numericalmethod.suanshu.stats.hmm.rabiner
-
This is the hidden Markov model as defined by Rabiner.
- HiddenMarkovModel(Vector, Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.hmm.rabiner.HiddenMarkovModel
-
Construct a Rabiner hidden Markov model.
- HiddenMarkovModel(HiddenMarkovModel) - Constructor for class com.numericalmethod.suanshu.stats.hmm.rabiner.HiddenMarkovModel
-
Copy constructor.
- HilbertMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype
-
A Hilbert matrix, H, is a symmetric matrix with entries being the unit fractions
H[i][j] = 1 / (i + j -1)
- HilbertMatrix(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.HilbertMatrix
-
Construct a Hilbert matrix.
- HilbertSpace<H,F extends Field<F> & java.lang.Comparable<F>> - Interface in com.numericalmethod.suanshu.mathstructure
-
A Hilbert space is an inner product space, an abstract vector space in which distances and angles can be measured.
- HmmBaumWelch - Class in com.numericalmethod.suanshu.stats.hmm.mixture
-
The Baum–Welch algorithm is used to find the unknown parameters of a hidden Markov model (HMM)
by making use of the forward-backward algorithm.
- HmmBaumWelch(double[], HiddenMarkovModel, double, int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.HmmBaumWelch
-
Construct a mixture HMM model by training an initial model using the Baum-Welch algorithm.
- HmmBaumWelch.TrainedModel - Class in com.numericalmethod.suanshu.stats.hmm.mixture
-
the result of the Baum-Welch algorithm
- HMMDistribution - Interface in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
This is the conditional distribution of the observations in each state
(possibly differently parameterized) of a mixture hidden Markov model.
- HmmForwardBackward - Class in com.numericalmethod.suanshu.stats.hmm.mixture
-
The implementation of the forward-backward algorithm computes the log of forward and backward probabilities,
give a sequence of observations and the hmm model.
- HmmForwardBackward(HiddenMarkovModel) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.HmmForwardBackward
-
Construct an instance of HmmForwardBackward to compute
the forward and backward probabilities and the log-likelihood.
- HmmForwardBackward - Class in com.numericalmethod.suanshu.stats.hmm.rabiner
-
The forward–backward algorithm is an inference algorithm for hidden Markov models
which computes the posterior marginals of all hidden state variables given a sequence of observations.
- HmmForwardBackward(HiddenMarkovModel) - Constructor for class com.numericalmethod.suanshu.stats.hmm.rabiner.HmmForwardBackward
-
Construct the forward and backward probability matrix generator for an HMM model.
- HmmGamma - Class in com.numericalmethod.suanshu.stats.hmm.rabiner
-
γ is the probability of the system in state si,
given the model and observation sequence.
- HmmGamma(HiddenMarkovModel) - Constructor for class com.numericalmethod.suanshu.stats.hmm.rabiner.HmmGamma
-
Construct the gamma matrix generator.
- HmmInnovation - Class in com.numericalmethod.suanshu.stats.hmm
-
An HMM innovation consists of a state and an observation in the state.
- HmmInnovation(int, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.HmmInnovation
-
Construct an HMM innovation.
- HmmTrainByEM - Class in com.numericalmethod.suanshu.stats.hmm.rabiner
-
This implementation trains an HMM model by observations using an EM algorithm.
- HmmTrainByEM(int[], HiddenMarkovModel, int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.rabiner.HmmTrainByEM
-
Construct an HMM model by training an initial model using an EM algorithm.
- HmmViterbi - Class in com.numericalmethod.suanshu.stats.hmm.mixture
-
The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states
– called the Viterbi path –
that results in a sequence of observed events,
especially in the context of Markov information sources and hidden Markov models.
- HmmViterbi(HiddenMarkovModel) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.HmmViterbi
-
Construct an Viterbi algorithm for an HMM.
- HmmXi - Class in com.numericalmethod.suanshu.stats.hmm.rabiner
-
ξ is the probability of the system being in state si at time t and state sj at time t+1,
given the model and observation sequence.
- HmmXi(HiddenMarkovModel) - Constructor for class com.numericalmethod.suanshu.stats.hmm.rabiner.HmmXi
-
Construct the xi matrices generator.
- HomogeneousPathFollowing - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing
-
This implementation solves a Semi-Definite Programming problem using the Homogeneous Self-Dual Path-Following algorithm.
- HomogeneousPathFollowing(double, double, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowing
-
Construct a Homogeneous Self-Dual Path-Following minimizer to solve semi-definite programming problems.
- HomogeneousPathFollowing(double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowing
-
Construct a Homogeneous Self-Dual Path-Following minimizer to solve semi-definite programming problems.
- HomogeneousPathFollowing.Solution - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing
-
This is the solution to a Semi-Definite Programming problem using the Homogeneous Self-Dual Path-Following algorithm.
- HornerScheme - Class in com.numericalmethod.suanshu.analysis.function.polynomial
-
Horner scheme is an algorithm for the efficient evaluation of polynomials in monomial form.
- HornerScheme(Polynomial, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.HornerScheme
-
Evaluate a polynomial at x.
- Householder - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
A Householder transformation in the 3-dimensional space is the reflection of a vector in the plane.
- Householder(Vector) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.Householder
-
Construct a Householder matrix from the vector that defines the hyperplane orthogonal to the vector.
- Householder.Context - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
This is the context information about a Householder transformation.
- HouseholderReflection - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.qr
-
Successive Householder reflections gradually transform a matrix A to the upper triangular form.
- HouseholderReflection(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.HouseholderReflection
-
Run the Householder reflection process to orthogonalize a matrix.
- HouseholderReflection(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.HouseholderReflection
-
Run the Householder reflection process to orthogonalize a matrix.
- Hp - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing
-
This is the symmetrization operator as defined in eq.
- Hp(Matrix) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.Hp
-
Construct a symmetrization operator.
- Hp() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.Hp
-
Construct the symmetrization operator using an identity matrix.
- Huang - Class in com.numericalmethod.suanshu.optimization.unconstrained.quasinewton
-
Huang's updating formula is a family of formulas which encompasses
the rank-one, DFP, BFGS as well as some other formulas.
- Huang(double, double, double, double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.Huang
-
Construct a multivariate minimizer using Huang's method.
- Huang.HuangImpl - Class in com.numericalmethod.suanshu.optimization.unconstrained.quasinewton
-
an implementation of Huang's formula.
- HuangImpl(C2OptimProblem) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.Huang.HuangImpl
-
- HypothesisTest - Class in com.numericalmethod.suanshu.stats.test
-
A statistical hypothesis test is a method of making decisions using experimental data.
- HypothesisTest(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
Construct an instance of HypothesisTest from samples.
- i - Variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.Coordinates
-
the row index
- I - Static variable in class com.numericalmethod.suanshu.number.complex.Complex
-
a number representing 0.0 + 1.0i, the square root of -1
- I - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- I - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Expectation
-
the integrator to compute the integral for each filtration/path
- id - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk.MultiVariateRealization
-
the ID of this particular realization
- id - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk.Realization
-
the ID of this particular realization
- idempotent(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a matrix is idempotent.
- identity(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a matrix is an identity matrix, up to a precision.
- Identity - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the link function:
- Identity() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Identity
-
- IdentityPreconditioner - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
-
This identity preconditioner is used when no preconditioning is applied.
- IdentityPreconditioner() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
-
- ifelse(double[], R.ifelse) - Static method in class com.numericalmethod.suanshu.misc.R
-
Return a value with the same shape as test
which is filled with elements selected from either yes or no
depending on whether the element of test is true or false.
- IID - Class in com.numericalmethod.suanshu.stats.random.multivariate
-
An i.i.d.
- IID(RandomNumberGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.multivariate.IID
-
Construct a rvg that outputs vectors that have i.i.d.
- ILPBranchAndBound - Class in com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb
-
This is a Branch-and-Bound algorithm that solves Integer Linear Programming problems.
- ILPBranchAndBound(ILPBranchAndBound.ActiveListFactory) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPBranchAndBound
-
Construct a Branch-and-Bound minimizer to solve Integer Linear Programming problems.
- ILPBranchAndBound() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPBranchAndBound
-
Construct a Branch-and-Bound minimizer to solve Integer Linear Programming problems.
- ILPBranchAndBound.ActiveListFactory - Interface in com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb
-
This factory constructs a new instance of ActiveList for each Integer Linear Programming problem.
- ILPNode - Class in com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb
-
This is the branch-and-bound node used in conjunction with
ILPBranchAndBound to solve an Integer Linear Programming problem.
- ILPNode(ILPProblem) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPNode
-
Construct a BB node and associate it with an ILP problem.
- ILPProblem - Interface in com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem
-
A linear program in real variables is said to be integral if it has at least one optimal solution which is integral.
- ILPProblemImpl1 - Class in com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem
-
This implementation is an ILP problem, in which the variables can be real or integral.
- ILPProblemImpl1(Vector, LinearGreaterThanConstraints, LinearLessThanConstraints, LinearEqualityConstraints, BoxConstraints, int[], double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
Construct an ILP problem, in which the variables can be real or integral.
- imaginary() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Get the imaginary part of this complex number.
- ImmutableMatrix - Class in com.numericalmethod.suanshu.matrix.doubles
-
This is a read-only view of a
Matrix instance.
- ImmutableMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
Construct a read-only version of a matrix.
- ImmutableVector - Class in com.numericalmethod.suanshu.vector.doubles
-
This is a read-only view of a
Vector instance.
- ImmutableVector(Vector) - Constructor for class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
Construct a read-only version of a vector.
- index() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector.Entry
-
Get the index of this entry in the sparse vector, counting from 1.
- index - Variable in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.BoxConstraints.Bound
-
the index to the variable, counting from 1
- index - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
-
the index of a variable, an inequality, an equality, etc., counting from 1
- index - Variable in class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
the index of an integral variable
- InformationCriteria - Class in com.numericalmethod.suanshu.stats.regression.linear.ols
-
The information criteria measure the goodness of fit of an estimated statistical model.
- informationCriteria - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression
-
the model selection criteria
- initialization() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
Initialize the first population.
- initialization() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
The initial population is generated by putting a uniform mesh/grid/net over the entire region.
- initials - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- innerProduct(H) - Method in interface com.numericalmethod.suanshu.mathstructure.HilbertSpace
-
<·,·> : H × H → F
Inner product formalizes the geometrical notions such as the length of a vector and the angle between two vectors.
- innerProduct(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- InnerProduct - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
The Frobenius inner product is the component-wise inner product of two matrices as though they are vectors.
- InnerProduct(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.InnerProduct
-
Compute the inner product of two matrices.
- innerProduct(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- innerProduct(Vector, Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- innerProduct(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- innerProduct(Vector) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Inner product in the Euclidean space is the dot product.
- InnovationAlgorithm - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This is an implementation, adapted for an ARMA process, of the innovation algorithm,
which is an efficient way of obtaining a one step least square linear predictor.
- InnovationAlgorithm(MultiVariateTimeSeries, ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.InnovationAlgorithm
-
Construct an instance of InnovationAlgorithm for a multivariate ARMA time series.
- InnovationAlgorithm - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess
-
The innovation algorithm is an efficient way of
obtaining a one step least square linear predictor for a linear time series {Xt} with known covariance structure.
- InnovationAlgorithm(MultiVariateTimeSeries, AutoCovarianceFunction) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.InnovationAlgorithm
-
Construct an instance of InnovationAlgorithm for a multivariate time series with known auto-covariance structure.
- InnovationAlgorithm - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess
-
The innovation algorithm is an efficient way of
obtaining a one step least square linear predictor for a linear time series {Xt} with known auto-covariance.
- InnovationAlgorithm(TimeSeries, AutoCovarianceFunction) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.InnovationAlgorithm
-
Construct an instance of InnovationAlgorithm for a univariate time series with known auto-covariance structure.
- InnovationAlgorithmImpl - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess
-
This class implements the part of the innovation algorithm that computes the prediction coefficients, V and Θ.
- InnovationAlgorithmImpl() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.InnovationAlgorithmImpl
-
- intArray2doubleArray(int...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert an int array to a double array.
- intArray2List(int[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert an int array to a list.
- IntegerDomain(int, int[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
Construct the integral domain for an integral variable.
- IntegerDomain(int, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
Construct the integral domain for an integral variable.
- IntegerDomain(int, int, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
Construct the integral domain for an integral variable.
- integral(Filtration) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integrator
-
Integrate the function for a given filtration.
- IntegralConstrainedCellFactory - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained
-
This implementation defines the constrained Differential Evolution operators that solve an Integer Programming problem.
- IntegralConstrainedCellFactory(DEOptimCellFactory, IntegralConstrainedCellFactory.IntegerConstraint) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory
-
Construct an instance of IntegralConstrainedCellFactory.
- IntegralConstrainedCellFactory.AllIntegers - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained
-
This integral constraint makes all variables in the objective function integral variables.
- IntegralConstrainedCellFactory.IntegerConstraint - Interface in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained
-
The integral constraints are defined by implementing this interface.
- IntegralConstrainedCellFactory.SomeIntegers - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained
-
This integral constraint makes some variables in the objective function integral variables.
- IntegralDB - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
This class implements the following class of integrals.
- IntegralDB(FiltrationFunction) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDB
-
Construct an integral for f with respect to dB.
- IntegralDt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
This class implements the following class of integrals.
- IntegralDt(FiltrationFunction) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDt
-
Construct an integral for f with respect to dt.
- integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.ChangeOfVariable
-
- integrate(UnivariateRealFunction, double, double) - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Integrator
-
Integrate function f from a to b,
\[
\int_a^b\! f(x)\, dx
\]
- integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.NewtonCotes
-
- integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Riemann
-
- integrate(UnivariateRealFunction, double, double, SubstitutionRule) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Riemann
-
Integrate a function, f, from a to b possibly using change of variable.
- integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Romberg
-
- integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Simpson
-
- Integrator - Interface in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
This defines the interface for the numerical integration of definite integrals of univariate functions.
- Integrator - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
The class represents an integral for a function, in the Lebesgue sense.
- Integrator(FiltrationFunction) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integrator
-
Construct an integral from an integrand.
- interpolate(OrderedPairs) - Method in interface com.numericalmethod.suanshu.analysis.interpolation.Interpolation
-
Construct a real valued function from a discrete set of data points.
- Interpolation - Interface in com.numericalmethod.suanshu.analysis.interpolation
-
Interpolation is a method of constructing new data points within the range of a discrete set of known data points.
- Interval<T extends java.lang.Comparable<? super T>> - Class in com.numericalmethod.suanshu.interval
-
For a partially ordered set, there is a binary relation, denoted as ≤, that indicates that,
for certain pairs of elements in the set, one of the elements precedes the other.
- Interval(T, T) - Constructor for class com.numericalmethod.suanshu.interval.Interval
-
Construct an interval.
- IntervalRelation - Enum in com.numericalmethod.suanshu.interval
-
Allen's Interval Algebra is a calculus for temporal reasoning that was introduced by James F.
- Intervals<T extends java.lang.Comparable<? super T>> - Class in com.numericalmethod.suanshu.interval
-
This is a disjoint set of intervals.
- Intervals() - Constructor for class com.numericalmethod.suanshu.interval.Intervals
-
Construct an empty set of intervals.
- Intervals(Interval<T>) - Constructor for class com.numericalmethod.suanshu.interval.Intervals
-
Construct a set that contains only one interval.
- Intervals(T, T) - Constructor for class com.numericalmethod.suanshu.interval.Intervals
-
Construct a set that contains only one interval [begin, end].
- Intervals(Interval<T>...) - Constructor for class com.numericalmethod.suanshu.interval.Intervals
-
Construct a set of intervals.
- Intervals(Intervals<T>) - Constructor for class com.numericalmethod.suanshu.interval.Intervals
-
Copy constructor.
- intValue() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- intValue() - Method in class com.numericalmethod.suanshu.number.Real
-
- intValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
- invdet() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.HilbertMatrix
-
One over the determinant of H: 1/|H|, which is an integer.
- inverse() - Method in interface com.numericalmethod.suanshu.mathstructure.Field
-
For each a in F, there exists an element b in F such that a × b = b × a = 1.
- Inverse - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
For a square matrix A, the inverse, A-1, if exists, satisfies
A.multiply(A.inverse()) == A.ONE()
There are multiple ways to compute the inverse of a matrix.
- Inverse(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.Inverse
-
Construct a the inverse of a matrix.
- Inverse(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.Inverse
-
Construct a the inverse of a matrix.
- Inverse(UpperTriangularMatrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.Inverse
-
Construct the inverse of an upper triangular matrix.
- Inverse(LowerTriangularMatrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.Inverse
-
Construct the inverse of a lower triangular matrix.
- inverse() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- inverse() - Method in class com.numericalmethod.suanshu.number.Real
-
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Cloglog
-
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Identity
-
- Inverse - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the link function:
- Inverse() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Inverse
-
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Inverse
-
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.InverseSquared
-
- inverse(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkFunction
-
Inverse of the link function, i.e., g-1(x).
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Log
-
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Logit
-
Inverse of the link function, i.e., g-1(x).
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Probit
-
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Sqrt
-
- InverseGaussian - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
The Inverse Gaussian distribution for the error distribution in a GLM model.
- InverseGaussian() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
-
Construct an instance of InverseGaussian.
- InverseGaussian(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
-
Construct an instance of InverseGaussian with an overriding link function.
- InverseGaussian - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family
-
The quasi InverseGaussian family of GLM.
- InverseGaussian() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.InverseGaussian
-
Create an instance of InverseGaussian.
- InverseGaussian(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.InverseGaussian
-
Create an instance of InverseGaussian with an overriding link function.
- InverseIteration - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
-
Inverse iteration is an iterative eigenvalue algorithm.
- InverseIteration(Matrix, double, InverseIteration.StoppingCriterion) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.InverseIteration
-
Construct an instance of InverseIteration to find the corresponding eigenvector.
- InverseIteration(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.InverseIteration
-
Construct an instance of InverseIteration to find the corresponding eigenvector.
- InverseIteration.StoppingCriterion - Interface in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
-
This interface defines the convergence criterion.
- InverseNonExistent() - Constructor for exception com.numericalmethod.suanshu.mathstructure.Field.InverseNonExistent
-
Construct an instance of InverseNonExistent
- InverseSquared - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the link function:
- InverseSquared() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.InverseSquared
-
- InverseTransformSampling - Class in com.numericalmethod.suanshu.stats.random.univariate
-
Inverse transform sampling (also known as inversion sampling, the inverse probability integral transform, the inverse transformation method, Smirnov transform, golden rule, etc.)
is a basic method for pseudo-random number sampling,
i.e.
- InverseTransformSampling(ProbabilityDistribution, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.InverseTransformSampling
-
Construct a random number generator to sample from a distribution.
- InverseTransformSampling(ProbabilityDistribution) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.InverseTransformSampling
-
Construct a random number generator to sample from a distribution.
- InverseTransformSamplingExpRng - Class in com.numericalmethod.suanshu.stats.random.univariate.exp
-
This is a pseudo random number generator that samples from the exponential distribution using the inverse transform sampling method.
- InverseTransformSamplingExpRng(double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.exp.InverseTransformSamplingExpRng
-
Construct a random number generator to sample from the exponential distribution using the inverse transform sampling method.
- InverseTransformSamplingExpRng(double) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.exp.InverseTransformSamplingExpRng
-
Construct a random number generator to sample from the exponential distribution using the inverse transform sampling method.
- InverseTransformSamplingExpRng() - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.exp.InverseTransformSamplingExpRng
-
Construct a random number generator to sample from the standard exponential distribution using the inverse transform sampling method.
- InverseTransformSamplingGammaRng - Class in com.numericalmethod.suanshu.stats.random.univariate.gamma
-
- InverseTransformSamplingGammaRng(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.gamma.InverseTransformSamplingGammaRng
-
Deprecated.
Construct a random number generator to sample from the gamma distribution using the inverse transform sampling method.
- InverseTransformSamplingGammaRng() - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.gamma.InverseTransformSamplingGammaRng
-
Deprecated.
Construct a random number generator to sample from the standard gamma distribution using the inverse transform sampling method.
- Invertibility - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class computes the inverse representation of an Autoregressive Moving Average (ARMA) model.
- Invertibility(ARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.Invertibility
-
Construct the inverse representation of an ARMA model.
- Invertibility(ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.Invertibility
-
- InvertingVariable - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This is the inverting-variable transformation.
- InvertingVariable(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.InvertingVariable
-
Construct an InvertingVariable substitution rule.
- invOfwAtwA() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
(wA' %*% wA)-1
- IPMinimizer<T extends IPProblem,S extends MinimizationSolution<Vector>> - Interface in com.numericalmethod.suanshu.optimization.constrained.integer
-
An Integer Programming minimizer minimizes an objective function subject to equality/inequality constraints as well as integral constraints.
- IPProblem - Interface in com.numericalmethod.suanshu.optimization.constrained.integer
-
An Integer Programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers.
- IPProblemImpl1 - Class in com.numericalmethod.suanshu.optimization.constrained.integer
-
This is an implementation of a general Integer Programming problem in which some variables take only integers.
- IPProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints, int[], double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.IPProblemImpl1
-
Construct a constrained optimization problem with integral constraints.
- IPProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints, int[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.IPProblemImpl1
-
Construct a constrained optimization problem with integral constraints.
- is(IntervalRelation, Interval<T>) - Method in class com.numericalmethod.suanshu.interval.Interval
-
Check whether this and Y satisfies a certain Allen's interval relation.
- isAllZeros(double[], double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if a double array contains only 0s, entry-by-entry.
- isArray(Table) - Static method in class com.numericalmethod.suanshu.datastructure.DimensionCheck
-
Check if a table is a row or a column.
- isBetween(Interval<T>, Interval<T>) - Method in enum com.numericalmethod.suanshu.interval.IntervalRelation
-
Check if X and Y satisfy a certain relation.
- isBracketing(double, double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch.Solution
-
Check whether [xl, xu] is bracketing x.
- isCandidate() - Method in interface com.numericalmethod.suanshu.algorithm.bb.BBNode
-
Check if this node is a possible solution to the original problem, e.g., not pruned.
- isCandidate() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPNode
-
- isColumn(Table) - Static method in class com.numericalmethod.suanshu.datastructure.DimensionCheck
-
Check if a table is a column.
- isConverged() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
This is the convergence criterion.
- isConverged() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
This genetic algorithm terminates if
the minimum does not improve for a fixed number of iterations, or
the maximum number of iterations is exceeded.
- isEmpty() - Method in interface com.numericalmethod.suanshu.algorithm.bb.ActiveList
-
Returns true if this collection contains no elements.
- isFat(Table) - Static method in class com.numericalmethod.suanshu.datastructure.DimensionCheck
-
Check if a table is fat.
- isFeasible() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Check if this table is feasible.
- isFixedIndex(int) - Method in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
Check whether a particular index corresponds a fixed variable/value.
- isFree(int) - Method in interface com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Check whether xi is a free variable after handling the box constraints.
- isFree(int) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- isFree(int) - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.ILPProblemImpl1
-
- isHessenberg(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.Hessenberg
-
Check if H is upper Hessenberg.
- isInfinite(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.Complex
-
Check if a complex number is an infinity;
i.e., either the real or the imaginary part is infinite, c.f., Double.isInfinite(),
and the number is not a NaN.
- isInKernel(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.operation.VectorSpace
-
- IsMatrix - Class in com.numericalmethod.suanshu.matrix.doubles
-
These are the boolean operators that take a matrix or a vector and check if it satisfies a certain property.
- isMinFound() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch.Solution
-
the convergence criterion
- isMinFound() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Brent.Solution
-
the convergence criterion
- isMinFound() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Fibonacci.Solution
-
This algorithm stops only after a pre-specified number of iterations.
- isMinFound() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Golden.Solution
-
- isNaN(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.Complex
-
Check if a complex number is an NaN;
i.e., either the real or the imaginary part is an NaN.
- isNegative(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if d is negative.
- isNegligible(Matrix, int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.Hessenberg.DefaultDeflationCriterion
-
Check if
H[i,j] is negligible by Steward's deflation criterion.
- isNegligible(Matrix, int, int, double) - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.Hessenberg.DeflationCriterion
-
Check whether a sub-diagonal element is sufficiently small.
- isNullRejected(double) - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
Use p-value to check whether the null hypothesis can be rejected for given significance level (size) alpha.
- isNumber(double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if a double is a number, i.e., it is not ∞ or NaN.
- isPositive(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if d is positive.
- isPow2(int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if an integer is a power of 2.
- isQuasiTriangular - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.Hessenberg.Deflation
-
true if the matrix is a quasi-triangular matrix
- isReal(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.Complex
-
Check if this complex number is a real number;
i.e., the imaginary part is 0.
- isReal(Number) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
-
Check if a number is a real number.
- isReducible(Matrix, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.Hessenberg
-
Check if H is upper Hessenberg and is reducible.
- isReducible() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearEqualityConstraints
-
Check if we can reduce the number of linear equalities.
- isResidualSmall(double) - Method in class com.numericalmethod.suanshu.algorithm.iterative.tolerance.AbsoluteTolerance
-
- isResidualSmall(double) - Method in class com.numericalmethod.suanshu.algorithm.iterative.tolerance.RelativeTolerance
-
- isResidualSmall(double) - Method in interface com.numericalmethod.suanshu.algorithm.iterative.tolerance.Tolerance
-
Checks if the updated residual satisfies the tolerance criteria.
- isRow(Table) - Static method in class com.numericalmethod.suanshu.datastructure.DimensionCheck
-
Check if a table is a row.
- isSameDimension(Table, Table) - Static method in class com.numericalmethod.suanshu.datastructure.DimensionCheck
-
Check if two tables have the same dimension.
- isSatisfied(Constraints, Vector, double) - Static method in class com.numericalmethod.suanshu.optimization.constrained.constraint.ConstraintsUtils
-
Check if the constraints are satisfied.
- isSatisfied(Constraints, Vector) - Static method in class com.numericalmethod.suanshu.optimization.constrained.constraint.ConstraintsUtils
-
Check if the constraints are satisfied.
- isSpanned(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.operation.VectorSpace
-
Check whether a vector is in the span of the basis.
- isSquare(Table) - Static method in class com.numericalmethod.suanshu.datastructure.DimensionCheck
-
Check if a table is square.
- isTall(Table) - Static method in class com.numericalmethod.suanshu.datastructure.DimensionCheck
-
Check if a table is tall.
- isTrue(double, int) - Method in interface com.numericalmethod.suanshu.misc.R.which
-
Decide whether x is to be selected.
- isUnique() - Method in interface com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.QPSolution
-
Return true if the quadratic programming problem has only one solution.
- isUnreduced(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
A bi-diagonal matrix is unreduced if it has no 0 on both the super and main diagonals.
- isValidated(Matrix) - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159
-
Check whether a matrix satisfies the row and column sums.
- IsVector - Class in com.numericalmethod.suanshu.vector.doubles
-
These are the utility functions to validate input arguments for vector operations.
- IsVector.SizeMismatch - Exception in com.numericalmethod.suanshu.vector.doubles
-
This is the exception thrown when an operation is performed on
two vectors with different sizes.
- IsVector.VectorAccessException - Exception in com.numericalmethod.suanshu.vector.doubles
-
This is the exception thrown when any invalid access to a
Vector instance is detected.
- isWeekend(DateTime) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Check if the given time is a weekend.
- isZero() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel
-
Check if the kernel has zero dimension, that is, if A has full rank.
- isZero(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if d is zero.
- iter - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- iter - Variable in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch.Solution
-
the current iteration count
- IteratesMonitor<S> - Class in com.numericalmethod.suanshu.algorithm.iterative.monitor
-
- IteratesMonitor() - Constructor for class com.numericalmethod.suanshu.algorithm.iterative.monitor.IteratesMonitor
-
Construct a monitor to keep track of the states in all iterations.
- iteration - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- IterationBody<T> - Interface in com.numericalmethod.suanshu.parallel
-
This interface defines the code snippet to be run in parallel.
- IterationMonitor<S> - Interface in com.numericalmethod.suanshu.algorithm.iterative.monitor
-
To debug an iterative algorithm, such as in
IterativeMethod, it is
useful to keep track of the all states generated in the iterations.
- IterativeIntegrator - Interface in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
An iterative integrator computes an integral by a series of sums, which approximates the value of the integral.
- IterativeLinearSystemSolver - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative
-
An iterative method for solving an N-by-N (or non-square) linear system
Ax = b involves a sequence of matrix-vector multiplications.
- IterativeLinearSystemSolver.Solution - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative
-
This is the solution to a system of linear equations using an iterative
solver.
- IterativeMethod<S> - Interface in com.numericalmethod.suanshu.algorithm.iterative
-
An iterative method is a mathematical procedure that generates a sequence of
improving approximate solutions for a class of problems.
- IterativeMinimizer<S> - Interface in com.numericalmethod.suanshu.optimization.problem
-
Many minimization algorithms work by starting from some given initials and iteratively moving toward an approximate solution.
- iterator() - Method in class com.numericalmethod.suanshu.algorithm.Combination
-
- iterator() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLMSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk.MultiVariateRealization
-
- iterator() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization
-
- Iterator(int, int, long) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization.Iterator
-
Construct a realization of a multivariate stochastic process.
- iterator() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk.Realization
-
- iterator() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.Realization
-
- Iterator(int, long) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.Realization.Iterator
-
Construct a realization of a univariate stochastic process.
- iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.DateTimeGenericTimeSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.OneDimensionTimeSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
-
- IWLS - Class in com.numericalmethod.suanshu.stats.regression.linear.glm
-
We estimate parameters ß in a GLM model using the Iteratively Re-weighted Least Squares algorithm.
- IWLS(double, int) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
-
Construct an instance to run the Iteratively Re-weighted Least Squares algorithm.
- L() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Get the lower triangular matrix L, such that P * A = L * U.
- L() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
-
- L() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Cholesky
-
Get the lower triangular matrix L.
- L() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Doolittle
-
- L() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LDL
-
Get L as in the LDL decomposition.
- L() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LU
-
- L() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LUDecomposition
-
Get the lower triangular matrix L as in the LU decomposition.
- L() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Get the lower triangular matrix L in the LDL decomposition.
- Label(SimplexTable.LabelType, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
-
Construct a label for a row or column in the simplex table.
- lag(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
Construct an instance of SimpleMultiVariateTimeSeries by lagging the time series.
- lag(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
Construct an instance of SimpleMultiVariateTimeSeries by lagging the time series.
- lag(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
-
/**
Construct an instance of SimpleTimeSeries by lagging the time series.
- lag(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
-
Construct an instance of SimpleTimeSeries by lagging the time series.
- lagAdjust - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
indicate whether the distribution is adjusted for lags
- lagOrder - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
the lag order used to calculate the test statistics; lagOrder = 0 yields the Dickey-Fuller distribution
- lagOrder - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
-
the lag order
- lambda - Variable in class com.numericalmethod.suanshu.matrix.doubles.operation.Householder.Context
-
the norm of v with the sign chosen to be the opposite of the first coordinate of v
- Lambda(double, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaDistribution.Lambda
-
Store the Beta distribution parameters.
- Lambda(int, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialDistribution.Lambda
-
Store the Binomial distribution parameters.
- Lambda(double, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaDistribution.Lambda
-
Store the Gamma distribution parameters.
- Lambda(double, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalDistribution.Lambda
-
Construct a Log-Normal distribution.
- Lambda(double, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalDistribution.Lambda
-
Construct a Normal distribution.
- lambdaCol - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
- Lanczos - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Lanczos approximation is a method for computing the Gamma function numerically, published by Cornelius Lanczos in 1964.
- Lanczos(double, int, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.Lanczos
-
Construct a Lanczos approximation instance.
- Lanczos() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.Lanczos
-
Construct a Lanczos approximation instance using default parameters.
- lastValue() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk.MultiVariateRealization
-
- lastValue() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization
-
Get the ending value of a realization,
i.e., the value at the end of the time interval, e.g., ω(T).
- lastValue() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk.Realization
-
- lastValue() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.Realization
-
Get the ending value of a realization,
i.e., the value at the end of the time interval, e.g., ω(T).
- LDL - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.triangle
-
The LDL decomposition decomposes a real and symmetric (hence square) matrix A into A = L * D * Lt.
- LDL(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LDL
-
Run the LDL decomposition on a real and symmetric (hence square) matrix.
- LDL(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LDL
-
Run the LDL decomposition on a real and symmetric (hence square) matrix.
- LeastPth<T> - Class in com.numericalmethod.suanshu.optimization.minmax
-
The least p-th minmax algorithm minimizes the maximal error/loss (function):
\[
\min_x \max_{\omega \in S} e(x, \omega)
\]
\(e(x, \omega)\) is the error or loss function.
- LeastPth(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.minmax.LeastPth
-
Construct a minmax minimizer using the Least p-th method.
- Lebesgue - Class in com.numericalmethod.suanshu.analysis.integration.univariate
-
Lebesgue integration is the general theory of integration of a function with respect to a general measure.
- Lebesgue(double[], double[]) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.Lebesgue
-
Construct a Lebesgue integral.
- LEcuyer - Class in com.numericalmethod.suanshu.stats.random.univariate.uniform.linear
-
This is the uniform random number generator recommended by L'Ecuyer in 1996.
- LEcuyer() - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.LEcuyer
-
Construct a LEcuyer pseudo uniform random generator.
- LEcuyer(long, long, long, long, long, long) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.LEcuyer
-
Construct a LEcuyer pseudo uniform random generator and then seed.
- leftConfidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
Compute the one sided left confidence interval, [0, a]
- leftConfidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
Compute the one sided left confidence interval, [0, a]
- Lehmer - Class in com.numericalmethod.suanshu.stats.random.univariate.uniform.linear
-
Lehmer proposed a general linear congruential generator that generates pseudo-random numbers in [0, 1].
- Lehmer(long, long, long) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.Lehmer
-
Construct a Lehmer (pure) linear congruential generator.
- Lehmer(long, long, long, long) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.Lehmer
-
Construct a skipping ahead Lehmer (pure) linear congruential generator.
- Lehmer() - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.Lehmer
-
Construct a Lehmer (pure) linear congruential generator.
- length() - Method in class com.numericalmethod.suanshu.analysis.sequence.Fibonacci
-
- length() - Method in interface com.numericalmethod.suanshu.analysis.sequence.Sequence
-
Get the number of computed terms in the sequence.
- LessThanConstraints - Interface in com.numericalmethod.suanshu.optimization.constrained.constraint
-
The domain of an optimization problem may be restricted by less-than or equal-to constraints.
- Levene - Class in com.numericalmethod.suanshu.stats.test.variance
-
The Levene test tests for the equality of variance of groups.
- Levene(double...) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.Levene
-
Perform the Levene test to test for homeogeneity of variance across groups.
- Levene(Levene.Type, double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.Levene
-
Perform the Levene test to test for homeogeneity of variance across groups.
- Levene.Type - Enum in com.numericalmethod.suanshu.stats.test.variance
-
the implementations available when computing the absolute deviations
- leverage - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
-
leverage; the bigger the leverage for an observation, the bigger influence on the prediction
- Lilliefors - Class in com.numericalmethod.suanshu.stats.test.distribution.normality
-
Lilliefors test tests the null hypothesis that data come from a normally distributed population with an estimated sample mean and variance.
- Lilliefors(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.normality.Lilliefors
-
Perform the Lilliefors test to test for the null hypothesis that data come from a normally distributed population with an estimated sample mean and variance.
- LILSparseMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
-
The list of lists (LIL) format for sparse matrix stores one list per row,
where each entry stores a column index and value.
- LILSparseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
Construct a sparse matrix in LIL format.
- LILSparseMatrix(int, int, int[], int[], double[]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
Construct a sparse matrix in LIL format.
- LILSparseMatrix(int, int, List<SparseEntry>) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
Construct a sparse matrix in LIL format by a list of non-zero entries.
- LILSparseMatrix(LILSparseMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
Copy constructor.
- LinearCongruentialGenerator - Interface in com.numericalmethod.suanshu.stats.random.univariate.uniform.linear
-
A linear congruential generator (LCG) produces a sequence of pseudo-random numbers
based on a linear recurrence relation.
- LinearConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.constraint.linear
-
This is a collection of linear constraints for a real-valued optimization problem.
- LinearConstraints(Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearConstraints
-
Construct a collection of linear constraints.
- LinearEqualityConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.constraint.linear
-
This is a collection of linear equality constraints.
- LinearEqualityConstraints(Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearEqualityConstraints
-
Construct a collection of linear equality constraints.
- LinearGreaterThanConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.constraint.linear
-
This is a collection of linear greater-than-or-equal-to constraints.
- LinearGreaterThanConstraints(Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearGreaterThanConstraints
-
Construct a collection of linear greater-than or equal-to constraints.
- linearInterpolate(double, double, double, double, double) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
Linear interpolation between two points.
- LinearInterpolator - Class in com.numericalmethod.suanshu.analysis.interpolation
-
Define a univariate function by linearly interpolating between adjacent points.
- LinearInterpolator(OrderedPairs) - Constructor for class com.numericalmethod.suanshu.analysis.interpolation.LinearInterpolator
-
Construct a univariate function by linearly interpolating between adjacent points.
- LinearKalmanFilter - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
The Kalman filter, also known as linear quadratic estimation (LQE),
is an algorithm which uses a series of measurements observed over time,
containing noise (random variations) and other inaccuracies,
and produces estimates of unknown variables that tend to be more precise than those that would be based on a single measurement alone.
- LinearKalmanFilter(DLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Construct a Kalman filter from a multivariate controlled dynamic linear model.
- LinearKalmanFilter - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
The Kalman filter, also known as linear quadratic estimation (LQE),
is an algorithm which uses a series of measurements observed over time,
containing noise (random variations) and other inaccuracies,
and produces estimates of unknown variables that tend to be more precise than those that would be based on a single measurement alone.
- LinearKalmanFilter(DLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Construct a Kalman filter from a univariate controlled dynamic linear model.
- LinearLessThanConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.constraint.linear
-
This is a collection of linear less-than-or-equal-to constraints.
- LinearLessThanConstraints(Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearLessThanConstraints
-
Construct a collection of linear less-than or equal-to constraints.
- LinearRepresentation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class computes the linear representation of an Autoregressive Moving Average (ARMA) model.
- LinearRepresentation(ARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.LinearRepresentation
-
Construct the linear representation of an ARMA model.
- LinearRepresentation(ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.LinearRepresentation
-
- LinearRepresentation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
This class computes the linear representation of an Autoregressive Moving Average (ARMA) model.
- LinearRepresentation(ARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
-
Construct the linear representation of an ARMA model.
- LinearRepresentation(ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
-
Construct the linear representation of an ARMA model.
- LinearRoot - Class in com.numericalmethod.suanshu.analysis.function.polynomial.root
-
This is a solver for finding the roots of a linear equation.
- LinearRoot() - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.root.LinearRoot
-
- LinearSystemSolver - Class in com.numericalmethod.suanshu.matrix.doubles.linearsystem
-
Solve a system of linear equations in the form:
Ax = b,
We assume that, after row reduction, A has no more rows than columns.
- LinearSystemSolver(double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LinearSystemSolver
-
Construct a solver for a linear system of equations.
- LinearSystemSolver.NoSolution - Exception in com.numericalmethod.suanshu.matrix.doubles.linearsystem
-
This is the runtime exception thrown when it fails to solve a system of linear
equations.
- LinearSystemSolver.Solution - Interface in com.numericalmethod.suanshu.matrix.doubles.linearsystem
-
This is the solution to a linear system of equations.
- LineSearch - Interface in com.numericalmethod.suanshu.optimization.unconstrained.linesearch
-
A line search is often used in another minimization algorithm to improve the current solution in one iteration step.
- linesearch(Vector, Vector) - Method in interface com.numericalmethod.suanshu.optimization.unconstrained.linesearch.LineSearch.Solution
-
Get the increment α so that f(x + α * d) is (approximately) minimized.
- linesearch - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.SteepestDescentImpl
-
- LineSearch.Solution - Interface in com.numericalmethod.suanshu.optimization.unconstrained.linesearch
-
This is the solution to a line search minimization.
- link() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Family
-
Get the link function of this distribution.
- link() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.QuasiFamily
-
- LinkFunction - Interface in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This interface represents a link function g(x) in the Generalized Linear Model (GLM).
- LjungBox - Class in com.numericalmethod.suanshu.stats.test.timeseries.portmanteau
-
The Ljung–Box test (named for Greta M.
- LjungBox(double[], int, int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.LjungBox
-
- LMProblem - Class in com.numericalmethod.suanshu.stats.regression.linear
-
This class represents a linear regression or a linear model (LM) problem.
- LMProblem(Vector, Matrix, boolean, Vector) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Construct a linear regression problem.
- LMProblem(Vector, Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Construct a linear regression problem, assuming a constant term (the intercept).
- LMProblem(Vector, Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Construct a linear regression problem, assuming equal weights to all observations.
- LMProblem(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Construct a linear regression problem, assuming
a constant term (the intercept)
equal weights to all observations
- LMProblem(LMProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Copy constructor.
- loadings() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FAEstimator
-
Get the rotated loading matrix.
- loadings() - Method in interface com.numericalmethod.suanshu.stats.pca.PCA
-
Get the matrix of variable loadings.
- loadings(int) - Method in interface com.numericalmethod.suanshu.stats.pca.PCA
-
Get the loading vector of the i-th principal component.
- loadings() - Method in class com.numericalmethod.suanshu.stats.pca.PCAbyEigen
-
Get the matrix of variable loadings.
- loadings() - Method in class com.numericalmethod.suanshu.stats.pca.PCAbySVD
-
Get the matrix of variable loadings.
- LocalSearchCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local.LocalSearchCellFactory.LocalSearchCell
-
- LocalSearchCellFactory<P extends OptimProblem,T extends Minimizer<OptimProblem,IterativeMinimizer<Vector>>> - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local
-
A LocalSearchCellFactory produces LocalSearchCellFactory.LocalSearchCells.
- LocalSearchCellFactory(LocalSearchCellFactory.MinimizerFactory<T>, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local.LocalSearchCellFactory
-
Construct an instance of a LocalSearchCellFactory.
- LocalSearchCellFactory.LocalSearchCell - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local
-
A LocalSearchCell implements the two genetic operations.
- LocalSearchCellFactory.MinimizerFactory<U extends Minimizer<OptimProblem,IterativeMinimizer<Vector>>> - Interface in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local
-
This factory constructs a new Minimizer for each mutation operation.
- log(BigDecimal) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute log(x).
- log(BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute log(x) up to a scale.
- log(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Natural logarithm of a complex number.
- log(double[]) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the logs of values.
- Log - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the link function:
- Log() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Log
-
- logBackward(double[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.HmmForwardBackward
-
Get the log-transformed backward probability matrix.
- LogBeta - Class in com.numericalmethod.suanshu.analysis.function.special.beta
-
This class represents the log of Beta function log(B(x, y)).
- LogBeta() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.beta.LogBeta
-
- logForward(double[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.HmmForwardBackward
-
Get the log-transformed forward probability matrix.
- logGamma(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.Lanczos
-
Compute log-gamma for a positive value x.
- logGamma(BigDecimal) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.Lanczos
-
Compute log-gamma for a positive value x to arbitrary precision.
- LogGamma - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The log-Gamma function, \(\log (\Gamma(z))\), for positive real numbers, is the log of the Gamma function.
- LogGamma(LogGamma.Method, Lanczos) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.LogGamma
-
Construct an instance of log-Gamma.
- LogGamma() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.LogGamma
-
Construct an instance of log-Gamma.
- LogGamma.Method - Enum in com.numericalmethod.suanshu.analysis.function.special.gamma
-
the methods available to compute \(\log (\Gamma(z))\)
- logGammaQuick(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.Lanczos
-
Compute log-gamma for a positive value x.
- Logistic - Class in com.numericalmethod.suanshu.stats.regression.linear.logistic
-
A logistic regression (sometimes called the logistic model or logit model)
is used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve.
- Logistic(LMProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.logistic.Logistic
-
Construct a Logistic instance.
- LogisticProblem - Class in com.numericalmethod.suanshu.stats.regression.linear.logistic
-
This class represents a logistic regression problem.
- LogisticProblem(LMProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticProblem
-
- Logit - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the link function:
- Logit() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Logit
-
- logLikelihood() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FAEstimator
-
Get the log-likelihood value.
- logLikelihood - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.HmmBaumWelch.TrainedModel
-
the log-likelihood
- logLikelihood(double[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.HmmForwardBackward
-
Get the log-likelihood.
- logLikelihood() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.Fitting
-
- logLikelihood() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
-
- logMu - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalDistribution.Lambda
-
the log-mean μ ∈ R
- LogNormalDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
A log-normal distribution is a probability distribution of a random variable whose logarithm is normally distributed.
- LogNormalDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
Construct a log-normal distribution.
- LogNormalDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Log-Normal distribution to model the observations.
- LogNormalDistribution(LogNormalDistribution.Lambda[], boolean, boolean) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalDistribution
-
Construct a Log-Normal distribution for each state in the HMM model.
- LogNormalDistribution(LogNormalDistribution.Lambda[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalDistribution
-
Construct a Log-Normal distribution for each state in the HMM model.
- LogNormalDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
the Log-Normal distribution parameters
- LogNormalRng - Class in com.numericalmethod.suanshu.stats.random.univariate
-
This random number generator samples from the log-normal distribution.
- LogNormalRng(NormalRng) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.LogNormalRng
-
Construct a random number generator to sample from the log-normal distribution.
- LogNormalRng(double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.LogNormalRng
-
Construct a random number generator to sample from the log-normal distribution.
- logSigma - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalDistribution.Lambda
-
the log-standard deviation; shape
- longValue() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- longValue() - Method in class com.numericalmethod.suanshu.number.Real
-
- longValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
- LoopBody - Interface in com.numericalmethod.suanshu.parallel
-
The implementation of this interface contains the code inside a for-loop
construct.
- lower() - Method in class com.numericalmethod.suanshu.interval.RealInterval
-
Get the lower bound of this interval.
- lower() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.BoxConstraints.Bound
-
- lower - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- lowerBidiagonal(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a matrix is lower bidiagonal, up to a precision.
- lowerBound() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.CauchyPolynomial
-
Cauchy's lower bound on polynomial zeros is the unique positive root of the Cauchy polynomial.
- LowerBoundConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.constraint.linear
-
This is a lower bound constraints such that for all xi's,
xi ≥ b
- LowerBoundConstraints(RealScalarFunction, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LowerBoundConstraints
-
Construct a lower bound constraints for all variables in a function.
- lowerTriangular(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a matrix is lower triangular, up to a precision.
- LowerTriangularMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle
-
A lower triangular matrix has 0 entries where column index > row index.
- LowerTriangularMatrix(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
Construct a lower triangular matrix of dimension dim * dim.
- LowerTriangularMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
Construct a lower triangular matrix from a 2D double[][] array.
- LowerTriangularMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
Construct a lower triangular matrix from a matrix.
- LowerTriangularMatrix(LowerTriangularMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
Copy constructor.
- LPBoundedMinimizer - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution
-
This is the solution to a bounded linear programming problem.
- LPBoundedMinimizer(SimplexTable) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
Construct the solution for a bounded linear programming problem.
- LPCanonicalSolver - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver
-
This is an LP solver that solves a canonical LP problem in the following form.
- LPCanonicalSolver() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPCanonicalSolver
-
- LPDimensionNotMatched - Exception in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when the dimensions of the objective function and constraints of a linear programming problem are inconsistent.
- LPDimensionNotMatched(String) - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception.LPDimensionNotMatched
-
Construct an instance of LPDimensionNotMatched.
- LPEmptyCostVector - Exception in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when there is no objective function in a linear programming problem.
- LPEmptyCostVector() - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception.LPEmptyCostVector
-
- LPException - Exception in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when there is any problem when solving a linear programming problem.
- LPException() - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception.LPException
-
Construct an instance of LPException.
- LPInfeasible - Exception in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when the LP problem is infeasible, i.e., no solution.
- LPInfeasible() - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception.LPInfeasible
-
- LPMinimizer - Interface in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp
-
An LP minimizer minimizes the objective of an LP problem, satisfying all the constraints.
- LPNoConstraint - Exception in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when there is no linear constraint found for the LP problem.
- LPNoConstraint() - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception.LPNoConstraint
-
- LPProblem - Interface in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem
-
A linear programming (LP) problem minimizes a linear objective function subject to a collection of linear constraints.
- LPProblemImpl1 - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem
-
This is an implementation of a linear programming problem,
LPProblem.
- LPProblemImpl1(Vector, LinearGreaterThanConstraints, LinearLessThanConstraints, LinearEqualityConstraints, BoxConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
Construct a general linear programming problem.
- LPProblemImpl1(Vector, LinearGreaterThanConstraints, LinearEqualityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
Construct a general linear programming problem with only greater-than-or-equal-to and equality constraints.
- LPRuntimeException - Exception in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when there is any problem when constructing a linear programming problem.
- LPRuntimeException() - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception.LPRuntimeException
-
Construct an instance of LPRuntimeException.
- LPRuntimeException(String) - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception.LPRuntimeException
-
Construct an instance of LPRuntimeException.
- LPSimplexMinimizer - Interface in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution
-
A simplex LP minimizer can be read off from the solution simplex table.
- LPSimplexSolution - Interface in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution
-
The solution to a linear programming problem using a simplex method contains an
LPSimplexMinimizer.
- LPSimplexSolver<P extends LPProblem> - Interface in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver
-
A simplex solver works toward an LP solution by sequentially applying Jordan exchange to a simplex table.
- LPSolution<T extends LPMinimizer> - Interface in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp
-
A solution to an LP problem contains all information about solving an LP problem such as
whether the problem has a solution (bounded), how many minimizers it has, and the minimum.
- LPSolver<P extends LPProblem,S extends LPSolution<?>> - Interface in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp
-
An LP solver solves a Linear Programming (LP) problem.
- LPTwoPhaseSolver - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver
-
This implementation solves a linear programming problem,
LPProblem, using a two-step approach.
- LPTwoPhaseSolver(LPSimplexSolver<CanonicalLPProblem1>) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
-
Construct an LP solver to solve LP problems.
- LPTwoPhaseSolver() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
-
Construct an LP solver to solve LP problems.
- LPUnbounded - Exception in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when the LP problem is unbounded.
- LPUnbounded(int) - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception.LPUnbounded
-
Construct an instance of LPUnbounded.
- LPUnboundedMinimizer - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution
-
This is the solution to an unbounded linear programming problem.
- LPUnboundedMinimizer(SimplexTable, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
Construct the solution for an unbounded linear programming problem.
- LPUnboundedMinimizerScheme2 - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution
-
This is the solution to an unbounded linear programming problem found in scheme 2.
- LPUnboundedMinimizerScheme2(SimplexTable, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2
-
Construct the solution for an unbounded linear programming problem as a result of applying scheme 2.
- lr - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.Hessenberg.Deflation
-
H33 an upper quasi-triangular in Algorithm 7.5.2 has dimension \((n-l_r) \times (n-l_r)\).
- LSProblem - Class in com.numericalmethod.suanshu.matrix.doubles.linearsystem
-
This is the problem of solving a system of linear equations.
- LSProblem(Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LSProblem
-
Construct a system of linear equations Ax = b.
- Lt() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Cholesky
-
Get the transpose of the lower triangular matrix, L'.
- Lt() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LDL
-
Get the transpose of L as in the LDL decomposition.
- Lt() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Get the transpose of the lower triangular matrix L in the LDL decomposition.
- LU - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.triangle
-
LU decomposition decomposes an n x n matrix A so that P * A = L * U.
- LU(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LU
-
Run the LU decomposition on a square matrix.
- LU(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LU
-
Run the LU decomposition on a square matrix.
- LUDecomposition - Interface in com.numericalmethod.suanshu.matrix.doubles.factorization.triangle
-
LU decomposition decomposes an n x n matrix A so that P * A = L * U.
- LUSolver - Class in com.numericalmethod.suanshu.matrix.doubles.linearsystem
-
Use LU decomposition to solve Ax = b where A is square and
det(A) != 0.
- LUSolver() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LUSolver
-
- m() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Get the dimension of the system, i.e., m = the dimension of y.
- M - Variable in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
number of observations in group 1
- m0() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLM
-
Get the the mean of x0.
- m0() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Get the the mean of x0.
- MA(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Get the i-th MA coefficient; AR(0) = 1.
- MA() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Get the MA coefficients, excluding the initial 1.
- MA - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
the MA coefficients
- MA(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the i-th MA coefficient; MA(0) = 1.
- MA() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the MA coefficients, excluding the initial 1.
- MACH_EPS - Static variable in class com.numericalmethod.suanshu.Constant
-
the machine epsilon
This is the difference between 1 and the smallest exactly representable number greater than 1.
- MACH_SCALE - Static variable in class com.numericalmethod.suanshu.Constant
-
the scale for the machine epsilon
- MADecomposition - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess
-
This class decomposes a time series into the trend, seasonal and the stationary random components
using the Moving Average Estimation with symmetric window.
- MADecomposition(TimeSeries, double[], int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
Decompose a time series into the trend, seasonal and the stationary random components using the Moving Average Estimation.
- MADecomposition(TimeSeries, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
Decompose a periodic time series into the seasonal and stationary random components using no MA filter.
- MADecomposition(TimeSeries, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
Decompose a time series into the trend, seasonal and the stationary random components using the default filter.
- magicSquare(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
- makePeriodicInstants(DateTime, Period, int) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Make a list of periodic time instants.
- MAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a multivariate MA model.
- MAModel(Vector, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.MAModel
-
Construct a multivariate MA model.
- MAModel(Vector, Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.MAModel
-
Construct a multivariate MA model with unit variance.
- MAModel(Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.MAModel
-
Construct a zero-mean multivariate MA model.
- MAModel(Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.MAModel
-
Construct a zero-mean multivariate MA model with unit variance.
- MAModel(MAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.MAModel
-
Copy constructor.
- MAModel(MAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.MAModel
-
Cast a univariate MA model to a multivariate model.
- MAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
This class represents an MA model.
- MAModel(double, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a univariate MA model.
- MAModel(double, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a univariate MA model with unit variance.
- MAModel(double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a zero-mean univariate MA model.
- MAModel(double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a zero-mean univariate MA model with unit variance.
- MAModel(MAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Copy constructor.
- MarsagliaBray1964 - Class in com.numericalmethod.suanshu.stats.random.univariate.normal
-
The polar method (attributed to George Marsaglia, 1964) is a pseudo-random number sampling method for generating a pair of independent standard normal random variables.
- MarsagliaBray1964(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.normal.MarsagliaBray1964
-
Construct a random number generator to sample from the standard Normal distribution.
- MarsagliaBray1964() - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.normal.MarsagliaBray1964
-
Construct a random number generator to sample from the standard Normal distribution.
- MarsagliaTsang2000 - Class in com.numericalmethod.suanshu.stats.random.univariate.gamma
-
Marsaglia-Tsang is a procedure for generating a gamma variate as the cube of a suitably scaled normal variate.
- MarsagliaTsang2000(double, double, RandomStandardNormalNumberGenerator, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.gamma.MarsagliaTsang2000
-
Construct a random number generator to sample from the gamma distribution.
- MarsagliaTsang2000(double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.gamma.MarsagliaTsang2000
-
Construct a random number generator to sample from the gamma distribution.
- MarsagliaTsang2000() - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.gamma.MarsagliaTsang2000
-
Construct a random number generator to sample from the standard gamma distribution.
- MAT - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
MAT is the inverse operator of
SVEC.
- MAT(Vector) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.MAT
-
Construct the MAT of a vector.
- MathTable - Class in com.numericalmethod.suanshu.datastructure
-
A mathematical table consists of numbers showing the results of calculation with varying arguments.
- MathTable(String...) - Constructor for class com.numericalmethod.suanshu.datastructure.MathTable
-
Construct an empty table by headers.
- MathTable(int) - Constructor for class com.numericalmethod.suanshu.datastructure.MathTable
-
Construct an empty table.
- MathTable.Row - Class in com.numericalmethod.suanshu.datastructure
-
A row is indexed by a number and contains multiple values.
- Matrix - Interface in com.numericalmethod.suanshu.matrix.doubles
-
This interface defines a
Matrix as a
Ring, a
Table, and a few more methods not already defined in its mathematical definition.
- Matrix<T extends Matrix<T,F>,F extends Field<F>> - Interface in com.numericalmethod.suanshu.matrix.generic
-
This class defines a matrix over a field.
- MatrixAccess - Interface in com.numericalmethod.suanshu.matrix.doubles
-
This interface defines the methods for accessing entries in a matrix.
- MatrixAccess<F extends Field<F>> - Interface in com.numericalmethod.suanshu.matrix.generic
-
This interface defines the methods for accessing entries in a matrix over a field.
- MatrixAccessException - Exception in com.numericalmethod.suanshu.matrix
-
This is the runtime exception thrown when trying to access an invalid entry in a matrix, e.g., A[0, 0].
- MatrixAccessException() - Constructor for exception com.numericalmethod.suanshu.matrix.MatrixAccessException
-
Construct an instance of MatrixAccessException.
- MatrixAccessException(String) - Constructor for exception com.numericalmethod.suanshu.matrix.MatrixAccessException
-
Construct an instance of MatrixAccessException with a message.
- MatrixMathOperation - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation
-
This interface defines some standard operations for generic matrices.
- MatrixMeasure - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
A measure, μ, of a matrix, A, is a map from the Matrix space to the Real line.
- MatrixMeasure() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixMeasure
-
- MatrixMismatchException - Exception in com.numericalmethod.suanshu.matrix
-
This is the runtime exception thrown when an operation acts on matrices that have incompatible dimensions.
- MatrixMismatchException() - Constructor for exception com.numericalmethod.suanshu.matrix.MatrixMismatchException
-
Construct an instance of MatrixMismatchException.
- MatrixMismatchException(String) - Constructor for exception com.numericalmethod.suanshu.matrix.MatrixMismatchException
-
Construct an instance of MatrixMismatchException with a message.
- MatrixRing - Interface in com.numericalmethod.suanshu.matrix.doubles
-
A matrix ring is the set of all
n × n matrices over an arbitrary
Ring R.
- MatrixSingularityException - Exception in com.numericalmethod.suanshu.matrix
-
This is the runtime exception thrown when an operation acts on a singular matrix, e.g., applying LU decomposition to a singular matrix.
- MatrixSingularityException() - Constructor for exception com.numericalmethod.suanshu.matrix.MatrixSingularityException
-
Construct an instance of MatrixSingularityException.
- MatrixSingularityException(String) - Constructor for exception com.numericalmethod.suanshu.matrix.MatrixSingularityException
-
Construct an instance of MatrixSingularityException with a message.
- MatrixStorageImpl - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype
-
There are multiple ways to implement the storage data structure depending on the matrix type for optimization purpose.
- MatrixStorageImpl(int, int, MatrixAccess) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
Construct a MatrixStorageImpl to wrap a storage for access.
- MatrixTable - Interface in com.numericalmethod.suanshu.matrix.doubles
-
A matrix is represented by a rectangular table structure with accessors.
- MatrixUtils - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
These are the utility functions to apply to matrices.
- MatrixUtils() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixUtils
-
- MatthewsDavies - Class in com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite
-
Matthews and Davies propose the following way
to coerce a non-positive definite Hessian matrix to become symmetric, positive definite.
- MatthewsDavies(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Construct a symmetric, positive definite matrix using the Matthews-Davies algorithm.
- max(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixMeasure
-
Compute the maximal entry in a matrix.
- max(double...) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the maximum of the values.
- max(int...) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the maximum of the values.
- Max - Class in com.numericalmethod.suanshu.stats.descriptive.rank
-
The maximum of a sample is the biggest value in the sample.
- Max() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
Construct an empty Max calculator.
- Max(double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
Construct a Max calculator,
initialized with a sample.
- Max(Max) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
Copy constructor.
- max(DateTime, DateTime) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Return the later of two DateTime instances.
- maxDomain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
Get the biggest abscissae.
- maximizer() - Method in interface com.numericalmethod.suanshu.optimization.unconstrained.MultivariateMaximizer.Solution
-
Get the maximizer (solution) to the maximization problem.
- maximum() - Method in interface com.numericalmethod.suanshu.optimization.unconstrained.MultivariateMaximizer.Solution
-
Get the maximum found.
- maxIndex(boolean, int, int, double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get the index of the maximum of the values.
- maxIndex(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get the index of the maximum of the values.
- maxIterations - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver
-
- maxIterations - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- maxIterations - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent
-
the maximum number of iterations
- maxIterations - Variable in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch
-
the maximum number of iterations
- maxIterations - Variable in class com.numericalmethod.suanshu.optimization.univariate.GridSearch
-
the maximum number of iterations
- maxIterations - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
-
the maximum number of iterations
- MaxIterationsExceededException(int) - Constructor for exception com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction.MaxIterationsExceededException
-
Construct a new MaxIterationsExceededException, indicating the number of iterations.
- maxPQ() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Get the maximum of AR length or MA length.
- maxPQ() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the maximum of AR length or MA length.
- maxPQ() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the maximum of ARCH length or GARCH length.
- McCormick - Class in com.numericalmethod.suanshu.optimization.unconstrained.quasinewton
-
- McCormick(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.McCormick
-
- MCUtils - Class in com.numericalmethod.suanshu.stats.markovchain
-
These are the utility functions to examine a Markov chain.
- Mean - Class in com.numericalmethod.suanshu.stats.descriptive.moment
-
The mean of a sample is the sum of all numbers in the sample,
divided by the sample size.
- Mean() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
Construct an empty Mean calculator.
- Mean(double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
Construct a Mean calculator,
initialized with a sample.
- Mean(Mean) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
Copy constructor.
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
Get the mean of this distribution.
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- mean() - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
Get the mean of this distribution.
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
Get the mean of this distribution.
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- mean() - Method in interface com.numericalmethod.suanshu.stats.pca.PCA
-
Get the sample means that were subtracted.
- mean() - Method in class com.numericalmethod.suanshu.stats.pca.PCAbySVD
-
Get the sample means that were subtracted.
- mean() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Expectation
-
Compute the mean of the integral.
- mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- mean1 - Variable in class com.numericalmethod.suanshu.stats.test.mean.T
-
mean for sample 1
- mean2 - Variable in class com.numericalmethod.suanshu.stats.test.mean.T
-
mean for sample 2
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
Get the median of this distribution.
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- median() - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
Get the median of this distribution.
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- MersenneTwister - Class in com.numericalmethod.suanshu.stats.random.univariate.uniform
-
Mersenne Twister is one of the best pseudo random number generators available.
- MersenneTwister() - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.uniform.MersenneTwister
-
Construct a random number generator to sample uniformly from [0, 1].
- MersenneTwister(long...) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.uniform.MersenneTwister
-
Construct a random number generator to sample uniformly from [0, 1].
- Midpoint - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
The midpoint rule computes an approximation to a definite integral,
made by finding the area of a collection of rectangles whose heights are determined by the values of the function.
- Midpoint(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Midpoint
-
Construct an integrator that implements the Midpoint rule.
- Milstein - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde
-
The Milstein method is a first-order numerical procedure for integrating stochastic differential equations (SDEs) with a given initial value.
- Milstein(SDE, TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Milstein
-
Simulate an SDE using the Milstein scheme at time points specified.
- Milstein(SDE, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Milstein
-
Simulate an SDE using the Milstein scheme at even time points, [0, 1, ......, T].
- Milstein - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
Milstein scheme is a first-order approximation to a continuous-time SDE.
- Milstein(SDE) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Milstein
-
Discretize a univariate SDE using the Milstein scheme.
- min(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixMeasure
-
Compute the minimal entry in a matrix.
- min(double...) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the minimum of the values.
- Min - Class in com.numericalmethod.suanshu.stats.descriptive.rank
-
The minimum of a sample is the smallest value in the sample.
- Min() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
Construct an empty Min calculator.
- Min(double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
Construct a Min calculator,
initialized with a sample.
- Min(Min) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
Copy constructor.
- min(DateTime, DateTime) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Return the earlier of two DateTime instances.
- minDomain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
Get the smallest abscissae.
- MinimalResidualSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Minimal Residual method (MINRES) is useful for solving a symmetric n-by-n
linear system (possibly indefinite or singular).
- MinimalResidualSolver(PreconditionerFactory, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
Construct a MINRES solver.
- MinimalResidualSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
Construct a MINRES solver.
- MinimizationSolution<T> - Interface in com.numericalmethod.suanshu.optimization.problem
-
This is the solution to a minimization problem,
OptimProblem.
- minimizer() - Method in class com.numericalmethod.suanshu.algorithm.bb.BranchAndBound
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPoint.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.activeset.QPPrimalActiveSetSolver.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
This is the same as the u vector, such that the direction of arbitrarily negative can be computed by adjusting λ.
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2
-
- minimizer() - Method in interface com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.QPSolution
-
Get a minimizing vector.
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- Minimizer<P extends OptimProblem,S extends MinimizationSolution<?>> - Interface in com.numericalmethod.suanshu.optimization
-
This interface represents an optimization algorithm that minimizes a real valued objective function, one or multi dimension.
- minimizer() - Method in interface com.numericalmethod.suanshu.optimization.problem.MinimizationSolution
-
Get the minimizer (solution) to the minimization problem.
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.SteepestDescentImpl
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearch.Solution
-
- minimizers() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
Get all optimal minimizers.
- minimum() - Method in class com.numericalmethod.suanshu.algorithm.bb.BranchAndBound
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPoint.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.activeset.QPPrimalActiveSetSolver.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- minimum() - Method in interface com.numericalmethod.suanshu.optimization.problem.MinimizationSolution
-
Get the (approximate) minimum found.
- minimum() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.SteepestDescentImpl
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearch.Solution
-
- minIndex(boolean, int, int, double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get the index of the minimum of the values.
- minIndex(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get the index of the minimum of the values.
- MinMaxMinimizer<T> - Interface in com.numericalmethod.suanshu.optimization.minmax
-
A minmax minimizer minimizes a minmax problem.
- MinMaxProblem<T> - Interface in com.numericalmethod.suanshu.optimization.minmax
-
A minmax problem is a decision rule used in decision theory, game theory, statistics and philosophy for minimizing the possible loss while maximizing the potential gain.
- minus(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- minus(G) - Method in interface com.numericalmethod.suanshu.mathstructure.AbelianGroup
-
- : G × G → G
The operation "
-" is not in the definition of of an additive group but can be deduced.
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- minus(Matrix) - Method in interface com.numericalmethod.suanshu.matrix.doubles.MatrixRing
-
this - that
- minus(DenseData) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
-
Subtract the elements in this by that, element-by-element.
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Compute the difference between two diagonal matrices.
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- minus(MatrixAccess, MatrixAccess) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
A1 - A2
- minus(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.ParallelMatrixMathOperation
-
- minus(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- minus(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- minus(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
- minus(ComplexMatrix) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- minus(GenericMatrix<F>) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
-
- minus(RealMatrix) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- minus(Complex) - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- minus(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
-
- minus(double[], double[]) - Method in interface com.numericalmethod.suanshu.number.doublearray.DoubleArrayOperation
-
Subtract one double array from another.
- minus(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.ParallelDoubleArrayOperation
-
- minus(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.SimpleDoubleArrayOperation
-
- minus(Real) - Method in class com.numericalmethod.suanshu.number.Real
-
- minus(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- minus(double) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- minus(Vector, Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- minus(Vector, double) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- minus(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- minus(double) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- minus(Vector) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
\(this - that\)
- minus(double) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Subtract a constant from all entries in this vector.
- minusWeekdayPeriod(DateTime, Period) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Subtract a weekday-period (i.e., skipping weekends) from a DateTime.
- MixedRule - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
The mixed rule is good for functions that fall off rapidly at infinity, e.g., \(e^{x^2}\) or \(e^x\)
The integral region is \((0, +\infty)\).
- MixedRule(UnivariateRealFunction, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.MixedRule
-
Construct a MixedRule substitution rule.
- ML - Variable in class com.numericalmethod.suanshu.stats.regression.linear.logistic.Logistic
-
the maximum log-likelihood
- mod(long, long) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
Compute the positive modulus of a number.
- model - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.HmmBaumWelch.TrainedModel
-
the newly trained model as a result of the Baum-Welch algorithm
- modpow(long, long, long) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
be mod m
- modulus() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Get the modulus.
- modulus() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
- modulus() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.LEcuyer
-
- modulus() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.Lehmer
-
- modulus() - Method in interface com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.LinearCongruentialGenerator
-
Get the modulus of this linear congruential generator.
- modulus() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.MRG
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- moment(double) - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
The moment generating function is the expected value of etX.
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- Moments - Class in com.numericalmethod.suanshu.stats.descriptive.moment
-
Compute the central moment of a data set incrementally.
- Moments(int) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
Construct an empty moment calculator, computing all moments up to and including the order-th moment.
- Moments(int, double...) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
Construct a moment calculator, computing all moments up to and including the order-th moment.
- Moments(Moments) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
Copy constructor.
- Monoid<G> - Interface in com.numericalmethod.suanshu.mathstructure
-
A monoid is a group with a binary operation (×), satisfying the group axioms:
closure
associativity
existence of multiplicative identity
- moveColumn2End(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
Swap a column of a permutation matrix with the last column.
- moveRow2End(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
Swap a row of the permutation matrix with the last row.
- MovingAverage - Class in com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles
-
This applies a linear filter to a univariate time series using the moving average estimation.
- MovingAverage(double[], MovingAverage.Side) - Constructor for class com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverage
-
Construct a moving average filter.
- MovingAverage(double[]) - Constructor for class com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverage
-
Construct a moving average filter using a symmetric window.
- MovingAverage.Side - Enum in com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles
-
the types of moving average filtering available
- MovingAverageByExtension - Class in com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles
-
This implements a moving average filter with these properties:
1) both past and future observations are used in smoothing;
2) the head is prepended with the first element in the inputs (x_t = x_1 for t < 1);
3) the tail is appended with the last element in the inputs (x_t = x_n for t > n).
- MovingAverageByExtension(double[]) - Constructor for class com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverageByExtension
-
Construct a moving average filter with prepending and appending.
- MRG - Class in com.numericalmethod.suanshu.stats.random.univariate.uniform.linear
-
A Multiple Recursive Generator (MRG) is a linear congruential generator which takes this form:
- MRG(long, long...) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.MRG
-
Construct a Multiple Recursive Generator.
- mu - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalDistribution.Lambda
-
the mean
- mu() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.Fitting
-
Get μ as in
- mu() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
-
- mu - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.Brownian
-
μ, the drift
- mu - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantDrift
-
the drift
- mu - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.SDE
-
the drift
- mu - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.Brownian
-
μ, the drift
- mu - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.SDE
-
the drift
- mu - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
the intercept (constant) vector
- mu() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Get the intercept vector.
- mu() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the intercept vector.
- mu - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
the intercept (constant) term
- mu() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the intercept term.
- MultinomialRvg - Class in com.numericalmethod.suanshu.stats.random.multivariate
-
A multinomial distribution puts N objects into K bins according to the bins' probabilities.
- MultinomialRvg(int, double[], RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.multivariate.MultinomialRvg
-
Construct a multinomial random vector generator.
- MultinomialRvg(int, double[]) - Constructor for class com.numericalmethod.suanshu.stats.random.multivariate.MultinomialRvg
-
Construct a multinomial random vector generator.
- MultipleExecutionException - Exception in com.numericalmethod.suanshu.parallel
-
This exception is thrown when any of the parallel tasks throws an exception
during execution.
- MultipleExecutionException(List<?>, List<ExecutionException>) - Constructor for exception com.numericalmethod.suanshu.parallel.MultipleExecutionException
-
Construct an exception with the (partial) results and all exceptions encountered during execution.
- MultiplicativeModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess
-
The multiplicative model of a time series is a multiplicative composite of the trend, seasonality and irregular random components.
- MultiplicativeModel(double[], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MultiplicativeModel
-
Construct a univariate time series by multiplying the components.
- MultiplicativeModel(double[], double[], RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MultiplicativeModel
-
Construct a univariate time series by multiplying the components.
- MultiplierPenalty - Class in com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod
-
A multiplier penalty function allows different weights to be assigned to the constraints.
- MultiplierPenalty(Constraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.MultiplierPenalty
-
Construct a multiplier penalty function from a collection of constraints.
- MultiplierPenalty(Constraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.MultiplierPenalty
-
Construct a multiplier penalty function from a collection of constraints.
- MultiplierPenalty(Constraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.MultiplierPenalty
-
Construct a multiplier penalty function from a collection of constraints.
- multiply(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- multiply(G) - Method in interface com.numericalmethod.suanshu.mathstructure.Monoid
-
× : G × G → G
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- multiply(Vector) - Method in interface com.numericalmethod.suanshu.matrix.doubles.Matrix
-
Right multiply this matrix, A, by a vector.
- multiply(Matrix) - Method in interface com.numericalmethod.suanshu.matrix.doubles.MatrixRing
-
this * that
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
this * that
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Compute the product of two diagonal matrices.
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
Left multiplication by G, namely, G * A.
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- multiply(MatrixAccess, MatrixAccess) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
A1 * A2
- multiply(MatrixAccess, Vector) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
A * v
- multiply(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.ParallelMatrixMathOperation
-
- multiply(MatrixAccess, Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.ParallelMatrixMathOperation
-
- multiply(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- multiply(MatrixAccess, Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
Left multiplication by P.
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
Left multiplication by P.
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
- multiply(ComplexMatrix) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- multiply(GenericMatrix<F>) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
-
- multiply(RealMatrix) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- multiply(Complex) - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Compute the product of this complex number and that complex number.
- multiply(Real) - Method in class com.numericalmethod.suanshu.number.Real
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- multiply(Vector, Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- multiply(Vector) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Multiply this by that, entry-by-entry.
- MultivariateMaximizer - Class in com.numericalmethod.suanshu.optimization.unconstrained
-
A maximization problem is simply minimizing the negative of the objective function.
- MultivariateMaximizer(T) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.MultivariateMaximizer
-
Construct a multivariate maximizer to maximize an objective function.
- MultivariateMaximizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.MultivariateMaximizer
-
Construct a multivariate maximizer to maximize an objective function.
- MultivariateMaximizer.Solution - Interface in com.numericalmethod.suanshu.optimization.unconstrained
-
- MultivariateMinimizer<S extends MinimizationSolution<?>> - Interface in com.numericalmethod.suanshu.optimization.unconstrained
-
This is a minimizer that minimizes a twice continuously differentiable, multivariate function.
- MultiVariateRealization - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate
-
This interface defines the realization for a multivariate stochastic process, as well as
the Iterator for generating (reading) the realization.
- MultiVariateRealization - Interface in com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime
-
A multivariate realization is a multivariate time series indexed by real numbers, e.g., real time.
- MultiVariateRealization.Entry - Class in com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime
-
This is the TimeSeries.Entry for a real number -indexed multivariate time series.
- MultiVariateRealization.Iterator - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate
-
This Iterator support lazy evaluation/generation of a realization from a stochastic process.
- MultiVariateTimeSeries<T extends java.lang.Comparable,E extends MultiVariateTimeSeries.Entry<T>> - Interface in com.numericalmethod.suanshu.stats.timeseries.multivariate
-
A multivariate time series is a sequence of vectors indexed by some notion of time.
- MultiVariateTimeSeries - Interface in com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime
-
This is a multivariate time series indexed by integers.
- MultiVariateTimeSeries.Entry<T> - Class in com.numericalmethod.suanshu.stats.timeseries.multivariate
-
This is the TimeSeries.Entry for a multivariate time series.
- MultiVariateTimeSeries.Entry - Class in com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime
-
This is the TimeSeries.Entry for an integer -indexed multivariate time series.
- mutate() - Method in interface com.numericalmethod.suanshu.optimization.geneticalgorithm.Chromosome
-
Construct a Chromosome by mutation.
- mutate() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Best2Bin.DeBest2BinCell
-
- mutate() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell
-
- mutate() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell
-
- mutate() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell
-
- mutate() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local.LocalSearchCellFactory.LocalSearchCell
-
Mutate by a local search in the neighborhood.
- mutate() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell
-
Mutate by random disturbs in a neighborhood.
- Mutex - Class in com.numericalmethod.suanshu.parallel
-
Provides mutual exclusive execution of a Runnable.
- Mutex() - Constructor for class com.numericalmethod.suanshu.parallel.Mutex
-
- MWC8222 - Class in com.numericalmethod.suanshu.stats.random.univariate.uniform
-
Marsaglia's MWC256 (also known as MWC8222) is a multiply-with-carry generator.
- MWC8222() - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.uniform.MWC8222
-
Construct a random number generator to sample uniformly from [0, 1].
- MyCutter(ILPProblem) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane.GomoryMixedCut.MyCutter
-
Construct a Gomory mixed cutter.
- MyCutter(PureILPProblem) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane.GomoryPureCut.MyCutter
-
Construct a Gomory pure cutter.
- MySteepestDescent(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.GaussNewton.MySteepestDescent
-
- N() - Method in class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable
-
Get the number of data points.
- n - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- n() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPDualProblem
-
Get the dimension of the square matrices C and As.
- n() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPPrimalProblem
-
Get the dimension of the system, i.e., the dimension of x, the number of variables.
- n(int) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Get the number of columns of Ai.
- n() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Get the number of rows of the multivariate time series used in regression.
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.Covariance
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Quantile
-
- N() - Method in interface com.numericalmethod.suanshu.stats.descriptive.Statistic
-
Get the size of the sample.
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.SynchronizedStatistic
-
- N - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.EvenlySpacedGrid
-
the number of time points
- N - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.UnitGrid
-
the number of time points
- n - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Expectation
-
the number of discretization in the integral time interval
- n - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
the number of observations
- n - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
the number of observations
- n - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
the total number of observations of the two samples
- N - Variable in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBeraDistribution
-
the number of observations in a sample
- n - Variable in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
the number of observations
- N - Variable in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
total number of observations
- N - Variable in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
number of observations in group 2
- N - Variable in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
number of observations in group 2
- n1 - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
the number of observations of the first sample
- n2 - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
the number of observations of the second sample
- NaiveRule - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
-
This pivoting rule chooses the column with the most negative reduced cost.
- NaiveRule() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
-
- NaN - Static variable in class com.numericalmethod.suanshu.number.complex.Complex
-
a number representing the complex Not-a-Number (NaN)
- nB() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.Brownian
-
- nB() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.DiscretizedSDE
-
Get the number of independent driving Brownian motions.
- nB() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Euler
-
- nB() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
-
Get the number of independent Brownian motions.
- nB - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.SDE
-
number of independent driving Brownian motions
- nChildren() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
Get the number of children before populating the next generation.
- nCols() - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
- nCols() - Method in interface com.numericalmethod.suanshu.datastructure.Table
-
Get the number of columns.
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
-
- nCols() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
-
Deprecated.
- nCols() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.Diffusion
-
Get the number of independent Brownian motions.
- nColumns() - Method in class com.numericalmethod.suanshu.datastructure.MathTable
-
Get the number of columns in the table.
- NEGATIVE_INFINITY - Static variable in class com.numericalmethod.suanshu.number.complex.Complex
-
a number representing -∞ + -∞i
- NelderMead - Class in com.numericalmethod.suanshu.optimization.unconstrained
-
The Nelder–Mead method is a nonlinear optimization technique,
which is well-defined for twice differentiable and unimodal problems.
- NelderMead(double, double, double, double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead
-
Construct a Nelder-Mead multivariate minimizer.
- NelderMead(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead
-
Construct a Nelder-Mead multivariate minimizer.
- NelderMead.Solution - Class in com.numericalmethod.suanshu.optimization.unconstrained
-
This is the solution to an optimization problem by the Nelder-Mead method.
- nEqualities() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
Get the number of equality constraints.
- NevilleTable - Class in com.numericalmethod.suanshu.analysis.interpolation
-
Neville's algorithm is a polynomial interpolation algorithm.
- NevilleTable(int, OrderedPairs) - Constructor for class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable
-
Construct a Neville table of size n, initialized with data {(x, y)}.
- NevilleTable(OrderedPairs) - Constructor for class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable
-
Construct a Neville table of size n, initialized with data {(x, y)}.
- NevilleTable() - Constructor for class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable
-
Construct an empty Neville table.
- newActiveList() - Method in interface com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPBranchAndBound.ActiveListFactory
-
Construct a new instance of ActiveList for an Integer Linear Programming problem.
- newCellFactory() - Method in interface com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptim.NewCellFactory
-
Construct a new instance of DEOptimCellFactory for a minimization problem.
- newCellFactory() - Method in interface com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.NewCellFactoryCtor
-
Construct a new instance of SimpleCellFactory for a minimization problem.
- newEMDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaDistribution
-
- newEMDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialDistribution
-
- newEMDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.ExponentialDistribution
-
- newEMDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaDistribution
-
- newEMDistribution(Object[]) - Method in interface com.numericalmethod.suanshu.stats.hmm.mixture.distribution.HMMDistribution
-
Construct a new distribution from a set of parameters, one set per state.
- newEMDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalDistribution
-
- newEMDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalDistribution
-
- newEMDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.PoissonDistribution
-
- newInstance(Matrix) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.PreconditionerFactory
-
- newInstance() - Method in interface com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPMinimizer.ConstrainedMinimizerFactory
-
Construct a new instance of ConstrainedMinimizer to solve a real valued minimization problem.
- newInstance() - Method in interface com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.local.LocalSearchCellFactory.MinimizerFactory
-
Construct a new instance of Minimizer for a mutation operation.
- Newton - Class in com.numericalmethod.suanshu.analysis.uniroot
-
The Newton–Raphson method is as follows:
one starts with an initial guess which is reasonably close to the true root,
then the function is approximated by its tangent line (which can be computed using the tools of calculus),
and one computes the x-intercept of this tangent line (which is easily done with elementary algebra).
- Newton(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.uniroot.Newton
-
Construct an instance of Newton's root finding algorithm.
- NewtonCotes - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
The Newton–Cotes formulae, also called the Newton–Cotes quadrature rules or simply Newton–Cotes rules,
are a group of formulae for numerical integration (also called quadrature) based on evaluating the integrand at equally-spaced points.
- NewtonCotes(int, NewtonCotes.Type, double, int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.NewtonCotes
-
Construct an instance of the Newton–Cotes quadrature.
- NewtonCotes.Type - Enum in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
There are two types of the Newton-Cotes method: OPEN and CLOSED.
- NewtonRaphson - Class in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent
-
The Newton-Raphson method is a second order steepest descent method that is based on
the quadratic approximation of the Taylor series.
- NewtonRaphson(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.NewtonRaphson
-
Construct a multivariate minimizer using the Newton-Raphson method.
- NewtonRaphson.NewtonRaphsonImpl - Class in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent
-
- NewtonRaphsonImpl(C2OptimProblem) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.NewtonRaphson.NewtonRaphsonImpl
-
- newVariation(RealScalarFunction, EqualityConstraints) - Method in interface com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetSolverForOnlyEqualityConstraint1.VariationFactory
-
Construct a new instance of SQPASEVariation for an SQP problem.
- newVariation(RealScalarFunction, EqualityConstraints, GreaterThanConstraints) - Method in interface com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver.VariationFactory
-
Construct a new instance of SQPASVariation for an SQP problem.
- nExogenousFactors() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
the number of factors, excluding the intercept
- next(int, UnivariateRealFunction, double, double, double) - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.IterativeIntegrator
-
Compute a refined sum for the integral.
- next(int, UnivariateRealFunction, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.NewtonCotes
-
- next(int, UnivariateRealFunction, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Simpson
-
- next() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector.Iterator
-
- next(Vector) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLMSim
-
Get the next innovation.
- next(double) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSim
-
Get the next innovation.
- next() - Method in class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel
-
Get the next simulated innovation - state and observation.
- next() - Method in interface com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedGenerator.Generator
-
Returns the next value in the underlying generated sequence.
- next() - Method in class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedGenerator
-
Returns the next value in the generated sequence.
- next() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization.Iterator
-
- next() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.Realization.Iterator
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel
-
Get the next simulated observation.
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Get the next simulated state.
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedRLG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedRNG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.beta.Cheng1978
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.beta.VanDerWaerden1969
-
Deprecated.
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.BinomialRng
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.exp.Ziggurat2000Exp
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.gamma.KunduGupta2007
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.gamma.MarsagliaTsang2000
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.gamma.XiTanLiu2010a
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.gamma.XiTanLiu2010b
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.InverseTransformSampling
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.LogNormalRng
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.normal.BoxMuller
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.normal.MarsagliaBray1964
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.normal.NormalRng
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.normal.Ziggurat2000
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.normal.Zignor2005
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.poisson.Knuth1969
-
- nextDouble() - Method in interface com.numericalmethod.suanshu.stats.random.univariate.RandomNumberGenerator
-
Get the next random double.
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.LEcuyer
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.Lehmer
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.MRG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.MersenneTwister
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.MWC8222
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.SHR0
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.SHR3
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.UniformRng
-
- nextInt() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.SHR0
-
- nextInt() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.SHR3
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedRLG
-
- nextLong() - Method in interface com.numericalmethod.suanshu.stats.random.univariate.RandomLongGenerator
-
Get the next random long.
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.LEcuyer
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.Lehmer
-
All built-in linear random number generators in this library
ultimately call this function to generate random numbers.
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.MRG
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.MersenneTwister
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.MWC8222
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.SHR0
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.SHR3
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.UniformRng
-
- nextRealization(Vector) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.RandomWalk
-
- nextRealization(Vector) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.Construction
-
Construct a realization of a stochastic process.
- nextRealization(Vector) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk
-
- nextRealization(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.RandomWalk
-
- nextRealization(double) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Construction
-
Construct a realization of the stochastic process.
- nextRealization(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk
-
- nextSample() - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159
-
Construct a random matrix based on the row and column sums.
- nextState() - Method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Get the next simulated state.
- nextValue() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization.Iterator
-
- nextValue() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.Realization.Iterator
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedRVG
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.multivariate.IID
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.multivariate.MultinomialRvg
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.multivariate.NormalRvg
-
- nextVector() - Method in interface com.numericalmethod.suanshu.stats.random.multivariate.RandomVectorGenerator
-
Get the next random vector.
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.multivariate.UniformDistributionOverBox
-
- nextWeekDay(DateTime) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Get the next weekday, i.e., skipping Saturdays and Sundays.
- nFactors() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FactorAnalysis
-
Get the number of factors.
- nFactors() - Method in interface com.numericalmethod.suanshu.stats.pca.PCA
-
Get the number of variables in the original data.
- nFactors() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
the number of factors, including the intercept if any
- nGreaterThanInequalities() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
Get the number of greater-than-or-equal-to constraints.
- nLags - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.AutoCovariance
-
the number of lags in the result
- nNoChanges - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- nNonZeros() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- nNonZeros() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- nNonZeros() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- nNonZeros() - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseStructure
-
Get the number of non-zero entries in the structure.
- nNonZeros() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- no(double) - Method in interface com.numericalmethod.suanshu.misc.R.ifelse
-
Return value for a false element of test.
- nObs() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FactorAnalysis
-
Get the number of observations.
- nObs() - Method in interface com.numericalmethod.suanshu.stats.pca.PCA
-
Get the number of observations in the original data; sample size.
- nObs() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
the number of observations
- NoChangeOfVariable - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This is a dummy substitution rule that does not change any variable.
- NoChangeOfVariable(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
-
Construct an NoChangeOfVariable substitution rule.
- NonNegativityConstraintOptimProblem - Class in com.numericalmethod.suanshu.optimization.constrained.problem
-
This is a constrained optimization problem for a function which has all non-negative variables.
- NonNegativityConstraintOptimProblem(RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.problem.NonNegativityConstraintOptimProblem
-
Construct a constrained optimization problem with only non-negative variables.
- NonNegativityConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.constraint.linear
-
These constraints ensures that for all variables are non-negative.
- NonNegativityConstraints(RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.NonNegativityConstraints
-
Construct a lower bound constraints for all variables in a function.
- NonParametricBootstrap - Class in com.numericalmethod.suanshu.stats.sampling.resampling.bootstrap
-
This is the classical bootstrap method described in the reference.
- NonParametricBootstrap(double[]) - Constructor for class com.numericalmethod.suanshu.stats.sampling.resampling.bootstrap.NonParametricBootstrap
-
Construct a bootstrap sample generator.
- norm() - Method in interface com.numericalmethod.suanshu.mathstructure.BanachSpace
-
|·| : B → F
norm assigns a strictly positive length or size to all vectors in the vector space,
other than the zero vector.
- norm() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- norm(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- norm(int) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- norm() - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- norm(Vector, int) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- norm(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- norm() - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- norm(int) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- norm() - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Compute the length or magnitude or Euclidean norm of a vector, namely, \(\|v\|\).
- norm(int) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Get the norm of a vector.
- NormalDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The Normal distribution has its density a Gaussian function.
- NormalDistribution() - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
Construct an instance of the standard Normal distribution with mean 0 and standard deviation 1.
- NormalDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
Construct a Normal distribution with mean mu and standard deviation sigma.
- NormalDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Normal distribution to model the observations.
- NormalDistribution(NormalDistribution.Lambda[], boolean, boolean) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalDistribution
-
Construct a Normal distribution for each state in the HMM model.
- NormalDistribution(NormalDistribution.Lambda[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalDistribution
-
Construct a Normal distribution for each state in the HMM model.
- NormalDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
the Normal distribution parameters
- NormalRng - Class in com.numericalmethod.suanshu.stats.random.univariate.normal
-
This is a random number generator that generates random deviates according to the Normal distribution.
- NormalRng(double, double, RandomStandardNormalNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.normal.NormalRng
-
Construct a random number generator to sample from the Normal distribution.
- NormalRng(double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.normal.NormalRng
-
Construct a random number generator to sample from the Normal distribution.
- NormalRvg - Class in com.numericalmethod.suanshu.stats.random.multivariate
-
A multivariate Normal random vector is said to be p-variate normally distributed
if every linear combination of its p components has a univariate normal distribution.
- NormalRvg(Vector, Matrix, RandomLongGenerator, double) - Constructor for class com.numericalmethod.suanshu.stats.random.multivariate.NormalRvg
-
Construct a multivariate Normal random vector generator.
- NormalRvg(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.random.multivariate.NormalRvg
-
Construct a multivariate Normal random vector generator.
- NormalRvg(int) - Constructor for class com.numericalmethod.suanshu.stats.random.multivariate.NormalRvg
-
Construct a standard multivariate Normal random vector generator.
- NoRootFoundException - Exception in com.numericalmethod.suanshu.analysis.uniroot
-
This is the Exception thrown when it fails to find a root.
- NoRootFoundException(double, double) - Constructor for exception com.numericalmethod.suanshu.analysis.uniroot.NoRootFoundException
-
Construct a NoRootFoundException.
- NoSolution(String) - Constructor for exception com.numericalmethod.suanshu.matrix.doubles.linearsystem.LinearSystemSolver.NoSolution
-
Construct an LinearSystemSolver.NoSolution exception.
- nParams() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Compute the number of parameters for the estimation/fitting.
- nPopulation() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
Get the size of the population pool, that is the number of chromosomes.
- nRows() - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
- nRows() - Method in interface com.numericalmethod.suanshu.datastructure.Table
-
Get the number of rows.
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
-
- nRows() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
-
Deprecated.
- nRows() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.Diffusion
-
Get the dimension of the process.
- nSamples() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
Get the number of samples in the empirical distribution.
- nSim - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Expectation
-
the number of simulations
- nSim - Variable in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBera
-
- nSim - Variable in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBeraDistribution
-
the number of Monte Carlo simulation paths
- nSim - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution
-
the number of simulations
- nSim - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution1
-
Deprecated.
the number of simulations
- nSim - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
the number of simulations
- nStableIterations - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- nStates() - Method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Get the number of states.
- nSymbols() - Method in class com.numericalmethod.suanshu.stats.hmm.rabiner.HiddenMarkovModel
-
Get the number of observation symbols per state.
- nT - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution
-
the number of grid points in interval [0, 1]
- nT - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution1
-
Deprecated.
the number of grid point in interval [0, 1]
- nullDeviance - Variable in class com.numericalmethod.suanshu.stats.regression.linear.logistic.Residuals
-
the null deviance
- nullity() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel
-
Get the nullity of A.
- nullity(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixMeasure
-
- NullMonitor<S> - Class in com.numericalmethod.suanshu.algorithm.iterative.monitor
-
- NullMonitor() - Constructor for class com.numericalmethod.suanshu.algorithm.iterative.monitor.NullMonitor
-
- NumberUtils - Class in com.numericalmethod.suanshu.number
-
These are the utility functions to manipulate Numbers.
- NumberUtils.Comparable<T extends java.lang.Number> - Interface in com.numericalmethod.suanshu.number
-
We need a precision parameter to determine whether two numbers are close enough to be treated as equal.
- nVariables() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FactorAnalysis
-
Get the number of variables in the original data set.
- p() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.QuadraticFunction
-
- p() - Method in class com.numericalmethod.suanshu.analysis.function.special.beta.BetaRegularized
-
Get p, the shape parameter.
- P() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Get the permutation matrix, P, such that P * A = L * U.
- P() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
-
- P() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.GramSchmidt
-
- P() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.HouseholderReflection
-
Get P, the pivoting matrix in the QR decomposition.
- P() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.QR
-
- P() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.qr.QRDecomposition
-
Get P, the pivoting matrix in the QR decomposition.
- P() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Doolittle
-
- P() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LU
-
- P() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LUDecomposition
-
Get the permutation matrix P as in P * A = L * U.
- p() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPDualProblem
-
Get the dimension of the system, i.e., p = the dimension of y, the number of variables.
- p() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPPrimalProblem
-
Get the size of b.
- p - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- p - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialDistribution.Lambda
-
the success probability in each trial
- p() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Get the number of AR terms.
- p() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the order of the VECM model.
- p() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the number of AR terms.
- p() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the number of GARCH terms.
- Pair - Class in com.numericalmethod.suanshu.analysis.function.tuple
-
An ordered pair (x,y) is a pair of mathematical objects.
- Pair(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.tuple.Pair
-
Construct a pair.
- PanelRegression - Interface in com.numericalmethod.suanshu.stats.regression.panel
-
This interface defines methods for solving panel regression.
- PanelRegressionResult - Class in com.numericalmethod.suanshu.stats.regression.panel
-
Stores the result of a panel regression.
- PanelRegressionResult(Beta, Residuals) - Constructor for class com.numericalmethod.suanshu.stats.regression.panel.PanelRegressionResult
-
- parallel - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
This indicate if the algorithm is to run in parallel (multi-core).
- parallel - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- ParallelDoubleArrayOperation - Class in com.numericalmethod.suanshu.number.doublearray
-
This is a multi-threaded implementation of the array math operations.
- ParallelDoubleArrayOperation() - Constructor for class com.numericalmethod.suanshu.number.doublearray.ParallelDoubleArrayOperation
-
- ParallelExecutor - Class in com.numericalmethod.suanshu.parallel
-
This class provides a framework for executing an algorithm in parallel.
- ParallelMatrixMathOperation - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation
-
This is a multi-threaded implementation of the matrix math operations.
- ParallelMatrixMathOperation() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.ParallelMatrixMathOperation
-
- parse(String) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
-
Construct a number from a String.
- parseArray(String...) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
-
Convert an array of numbers in String to an array of numbers in Number.
- PartialAutoCorrelation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample
-
This computes the sample partial Auto-Correlation Function (PACF) for a univariate data set.
- PartialAutoCorrelation(TimeSeries, AutoCovariance.Type) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.PartialAutoCorrelation
-
- PartialAutoCorrelation(TimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.PartialAutoCorrelation
-
- paste(Collection<String>, String) - Static method in class com.numericalmethod.suanshu.misc.R
-
Concatenate Strings into one String.
- path - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- PCA - Interface in com.numericalmethod.suanshu.stats.pca
-
Principal Component Analysis (PCA) is a mathematical procedure that
uses an orthogonal transformation to convert a set of observations of possibly correlated variables into
a set of values of uncorrelated variables called principal components.
- PCAbyEigen - Class in com.numericalmethod.suanshu.stats.pca
-
This class performs a principal component analysis (PCA) on the given data matrix.
- PCAbyEigen(Matrix, boolean, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.pca.PCAbyEigen
-
Perform a principal component analysis, using the eigen method,
on a given data matrix with an optional correlation (or covariance) matrix provided.
- PCAbyEigen(Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.stats.pca.PCAbyEigen
-
Perform a principal component analysis, using the eigen method, on a given data matrix.
- PCAbyEigen(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.pca.PCAbyEigen
-
Perform a principal component analysis, using the eigen method and the preferred correlation matrix,
on a given data matrix.
- PCAbySVD - Class in com.numericalmethod.suanshu.stats.pca
-
This class performs a Principal Component Analysis (PCA) on the given data matrix
using the preferred singular value decomposition (SVD) method.
- PCAbySVD(Matrix, boolean, boolean, Vector, Vector) - Constructor for class com.numericalmethod.suanshu.stats.pca.PCAbySVD
-
Perform a Principal Component Analysis, using the preferred SVD method,
on a given data matrix with (optional) mean vector and scaling vector provided.
- PCAbySVD(Matrix, boolean, boolean) - Constructor for class com.numericalmethod.suanshu.stats.pca.PCAbySVD
-
Perform a principal component analysis, using the preferred SVD method, on a given data matrix (possibly centered and/or scaled).
- PCAbySVD(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.pca.PCAbySVD
-
Perform a principal component analysis, using the preferred SVD method, on a centered and scaled data matrix.
- Pearson - Class in com.numericalmethod.suanshu.optimization.unconstrained.quasinewton
-
This is the Pearson method.
- Pearson(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.Pearson
-
Construct a multivariate minimizer using the Pearson method.
- pearsonStat(Matrix, Matrix, boolean) - Static method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquare4Independence
-
Compute the Pearson's cumulative test statistic, which asymptotically approaches a χ2 distribution.
- PenaltyFunction - Class in com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod
-
A function P: Rn -> R is a penalty function for a constrained optimization problem if it has these properties.
- PenaltyFunction() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyFunction
-
- PenaltyMethodMinimizer - Class in com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod
-
The penalty method is an algorithm for solving a constrained minimization problem with general constraints.
- PenaltyMethodMinimizer(PenaltyMethodMinimizer.PenaltyFunctionFactory, double, T) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
Construct a constrained minimizer using the penalty method.
- PenaltyMethodMinimizer(double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
Construct a constrained minimizer using the penalty method.
- PenaltyMethodMinimizer() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
Construct a constrained minimizer using the penalty method.
- PenaltyMethodMinimizer.PenaltyFunctionFactory - Interface in com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod
-
For each constrained optimization problem, the solver creates a new penalty function for it.
- permutation(int, int) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
Compute the permutation function.
- permutation(int, int) - Static method in class com.numericalmethod.suanshu.number.big.BigIntegerUtils
-
Compute the permutation function.
- PermutationMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype
-
A permutation matrix is a square matrix that has exactly one entry '1' in each row and each column and 0's elsewhere.
- PermutationMatrix(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
Construct an identity permutation matrix.
- PermutationMatrix(int[]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
Construct a permutation matrix from an 1D double[].
- PermutationMatrix(PermutationMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
Copy constructor.
- phi - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- phi - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
the AR coefficients
- PI - Static variable in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
the value of PI
- PI() - Method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Get the initial state probabilities.
- PI - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.Invertibility
-
the coefficients of the linear representation of the time series
- pi() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the impact matrix.
- PI_SQ - Static variable in class com.numericalmethod.suanshu.Constant
-
\(\pi^2\)
- Pivot(int, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot
-
Construct a pivot.
- plusWeekdayPeriod(DateTime, Period) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Add a weekday-period (i.e., skipping weekends) to a DateTime.
- Poisson - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
The Poisson distribution for the error distribution in a GLM model.
- Poisson() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Poisson
-
Construct an instance of Poisson.
- Poisson(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Poisson
-
Construct an instance of Poisson with an overriding link function.
- Poisson - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family
-
The quasi Poisson family of GLM.
- Poisson() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Poisson
-
Create an instance of Poisson.
- Poisson(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Poisson
-
Create an instance of Poisson with an overriding link function.
- PoissonDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The Poisson distribution (or Poisson law of small numbers) is a discrete probability distribution that
expresses the probability of a given number of events occurring in a fixed interval of time and/or space
if these events occur with a known average rate and independently of the time since the last event.
- PoissonDistribution(double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
Construct a Poisson distribution.
- PoissonDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Poisson distribution to model the observations.
- PoissonDistribution(Double[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.PoissonDistribution
-
Construct a Poisson distribution for each state in the HMM model.
- Polynomial - Class in com.numericalmethod.suanshu.analysis.function.polynomial
-
A polynomial is a
UnivariateRealFunction that represents a finite length expression constructed from variables and constants,
using the operations of addition, subtraction, multiplication, and constant non-negative whole number exponents.
- Polynomial(double...) - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Construct a polynomial from an array of coefficients.
- Polynomial(Polynomial) - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Copy constructor.
- PolyRoot - Class in com.numericalmethod.suanshu.analysis.function.polynomial.root
-
This is a solver for finding the roots of a polynomial equation.
- PolyRoot() - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.root.PolyRoot
-
- PolyRootSolver - Interface in com.numericalmethod.suanshu.analysis.function.polynomial.root
-
A root (or a zero) of a polynomial p is a member x in the domain of p such that p(x) vanishes.
- pop() - Method in interface com.numericalmethod.suanshu.algorithm.bb.ActiveList
-
Get the next node.
- population - Variable in class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
This is the (current) population pool.
- POSITIVE_INFINITY - Static variable in class com.numericalmethod.suanshu.number.complex.Complex
-
a number representing +∞ + ∞i
- positiveDefinite(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a square matrix is positive definite; the matrix needs not be symmetric.
- PositiveDefiniteMatrixByPositiveDiagonal - Class in com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite
-
This class "converts" a matrix into a symmetric, positive definite matrix, if it is not already so, by
forcing the diagonal entries in the eigen decomposition to a small non-negative number, e.g., 0.
- PositiveDefiniteMatrixByPositiveDiagonal(Matrix, double, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite.PositiveDefiniteMatrixByPositiveDiagonal
-
Construct a positive definite matrix
by forcing the diagonal entries in the eigen decomposition to a small non-negative number, e.g., 0.
- positiveSemiDefinite(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a square matrix is positive definite, up to a precision.
- PositiveSemiDefiniteMatrixNonNegativeDiagonal - Class in com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite
-
This class "converts" a matrix into a symmetric, positive semi-definite matrix, if it is not already so, by
forcing the negative diagonal entries in the eigen decomposition to 0.
- PositiveSemiDefiniteMatrixNonNegativeDiagonal(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.positivedefinite.PositiveSemiDefiniteMatrixNonNegativeDiagonal
-
Construct a positive semi-definite matrix
by forcing the negative diagonal entries in the eigen decomposition to 0.
- pow(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- Pow - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
This is a square matrix A to the power of an integer n, An.
- Pow(Matrix, int, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.Pow
-
Construct the power matrix An so that
An = basescale * B
- Pow(Matrix, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.Pow
-
Construct the power matrix An so that
An = (1e100)scale * B
- pow(BigDecimal, BigDecimal) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute a to the power of b.
- pow(BigDecimal, BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute a to the power of b.
- pow(BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute a to the power of n, where n is an integer.
- pow(BigDecimal, int, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute a to the power of n, where n is an integer.
- pow(Complex, Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
z1 to the power z2.
- pow(double) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- pow(Vector, double) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- pow(double) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- pow(double) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Take the exponentiation of all entries in this vector, entry-by-entry.
- Powell - Class in com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection
-
Powell's algorithm, starting from an initial point, performs a series
of line searches in one iteration.
- Powell(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.Powell
-
Construct a multivariate minimizer using the Powell method.
- Powell.PowellImpl - Class in com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection
-
an implementation of Powell's algorithm
- PowellImpl(C2OptimProblem) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.Powell.PowellImpl
-
- PowerLawSingularity - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This transformation is good for an integral which diverges at one of the end points.
- PowerLawSingularity(PowerLawSingularity.PowerLawSingularityType, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
-
Construct a PowerLawSingularity substitution rule.
- PowerLawSingularity.PowerLawSingularityType - Enum in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
the type of end point divergence
- Preconditioner - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
-
Preconditioning reduces the condition number of the
coefficient matrix of a linear system to accelerate the convergence
when the system is solved by an iterative method.
- PreconditionerFactory - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
-
This constructs a new instance of
Preconditioner for a coefficient matrix.
- previousWeekDay(DateTime) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils
-
Get the previous weekday, i.e., skipping Saturdays and Sundays.
- pricing(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
-
This is pivot column selection (pricing) rule.
- pricing(SimplexTable) - Method in interface com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting
-
This is pivot column selection (pricing) rule.
- pricing(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SmallestSubscriptRule
-
This is pivot column selection (pricing) rule.
- PrimalDualInteriorPoint - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint
-
This implementation solves a Dual Second Order Conic Programming problem using the Primal Dual Interior Point algorithm.
- PrimalDualInteriorPoint(double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPoint
-
Construct a Primal Dual Interior Point minimizer to solve Dual Second Order Conic Programming problems.
- PrimalDualInteriorPoint(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPoint
-
Construct a Primal Dual Interior Point minimizer to solve Dual Second Order Conic Programming problems.
- PrimalDualInteriorPoint.Solution - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint
-
This is the solution to a Dual Second Order Conic Programming problem using the Primal Dual Interior Point algorithm.
- PrimalDualPathFollowing - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing
-
The Primal-Dual Path-Following algorithm is an interior point method that solves Semi-Definite Programming problems.
- PrimalDualPathFollowing(double, double, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing
-
Construct a Primal-Dual Path-Following minimizer to solve semi-definite programming problems.
- PrimalDualPathFollowing(double, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing
-
Construct a Primal-Dual Path-Following minimizer to solve semi-definite programming problems.
- PrimalDualPathFollowing(double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing
-
Construct a Primal-Dual Path-Following minimizer to solve semi-definite programming problems.
- PrimalDualPathFollowing.Solution - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing
-
This is the solution to a Semi-Definite Programming problem using the Primal-Dual Path-Following algorithm.
- PrimalDualSolution - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint
-
The vector set {x, s, y} is a solution to both the primal and dual SOCP problems.
- PrimalDualSolution(Vector, Vector, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution
-
Construct a solution to a primal and a dual SOCP problems.
- prob - Variable in class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159.RandomMatrix
-
the probability of observing this matrix
- ProbabilityDistribution - Interface in com.numericalmethod.suanshu.stats.distribution.univariate
-
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete),
or the probability of the value falling within a particular interval (when the variable is continuous).
- ProbabilityMassFunction<X> - Interface in com.numericalmethod.suanshu.stats.distribution
-
A probability mass function (pmf) is a function that gives the probability that a discrete random variable is exactly equal to some value.
- Probit - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the link function:
Inverse of cumulative distribution function of a NormalDistribution distribution N(0, 1).
- Probit() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Probit
-
- problem - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- problem - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.SteepestDescentImpl
-
- problem - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
-
the generalized linear regression problem to be solved
- problem - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
-
the quasi- generalized linear regression problem to be solved
- problem - Variable in class com.numericalmethod.suanshu.stats.regression.linear.logistic.Logistic
-
the logistic regression problem to be solved
- problem - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression
-
the ordinary linear regression problem to be solved
- problem - Variable in class com.numericalmethod.suanshu.stats.regression.linear.Residuals
-
the linear regression problem to be solved
- process() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightPhase1
-
Find a feasible table, if any.
- process() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightScheme2
-
Remove equalities and free variables, if possible.
- product(GivensMatrix[]) - Static method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
Given an array of Givens matrices {Gi},
compute G, where
G = G1 * G2 * ...
- product(Householder[], int, int, int, int) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.Householder
-
Compute Q from Householder matrices {Qi}.
- product(Householder[], int, int) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.Householder
-
Compute Q from Householder matrices {Qi}.
- Projection - Class in com.numericalmethod.suanshu.analysis.function.rn2r1
-
- Projection(RealVectorFunction, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.Projection
-
Construct a \(R^n \rightarrow R\) projection from a \(R^n \rightarrow R^m\) function f.
- Projection - Class in com.numericalmethod.suanshu.vector.doubles.operation
-
Project a vector v on another vector w or a set of vectors (basis) {wi}.
- Projection(Vector, List<Vector>) - Constructor for class com.numericalmethod.suanshu.vector.doubles.operation.Projection
-
Project a vector v onto a set of basis {wi}.
- Projection(Vector, Vector[]) - Constructor for class com.numericalmethod.suanshu.vector.doubles.operation.Projection
-
Project a vector v onto a set of basis {wi}.
- Projection(Vector, Vector) - Constructor for class com.numericalmethod.suanshu.vector.doubles.operation.Projection
-
Project a vector v onto another vector.
- proportionVar() - Method in interface com.numericalmethod.suanshu.stats.pca.PCA
-
Get the proportion of overall variance explained by each of the principal components.
- proportionVar(int) - Method in interface com.numericalmethod.suanshu.stats.pca.PCA
-
Get the proportion of overall variance explained by the i-th principal component.
- proportionVar() - Method in class com.numericalmethod.suanshu.stats.pca.PCAbyEigen
-
Get the proportion of overall variance explained by each of the principal components.
- PseudoInverse - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
The Moore–Penrose pseudo-inverse of an m x n matrix A is A+.
- PseudoInverse(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.PseudoInverse
-
Construct the Moore–Penrose pseudo-inverse matrix of a matrix.
- PseudoInverse(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.PseudoInverse
-
Construct the Moore–Penrose pseudo-inverse matrix of A.
- psi() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FAEstimator
-
Get the estimated (optimal) psi, E(ee'), p.
- psi - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
the coefficients of the deterministic terms (excluding the intercept term)
- psi() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Get the coefficients of the deterministic terms.
- PSI - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.LinearRepresentation
-
the coefficients of the linear representation of the time series
- psi() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the coefficients of the deterministic terms.
- psi - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
the coefficients of the deterministic terms (excluding the intercept term)
- psi() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the coefficients of the deterministic terms.
- psi() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
-
Get a copy of the linear representation coefficients.
- PureILPProblem - Class in com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem
-
This is a pure integer linear programming problem, in which all variables are integral.
- PureILPProblem(Vector, LinearGreaterThanConstraints, LinearLessThanConstraints, LinearEqualityConstraints, BoxConstraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.linear.problem.PureILPProblem
-
Construct a pure ILP problem.
- pValue() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FAEstimator
-
Calculate the p-value of the test statistics, given the degree of freedom.
- pValue - Variable in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
p-value for the test statistics
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
Get the p-value.
- pValue(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Compute the two-sided p-value for a critical value.
- pValue(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Compute the two-sided p-value for a critical value.
- pValue1SidedGreater - Variable in class com.numericalmethod.suanshu.stats.test.mean.T
-
right, one-sided p-value
- pValue1SidedGreater - Variable in class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
right, one-sided p-value
- pValue1SidedGreater - Variable in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum
-
right, one-sided p-value
- pValue1SidedGreater(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Compute the one-sided p-value for the statistics greater than a critical value.
- pValue1SidedGreater - Variable in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank
-
right, one-sided p-value
- pValue1SidedGreater(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Compute the one-sided p-value for the statistics greater than a critical value.
- pValue1SidedGreater - Variable in class com.numericalmethod.suanshu.stats.test.variance.F
-
right, one-sided p-value
- pValue1SidedLess - Variable in class com.numericalmethod.suanshu.stats.test.mean.T
-
left, one-sided p-value
- pValue1SidedLess - Variable in class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
left, one-sided p-value
- pValue1SidedLess - Variable in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum
-
left, one-sided p-value
- pValue1SidedLess - Variable in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank
-
left, one-sided p-value
- pValue1SidedLess - Variable in class com.numericalmethod.suanshu.stats.test.variance.F
-
left, one-sided p-value
- pvalueZ1 - Variable in class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
-
the p-value for Z1
- pvalueZ2 - Variable in class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
-
the p-value for Z2
- R() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.GramSchmidt
-
- R() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.HouseholderReflection
-
- R() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.QR
-
- R() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.qr.QRDecomposition
-
Get the upper triangular matrix R in the QR decomposition, A = QR.
- R - Class in com.numericalmethod.suanshu.misc
-
These are some R-equivalent utility functions.
- r - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot
-
the pivot row
- r - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- r(CointegrationMLE, double) - Method in class com.numericalmethod.suanshu.stats.cointegration.JohansenTest
-
Get the (most likely) order of cointegration.
- R.ifelse - Interface in com.numericalmethod.suanshu.misc
-
Return a value with the same shape as test
which is filled with elements selected from either yes or no
depending on whether the element of test is true or false.
- R.which - Interface in com.numericalmethod.suanshu.misc
-
Decide whether x satisfies the boolean test.
- R1toConstantMatrix - Class in com.numericalmethod.suanshu.analysis.function.matrix
-
A constant matrix function maps a real number to a constant matrix: \(R^n \rightarrow A\).
- R1toConstantMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.analysis.function.matrix.R1toConstantMatrix
-
Construct a constant matrix function.
- R1toMatrix - Class in com.numericalmethod.suanshu.analysis.function.matrix
-
This is a function that maps from R1 to a Matrix space.
- R1toMatrix() - Constructor for class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
-
- R2 - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
-
diagnostic measure: the R-squared
- R2toMatrix - Class in com.numericalmethod.suanshu.analysis.function.matrix
-
This is a function that maps from R2 to a Matrix space.
- R2toMatrix() - Constructor for class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
-
- Rand1Bin - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim
-
The Rand-1-Bin rule is defined by:
mutation by adding a scaled, randomly sampled vector difference to a third vector (differential mutation);
crossover by performing a uniform crossover (discrete recombination).
- Rand1Bin(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Rand1Bin
-
Construct an instance of Rand1Bin.
- Rand1Bin(double, double) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.Rand1Bin
-
Construct an instance of Rand1Bin.
- Rand1Bin.DeRand1BinCell - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim
-
This chromosome defines the Rand-1-Bin rule.
- random - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
the stationary random component of the time series after the trend and seasonal components are removed
- RandomBetaGenerator - Interface in com.numericalmethod.suanshu.stats.random.univariate.beta
-
This is a random number generator that generates random deviates according to the Beta distribution.
- RandomExpGenerator - Interface in com.numericalmethod.suanshu.stats.random.univariate.exp
-
This is a random number generator that generates random deviates according to the exponential distribution.
- RandomGammaGenerator - Interface in com.numericalmethod.suanshu.stats.random.univariate.gamma
-
This is a random number generator that generates random deviates according to the Gamma distribution.
- RandomLongGenerator - Interface in com.numericalmethod.suanshu.stats.random.univariate
-
A (pseudo) random number generator that generates a sequence of longs that lack any pattern and are uniformly distributed.
- RandomMatrix(Matrix, double) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159.RandomMatrix
-
- RandomNumberGenerator - Interface in com.numericalmethod.suanshu.stats.random.univariate
-
A (pseudo) random number generator is an algorithm designed to generate a sequence of numbers that lack any pattern.
- RandomProcess - Interface in com.numericalmethod.suanshu.stats.stochasticprocess
-
This interface represents a random process a.k.a.
- RandomStandardNormalNumberGenerator - Interface in com.numericalmethod.suanshu.stats.random.univariate.normal
-
This is a random number generator that generates random deviates according to the standard Normal distribution.
- RandomVectorGenerator - Interface in com.numericalmethod.suanshu.stats.random.multivariate
-
A (pseudo) multivariate random number generator samples a random vector from a multivariate distribution.
- RandomWalk - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian
-
This is the Random Walk construction of a multivariate Brownian motion.
- RandomWalk(int, TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.RandomWalk
-
Construct a multi-dimensional Brownian motion at time points specified.
- RandomWalk(int, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.RandomWalk
-
Construct a multi-dimensional Brownian motion at even time points, [0, 1, ......, T].
- RandomWalk - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde
-
This is the Random Walk construction of a stochastic process per SDE specification.
- RandomWalk(DiscretizedSDE, TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk
-
Construct a multivariate stochastic process from an SDE.
- RandomWalk - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian
-
This is the Random Walk construction of a univariate Brownian motion.
- RandomWalk(TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.RandomWalk
-
Construct a univariate Brownian motion at time points specified.
- RandomWalk(int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.RandomWalk
-
Construct a univariate Brownian motion at even time points, [0, 1, ......, T].
- RandomWalk - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde
-
This is the Random Walk construction of a stochastic process per SDE specification.
- RandomWalk(DiscretizedSDE, TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk
-
Construct a univariate stochastic process from an SDE.
- RandomWalk.MultiVariateRealization - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde
-
- RandomWalk.Realization - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde
-
- rank() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.GramSchmidt
-
- rank() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.HouseholderReflection
-
This implementation computes the rank by counting the number of non-zero rows in R.
- rank() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.QR
-
- rank() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.qr.QRDecomposition
-
Get the numerical rank of A as computed by the QR decomposition.
- rank() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel
-
Get the rank of A.
- rank(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixMeasure
-
Compute the numerical rank of a matrix.
- rank(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixMeasure
-
Compute the numerical rank of a matrix.
- rank() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Get the rank of the system, i.e., the number of (real) eigenvalues.
- Rank - Class in com.numericalmethod.suanshu.stats.descriptive.rank
-
Rank is a relationship between a set of items such that,
for any two items, the first is either "ranked higher than", "ranked lower than" or "ranked equal to" the second.
- Rank(double[], double) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
Compute the sample ranks of the values.
- Rank(double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
Compute the sample ranks of the values.
- rank(int) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
Get the rank of the i-th element.
- rank() - Method in class com.numericalmethod.suanshu.vector.doubles.operation.VectorSpace
-
Get the rank of this vector space.
- RankOne - Class in com.numericalmethod.suanshu.optimization.unconstrained.quasinewton
-
The Rank One method is a quasi-Newton method
to solve unconstrained nonlinear optimization problems.
- RankOne(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.RankOne
-
Construct a multivariate minimizer using the Rank One method.
- ranks() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
Get the ranks of the values.
- ratioTest(SimplexTable, int) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
-
This is pivot row selection (Ratio test) rule.
- ratioTest(SimplexTable, int) - Method in interface com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting
-
This is pivot row selection (Ratio test) rule.
- RayleighDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The L2 norm of (x1, x2), where xi's are normal, uncorrelated, equal variance and
have the Rayleigh distributions.
- RayleighDistribution(double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
Construct a Rayleigh distribution.
- RayleighRng - Class in com.numericalmethod.suanshu.stats.random.univariate
-
This random number generator samples from the Rayleigh distribution using the inverse transform sampling method.
- RayleighRng(double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.RayleighRng
-
Construct a random number generator to sample from the Rayleigh distribution.
- RayleighRng(double) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.RayleighRng
-
Construct a random number generator to sample from the Rayleigh distribution.
- rbind(Vector...) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Combine an array of vectors by rows.
- rbind(List<Vector>) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Combine a list of array of vectors by rows.
- rbind(Matrix...) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Combine an array of matrices by rows.
- readObject(ObjectInputStream) - Method in class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedGenerator
-
- real() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Get the real part of this complex number.
- Real - Class in com.numericalmethod.suanshu.number
-
A real number is an arbitrary precision number.
- Real(double) - Constructor for class com.numericalmethod.suanshu.number.Real
-
Construct a Real from a double.
- Real(long) - Constructor for class com.numericalmethod.suanshu.number.Real
-
Construct a Real from an integer.
- Real(BigDecimal) - Constructor for class com.numericalmethod.suanshu.number.Real
-
Construct a Real from a BigDecimal.
- Real(BigInteger) - Constructor for class com.numericalmethod.suanshu.number.Real
-
Construct a Real from a BigInteger.
- Real(String) - Constructor for class com.numericalmethod.suanshu.number.Real
-
Construct a Real from a String.
- RealInterval - Class in com.numericalmethod.suanshu.interval
-
This is an interval on the real line.
- RealInterval(Double, Double) - Constructor for class com.numericalmethod.suanshu.interval.RealInterval
-
Construct an interval on the real line.
- Realization - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate
-
This interface defines the Iterator for generating (reading) a realization of a univariate random process.
- Realization - Interface in com.numericalmethod.suanshu.stats.timeseries.univariate.realtime
-
This is a univariate time series indexed real numbers.
- Realization.Entry - Class in com.numericalmethod.suanshu.stats.timeseries.univariate.realtime
-
This is the TimeSeries.Entry for a real number -indexed univariate time series.
- Realization.Iterator - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate
-
This Iterator support lazy evaluation/generation of a realization from a stochastic process.
- RealMatrix - Class in com.numericalmethod.suanshu.matrix.generic.matrixtype
-
- RealMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- RealMatrix(Real[][]) - Constructor for class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- RealMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- RealScalarFunction - Interface in com.numericalmethod.suanshu.analysis.function.rn2r1
-
A real valued function a \(R^n \rightarrow R\) function, \(y = f(x_1, ..., x_n)\).
- RealScalarFunctionChromosome - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid
-
This chromosome encodes a real valued function.
- RealScalarFunctionChromosome(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
Construct an instance of RealScalarFunctionChromosome.
- RealScalarSubFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1
-
- RealScalarSubFunction(RealScalarFunction, Map<Integer, Double>) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.RealScalarSubFunction
-
Construct a scalar sub-function.
- RealVectorFunction - Interface in com.numericalmethod.suanshu.analysis.function.rn2rm
-
A vector-valued function a \(R^n \rightarrow R^m\) function, \([y_1,...,y_m] = f(x_1,...,x_n)\).
- RealVectorSubFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2rm
-
- RealVectorSubFunction(RealVectorFunction, Map<Integer, Double>) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2rm.RealVectorSubFunction
-
Construct a vector-valued sub-function.
- reduce(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.Hessenberg
-
- reducedRowEchelonForm(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a matrix is in the reduced row echelon form, up to a precision.
- Reference<T> - Class in com.numericalmethod.suanshu.parallel
-
- Reference() - Constructor for class com.numericalmethod.suanshu.parallel.Reference
-
- reflect(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.Householder
-
Apply the Householder matrix, H, to a column vector, x.
- reflect(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.Householder
-
Apply the Householder matrix, H, to a matrix (a set of column vectors), A.
- reflectRows(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.Householder
-
Apply the Householder matrix, H, to a matrix (a set of row vectors), A.
- relations(Interval<T>) - Method in class com.numericalmethod.suanshu.interval.Interval
-
Determine the interval relations between this and Y.
- relativeError(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Compute the relative error for {x1, x0}.
- RelativeTolerance - Class in com.numericalmethod.suanshu.algorithm.iterative.tolerance
-
The stopping criteria is that the norm of the residual r relative to
the input base is equal to or smaller than the specified
tolerance, that is,
||r||2/base ≤ tolerance
- RelativeTolerance(double) - Constructor for class com.numericalmethod.suanshu.algorithm.iterative.tolerance.RelativeTolerance
-
- RelativeTolerance(double, double) - Constructor for class com.numericalmethod.suanshu.algorithm.iterative.tolerance.RelativeTolerance
-
Construct an instance with specified tolerance.
- remainder() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.HornerScheme
-
Get the remainder, P(x0).
- remove() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector.Iterator
-
Overridden to avoid the vector being altered.
- renameCol(int, Object) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Rename column i.
- renameRow(int, Object) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Rename row i.
- rep(double, int) - Static method in class com.numericalmethod.suanshu.misc.R
-
Generate an array of doubles of repeated values.
- rep(int, int) - Static method in class com.numericalmethod.suanshu.misc.R
-
Generate an array of ints of repeated values.
- replace(Matrix, int, int, int, int, Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Replace a sub-matrix of a matrix with a smaller matrix.
- Resampling - Interface in com.numericalmethod.suanshu.stats.sampling.resampling
-
Specify the re-sampling method.
- residuals - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
-
the residual analysis of this GLM regression
- residuals - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
-
the residual analysis of this quasi GLM regression
- Residuals - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.quasi
-
Residual analysis of the results of a quasi Generalized Linear Model regression.
- Residuals - Class in com.numericalmethod.suanshu.stats.regression.linear.glm
-
Residual analysis of the results of a Generalized Linear Model regression.
- Residuals(GLMProblem, Vector) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.Residuals
-
Perform the residual analysis for a GLM problem.
- residuals - Variable in class com.numericalmethod.suanshu.stats.regression.linear.logistic.Logistic
-
the residual analysis of this regression
- Residuals - Class in com.numericalmethod.suanshu.stats.regression.linear.logistic
-
Residual analysis of the results of a logistic regression.
- residuals - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression
-
the residual analysis of this OLS regression
- Residuals - Class in com.numericalmethod.suanshu.stats.regression.linear.ols
-
Residual analysis of the results of an Ordinary Least Square linear regression model.
- Residuals - Class in com.numericalmethod.suanshu.stats.regression.linear
-
The residual of a sample is the difference between the sample and the estimated function (fitted) value.
- Residuals(LMProblem, Vector) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.Residuals
-
Create an instance of Residuals for a linear regression problem.
- residuals - Variable in class com.numericalmethod.suanshu.stats.regression.linear.Residuals
-
the residuals, ε
- reverse(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Reverse a double array.
- reverse(int...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Reverse an int array.
- rho() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
Get ρ as discussed in the reference.
- Ridders - Class in com.numericalmethod.suanshu.analysis.differentiation
-
Ridders' method computes the numerical derivative of a function.
- Ridders(UnivariateRealFunction, int, double, int) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
Construct the derivative function of a univariate function using Ridder's method.
- Ridders(UnivariateRealFunction, int) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
Construct the derivative function of a univariate function using Ridder's method.
- Ridders(RealScalarFunction, int[], double, int) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
Construct the derivative function of a vector-valued function using Ridder's method.
- Ridders(RealScalarFunction, int[]) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
Construct the derivative function of a vector-valued function using Ridder's method.
- Riemann - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
This is a wrapper class that integrates a function by using an appropriate integrator together with Romberg's method.
- Riemann(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Riemann
-
Construct an integrator.
- Riemann() - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Riemann
-
Construct an integrator.
- rightConfidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
Compute the one sided right confidence interval, [a, ∞)
- rightConfidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
Compute the one sided right confidence interval, [a, ∞)
- rightMultiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
Right multiplication by G, namely, A * G.
- rightMultiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
Right multiplication by P.
- rightTailApproximation - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
true if we use approximation for the right tail to speed up computation; up to 7 digit of accuracy
- Ring<R> - Interface in com.numericalmethod.suanshu.mathstructure
-
A ring is a set R equipped with two binary operations called addition and multiplication:
+ : R × R → R and
· : R × R → R
To qualify as a ring, the set and two operations, (R, +, ⋅), must satisfy the requirements known as the ring axioms.
- RngUtils - Class in com.numericalmethod.suanshu.stats.random
-
This class provides static methods that wraps random number generators to
produce synchronized generators.
- RntoMatrix - Interface in com.numericalmethod.suanshu.analysis.function.matrix
-
This interface is a function that maps from Rn to a Matrix space.
- Romberg - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
Romberg's method computes an integral by generating a sequence of estimations of the integral value and then doing an extrapolation.
- Romberg(IterativeIntegrator) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Romberg
-
Extend an integrator using Romberg's method.
- ROOT_2 - Static variable in class com.numericalmethod.suanshu.Constant
-
\(\sqrt{2}\)
- ROOT_2_PI - Static variable in class com.numericalmethod.suanshu.Constant
-
\(\sqrt{2\pi}\)
- ROOT_PI - Static variable in class com.numericalmethod.suanshu.Constant
-
\(\sqrt{\pi}\)
- rotate(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- round(double, DoubleUtils.RoundingScheme) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Round up or down a number to an integer.
- round(double, int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Round a number to the precision specified.
- round(Vector) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.AllIntegers
-
- round(Vector) - Method in interface com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.IntegerConstraint
-
- round(Vector) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.SomeIntegers
-
- rowEchelonForm(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a matrix is in the row echelon form, up to a precision.
- rows(Matrix, int[]) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Construct a sub-matrix from the rows of a matrix.
- rows(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Construct a sub-matrix from the rows of a matrix.
- rowSums(MatrixTable) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixUtils
-
Get the row sums.
- RSS - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
-
diagnostic measure: the sum of squared residuals, Σ(ε^2)
- run() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
Run the genetic algorithm.
- run(T) - Method in interface com.numericalmethod.suanshu.parallel.IterationBody
-
Execute a (parallel) task.
- run(int) - Method in interface com.numericalmethod.suanshu.parallel.LoopBody
-
This method contains the code inside the for-loop, as in a native
for-loop like this:
- run(int, AutoCovarianceFunction) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.InnovationAlgorithmImpl
-
Run the Innovation Algorithm to compute the prediction parameters.
- S - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.CentralPath
-
This is the auxiliary helper to solve the dual problem.
- s - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution
-
This is the auxiliary helper to solve the dual problem.
- s - Variable in exception com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.exception.LPUnbounded
-
This is the pricing column that does not have an eligible row that passes the ratio test,
hence the problem is unbounded.
- s - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot
-
the pivot column
- s() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
\[
s = \sum(t_i^2 - t_i)
\]
- S() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FactorAnalysis
-
Get the covariance (or correlation) matrix.
- sample() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis
-
Get the sample kurtosis (biased estimator).
- sample() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
Get the sample skewness (biased estimator).
- sampleSize - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
the (finite) sample size
- scalar(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
- scale() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.Pow
-
Get the exponential of the coefficient.
- scale() - Method in interface com.numericalmethod.suanshu.stats.pca.PCA
-
Get the scalings applied to each variable.
- scale() - Method in class com.numericalmethod.suanshu.stats.pca.PCAbyEigen
-
Get the scalings applied to each variable.
- scale() - Method in class com.numericalmethod.suanshu.stats.pca.PCAbySVD
-
Get the scalings applied to each variable,
the scaling vector to be divided.
- scaleColumn(int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
Scale a column:
A[, j] = c * A[, j]
- scaled(Real) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- scaled(double) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- scaled(F) - Method in interface com.numericalmethod.suanshu.mathstructure.VectorSpace
-
× : F × V → V
The result of applying this function to a scalar,
c, in
F and
v in
V is denoted
cv.
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- scaled(double) - Method in interface com.numericalmethod.suanshu.matrix.doubles.Matrix
-
Scale this matrix, A, by a constant.
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
-
Multiply the elements in this by a scalar, element-by-element.
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- scaled(MatrixAccess, double) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
c * A
- scaled(MatrixAccess, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.ParallelMatrixMathOperation
-
- scaled(MatrixAccess, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- scaled(Real) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
- scaled(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
- scaled(Complex) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- scaled(F) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
-
- scaled(Real) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- scaled(double[], double) - Method in class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
-
- scaled(double[], double) - Method in interface com.numericalmethod.suanshu.number.doublearray.DoubleArrayOperation
-
Scale a double array.
- scaled(double[], double) - Method in class com.numericalmethod.suanshu.number.doublearray.ParallelDoubleArrayOperation
-
- scaled(double[], double) - Method in class com.numericalmethod.suanshu.number.doublearray.SimpleDoubleArrayOperation
-
- scaled(double) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- scaled(Real) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- scaled(Vector, double) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- scaled(Vector, Real) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- scaled(double) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- scaled(Real) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- scaled(double) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Scale this vector by a constant, entry-by-entry.
- scaled(Real) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Scale this vector by a constant, entry-by-entry.
- scaledAlpha(int[]) - Method in class com.numericalmethod.suanshu.stats.hmm.rabiner.HmmForwardBackward
-
Get the scaled forward probability matrix, dimension (T * N).
- scaledBeta(int[]) - Method in class com.numericalmethod.suanshu.stats.hmm.rabiner.HmmForwardBackward
-
Get the scaled backward probability matrix, dimension (T * N).
- ScaledPolynomial - Class in com.numericalmethod.suanshu.analysis.function.polynomial
-
This constructs a scaled polynomial that has neither too big or too small coefficients,
hence avoiding overflow or underflow.
- ScaledPolynomial(Polynomial, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.ScaledPolynomial
-
Construct a scaled polynomial.
- ScaledPolynomial(Polynomial) - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.ScaledPolynomial
-
Construct a scaled polynomial.
- scaleRow(int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
Scale a row:
A[i, ] = c * A[i, ]
- ScientificNotation - Class in com.numericalmethod.suanshu.number
-
Scientific notation expresses a number in this form
x = a * 10b
a is called the significand or mantissa, and 1 ≤ |a| < 10.
- ScientificNotation(double, int) - Constructor for class com.numericalmethod.suanshu.number.ScientificNotation
-
Construct the scientific notation of a number in this form: x = a * 10b.
- ScientificNotation(BigDecimal, int) - Constructor for class com.numericalmethod.suanshu.number.ScientificNotation
-
Construct the scientific notation of a number in this form: x = a * 10b.
- ScientificNotation(BigDecimal) - Constructor for class com.numericalmethod.suanshu.number.ScientificNotation
-
Construct the scientific notation of a number.
- ScientificNotation(BigInteger) - Constructor for class com.numericalmethod.suanshu.number.ScientificNotation
-
Construct the scientific notation of an integer.
- ScientificNotation(long) - Constructor for class com.numericalmethod.suanshu.number.ScientificNotation
-
Construct the scientific notation of a long.
- ScientificNotation(double) - Constructor for class com.numericalmethod.suanshu.number.ScientificNotation
-
Construct the scientific notation of a double.
- scores() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FAEstimator
-
Get the matrix of scores, computed using either Thompson's (1951) scores,
or Bartlett's (1937) weighted least-squares scores.
- scores() - Method in interface com.numericalmethod.suanshu.stats.pca.PCA
-
Get the scores of supplied data on the principal components.
- scoringRule() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FactorAnalysis
-
Get the scoring rule.
- sde - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk
-
the SDE specification, in discretized form
- sde - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Euler
-
the continuous-time multivariate SDE
- SDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
This class represents a multi-dimensional, continuous-time, Stochastic Differential Equation (SDE) of this form:
dX(t) = μ(t, Xt, Zt, ...) * dt + σ(t, Xt, Zt, ...) * dB(t).
- SDE(Drift, Diffusion, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.SDE
-
Construct a multi-dimensional diffusion type stochastic differential equation.
- sde - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk
-
the SDE specification, in discretized form
- sde - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Euler
-
the continuous-time univariate SDE
- sde - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Milstein
-
the continuous-time SDE specification
- SDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
This class represents a univariate, continuous-time Stochastic Differential Equation of this form:
dX(t) = μ(t, Xt, Zt, ...) * dt + σ(t, Xt, Zt, ...) * dB(t).
- SDE(Drift, Diffusion) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.SDE
-
Construct a univariate diffusion type stochastic differential equation.
- SDPDualProblem - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem
-
A dual SDP problem, as in eq.
- SDPDualProblem(Vector, SymmetricMatrix, SymmetricMatrix[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPDualProblem
-
Construct a dual SDP problem.
- SDPDualProblem.EqualityConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem
-
This is the collection of equality constraints:
\[
\sum_{i=1}^{p}y_i\mathbf{A_i}+\textbf{S} = \textbf{C}, \textbf{S} \succeq \textbf{0}
\]
- SDPPrimalProblem - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem
-
A Primal SDP problem, as in eq.
- SDPPrimalProblem(SymmetricMatrix, SymmetricMatrix[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPPrimalProblem
-
Construct a primal SDP problem.
- sdPrincipalComponent() - Method in interface com.numericalmethod.suanshu.stats.pca.PCA
-
Get the standard deviations of the principal components.
- sdPrincipalComponent(int) - Method in interface com.numericalmethod.suanshu.stats.pca.PCA
-
Get the standard deviation of the i-th principal component.
- sdPrincipalComponent() - Method in class com.numericalmethod.suanshu.stats.pca.PCAbyEigen
-
Get the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance or correlation matrix).
- sdPrincipalComponent() - Method in class com.numericalmethod.suanshu.stats.pca.PCAbySVD
-
Get the standard deviations of the principal components
(i.e., the square roots of the eigenvalues of the correlation (or covariance) matrix,
though the calculation is actually done with the singular values of the data matrix)
- search(BBNode...) - Method in class com.numericalmethod.suanshu.algorithm.bb.BranchAndBound
-
- search(S...) - Method in interface com.numericalmethod.suanshu.algorithm.iterative.IterativeMethod
-
Search for a solution that optimizes the objective function from the
given starting points.
- search(Vector...) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver.Solution
-
- search(CentralPath) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowing.Solution
-
- search(CentralPath...) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- search(CentralPath) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- search() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
Search for a solution that optimizes the objective function from the
given starting points.
- search(PrimalDualSolution...) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPoint.Solution
-
- search(PrimalDualSolution) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPoint.Solution
-
Search for a solution that optimizes the objective function from the
given starting point.
- search() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPoint.Solution
-
Search for a solution that optimizes the objective function from the
starting point given by K.
- search() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.activeset.QPPrimalActiveSetSolver.Solution
-
Search for a minimizer for the quadratic programming problem.
- search(Vector) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.activeset.QPPrimalActiveSetSolver.Solution
-
Search for a minimizer for the quadratic programming problem.
- search(QPSolution) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.activeset.QPPrimalActiveSetSolver.Solution
-
Search for a minimizer for the quadratic programming problem from the
given starting points.
- search(QPSolution...) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.activeset.QPPrimalActiveSetSolver.Solution
-
- search(Vector...) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver.Solution
-
- search(Vector) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver.Solution
-
Search for a solution that minimizes the objective function from the
given starting point.
- search(Vector, Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver.Solution
-
Search for a solution that minimizes the objective function from the
given starting point.
- search(Vector...) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolverForOnlyInequalityConstraint.Solution
-
- search(Vector...) - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution
-
Search for a solution that minimizes the objective function from the
given starting points.
- search(Vector...) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- search(Vector...) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead.Solution
-
Perform a Nelder-Mead search from an initial simplex.
- search(Vector...) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.SteepestDescentImpl
-
- search(double, double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch.Solution
-
- search(double, double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Brent.Solution
-
- search(double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Brent.Solution
-
- search(double, double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Fibonacci.Solution
-
- search(double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Fibonacci.Solution
-
- search(double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Golden.Solution
-
- search(double, double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearch.Solution
-
Search for a minimum within the interval [lower, upper].
- search(double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearch.Solution
-
- search(double, double, double) - Method in interface com.numericalmethod.suanshu.optimization.univariate.UnivariateMinimizer.Solution
-
Search for a minimum within the interval [lower, upper].
- search(double, double) - Method in interface com.numericalmethod.suanshu.optimization.univariate.UnivariateMinimizer.Solution
-
Search for a minimum within the interval [lower, upper].
- seasonal - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
the estimated seasonal effect of the time series
- seed(long...) - Method in class com.numericalmethod.suanshu.optimization.initialization.UniformDistributionOverBox1
-
Seed the random number generator to produce repeatable sequences.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedRLG
-
Delegate to the underlying random long generator.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedRNG
-
Delegate to the underlying random number generator.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.concurrent.ConcurrentCachedRVG
-
Delegate to the underlying random vector generator.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.multivariate.IID
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.multivariate.MultinomialRvg
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.multivariate.NormalRvg
-
- seed(long...) - Method in interface com.numericalmethod.suanshu.stats.random.multivariate.RandomVectorGenerator
-
Seed the random vector generator to produce repeatable sequences.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.multivariate.UniformDistributionOverBox
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.beta.Cheng1978
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.beta.VanDerWaerden1969
-
Deprecated.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.BinomialRng
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.exp.Ziggurat2000Exp
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.gamma.KunduGupta2007
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.gamma.MarsagliaTsang2000
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.gamma.XiTanLiu2010a
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.gamma.XiTanLiu2010b
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.InverseTransformSampling
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.LogNormalRng
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.normal.BoxMuller
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.normal.MarsagliaBray1964
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.normal.NormalRng
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.normal.Ziggurat2000
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.normal.Zignor2005
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.poisson.Knuth1969
-
- seed(long...) - Method in interface com.numericalmethod.suanshu.stats.random.univariate.RandomNumberGenerator
-
Seed the random number generator to produce repeatable sequences.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.LEcuyer
-
Seed the random number generator to produce repeatable sequences.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.Lehmer
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.linear.MRG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.MersenneTwister
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.MWC8222
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.SHR0
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.SHR3
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.univariate.uniform.UniformRng
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.sampling.resampling.bootstrap.NonParametricBootstrap
-
- seed(long...) - Method in interface com.numericalmethod.suanshu.stats.sampling.resampling.Resampling
-
Seed the random generator to produce repeatable sequences.
- seed(long) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.RandomWalk
-
- seed(long) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.Construction
-
Seed the construction process so that we may generate the same realizations.
- seed(long) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk
-
- seed(long) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.RandomWalk
-
- seed(long) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Construction
-
Seed the construction process so that we may generate the same realizations.
- seed(long) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk
-
- seed - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution1
-
Deprecated.
the seed (for randomly generating the simulations)
- select(double[], R.which) - Static method in class com.numericalmethod.suanshu.misc.R
-
Select the array elements which satisfy the boolean test.
- select(int[], R.which) - Static method in class com.numericalmethod.suanshu.misc.R
-
Select the array elements which satisfy the boolean test.
- seq(double, double, double) - Static method in class com.numericalmethod.suanshu.misc.R
-
Generate a sequence of doubles from from up to to with increments inc.
- seq(int, int, int) - Static method in class com.numericalmethod.suanshu.misc.R
-
Generate a sequence of ints from from up to to with increments inc.
- seq(int, int) - Static method in class com.numericalmethod.suanshu.misc.R
-
Generate a sequence of ints from from up to to with increments 1.
- Sequence - Interface in com.numericalmethod.suanshu.analysis.sequence
-
A sequence is an ordered list of (real) numbers.
- set(int, int, double) - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- set(int, int, double) - Method in interface com.numericalmethod.suanshu.matrix.doubles.MatrixAccess
-
Set the matrix entry at [i,j] to a value.
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- set(int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
Deprecated.
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
- set(int, int, F) - Method in interface com.numericalmethod.suanshu.matrix.generic.MatrixAccess
-
Set the matrix entry at [i,j] to a value.
- set(int, int, Complex) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- set(int, int, F) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
-
- set(int, int, Real) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- set(RealScalarFunction, EqualityConstraints) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
Associate this variation to a particular general constrained minimization problem with only equality constraints.
- set(RealScalarFunction, EqualityConstraints, GreaterThanConstraints) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPASVariation1
-
Associate this variation to a particular general constrained minimization problem.
- set(T) - Method in class com.numericalmethod.suanshu.parallel.Reference
-
- set(int, double) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- set(int, DenseVector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
Replace a sub-vector v[from : replacement.length] by a replacement starting at position from.
- set(int, double) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
This method is overridden to always throw VectorAccessException.
- set(int, double) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Change the value of an entry in this vector.
- setColumn(int, Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
Change the matrix column values to a vector value.
- setColumn(int, double...) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
Set the values for a column in the matrix, i.e., [*, j].
- setColumn(int, Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
Set the values for a column in the matrix, i.e., [*, j].
- setConcurrencyLevel(int) - Static method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
-
Sets the concurrency level for the ParallelExecutor.
- setDt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
-
Set the current time differential.
- setDt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtWt
-
- setDt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Set the current time differential.
- setDt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt
-
- setFT(Filtration) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.F_sum_BtDt
-
- setFT(Filtration) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.F_sum_tBtDt
-
- setFT(Filtration) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.FiltrationFunction
-
Set the filtration for this function.
- setInitials(BBNode...) - Method in class com.numericalmethod.suanshu.algorithm.bb.BranchAndBound
-
- setInitials(S...) - Method in interface com.numericalmethod.suanshu.algorithm.iterative.IterativeMethod
-
Supply the starting points for the search.
- setInitials(CentralPath...) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- setInitials(PrimalDualSolution...) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPoint.Solution
-
- setInitials(QPSolution...) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.activeset.QPPrimalActiveSetSolver.Solution
-
- setInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver.Solution
-
- setInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- setInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead.Solution
-
- setInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.SteepestDescentImpl
-
- setPopulation(List<Chromosome>) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
-
- setPopulation(List<Chromosome>) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Set the current generation.
- setRow(int, Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
Change the matrix row values to a vector value.
- setRow(int, double...) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
Set the values for a row in the matrix, i.e., [i, *].
- setRow(int, Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
Set the values for a row in the matrix, i.e., [i, *].
- setXt(Vector) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
-
Set the current value of the stochastic process.
- setXt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Set the current value of the stochastic process.
- setZt(Vector) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
-
Set the value of the Gaussian distribution innovation.
- setZt(Vector) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtWt
-
- setZt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Set the value of the Gaussian distribution innovation.
- setZt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt
-
- ShapiroWilk - Class in com.numericalmethod.suanshu.stats.test.distribution.normality
-
The Shapiro–Wilk test tests the null hypothesis that a sample comes from a normally distributed population.
- ShapiroWilk(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilk
-
Perform the Shapiro-Wilk test to test for the null hypothesis that a sample comes from a normally distributed population.
- ShapiroWilkDistribution - Class in com.numericalmethod.suanshu.stats.test.distribution.normality
-
Shapiro-Wilk distribution is the distribution of the Shapiro-Wilk statistics,
which tests the null hypothesis that a sample comes from a normally distributed population.
- ShapiroWilkDistribution(int) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
Construct a Shapiro-Wilk distribution.
- shellsort(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Sort an array using Shell sort.
- SHR0 - Class in com.numericalmethod.suanshu.stats.random.univariate.uniform
-
SHR0 is a simple uniform random number generator.
- SHR0() - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.uniform.SHR0
-
- SHR3 - Class in com.numericalmethod.suanshu.stats.random.univariate.uniform
-
SHR3 is a 3-shift-register generator with period 2^32-1.
- SHR3() - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.uniform.SHR3
-
- side - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov
-
the type of Kolmogorov-Smirnov statistic to be computed
- side - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
the type of KolmogorovDistribution two-sample distribution, i.e., equal, greater, less
- SiegelTukey - Class in com.numericalmethod.suanshu.stats.test.rank
-
Siegel–Tukey tests for differences in scale (variability) between two groups.
- SiegelTukey(double[], double[], double, boolean) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
Perform the Siegel-Tukey test to test for differences in scale (variability) between two groups.
- SiegelTukey(double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
Perform the Siegel-Tukey test to test for differences in scale (variability) between two groups.
- SiegelTukey(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
Perform the Siegel-Tukey test to test for differences in scale (variability) between two groups.
- sigma - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- sigma - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalDistribution.Lambda
-
the standard deviation
- sigma - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.Brownian
-
σ, the diffusion constant
- Sigma - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
This class provides an implementation of the diffusion coefficients in the form of a diffusion matrix.
- Sigma() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.Sigma
-
- sigma - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.SDE
-
the diffusion matrix
- sigma - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.Brownian
-
σ, the diffusion constant
- sigma - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.SDE
-
the diffusion coefficient
- sigma - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
the covariance matrix of white noise
- sigma() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
Get the covariance matrix of white noise.
- sigma() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the covariance matrix of white noise.
- sigma - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
the white noise variance
- sigma() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the white noise variance.
- sigma2(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Compute the conditional variance based on the past information.
- sigma2() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
Get a copy of the conditional variances.
- sigma_i_j(int, int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
-
Deprecated.
- sigma_i_j(int, int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.Sigma
-
Get the Ft adapted function D[i,j] element in the diffusion matrix.
- sign() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
Get the sign of the permutation matrix which is also the determinant.
- significand() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
Get the significand.
- signum(double[]) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the signs of values.
- SimilarMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
Given a matrix A and an invertible matrix P, we construct the similar matrix B s.t.,
B = P-1AP
- SimilarMatrix(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.SimilarMatrix
-
Construct the similar matrix B = P-1AP.
- SimpleCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell
-
- SimpleCellFactory - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid
-
A SimpleCellFactory produces SimpleCells.
- SimpleCellFactory(double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory
-
Construct an instance of a SimpleCellFactory.
- SimpleCellFactory.SimpleCell - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid
-
A SimpleCell implements the two genetic operations.
- SimpleDoubleArrayOperation - Class in com.numericalmethod.suanshu.number.doublearray
-
This is a simple, single-threaded implementation of the array math operations.
- SimpleDoubleArrayOperation() - Constructor for class com.numericalmethod.suanshu.number.doublearray.SimpleDoubleArrayOperation
-
- SimpleGridMinimizer - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid
-
This minimizer is a simple global optimization method.
- SimpleGridMinimizer(SimpleGridMinimizer.NewCellFactoryCtor, boolean, RandomLongGenerator, double, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
Construct a SimpleGridMinimizer to solve unconstrained minimization problems.
- SimpleGridMinimizer(boolean, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
Construct a SimpleGridMinimizer to solve unconstrained minimization problems.
- SimpleGridMinimizer.NewCellFactoryCtor - Interface in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid
-
This factory constructs a new SimpleCellFactory for each minimization problem.
- SimpleGridMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid
-
This is the solution to a minimization problem using SimpleGridMinimizer.
- SimpleMatrixMathOperation - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation
-
This is a generic, single-threaded implementation of matrix math operations.
- SimpleMatrixMathOperation() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- SimpleMC - Class in com.numericalmethod.suanshu.stats.markovchain
-
This is a time-homogeneous Markov chain with a finite state space.
- SimpleMC(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Construct a time-homogeneous Markov chain with a finite state space.
- SimpleMC(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Construct a time-homogeneous Markov chain with a finite state space using stationary state probabilities.
- SimpleMultiVariateTimeSeries - Class in com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime
-
This simple multivariate time series has its vectored values indexed by integers.
- SimpleMultiVariateTimeSeries(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
Construct an instance of SimpleMultiVariateTimeSeries.
- SimpleMultiVariateTimeSeries(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
Construct an instance of SimpleMultiVariateTimeSeries.
- SimpleMultiVariateTimeSeries(Vector...) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
Construct an instance of SimpleMultiVariateTimeSeries.
- SimpleMultiVariateTimeSeries(TimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
Construct an instance of SimpleMultiVariateTimeSeries from a univariate time series.
- SimpleTimeSeries - Class in com.numericalmethod.suanshu.stats.timeseries.univariate.realtime
-
This simple univariate time series has its double values indexed by integers.
- SimpleTimeSeries(double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
-
Construct an instance of SimpleTimeSeries.
- SimplexCuttingPlane - Class in com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane
-
The use of cutting planes to solve Mixed Integer Linear Programming (MILP) problems was introduced by Ralph E Gomory.
- SimplexCuttingPlane(LPSimplexSolver, SimplexCuttingPlane.CutterFactory) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane.SimplexCuttingPlane
-
Construct a cutting-plane minimizer to solve an MILP problem.
- SimplexCuttingPlane.CutterFactory - Interface in com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane
-
This factory constructs a new Cutter for each MILP problem.
- SimplexCuttingPlane.CutterFactory.Cutter - Interface in com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane
-
A Cutter defines how to cut a simplex table, i.e., how to relax a linear program so that
the current non-integer solution is no longer feasible to the relaxation.
- SimplexPivoting - Interface in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
-
A simplex pivoting finds a row and column to exchange to reduce the cost function.
- SimplexPivoting.Pivot - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
-
the pivot
- SimplexTable - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex
-
This is a simplex table used to solve a linear programming problem using a simplex method.
- SimplexTable(CanonicalLPProblem1, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Construct a simplex table from a canonical linear programming problem.
- SimplexTable(CanonicalLPProblem1) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Construct a simplex table from a canonical linear programming problem.
- SimplexTable(LPProblem, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Construct a simplex table from a general linear programming problem.
- SimplexTable(LPProblem) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Construct a simplex table from a general linear programming problem.
- SimplexTable(SimplexTable) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Copy constructor.
- SimplexTable.Label - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex
-
- SimplexTable.LabelType - Enum in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex
-
- Simpson - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
Simpson's rule can be thought of as a special case of Romberg's method.
- Simpson(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Simpson
-
Construct an integrator that implements Simpson's rule.
- sin(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Sine of a complex number.
- singular(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a square matrix is singular, i.e, having no inverse, up to a precision.
- sinh(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Hyperbolic sine of a complex number.
- size() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
- size() - Method in interface com.numericalmethod.suanshu.analysis.function.tuple.OrderedPairs
-
Get the number of points.
- size() - Method in class com.numericalmethod.suanshu.interval.Intervals
-
Get the number of disjoint intervals.
- size() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
-
Get the number of distinct eigenvalues.
- size() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LSProblem
-
Get the number of variables in the linear system.
- size() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- size() - Method in interface com.numericalmethod.suanshu.optimization.constrained.constraint.Constraints
-
Get the number of constraints.
- size() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.general.GeneralConstraints
-
- size() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearConstraints
-
- size() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
-
- size() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
-
- size() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLMSeries
-
- size() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.LinearKalmanFilter
-
Get T, the number of hidden states or observations.
- size() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
- size() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get T, the number of hidden states or observations.
- size - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialDistribution.Lambda
-
the size
- size() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk.MultiVariateRealization
-
- size() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.EvenlySpacedGrid
-
- size() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.TimeGrid
-
the number of time points
- size() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.UnitGrid
-
- size() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
-
Get the length of the history, excluding the initial value (0).
- size() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk.Realization
-
- size() - Method in class com.numericalmethod.suanshu.stats.timeseries.DateTimeGenericTimeSeries
-
- size() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
- size() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
- size() - Method in interface com.numericalmethod.suanshu.stats.timeseries.TimeSeries
-
Get the length of the time series.
- size() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
- size() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.OneDimensionTimeSeries
-
- size() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
-
- size() - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- size() - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- size() - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Get the length of this vector.
- SizeMismatch(int, int) - Constructor for exception com.numericalmethod.suanshu.vector.doubles.IsVector.SizeMismatch
-
Construct an instance of SizeMismatch.
- Sk - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.QuasiNewton.QuasiNewtonImpl
-
This is the approximate inverse of the Hessian matrix.
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
Get the skewness of this distribution.
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- skew() - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
Get the skewness of this distribution.
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
Get the skewness of this distribution.
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- Skewness - Class in com.numericalmethod.suanshu.stats.descriptive.moment
-
Skewness is a measure of the asymmetry of the probability distribution.
- Skewness() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
Construct an empty Skewness calculator.
- Skewness(double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
Construct a Skewness calculator,
initialized with a sample.
- Skewness(Skewness) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
Copy constructor.
- skewSymmetric(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a matrix is skew symmetric.
- SmallestSubscriptRule - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
-
Bland's smallest-subscript rule is for anti-cycling in choosing a pivot.
- SmallestSubscriptRule() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SmallestSubscriptRule
-
- SOCPDualProblem - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem
-
This is the Dual Second Order Conic Programming problem.
- SOCPDualProblem(Vector, Matrix[], Vector[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Construct a dual SODP problem.
- SOCPDualProblem.EqualityConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem
-
- SOCPGeneralProblem - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem
-
Many convex programming problems can be represented in the following form.
- SOCPGeneralProblem(Vector, Matrix[], Vector[], Vector[], double[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.problem.SOCPGeneralProblem
-
Construct a general Second Order Conic Programming problem.
- solution() - Method in interface com.numericalmethod.suanshu.algorithm.bb.BBNode
-
the solution to the sub-problem associated with this node
- Solution(PrimalDualPathFollowing, SDPDualProblem, double, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowing.Solution
-
solve the semi-definite programming problem using the Homogeneous Self-Dual Path-Following algorithm
- Solution(SDPDualProblem, double, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- Solution(RealScalarFunction, EqualityConstraints, GreaterThanConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver.Solution
-
- solution() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPNode
-
- Solution(RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- Solution(UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch.Solution
-
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.CubicRoot
-
Solve \(ax^3 + bx^2 + cx + d = 0\).
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.jenkinstraub.JenkinsTraubReal
-
Solve a polynomial equation.
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.LinearRoot
-
Solve ax + b = 0.
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.PolyRoot
-
Get the roots/zeros of a polynomial.
- solve(Polynomial) - Method in interface com.numericalmethod.suanshu.analysis.function.polynomial.root.PolyRootSolver
-
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuadraticRoot
-
Solve \(ax^2 + bx + c = 0\).
- solve(double, double, double, double, double) - Method in interface com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRoot.QuarticSolver
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRoot
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(double, double, double, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRootFerrari
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(double, double, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRootFerrari
-
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRootFerrari
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(double, double, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRootFormula
-
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRootFormula
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(UnivariateRealFunction, double, double, double...) - Method in class com.numericalmethod.suanshu.analysis.uniroot.Brent
-
- solve(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.uniroot.Brent
-
- solve(UnivariateRealFunction, double, double, double...) - Method in class com.numericalmethod.suanshu.analysis.uniroot.Halley
-
- solve(UnivariateRealFunction, double) - Method in class com.numericalmethod.suanshu.analysis.uniroot.Halley
-
- solve(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction, double) - Method in class com.numericalmethod.suanshu.analysis.uniroot.Halley
-
Search for a root, x, in the interval [lower, upper] such that f(x) = 0.
- solve(UnivariateRealFunction, double, double, double...) - Method in class com.numericalmethod.suanshu.analysis.uniroot.Newton
-
- solve(UnivariateRealFunction, double) - Method in class com.numericalmethod.suanshu.analysis.uniroot.Newton
-
- solve(UnivariateRealFunction, UnivariateRealFunction, double) - Method in class com.numericalmethod.suanshu.analysis.uniroot.Newton
-
Search for a root, x, in the interval [lower, upper] such that f(x) = 0.
- solve(UnivariateRealFunction, double, double, double...) - Method in interface com.numericalmethod.suanshu.analysis.uniroot.Uniroot
-
Search for a root, x, in the interval [lower, upper] such that f(x) = 0.
- solve(UpperTriangularMatrix, Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.BackwardSubstitution
-
Solve Ux = b.
- solve(LowerTriangularMatrix, Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.ForwardSubstitution
-
Solve Lx = b.
- solve(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LinearSystemSolver
-
Get a particular solution for the linear system, Ax = b.
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LUSolver
-
Solve Ax = b.
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.OLSSolverByQR
-
In the ordinary least square sense, solve
Ax = y
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.OLSSolverBySVD
-
In the ordinary least square sense, solve
Ax = y
- solve(LSProblem, IterationMonitor<Vector>) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver
-
Solves iteratively
Ax = b
until the solution converges, i.e., the norm of residual
(b - Ax) is less than or equal to the threshold.
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
- solve(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
-
Return the input vector x.
- solve(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.JacobiPreconditioner
-
Return P-1x, where P is the diagonal matrix
of A.
- solve(Vector) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.Preconditioner
-
Solve Mv = x, where M is the preconditioner matrix.
- solve(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner
-
Solve Mz = x using this SSOR preconditioner.
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
-
- solve(SDPDualProblem) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowing
-
- solve(SDPDualProblem) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing
-
- solve(SOCPDualProblem) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPoint
-
- solve(QPProblem) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.activeset.QPPrimalActiveSetSolver
-
- solve(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
-
- solve(CanonicalLPProblem1) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
-
- solve(SimplexTable) - Method in interface com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPSimplexSolver
-
Solve an LP problem by a simplex algorithm on a simplex table
- solve(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
-
- solve(LPProblem) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
-
- solve(QuadraticFunction, double) - Static method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.QPSimpleSolver
-
Solve an unconstrained quadratic programming problem of this form.
- solve(QuadraticFunction) - Static method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.QPSimpleSolver
-
Solve an unconstrained quadratic programming problem of this form.
- solve(QuadraticFunction, LinearEqualityConstraints, double) - Static method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.QPSimpleSolver
-
Solve a quadratic programming problem subject to equality constraints.
- solve(QuadraticFunction, LinearEqualityConstraints) - Static method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.QPSimpleSolver
-
Solve a quadratic programming problem subject to equality constraints.
- solve(ConstrainedOptimProblem) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
- solve(ConstrainedOptimProblem) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetSolverForOnlyEqualityConstraint1
-
- solve(RealScalarFunction, EqualityConstraints) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetSolverForOnlyEqualityConstraint1
-
Minimize a function subject to only equality constraints.
- solve(ConstrainedOptimProblem) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver
-
- solve(RealScalarFunction, GreaterThanConstraints) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolverForOnlyInequalityConstraint
-
Minimize a function subject to only inequality constraints.
- solve(ConstrainedOptimProblem) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolverForOnlyInequalityConstraint
-
- solve(BruteForceIPProblem) - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.bruteforce.BruteForceIPMinimizer
-
- solve(ILPProblem) - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPBranchAndBound
-
- solve(ILPProblem) - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.cuttingplane.SimplexCuttingPlane
-
- solve(OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.DEOptim
-
- solve(OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- solve(MinMaxProblem<T>) - Method in class com.numericalmethod.suanshu.optimization.minmax.LeastPth
-
- solve(P) - Method in interface com.numericalmethod.suanshu.optimization.Optimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.ConjugateGradient
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.FletcherReeves
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.Powell
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.Zangwill
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.linesearch.Fletcher
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.MultivariateMaximizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.BFGS
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.Huang
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.FirstOrder
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.GaussNewton.MySteepestDescent
-
- solve(RealVectorFunction, RntoMatrix) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.GaussNewton
-
Solve the minimization problem to minimize F = vf' * vf.
- solve(RealVectorFunction) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.GaussNewton
-
Solve the minimization problem to minimize F = vf' * vf.
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.NewtonRaphson
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent
-
Solve a minimization problem with a C2 objective function.
- solve(UnivariateRealFunction) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearch
-
Minimize a univariate function.
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Brent
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Fibonacci
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.Golden
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearch
-
- solve(UnivariateRealFunction) - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearch
-
Minimize a univariate function.
- solve(List<Vector>, List<Matrix>, boolean) - Method in class com.numericalmethod.suanshu.stats.regression.panel.OLSPanelRegression
-
- solve(List<Vector>, List<Matrix>, boolean) - Method in interface com.numericalmethod.suanshu.stats.regression.panel.PanelRegression
-
Solve panel regression of the following form:
\[
y_{t} = A_{t} x + \epsilon_{t}
\]
- SomeIntegers(IPProblem) - Constructor for class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.SomeIntegers
-
Construct the integral constraint from an Integer Programming problem.
- SORSweep - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
-
This is a building block for
SOR and
SSOR
to perform the forward or backward sweep.
- SORSweep(Matrix, Vector, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep
-
Construct an instance to perform forward or backward sweep for a linear
system Ax = b.
- SparseEntry - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
-
This is a (non-zero) entry in a sparse matrix.
- SparseEntry(Coordinates, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseEntry
-
Construct a sparse entry in a sparse matrix.
- SparseEntry.TopLeftFirstComparator - Enum in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
-
This Comparator sorts the matrix coordinates first from top to
bottom (rows), and then from left to right (columns).
- SparseMatrix - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
-
A sparse matrix stores only non-zero values.
- SparseStructure - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
-
This interface defines common operations on sparse structures such as sparse
vector or sparse matrix.
- SparseVector - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
-
A sparse vector stores only non-zero values.
- SparseVector(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
Construct a sparse vector.
- SparseVector(int, int[], double[]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
Construct a sparse vector.
- SparseVector(SparseVector) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
Copy constructor.
- SparseVector.Entry - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
-
- SparseVector.Iterator - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
-
This wrapper class overrides the Iterator.remove()
method to throw an exception when called.
- Spectrum - Interface in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
-
A spectrum is the set of eigenvalues of a matrix.
- SQPActiveSetSolver - Class in com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset
-
Sequential quadratic programming (SQP) is an iterative method for nonlinear optimization.
- SQPActiveSetSolver(SQPActiveSetSolver.VariationFactory, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver
-
Construct an SQP Active Set minimizer to solve general minimization problems with inequality constraints.
- SQPActiveSetSolver(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver
-
Construct an SQP Active Set minimizer to solve general minimization problems with inequality constraints.
- SQPActiveSetSolver.Solution - Class in com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset
-
This is the solution to a general minimization with only inequality constraints using the SQP Active Set algorithm.
- SQPActiveSetSolver.VariationFactory - Interface in com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset
-
This factory constructs a new instance of SQPASVariation for each SQP problem.
- SQPActiveSetSolverForOnlyEqualityConstraint1 - Class in com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint
-
This implementation is a modified version of Algorithm 15.1 in the reference to solve a general constrained optimization problem with only equality constraints.
- SQPActiveSetSolverForOnlyEqualityConstraint1(SQPActiveSetSolverForOnlyEqualityConstraint1.VariationFactory, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetSolverForOnlyEqualityConstraint1
-
Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.
- SQPActiveSetSolverForOnlyEqualityConstraint1(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetSolverForOnlyEqualityConstraint1
-
Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.
- SQPActiveSetSolverForOnlyEqualityConstraint1.VariationFactory - Interface in com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint
-
This factory constructs a new instance of SQPASEVariation for each SQP problem.
- SQPActiveSetSolverForOnlyEqualityConstraint2 - Class in com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint
-
- SQPActiveSetSolverForOnlyEqualityConstraint2(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetSolverForOnlyEqualityConstraint2
-
Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.
- SQPActiveSetSolverForOnlyEqualityConstraint2(double, double, int, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetSolverForOnlyEqualityConstraint2
-
Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.
- SQPActiveSetSolverForOnlyInequalityConstraint - Class in com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset
-
This implementation is a modified version of Algorithm 15.2 in the reference to solve a general constrained optimization problem with only inequality constraints.
- SQPActiveSetSolverForOnlyInequalityConstraint(SQPActiveSetSolver.VariationFactory, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolverForOnlyInequalityConstraint
-
Construct an SQP Active Set minimizer to solve general minimization problems with only inequality constraints.
- SQPActiveSetSolverForOnlyInequalityConstraint(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolverForOnlyInequalityConstraint
-
Construct an SQP Active Set minimizer to solve general minimization problems with only inequality constraints.
- SQPActiveSetSolverForOnlyInequalityConstraint.Solution - Class in com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset
-
This is the solution to a general minimization problem with only inequality constraints using the SQP Active Set algorithm.
- SQPASEVariation - Interface in com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint
-
This interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem with only equality constraints
using Sequential Quadratic Programming.
- SQPASEVariation1 - Class in com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint
-
This implementation is a modified version of the algorithm in the reference to solve a general constrained minimization problem
using Sequential Quadratic Programming.
- SQPASEVariation1(double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
Construct a variation.
- SQPASEVariation1() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
Construct a variation.
- SQPASEVariation2 - Class in com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint
-
This implementation tries to find an exact positive definite Hessian whenever possible.
- SQPASEVariation2(double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2
-
Construct a variation.
- SQPASEVariation2() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2
-
Construct a variation.
- SQPASVariation - Interface in com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset
-
This interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem
using Sequential Quadratic Programming.
- SQPASVariation1 - Class in com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset
-
This implementation is a modified version of Algorithm 15.4 in the reference to solve a general constrained minimization problem
using Sequential Quadratic Programming.
- SQPASVariation1(double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPASVariation1
-
Construct a variation.
- sqrt(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Square root of a complex number.
- sqrt(double[]) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the square roots of values.
- Sqrt - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the link function:
- Sqrt() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Sqrt
-
- squareQ() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.GramSchmidt
-
Get the square Q matrix.
- squareQ() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.HouseholderReflection
-
- squareQ() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.QR
-
- squareQ() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.qr.QRDecomposition
-
Get the square Q matrix.
- SSORPreconditioner - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
-
SSOR preconditioner is derived from a symmetric coefficient matrix A
which is decomposed as
A = D + L + Lt
The SSOR preconditioning matrix is defined as
M = (D + L)D-1(D + L)t
or, parameterized by ω
M(ω) = (1/(2 - ω))(D / ω + L)(D / ω)-1(D / ω + L)t
- SSORPreconditioner(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner
-
Construct an SSOR preconditioner with a symmetric coefficient matrix.
- StandardCumulativeNormal - Interface in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
The cumulative Normal distribution function describes the probability of a Normal random variable falling in the interval \((-\infty, x]\).
- standardDeviation() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
Get the standard deviation of the sample,
which is the square root of the variance.
- StandardInterval - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This transformation is for mapping integral region from [a, b] to [-1, 1].
- StandardInterval(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.StandardInterval
-
Construct a StandardInterval substitution rule.
- standardized() - Method in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
-
standard residual = residual / v1 / sqrt(RSS / (n-m))
- StandardLPProblem - Class in com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem
-
This is a linear programming problem in the standard form:
min c'x
s.t.
- StandardLPProblem(Vector, LinearEqualityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.StandardLPProblem
-
Construct a linear programming problem in the standard form.
- StandardNormalRng - Class in com.numericalmethod.suanshu.stats.random.univariate.normal
-
An alias for
Zignor2005 to provide a default implementation for sampling from the standard Normal distribution.
- StandardNormalRng() - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.normal.StandardNormalRng
-
- state - Variable in class com.numericalmethod.suanshu.stats.dlm.multivariate.DLMSim.Innovation
-
the simulated state
- state - Variable in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSim.Innovation
-
the simulated state
- StateEquation - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
This is the state equation in a controlled dynamic linear model.
- StateEquation(R1toMatrix, R1toMatrix, R1toMatrix, NormalRvg) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.StateEquation
-
Construct a state equation.
- StateEquation(R1toMatrix, R1toMatrix, R1toMatrix) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.StateEquation
-
Construct a state equation.
- StateEquation(R1toMatrix, R1toMatrix) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.StateEquation
-
Construct a state equation without control variables.
- StateEquation(Matrix, Matrix, Matrix, NormalRvg) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.StateEquation
-
Construct a time-invariant state equation.
- StateEquation(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.StateEquation
-
Construct a time-invariant state equation without control variables.
- StateEquation(StateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.StateEquation
-
Construct a multivariate state equation from a univariate state equation.
- StateEquation(StateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.StateEquation
-
Copy constructor.
- StateEquation - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
This is the state equation in a controlled dynamic linear model.
- StateEquation(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction, NormalRng) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Construct a state equation.
- StateEquation(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Construct a state equation.
- StateEquation(UnivariateRealFunction, UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Construct a state equation without control variables.
- StateEquation(double, double, double, NormalRng) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Construct a time-invariant state equation.
- StateEquation(double, double) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Construct a time-invariant state equation without control variables.
- StateEquation(StateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Copy constructor.
- Statistic - Interface in com.numericalmethod.suanshu.stats.descriptive
-
A statistic (singular) is a single measure of some attribute of a sample (e.g., its arithmetic mean value).
- StatisticFactory - Interface in com.numericalmethod.suanshu.stats.descriptive
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.factoranalysis.FAEstimator
-
Get the test statistics of the factor analysis.
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
Get the test statistics.
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.BreuschPagan
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.White
-
- stderr - Variable in class com.numericalmethod.suanshu.stats.regression.linear.Beta
-
the standard errors of the coefficients β^
- stderr - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
-
the standard error of the residuals
- stderr() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFitting
-
Get the asymptotic standard errors of the estimators.
- stderr() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Compute the asymptotic standard errors for the estimated parameters, φ and θ.
- SteepestDescent - Class in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent
-
A steepest descent algorithm finds the minimum by moving along the negative of the steepest gradient direction.
- SteepestDescent(LineSearch, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent
-
Construct a multivariate minimizer using a steepest descent method.
- SteepestDescent(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent
-
Construct a multivariate minimizer using a steepest descent method.
- SteepestDescent.SteepestDescentImpl - Class in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent
-
This is an implementation of the steepest descent method.
- SteepestDescentImpl(C2OptimProblem) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.SteepestDescentImpl
-
- SteepestDescentSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Steepest Descent method (SDM) solves a symmetric n-by-n linear system.
- SteepestDescentSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
Construct a Steepest Descent method (SDM) solver.
- SteepestDescentSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
Construct a Steepest Descent method (SDM) solver.
- step() - Method in class com.numericalmethod.suanshu.algorithm.bb.BranchAndBound
-
- step() - Method in interface com.numericalmethod.suanshu.algorithm.iterative.IterativeMethod
-
Do the next iteration.
- step() - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowing.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
Do the next iteration.
- step() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPoint.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.activeset.QPPrimalActiveSetSolver.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.constrained.general.sqp.activeset.SQPActiveSetSolver.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.GeneticAlgorithm
-
Run a step in genetic algorithm: produce the next generation of chromosome pool.
- step() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.SteepestDescentImpl
-
- StepFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
-
A step function (or staircase function) is a finite linear combination of indicator functions of intervals.
- StepFunction(double) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
Construct an empty step function.
- StepFunction(OrderedPairs, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
Construct a step function from a collection ordered pairs.
- StepFunction(OrderedPairs) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
Construct a step function from a collection ordered pairs.
- studentized() - Method in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
-
studentized residual = standardized * sqrt((n-m-1) / (n-m-standardized^2))
- SuanShuUtils - Class in com.numericalmethod.suanshu.misc
-
These are some miscellaneous utility functions that are commonly used throughout the SuanShu library.
- subarray(double[], int[]) - Static method in class com.numericalmethod.suanshu.misc.R
-
Get a sub-array of the original array with the given indices.
- subarray(int[], int[]) - Static method in class com.numericalmethod.suanshu.misc.R
-
Get a sub-array of the original array with the given indices.
- subDiagonal(Matrix) - Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.CreateVector
-
Get the sub-diagonal of a matrix as a vector.
- SubFunction<R> - Class in com.numericalmethod.suanshu.analysis.function
-
A sub-function, g, is defined over a subset of the domain of another (original) function, f.
- SubFunction(Function<Vector, R>, Map<Integer, Double>) - Constructor for class com.numericalmethod.suanshu.analysis.function.SubFunction
-
Construct a sub-function.
- subMatrix(Matrix, int, int, int, int) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Construct a sub-matrix from the four corners of a matrix.
- subMatrix(Matrix, int[], int[]) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
-
Construct a sub-matrix from the intersections of rows and columns of a matrix.
- SubMatrixRef - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
This is a 'reference' to a sub-matrix of a larger matrix without copying it.
- SubMatrixRef(Matrix, int, int, int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
Construct a sub-matrix reference.
- SubMatrixRef(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
Construct a reference to the whole matrix.
- SubstitutionRule - Interface in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
A substitution rule specifies \(x(t)\) and \(\frac{\mathrm{d} x}{\mathrm{d} t}\).
- subVector(Vector, int, int) - Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.CreateVector
-
Get a sub-vector from a vector.
- SuccessiveOverrelaxationSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
-
The Successive Overrelaxation method (SOR), is devised by applying
extrapolation to the
Gauss-Seidel method.
- SuccessiveOverrelaxationSolver(double, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
-
Construct a SOR solver with the extrapolation factor ω.
- sum(int, int) - Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Sum up the terms from from to to with the increment 1.
- sum(int, int, int) - Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Sum up the terms from from to to with the increment inc.
- sum(double, double, double) - Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Sum up the terms from from to to with the increment inc.
- sum(double[]) - Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Partial summation of the selected terms.
- sum(BigDecimal...) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Sum up the BigDecimal numbers.
- sum(double...) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Sum up big numbers.
- sum(double...) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the sum of the values.
- sum(int...) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the sum of the values.
- sum2(double...) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the sum of squares of the values.
- sum_BtDt - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.F_sum_BtDt
-
- sum_BtDt - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.F_sum_tBtDt
-
- sum_tBtDt - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.F_sum_tBtDt
-
- Summation - Class in com.numericalmethod.suanshu.analysis.sequence
-
Summation is the operation of adding a sequence of numbers; the result is their sum or total.
- Summation(Summation.Term, double) - Constructor for class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Construct a summation series.
- Summation(Summation.Term) - Constructor for class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Construct a finite summation series.
- Summation.Term - Interface in com.numericalmethod.suanshu.analysis.sequence
-
Define the terms in a summation series.
- SumOfPenalties - Class in com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod
-
This penalty function sums up the costs from a set of constituent penalty functions.
- SumOfPenalties(PenaltyFunction...) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.SumOfPenalties
-
Construct a sum-of-penalties penalty function from a set of penalty functions.
- sumsOfPowersOfDifferences(int, double, double...) - Static method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
Compute the power-th moment of an array of data with respect to a mean.
- sumToInfinity(int) - Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Sum up the terms from from to infinity with increment 1 until the series converges.
- sumToInfinity(double, double) - Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Sum up the terms from from to infinity with increment inc until the series converges.
- superDiagonal(Matrix) - Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.CreateVector
-
Get the super-diagonal of a matrix as a vector.
- SVD - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.svd
-
SVD decomposition decomposes a matrix A of dimension m x n, where m >= n, such that
U' * A * V = D, or U * D * V' = A.
- SVD(Matrix, boolean, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVD
-
Run the SVD decomposition on a matrix.
- SVD(Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVD
-
Run the SVD decomposition on a matrix.
- svd() - Method in class com.numericalmethod.suanshu.stats.pca.PCAbySVD
-
Get the singular value decomposition (SVD) of matrix X.
- SVDDecomposition - Interface in com.numericalmethod.suanshu.matrix.doubles.factorization.svd
-
SVD decomposition decomposes a matrix A of dimension m x n, where m >= n, such that
U' * A * V = D, or U * D * V' = A.
- SVEC - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
SVEC converts a symmetric matrix K = {Kij} into a vector of dimension n(n+1)/2.
- SVEC(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.SVEC
-
Construct the SVEC of a matrix.
- svecA() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowing.Solution
-
Toh, Todd, Tütüncü, Section 3.1, A^
- svecA() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowing.Solution
-
- swap(int, int) - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Perform a Jordan Exchange to swap row r with column s.
- swapColumn(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
Swap two columns of a permutation matrix.
- swapColumn(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
Swap columns:
- swapRow(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
Swap two rows of a permutation matrix.
- swapRow(int, int) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
Swap rows:
- symmetric(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a matrix is symmetric.
- SymmetricKronecker - Class in com.numericalmethod.suanshu.matrix.doubles.operation
-
Compute the symmetric Kronecker product of two matrices.
- SymmetricKronecker(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.SymmetricKronecker
-
Compute the symmetric Kronecker product of two matrices.
- SymmetricMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle
-
A symmetric matrix is a square matrix such that its transpose equals to itself, i.e.,
A[i][j] = A[j][i]
- SymmetricMatrix(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Construct a symmetric matrix of dimension dim * dim.
- SymmetricMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Construct a symmetric matrix from a 2D double[][] array.
- SymmetricMatrix(SymmetricMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Copy constructor.
- symmetricPositiveDefinite(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a square matrix is symmetric and positive definite.
- SymmetricSuccessiveOverrelaxationSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
-
The Symmetric Successive Overrelaxation method (SSOR) is like
SOR, but it performs in each
iteration one forward sweep followed by one backward sweep.
- SymmetricSuccessiveOverrelaxationSolver(double, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
-
Construct a SSOR solver with the extrapolation factor ω.
- synchronizedRLG(RandomLongGenerator) - Static method in class com.numericalmethod.suanshu.stats.random.RngUtils
-
- synchronizedRNG(RandomNumberGenerator) - Static method in class com.numericalmethod.suanshu.stats.random.RngUtils
-
- synchronizedRVG(RandomVectorGenerator) - Static method in class com.numericalmethod.suanshu.stats.random.RngUtils
-
- SynchronizedStatistic - Class in com.numericalmethod.suanshu.stats.descriptive
-
This is a thread-safe wrapper of
Statistic by synchronizing all public methods
so that only one thread at a time can access the instance.
- T() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.diagonalization.TriDiagonalization
-
Get T, such that T = Q * A * Q.
- T() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.qr.QRAlgorithm
-
Get the T matrix as in the real Schur canonical form Q'MQ = T.
- T() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Get the transformation matrix, T, such that T * A = U.
- T() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
-
Get the transformation matrix, T, such that T * A = U.
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- T() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel
-
Get the transformation matrix, T, such that T * A = U.
- t() - Method in interface com.numericalmethod.suanshu.matrix.doubles.MatrixRing
-
Get the transpose of this matrix.
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
The transpose of a diagonal matrix is the same as itself.
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
t(A)
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
The transpose of a symmetric matrix is the same as itself.
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
-
The transpose of a permutation matric is the same as its inverse.
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- T() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
Get the transformed matrix T.
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.FastKroneckerProduct
-
- t() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
- t() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
/[
t = \sum(t_i^3 - t_i)
\]
- t - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Beta
-
the z- or t-value for the regression coefficients β^
- t(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization.Iterator
-
Get the current timestamp of the realization.
- t() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtWt
-
Get the current time.
- T - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.EvenlySpacedGrid
-
the ending of the time interval
- t(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.EvenlySpacedGrid
-
the i-th time point in the time grid discretization
- T() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.EvenlySpacedGrid
-
- t(int) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.TimeGrid
-
the i-th time point in the time grid discretization
- T() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.TimeGrid
-
the last time point available
- t(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.UnitGrid
-
the i-th time point in the time grid discretization
- T() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.UnitGrid
-
- T(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
-
Get the t-th time point.
- T() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
-
Get the entire time grid.
- t(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.Realization.Iterator
-
Get the current timestamp of the realization.
- t() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt
-
Get the current time.
- T - Class in com.numericalmethod.suanshu.stats.test.mean
-
Student's TDistribution-test tests for the equality of means, for the one-sample case, against a hypothetical mean, and for two-sample case, of two populations.
- T(double[], double) - Constructor for class com.numericalmethod.suanshu.stats.test.mean.T
-
Construct a one-sample location test of whether the mean of a normally distributed population has a value specified in a null hypothesis.
- T(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.mean.T
-
Construct Welch's t test, an adaptation of Student's t-test, for the use with two samples having possibly unequal variances.
- T(double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.test.mean.T
-
Construct Welch's t test, an adaptation of Student's t-test, for the use with two samples having possibly unequal variances.
- T(double[], double[], boolean, double) - Constructor for class com.numericalmethod.suanshu.stats.test.mean.T
-
Construct a two sample location test of the null hypothesis that the means of two normally distributed populations are equal.
- T - Variable in class com.numericalmethod.suanshu.stats.test.mean.T
-
the associated TDistribution distribution
- T0 - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.EvenlySpacedGrid
-
the beginning of the time interval
- t0 - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Expectation
-
the beginning time of the integral time interval
- t1 - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Expectation
-
the ending time of the integral time interval
- ta() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential
-
Get the lower limit of the integral.
- ta() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.Exponential
-
- ta() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.InvertingVariable
-
- ta() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
-
- ta() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
-
- ta() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.StandardInterval
-
- ta() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.SubstitutionRule
-
Get the lower limit of the integral.
- Table - Interface in com.numericalmethod.suanshu.datastructure
-
A table is a means of arranging data in rows and columns.
- table - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
- tallR() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.GramSchmidt
-
- tallR() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.HouseholderReflection
-
- tallR() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.QR
-
- tallR() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.qr.QRDecomposition
-
Get the tall R matrix.
- tan(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Tangent of a complex number.
- tanh(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Hyperbolic tangent of a complex number.
- tb() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential
-
Get the upper limit of the integral.
- tb() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.Exponential
-
- tb() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.InvertingVariable
-
- tb() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
-
- tb() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
-
- tb() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.StandardInterval
-
- tb() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.SubstitutionRule
-
Get the upper limit of the integral.
- TDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The Student t distribution is the probability distribution of t, where
\[
t = \frac{\bar{x} - \mu}{s / \sqrt N}
\]
\(\bar{x}\) is the sample mean;
μ is the population mean;
s is the square root of the sample variance;
N is the sample size;
The importance of the Student's distribution is
when (as in nearly all practical statistical work) the population standard deviation is unknown and has to be estimated from the data.
- TDistribution(double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
Construct a Student's t distribution.
- test(double) - Method in interface com.numericalmethod.suanshu.misc.R.ifelse
-
Decide whether x satisfies the boolean test.
- testStatistics - Variable in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
the test statistics
- theta - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaDistribution.Lambda
-
the scale parameter
- theta(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
-
The canonical parameter of the distribution in terms of the mean μ.
- theta(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.ExponentialDistribution
-
The canonical parameter of the distribution in terms of the mean μ.
- theta(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gamma
-
- theta(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gaussian
-
- theta(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
-
- theta(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Poisson
-
- theta - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.ARIMAXModel
-
the MA coefficients
- theta(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.InnovationAlgorithmImpl
-
Get the coefficients of the linear predictor.
- threshold - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
-
the convergence threshold
- throwIfDifferentDimension(Table, Table) - Static method in class com.numericalmethod.suanshu.datastructure.DimensionCheck
-
Throws if
A1.nRows() != A2.nRows()
Or
A1.nCols() != A2.nCols()
- throwIfIncompatible4Multiplication(Table, Table) - Static method in class com.numericalmethod.suanshu.datastructure.DimensionCheck
-
Throws if
A1.nCols() != A2.nRows()
- throwIfIncompatible4Multiplication(Table, Vector) - Static method in class com.numericalmethod.suanshu.datastructure.DimensionCheck
-
Throws if
A.nCols() != v.size()
- throwIfInvalidColumn(Table, int) - Static method in class com.numericalmethod.suanshu.datastructure.DimensionCheck
-
Throws if accessing an out of range column.
- throwIfInvalidIndex(Vector, int) - Static method in class com.numericalmethod.suanshu.vector.doubles.IsVector
-
Check if an index is a valid index.
- throwIfInvalidRow(Table, int) - Static method in class com.numericalmethod.suanshu.datastructure.DimensionCheck
-
Throws if accessing an out of range row.
- throwIfNotEqualSize(Vector, Vector) - Static method in class com.numericalmethod.suanshu.vector.doubles.IsVector
-
Check if the input vectors have the same size.
- time(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
Get the i-th timestamp.
- time(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
Get the i-th time.
- TimeGrid - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.timepoints
-
This interface represents the discrete time points in [t1, tn = T] for a stochastic process.
- TimeInterval - Class in com.numericalmethod.suanshu.time
-
This is a time interval.
- TimeInterval(DateTime, DateTime) - Constructor for class com.numericalmethod.suanshu.time.TimeInterval
-
Construct a time interval from two time points.
- TimeIntervals - Class in com.numericalmethod.suanshu.time
-
- TimeIntervals() - Constructor for class com.numericalmethod.suanshu.time.TimeIntervals
-
Construct an empty collection of time interval.
- TimeIntervals(DateTime, DateTime) - Constructor for class com.numericalmethod.suanshu.time.TimeIntervals
-
Construct a collection consisting of one time interval.
- TimeIntervals(Interval<DateTime>) - Constructor for class com.numericalmethod.suanshu.time.TimeIntervals
-
Construct a collection consisting of one time interval.
- TimeIntervals(Interval<DateTime>...) - Constructor for class com.numericalmethod.suanshu.time.TimeIntervals
-
Construct a collection of time intervals.
- TimeIntervals(Intervals<DateTime>) - Constructor for class com.numericalmethod.suanshu.time.TimeIntervals
-
Copy constructor.
- timePoints - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk
-
the set of discretized time points
- timePoints - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk
-
the set of discretized time points
- TimeSeries<T extends java.lang.Comparable,V,E extends TimeSeries.Entry<T,V>> - Interface in com.numericalmethod.suanshu.stats.timeseries
-
A time series is a serially indexed collection of items.
- TimeSeries - Interface in com.numericalmethod.suanshu.stats.timeseries.univariate.realtime
-
This is a univariate time series indexed by integers.
- TimeSeries<T extends java.lang.Comparable,E extends TimeSeries.Entry<T>> - Interface in com.numericalmethod.suanshu.stats.timeseries.univariate
-
This is a univariate time series indexed by some notion of time.
- TimeSeries.Entry<T,V> - Interface in com.numericalmethod.suanshu.stats.timeseries
-
A time series is composed of a sequence of Entrys.
- TimeSeries.Entry - Class in com.numericalmethod.suanshu.stats.timeseries.univariate.realtime
-
This is the TimeSeries.Entry for an integer number -indexed univariate time series.
- TimeSeries.Entry<T> - Class in com.numericalmethod.suanshu.stats.timeseries.univariate
-
This is the TimeSeries.Entry for a univariate time series.
- timestamps() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
Get all the timestamps.
- timestamps() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
Get all the timestamps.
- to1DArray(MatrixTable) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixUtils
-
Get all matrix entries in the form of an 1D double[].
- to2DArray(MatrixTable) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixUtils
-
Get all matrix entries in the form of a 2D double[][] array.
- toArray() - Method in class com.numericalmethod.suanshu.datastructure.MathTable.Row
-
Convert the row to a double[], excluding the index.
- toArray() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- toArray() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
Get the sorted sample.
- toArray() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk.Realization
-
- toArray() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
Convert this multivariate time series into an array of vectors.
- toArray() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
- toArray() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.OneDimensionTimeSeries
-
- toArray() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
-
- toArray() - Method in interface com.numericalmethod.suanshu.stats.timeseries.univariate.TimeSeries
-
Convert this time series into an array, discarding the timestamps.
- toArray() - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- toArray() - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- toArray() - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
-
Cast this vector into a 1D double[].
- toBigDecimal() - Method in class com.numericalmethod.suanshu.number.Real
-
Convert this number to a BigDecimal.
- toDense() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- toDense() - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.Densifiable
-
Densify a matrix, i.e., convert a matrix implementation to the standard dense matrix,
DenseMatrix.
- toDense() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- toDense() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- toDense() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- toDense() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- toDense() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- toDense() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- toDouble() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Cast the complex number to a Double if it is a real number.
- toFamily() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Binomial
-
- toFamily() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Gamma
-
- toFamily() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Gaussian
-
- toFamily() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.InverseGaussian
-
- toFamily() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Poisson
-
- toFamily() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.QuasiFamily
-
- toGreaterThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.general.GeneralLessThanConstraints
-
- toGreaterThanConstraints() - Method in interface com.numericalmethod.suanshu.optimization.constrained.constraint.LessThanConstraints
-
Convert the less-than or equal-to constraints to greater-than or equal-to constraints.
- toGreaterThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.BoxConstraints
-
- toGreaterThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearLessThanConstraints
-
- Tolerance - Interface in com.numericalmethod.suanshu.algorithm.iterative.tolerance
-
The tolerance criteria for an iterative algorithm to stop.
- toLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.general.GeneralGreaterThanConstraints
-
- toLessThanConstraints() - Method in interface com.numericalmethod.suanshu.optimization.constrained.constraint.GreaterThanConstraints
-
Convert the greater-than or equal-to constraints to less-than or equal-to constraints.
- toLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.BoxConstraints
-
- toLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearGreaterThanConstraints
-
- toMatrix() - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
Get a copy of the flexible table in the form of a matrix.
- toMatrix() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk.MultiVariateRealization
-
- toMatrix() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
- toMatrix() - Method in interface com.numericalmethod.suanshu.stats.timeseries.multivariate.MultiVariateTimeSeries
-
Convert this multivariate time series into an m x n matrix, where
m is the dimension, and n the length.
- toMatrix() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
- toMatrix(TimeSeries<?, ?>) - Static method in class com.numericalmethod.suanshu.stats.timeseries.univariate.UnivariateTimeSeriesUtils
-
Cast a time series into a column matrix, discarding the timestamps.
- toStop(Vector) - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.InverseIteration.StoppingCriterion
-
Check whether we stop with the current eigenvector.
- toString() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- toString() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.QuadraticFunction
-
- toString() - Method in class com.numericalmethod.suanshu.datastructure.FlexibleTable
-
- toString() - Method in class com.numericalmethod.suanshu.interval.Interval
-
- toString() - Method in class com.numericalmethod.suanshu.interval.Intervals
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.CharacteristicPolynomial
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector.Entry
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.Householder.Context
-
- toString(MatrixTable) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixUtils
-
Get the String representation of a matrix.
- toString() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- toString(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Print out numbers to a string.
- toString(double[][]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Print out a 2D array, double[][] to a string.
- toString() - Method in class com.numericalmethod.suanshu.number.Real
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.BoxConstraints.Bound
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearConstraints
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPNode
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.Covariance
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Print out
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
-
- toString() - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- toString() - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- toVector(TimeSeries<?, ?>) - Static method in class com.numericalmethod.suanshu.stats.timeseries.univariate.UnivariateTimeSeriesUtils
-
Cast a time series into a vector, discarding the timestamps.
- tr(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixMeasure
-
Compute the sum of the diagonal elements, i.e., the trace of a matrix.
- train(HiddenMarkovModel, double[]) - Static method in class com.numericalmethod.suanshu.stats.hmm.mixture.HmmBaumWelch
-
Construct a trained mixture hidden Markov model, one iteration.
- train(HiddenMarkovModel, int[]) - Static method in class com.numericalmethod.suanshu.stats.hmm.rabiner.HmmTrainByEM
-
Construct a trained (discrete) hidden Markov model, one iteration.
- transform(double[]) - Method in interface com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.Filter
-
Transforms the input signal into the output signal.
- transform(double[]) - Method in class com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverage
-
- transform(double[]) - Method in class com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverageByExtension
-
- transpose(MatrixAccess) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
Get the transpose of A.
- transpose(MatrixAccess) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.ParallelMatrixMathOperation
-
- transpose(MatrixAccess) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- transposeSolve(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
-
Return the input vector x.
- transposeSolve(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.JacobiPreconditioner
-
Pt = P-1 for Jacobi preconditioner.
- transposeSolve(Vector) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.Preconditioner
-
Solve Mtv = x, where M is the preconditioner
matrix.
- transposeSolve(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner
-
Mtx = M-1x as M is symmetric.
- Trapezoidal - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
The Trapezoidal rule is a closed type Newton–Cotes formula, where the integral interval is evenly divided into N sub-intervals.
- Trapezoidal(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Trapezoidal
-
Construct an integrator that implements the Trapezoidal rule.
- trend - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution
-
the type of augmented Dickey-Fuller (ADF) test
- trend - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
the type of augmented Dickey-Fuller (ADF) test
- trend - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
the estimated trend of the time series
- tridiagonal(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
-
Check if a matrix is tridiagonal, up to a precision.
- TriDiagonalization - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.diagonalization
-
A tri-diagonal matrix A is a matrix such that
it has non-zero elements only in the main diagonal, the first diagonal below, and the first diagonal above.
- TriDiagonalization(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.diagonalization.TriDiagonalization
-
Run the tri-diagonalization process for a symmetric matrix.
- TridiagonalMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal
-
A tri-diagonal matrix has non-zero entries only on the super, main and sub diagonals.
- TridiagonalMatrix(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
Construct a 0 tri-diagonal matrix of dimension dim * dim.
- TridiagonalMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
Construct a tri-diagonal matrix from a 3-row 2D double[][] array such that:
the first row is the super diagonal with (dim - 1) entries;
the second row is the main diagonal with dim entries;
the third row is the sub diagonal with (dim - 1) entries.
- TridiagonalMatrix(TridiagonalMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
Copy constructor performing a deep copy.
- Trigamma - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The trigamma function is defined as the logarithmic derivative of the digamma function.
- Trigamma() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.Trigamma
-
- truncation - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
the number of truncated values
- TSS - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
-
diagnostic measure: the total sum of squares, Σ((y-y_mean)^2)
- type - Variable in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
-
the label type
- type - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov
-
the type of Kolmogorov-Smirnov test to be performed
- type - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
-
the trend type
- v() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.QuadraticMonomial
-
Get v as in (x2 + ux + v).
- V() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.diagonalization.BiDiagonalization
-
Get V, where
V' = Vk * ...
- V() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.GloubKahanSVD
-
- V() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVD
-
- V() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVDDecomposition
-
Get the V matrix as in SVD decomposition.
- v() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
When the problem is unbounded, the direction of arbitrarily negative can be computed by adjusting λ.
- v() - Method in class com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2
-
- V(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.ObservationEquation
-
Get V(t), the covariance matrix of vt.
- V(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.ObservationEquation
-
Get V(t), the variance of vt.
- V() - Method in class com.numericalmethod.suanshu.stats.pca.PCAbyEigen
-
Get the correlation (or covariance) matrix used for the PCA.
- value() - Method in interface com.numericalmethod.suanshu.algorithm.bb.BBNode
-
the value of this node
- value() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.Lebesgue
-
Get the integral value.
- value - Variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseEntry
-
the entry value
- value() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector.Entry
-
Get the value of this entry.
- value() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.InnerProduct
-
Get the value of the inner product.
- value() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.linear.bb.ILPNode
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.Covariance
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
- value(double) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Quantile
-
Compute the sample value corresponding to a quantile.
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Quantile
-
- value() - Method in interface com.numericalmethod.suanshu.stats.descriptive.Statistic
-
Get the value of the statistic.
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.SynchronizedStatistic
-
- value() - Method in class com.numericalmethod.suanshu.stats.sampling.resampling.bootstrap.BootstrapEstimator
-
The estimator value.
- value - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Expectation
-
the value of the integral
- value() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Expectation
-
Get the integral value.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.analysis.function.special.gamma.LogGamma.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.analysis.integration.univariate.riemann.NewtonCotes.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.PowerLawSingularity.PowerLawSingularityType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverage.Side
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.interval.IntervalRelation
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix.BidiagonalMatrixType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure.Reason
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseEntry.TopLeftFirstComparator
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.number.DoubleUtils.RoundingScheme
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.FirstOrder.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.cointegration.JohansenAsymptoticDistribution.Test
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.cointegration.JohansenAsymptoticDistribution.TrendType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.descriptive.rank.Quantile.QuantileType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.factoranalysis.FactorAnalysis.ScoringRule
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.random.univariate.uniform.UniformRng.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov.Side
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquare4Independence.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution1.Type
-
Deprecated.
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller.TrendType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.variance.Levene.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCovariance.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCH.GRADIENT
-
Returns the enum constant of this type with the specified name.
- values() - Static method in enum com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.analysis.function.special.gamma.LogGamma.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.analysis.integration.univariate.riemann.NewtonCotes.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.PowerLawSingularity.PowerLawSingularityType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverage.Side
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.interval.IntervalRelation
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix.BidiagonalMatrixType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure.Reason
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseEntry.TopLeftFirstComparator
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.number.DoubleUtils.RoundingScheme
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.optimization.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.FirstOrder.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.cointegration.JohansenAsymptoticDistribution.Test
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.cointegration.JohansenAsymptoticDistribution.TrendType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.descriptive.rank.Quantile.QuantileType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.factoranalysis.FactorAnalysis.ScoringRule
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.random.univariate.uniform.UniformRng.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov.Side
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquare4Independence.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution1.Type
-
Deprecated.
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller.TrendType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.variance.Levene.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCovariance.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCH.GRADIENT
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- VanDerWaerden - Class in com.numericalmethod.suanshu.stats.test.rank
-
The Van der Waerden test tests for the equality of all population distribution functions.
- VanDerWaerden(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.VanDerWaerden
-
Perform the Van Der Waerden test to test for the equality of all population distribution functions.
- VanDerWaerden1969 - Class in com.numericalmethod.suanshu.stats.random.univariate.beta
-
- VanDerWaerden1969(RandomGammaGenerator, RandomGammaGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.beta.VanDerWaerden1969
-
Deprecated.
Construct a random number generator to sample from the beta distribution.
- VanDerWaerden1969(double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.univariate.beta.VanDerWaerden1969
-
Deprecated.
Construct a random number generator to sample from the beta distribution.
- var() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Expectation
-
Compute the variance of the integral.
- var() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFitting
-
Get the variance of the white noise.
- var() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
- var() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Compute the unconditional variance of the GARCH model.
- var(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.InnovationAlgorithm
-
Get the mean squared error for prediction errors at time t for X^t+1, i.e.,
E(X_(t+1) - X^_(t+1))
- var1 - Variable in class com.numericalmethod.suanshu.stats.test.mean.T
-
variance for sample 1
- var2 - Variable in class com.numericalmethod.suanshu.stats.test.mean.T
-
variance for sample 2
- VARFitting - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class estimates the coefficients for a VAR model.
- VARFitting(MultiVariateTimeSeries, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARFitting
-
- Variance - Class in com.numericalmethod.suanshu.stats.descriptive.moment
-
The variance of a sample is the average squared deviations from the sample mean.
- Variance() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
Construct an empty Variance calculator.
- Variance(double[], boolean) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
Construct a Variance calculator,
initialized with a sample.
- Variance(double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
Construct an unbiased Variance calculator.
- Variance(Variance) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
Copy constructor.
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
Get the variance of this distribution.
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- variance() - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
Get the variance of this distribution.
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
Get the variance of this distribution.
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- variance(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
-
- variance(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.ExponentialDistribution
-
The variance function of the distribution in terms of the mean μ.
- variance(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gamma
-
- variance(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gaussian
-
- variance(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
-
- variance(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Poisson
-
- variance() - Method in class com.numericalmethod.suanshu.stats.sampling.resampling.bootstrap.BootstrapEstimator
-
The estimator variance, of which the convergence limit is decided by
sample size, not B.
- variance - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Expectation
-
the variance of the integral
- variance() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Expectation
-
Get the integral variance.
- variance() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- VARXModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a VARX (VAR model with eXogenous inputs) model.
- VARXModel(Vector, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX model.
- VARXModel(Vector, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX model with unit variance.
- VARXModel(Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a zero-mean VARX model.
- VARXModel(Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a zero-mean VARX model with unit variance.
- VARXModel(VECMTransitory) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX(p) from a transitory VECM(p).
- VARXModel(VECMLongrun) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX(p) from a long-run VECM(p).
- VARXModel(VARXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Copy constructor.
- VECM - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a Vector Error Correction Model (VECM).
- VECM(Vector, Matrix, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Construct a VECM(p) model.
- VECM(VECM) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Copy constructor.
- VECMLongrun - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a long-run Vector Error Correction Model (VECM).
- VECMLongrun(Vector, Matrix, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
-
Construct a long-run VECM(p) model.
- VECMLongrun(Matrix, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
-
Construct a zero-intercept (mu) long-run VECM(p) model.
- VECMLongrun(VARXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
-
Construct a long-run VECM(p) from a VARX(p).
- VECMLongrun(VECMLongrun) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
-
Copy constructor.
- VECMTransitory - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a transitory Vector Error Correction Model (VECM).
- VECMTransitory(Vector, Matrix, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
Construct a transitory VECM(p) model.
- VECMTransitory(Matrix, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
Construct a zero-intercept (mu) transitory VECM(p) model.
- VECMTransitory(VARXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
- VECMTransitory(VECMTransitory) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
Copy constructor.
- Vector - Interface in com.numericalmethod.suanshu.vector.doubles
-
An Euclidean vector is a geometric object that has both a magnitude/length and a direction.
- VectorAccessException(int, int) - Constructor for exception com.numericalmethod.suanshu.vector.doubles.IsVector.VectorAccessException
-
Construct an instance of VectorAccessException for out-of-range access.
- VectorAccessException(String) - Constructor for exception com.numericalmethod.suanshu.vector.doubles.IsVector.VectorAccessException
-
Construct an instance of VectorAccessException.
- VectorMathOperation - Class in com.numericalmethod.suanshu.vector.doubles.dense
-
This is a generic implementation of the math operations of
double based
Vector.
- VectorMathOperation() - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.VectorMathOperation
-
- VectorMonitor - Class in com.numericalmethod.suanshu.algorithm.iterative.monitor
-
- VectorMonitor() - Constructor for class com.numericalmethod.suanshu.algorithm.iterative.monitor.VectorMonitor
-
- VectorSpace<V,F extends Field<F>> - Interface in com.numericalmethod.suanshu.mathstructure
-
A vector space is a set V together with two binary operations that combine two entities to yield a third,
called vector addition and scalar multiplication.
- VectorSpace - Class in com.numericalmethod.suanshu.vector.doubles.operation
-
A vector space is a set of vectors that are closed under some operations.
- VectorSpace(Matrix, double) - Constructor for class com.numericalmethod.suanshu.vector.doubles.operation.VectorSpace
-
Construct a vector space from a matrix (a set of column vectors).
- VectorSpace(Matrix) - Constructor for class com.numericalmethod.suanshu.vector.doubles.operation.VectorSpace
-
Construct a vector space from a matrix (a set of column vectors).
- VectorSpace(List<Vector>, double) - Constructor for class com.numericalmethod.suanshu.vector.doubles.operation.VectorSpace
-
Construct a vector space from a list of vectors.
- VectorSpace(List<Vector>) - Constructor for class com.numericalmethod.suanshu.vector.doubles.operation.VectorSpace
-
Construct a vector space from a list of vectors.
- VectorSpace(double, Vector...) - Constructor for class com.numericalmethod.suanshu.vector.doubles.operation.VectorSpace
-
Construct a vector space from an array of vectors.
- VectorSpace(Vector...) - Constructor for class com.numericalmethod.suanshu.vector.doubles.operation.VectorSpace
-
Construct a vector space from an array of vectors.