M-Index (SuanShu 1.3.1 API Documentation)

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SuanShu, a Java numerical and statistical library

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M

m - Variable in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.LEcuyer: the modulus
m - Variable in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.Lehmer: the modulus
m - Variable in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.MRG: the modulus
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.Dlm: Get the the mean of x_0.
MA(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel: Get the i-th MA coefficient; AR(0) = 1.
MA() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel: Get the MA coefficients, excluding the initial 1.
MA - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel: the MA coefficients
MA(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel: Get the i-th MA coefficient; MA(0) = 1.
MA() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.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: Deprecated. Not supported yet.
MaModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma: This class represents a multivariate MA model.
MaModel(Vector, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.MaModel: Construct a multivariate MA model.
MaModel(Vector, Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.MaModel: Construct a multivariate MA model with unit variance.
MaModel(Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.MaModel: Construct a zero-mean multivariate MA model.
MaModel(Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.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.arima.arma.MaModel: Copy constructor.
MaModel(MaModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.MaModel: Cast a univariate MA model to a multivariate model.
MaModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma: This class represents an MA model.
MaModel(double, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.MaModel: Construct a univariate MA model.
MaModel(double, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.MaModel: Construct a univariate MA model with unit variance.
MaModel(double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.MaModel: Construct a zero-mean univariate MA model.
MaModel(double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.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.arima.arma.MaModel: Copy constructor.
MathTable - Class in com.numericalmethod.suanshu.datastructure: This class represents a mathematical table of numbers.
MathTable(String...) - Constructor for class com.numericalmethod.suanshu.datastructure.MathTable: Construct an empty table with headers.
MathTable(int) - Constructor for class com.numericalmethod.suanshu.datastructure.MathTable: Construct an empty table with a fixed number of columns.
MathTable.Row - Class in com.numericalmethod.suanshu.datastructure: This class represents a row in a mathematical table.
Matrix - Interface in com.numericalmethod.suanshu.matrix.doubles: This interface defines basic operations for a matrix, of which all entries are double.
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.
MatrixAccessException - Exception in com.numericalmethod.suanshu.matrix: This defines the RuntimeException 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 a MatrixAccessException.
MatrixAccessException(String) - Constructor for exception com.numericalmethod.suanshu.matrix.MatrixAccessException: Construct a MatrixAccessException with a message.
MatrixAccessor - Interface in com.numericalmethod.suanshu.matrix.doubles: This interface defines methods for accessing elements in a matrix.
MatrixAccessor<F extends Field<F>> - Interface in com.numericalmethod.suanshu.matrix.generic: This interface defines methods for accessing elements in a matrix.
MatrixData - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype: This interface defines for (an abstract) storage of matrix data.
MatrixDimension - Interface in com.numericalmethod.suanshu.matrix: A matrix is a rectangular arrangement of numbers.
MatrixList - Class in com.numericalmethod.suanshu.datastructure.list: This data structure represents a list of Matrixs.
MatrixList() - Constructor for class com.numericalmethod.suanshu.datastructure.list.MatrixList: Construct an empty list of Matrix.
MatrixList(MatrixList) - Constructor for class com.numericalmethod.suanshu.datastructure.list.MatrixList: Copy constructor to copy from another list of Matrixs.
MatrixMathImpl<T extends Matrix> - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype: This is one implementation of Matrix.
MatrixMathImpl(int, int, MatrixMathOperation) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixMathImpl: Construct a MatrixImpl, allowing overriding the default implementation.
MatrixMathImpl(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixMathImpl: Construct a MatrixImpl using SimpleMatrixMathOperation.
MatrixMathOperation - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation: This interface defines some standard operations for matrices.
MatrixMismatchException - Exception in com.numericalmethod.suanshu.matrix: This defines the RuntimeException thrown when operation acts on matrices that have incompatible dimensions.
MatrixMismatchException() - Constructor for exception com.numericalmethod.suanshu.matrix.MatrixMismatchException: Construct a MatrixMismatchException.
MatrixMismatchException(String) - Constructor for exception com.numericalmethod.suanshu.matrix.MatrixMismatchException: Construct a 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 defines the RuntimeException thrown when operation acts on a singular matrix.
MatrixSingularityException() - Constructor for exception com.numericalmethod.suanshu.matrix.MatrixSingularityException: Construct a MatrixSingularityException.
MatrixSingularityException(String) - Constructor for exception com.numericalmethod.suanshu.matrix.MatrixSingularityException: Construct a MatrixSingularityException with a message.
MatrixStorageImpl<T extends Matrix> - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype: This is one implementation of Matrix.
MatrixStorageImpl(int, int, MatrixData) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl: Construct a MatrixStorageImpl, allowing overriding the default implementation.
MatrixUtils - Class in com.numericalmethod.suanshu.matrix.doubles.operation: This class collects a set of utility functions to operate on matrices.
MatrixUtils() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixUtils
MatthewsDavies - Class in com.numericalmethod.suanshu.optimization.unconstrained.hessian: Matthews and Davies propose the following way to coerce a non-positive definite Hessian matrix to become positive definite.
MatthewsDavies(Matrix) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.hessian.MatthewsDavies: Construct a MatthewsDavies instance.
max(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.Measure: Compute the maximal entry in a matrix.
max(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils: 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.
maxIndex(boolean, int, int, double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils: Get the index to the maximum of the values.
maxIndex(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils: Get the index to the maximum of the values.
maxIteration(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeSolver.Problem: Override the maximum count of iterations.
maxIteration() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeSolver.Problem: Get the specified maximum number of iterations.
maxIterations - Variable in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.EulerMaclaurin: the maximum number of iterations for this iterative procedure For those integrals that do not converge, we need to put a bound on the number of iterations.
maxIterations() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.EulerMaclaurin
maxIterations() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.IterativeIntegrator: Get the maximum number of iterations for this iterative procedure.
maxIterations - Variable in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Riemann: the maximum number of iterations
maxIterations - Variable in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Simpson: the maximum number of iterations
maxIterations() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Simpson
maxIterations - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.linesearch.Fletcher: the maximum number of iterations
maxIterations - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS: the maximum number of iterations
maxPQ() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel: Get the maximum of AR length or MA length.
maxPQ() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.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: Deprecated. the McCormick algorithm does not seem to work; need further investigation; don't use it. TODO. Use BFGS instead.
McCormick() - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.McCormick: Deprecated. the McCormick algorithm does not seem to work; need further investigation; don't use it. TODO. Use BFGS instead.
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.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.NormalDistribution
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 interface com.numericalmethod.suanshu.stats.distribution.univariate.UnivariateDistribution: Get the mean of this distribution.
mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
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: Deprecated. Not supported yet.
mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution: Deprecated. Not supported yet.
mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution: Deprecated. Not supported yet.
mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution: Deprecated. Not supported yet.
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
Measure - Class in com.numericalmethod.suanshu.matrix.doubles.operation: A measure, μ, of a matrix, A, is a map from the DenseMatrix space to the real line.
Measure() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.Measure
median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
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: Deprecated. Not supported yet.
median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
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 interface com.numericalmethod.suanshu.stats.distribution.univariate.UnivariateDistribution: Get the median of this distribution.
median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution: Deprecated. Not supported yet.
median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution: Deprecated. Not supported yet.
median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution: Deprecated. Not supported yet.
median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution: Deprecated. Not supported yet.
median() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution: Deprecated. Not supported yet.
median() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution: Deprecated. Not supported yet.
MersenneTwister - Class in com.numericalmethod.suanshu.stats.random.pseudorandom: Mersenne Twister is one of the best pseudo random number generators available.
MersenneTwister() - Constructor for class com.numericalmethod.suanshu.stats.random.pseudorandom.MersenneTwister: Construct a pseudo uniform random number generator using the Mersenne Twister algorithm.
MersenneTwister(long...) - Constructor for class com.numericalmethod.suanshu.stats.random.pseudorandom.MersenneTwister: Construct a pseudo uniform random number generator implementing Mersenne Twister algorithm.
method - Variable in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel: the method to perform the LU decomposition
method - Variable in class com.numericalmethod.suanshu.stats.random.distribution.Uniform: the sampling algorithm
Midpoint - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann: The Midpoint rule is an OPEN type Newton–Cotes formula.
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.Measure: Compute the minimal entry in a matrix.
min(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils: Get the minimum of the values.
min() - Method in interface com.numericalmethod.suanshu.optimization.constrained.linearprogramming.LpSolution: Get the minimum.
min() - Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.LpBoundedSolution
min() - Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.LpUnboundedSolution
min() - Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.Tableau: Get the minimum inferred by this table.
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.
MinimalResidualSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary: The Minimal Residual method (MINRES) is useful for solving symmetric n-by-n linear systems (possibly indefinite or singular).
MinimalResidualSolver() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
minimize(Vector) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.QuasiNewton.QuasiNewtonImpl
minimize(Vector) - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.LineSearch: Perform a line search to minimize a function, starting from x_k.
minimizer() - Method in interface com.numericalmethod.suanshu.optimization.constrained.linearprogramming.LpSolution: Get a minimizing vector.
minimizer() - Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.LpBoundedSolution
minimizer() - Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.LpUnboundedSolution: 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.linearprogramming.simplex.Tableau: Get the minimizer inferred by this table.
Minimizer - Interface in com.numericalmethod.suanshu.optimization: This interface represents an optimization algorithm that minimizes a real valued objective function, one or multi dimension.
Minimizer - Class in com.numericalmethod.suanshu.optimization.univariate: This is a wrapper class to "cast" a BracketSearch into a Minimizer.
Minimizer(BracketSearch) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.Minimizer: Construct a Minimizer from a BracketSearch to minimize a univariate function.
Minimizer() - Constructor for class com.numericalmethod.suanshu.optimization.univariate.Minimizer: Construct a Minimizer from a Brent to minimize a univariate function.
minimizers() - Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.LpBoundedSolution: Get all the optimal minimizers.
minimum() - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyMethod
minimum() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.BruteForce
minimum() - Method in interface com.numericalmethod.suanshu.optimization.Minimizer: Get the minimum found.
minimum() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead
minimum() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent
minimum() - Method in class com.numericalmethod.suanshu.optimization.univariate.BracketSearch: Get the minimum found.
minimum() - Method in class com.numericalmethod.suanshu.optimization.univariate.Minimizer
minIndex(boolean, int, int, double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils: Get the index to the minimum of the values.
minIndex(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils: Get the index to the minimum of the values.
MinMaxProblem - Class in com.numericalmethod.suanshu.optimization.minmax: This class defines a minmax problem.
MinMaxProblem(MinMaxProblem.Error, MinMaxProblem.Gradient) - Constructor for class com.numericalmethod.suanshu.optimization.minmax.MinMaxProblem: Construct a minmax problem, from an objective (error) function and the gradient of its absolute value.
MinMaxProblem(MinMaxProblem.Error) - Constructor for class com.numericalmethod.suanshu.optimization.minmax.MinMaxProblem: Construct a minmax problem, from an objective (error) function.
MinMaxProblem.Error - Interface in com.numericalmethod.suanshu.optimization.minmax: e(x, ω): the minmax objective function family, parameterized by omega
MinMaxProblem.Gradient - Interface in com.numericalmethod.suanshu.optimization.minmax: the gradient of the absolute minmax objective function, for a given ω
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 - 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(DenseMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
minus(DiagonalMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix: this - that Subtract a DiagonalMatrix.
minus(TridiagonalMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
minus(LowerTriangularMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix: this - that Subtract a LowerTriangularMatrix.
minus(SymmetricMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
minus(UpperTriangularMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
minus(double[], double[]) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.CompositeDoubleArrayOperation
minus(double[], double[]) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.DoubleArrayOperation
minus(Matrix, Matrix) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation: A1 - A2
minus(double[], double[]) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.ParallelDoubleArrayOperation
minus(double[], double[]) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleDoubleArrayOperation
minus(Matrix, Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation: A1 - A2.
minus(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixMathImpl
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.ImmutableKroneckerProduct
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(Real) - Method in class com.numericalmethod.suanshu.number.Real
minus(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
minus(DenseVector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector: this - that
minus(double) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
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: v - s Subtract a scalar from all entries in the vector v.
MixedRule - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution: Mixed Rule is good for functions that fall off rapidly at infinity, e.g., e^x or e^x2.
MixedRule(UnivariateRealFunction, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.MixedRule: Construct an instance of 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() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.Garch: Get the fitted GARCH model.
modpow(long, long, long) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps: b^e mod m This implements a variant of the algorithm on page 244 of Bruce Schneier's Applied Cryptography, 2e, ISBN 0-471-11709-9.
modulus() - Method in class com.numericalmethod.suanshu.number.complex.Complex: Get the modulus.
modulus() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.CombinedLinearCongruentialGenerator
modulus() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.LEcuyer
modulus() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.Lehmer
modulus() - Method in interface com.numericalmethod.suanshu.stats.random.pseudorandom.linear.LinearCongruentialGenerator: Get the modulus of this linear congruential generator.
modulus() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.MRG
moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution: Deprecated. Not supported yet.
moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution: Deprecated. Not supported yet.
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.NormalDistribution
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 interface com.numericalmethod.suanshu.stats.distribution.univariate.UnivariateDistribution: The moment generating function, which is the expected value of e^tX This may not always exist.
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: Deprecated. Not supported yet.
moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution: Deprecated. Not supported yet.
moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution: Deprecated. Not supported yet.
moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution: Deprecated. Not supported yet.
moment(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution: Deprecated. Not supported yet.
moment(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution: Deprecated. Not supported yet.
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 instance to compute all moments up to the order-th moment.
Moments(int, double...) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Moments: Construct an instance to compute all moments up to the order-th moment, initialized with a sample.
Moments(Moments) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Moments: Copy constructor.
Monoid<G> - Interface in com.numericalmethod.suanshu.mathstructure: This interface represents a monoid.
moveColumn2End(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix: This swaps a column of a permutation matrix with the last column.
moveRow2End(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix: This swaps a row of a permutation matrix with the last row.
MovingAverage - Class in com.numericalmethod.suanshu.signalprocessing.filter: This class applies a linear filtering to a univariate time series using Moving Average estimation.
MovingAverage(double[], MovingAverage.Side) - Constructor for class com.numericalmethod.suanshu.signalprocessing.filter.MovingAverage: Create a MovingAverage filter.
MovingAverage(double[]) - Constructor for class com.numericalmethod.suanshu.signalprocessing.filter.MovingAverage: Create a MovingAverage filter using a symmetric window.
MovingAverage.Side - Enum in com.numericalmethod.suanshu.signalprocessing.filter: the types of moving average filtering available
MovingAverageByExtension - Class in com.numericalmethod.suanshu.signalprocessing.filter: This implements a Moving Average filter with these properties both past and future observations are used in smoothing the head is prepended with the first element in the inputs the tail is appended with the last element in the inputs Note that both past as well as future values are used in generating the outputs.
MovingAverageByExtension(double[]) - Constructor for class com.numericalmethod.suanshu.signalprocessing.filter.MovingAverageByExtension: Create a MovingAverageByExtension filter.
MRG - Class in com.numericalmethod.suanshu.stats.random.pseudorandom.linear: A Multiple Recursive Generator is a linear congruential generator which takes this form: x_i = (a₁ * x_i-1 + a₂ * x_i-2 + ... + a_k * x_i-k) mod m u_i = x_i / m
MRG(long, long...) - Constructor for class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.MRG: Construct a Multiple Recursive Generator.
mu - Variable in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution: the mean
mu - Variable in class com.numericalmethod.suanshu.stats.random.distribution.Gaussian: the mean
mu() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.Fitting: Get μ as in E(Y) = μ = g^-1(Xβ)
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 μ(t, Xt, Zt, ...)
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 μ(t, Xt, Zt, ...)
mu - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel: the intercept (constant) vector
mu() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel: Get the intercept vector.
mu() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.Vecm: Get the intercept vector.
mu - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel: the intercept (constant) term
mu() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel: Get the intercept term.
Multinomial - Class in com.numericalmethod.suanshu.stats.random.multivariate: This class generate random vectors from a multinomial distribution.
Multinomial(int, double[], RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.multivariate.Multinomial: Construct a Multinomial random vector generator.
Multinomial(int, double[]) - Constructor for class com.numericalmethod.suanshu.stats.random.multivariate.Multinomial: 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
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 be assigned to the constraints.
MultiplierPenalty(Constraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.MultiplierPenalty: Construct a MultiplierPenalty penalty function from a set of constraints.
MultiplierPenalty(Constraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.MultiplierPenalty: Construct a MultiplierPenalty penalty function from a set of constraints.
MultiplierPenalty(Constraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.MultiplierPenalty: Construct a MultiplierPenalty penalty function from a set 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(DenseMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix: Compute A %*% v, where v is a vector.
multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
multiply(DiagonalMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix: this %*% that Multiply by a DiagonalMatrix.
multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
multiply(LowerTriangularMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix: this %*% that Multiply by a LowerTriangularMatrix.
multiply(UpperTriangularMatrix) - 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 affects only the i-th and the j-th rows.
multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
multiply(Matrix, Matrix) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation: A1 %*% A2
multiply(Matrix, Vector) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation: A %*% v
multiply(Matrix, Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation: A1 %*% A2.
multiply(Matrix, Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
multiply(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixMathImpl
multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixMathImpl
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.ImmutableKroneckerProduct
multiply(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ImmutableKroneckerProduct
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(DenseVector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector: this * v Multiply this by v, entry-by-entry.
multiply(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
multiply(Vector) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector: this * that Multiply this by that, entry-by-entry.
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: This represents a multivariate time series indexed by real numbers, e.g., real time.
MultiVariateRealization.Entry - Class in com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime: the TimeSeries.Entry for a real number -indexed multivariate time series
MultiVariateRealization.Entry(double, Vector) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.MultiVariateRealization.Entry
MultiVariateRealization.Iterator - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate: This Iterator support lazy evaluation/generation of a realization from a stochastic process.
MultiVariateRealization.Iterator(MultiVariateRealization, int, long) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization.Iterator: Construct a realization of a multivariate stochastic process.
MultiVariateRealization.Iterator - Class in com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime: the Iterator to read a real number -indexed multivariate time series
MultiVariateRealization.Iterator(MultiVariateRealization) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.MultiVariateRealization.Iterator
MultiVariateTimeSeries<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 represents a multivariate time series indexed by integers.
MultiVariateTimeSeries.Entry<T> - Class in com.numericalmethod.suanshu.stats.timeseries.multivariate: the TimeSeries.Entry for a multivariate time series
MultiVariateTimeSeries.Entry(T, Vector) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.MultiVariateTimeSeries.Entry
MultiVariateTimeSeries.Entry - Class in com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime: the TimeSeries.Entry for an integer-indexed multivariate time series
MultiVariateTimeSeries.Entry(int, Vector) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.MultiVariateTimeSeries.Entry
MultiVariateTimeSeries.Iterator<E extends MultiVariateTimeSeries.Entry<?>> - Class in com.numericalmethod.suanshu.stats.timeseries.multivariate: the Iterator to read a multivariate time series
MultiVariateTimeSeries.Iterator(MultiVariateTimeSeries<?>) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.MultiVariateTimeSeries.Iterator
MultiVariateTimeSeries.Iterator - Class in com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime: the Iterator used to read an integer-indexed multivariate time series
MultiVariateTimeSeries.Iterator(MultiVariateTimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.MultiVariateTimeSeries.Iterator
Mutex - Class in com.numericalmethod.suanshu.parallel
Mutex() - Constructor for class com.numericalmethod.suanshu.parallel.Mutex

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