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SuanShu, a Java numerical and statistical library
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M

m() - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D
Get the number of interior x-axis grid points in the solution.
m() - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
Get the number of interior time-axis grid points in the solution.
m() - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
Get the number of interior time-axis grid points in the solution.
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() - Method in class com.numericalmethod.suanshu.stats.regression.lasso.lars.LeastAngleRegressionProblem
Get the number of predictors (number of columns of X).
m0() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLM
Get the the mean of x0.
m0() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
Get the the mean of x0.
MA() - Method in class com.numericalmethod.suanshu.stats.evt.timeseries.MARMAModel
Get the MA coefficients.
MA(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
Get the i-th MA coefficient; MA(0) = 1.
MA(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
Get the i-th MA coefficient; MA(0) = 1.
MacAddressVerifier - Class in com.numericalmethod.suanshu.license.verifier
 
MacAddressVerifier() - Constructor for class com.numericalmethod.suanshu.license.verifier.MacAddressVerifier
 
MacAddressVerifier(Collection<String>) - Constructor for class com.numericalmethod.suanshu.license.verifier.MacAddressVerifier
 
MACH_EPS - Static variable in class com.numericalmethod.suanshu.Constants
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.Constants
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 stationary random components using the Moving Average Estimation method with symmetric window.
MADecomposition(double[], double[], int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
Decompose a time series into the trend, seasonal and stationary random components using the Moving Average Estimation method.
MADecomposition(double[], int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
Decompose a time series into the trend, seasonal and stationary random components using the default filter.
MADecomposition(double[], 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.
magicSquare(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
Deprecated. Not supported yet.
MAGNETIC_FLUX_QUANTUM_PHI0 - Static variable in class com.numericalmethod.suanshu.PhysicalConstants
The magnetic flux quantum \(\Phi_0\) in webers (Wb).
MAGNETIC_MU0 - Static variable in class com.numericalmethod.suanshu.PhysicalConstants
The magnetic constant \(\mu_0\) in henries per meter (H m-1) or newtons per ampere squared (N A-2).
MAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
This class represents a univariate 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 univariate MA model with zero-mean.
MAModel(double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.MAModel
Construct a univariate MA model with unit variance and zero-mean.
MAModel(MAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.MAModel
Copy constructor.
marginalInverseTransform(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
Inverse of marginal transform.
marginalTransform(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
Transform to exponential margins under the GEV model.
MARMAModel - Class in com.numericalmethod.suanshu.stats.evt.timeseries
Simulation of max autoregressive moving average processes, i.e., MARMA(p, q) processes.
MARMAModel(UnivariateEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARMAModel
Create an instance with a given GEV distribution for generating innovations.
MARMAModel(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARMAModel
Create an instance with the AR and MA coefficients, using FrechetDistribution as the GEV distribution.
MARMAModel(double[], double[], UnivariateEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARMAModel
Create an instance with the AR and MA coefficients, and a GEV distribution for generating innovations.
MARMASim - Class in com.numericalmethod.suanshu.stats.evt.timeseries
Generate random numbers based on a given MARMA model.
MARMASim(MARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARMASim
Create an instance with the given MARMAModel.
MARMASim(MARMAModel, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARMASim
Create an instance with the given MARMAModel, but override the innovation generation by the the given generator.
MARMASim(MARMAModel, RandomNumberGenerator, double[]) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARMASim
Create an instance with the given MARMAModel and initial values, but override the innovation generation by the the given generator.
MARModel - Class in com.numericalmethod.suanshu.stats.evt.timeseries
This is equivalent to MARMA(p, 0).
MARModel(double[]) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARModel
Create an instance with the AR coefficients, using FrechetDistribution as the GEV distribution.
MARModel(double[], UnivariateEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARModel
Create an instance with the AR coefficients, and a GEV distribution for generating innovations.
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.
MatrixAccess - Interface in com.numericalmethod.suanshu.matrix.doubles
This interface defines the methods for accessing entries in a matrix.
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.
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.
max_abs_cor() - Method in class com.numericalmethod.suanshu.stats.regression.lasso.lars.LARSFitting.Estimators
Get the estimated sequence of maximal absolute correlations.
maxDomain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
Get the biggest abscissae.
MaximaDistribution - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate
The distribution of \(M\), where \(M=\max(x_1,x_2,...,x_n)\) and \(x_i\)'s are iid samples drawn from of a random variable \(X\) with cdf \(F(x)\).
MaximaDistribution(ProbabilityDistribution, int) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
Create an instance with the probability distribution of \(X\), and the number of iid samples to be drawn.
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.
MaximumLikelihoodFitting - Interface in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting
This interface defines model fitting by maximum likelihood algorithm.
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.SteepestDescentMinimizer
the maximum number of iterations
maxIterations - Variable in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer
the maximum number of iterations
maxIterations - Variable in class com.numericalmethod.suanshu.optimization.univariate.GridSearchMinimizer
the maximum number of iterations
maxOrder() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.DividedDifferences
Get the maximum order which is limited by the number of points given for the computation.
maxPQ() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
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 the ARCH length or GARCH length.
McCormickMinimizer - Class in com.numericalmethod.suanshu.optimization.unconstrained.quasinewton
Deprecated. the McCormick algorithm does not seem to work well; need further investigation; don't use it. TODO. Use BFGSMinimizer instead.
McCormickMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.McCormickMinimizer
Deprecated. the McCormick algorithm does not seem to work well; need further investigation; don't use it. TODO. Use BFGSMinimizer instead.
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 class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
 
mean() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
\[ \mu + \frac{\sigma}{1-\xi} \] for \(\xi < 1\).
mean() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
 
mean() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
 
mean() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution
 
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
 
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. 
mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated. 
mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated. 
mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
Deprecated. 
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() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
Get the mean of the first sample.
mean2() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
Get the mean of the second sample.
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
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.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.evt.evd.univariate.GeneralizedEVD
 
median() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
\[ \mu + \frac{\sigma( 2^{\xi} -1)}{\xi} \]
median() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
 
median() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
 
median() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution
 
median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated. 
median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated. 
median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated. 
median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
Deprecated. 
median() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
Deprecated. 
median() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
Deprecated. 
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.newtoncotes
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.newtoncotes.Midpoint
Construct an integrator that implements the Midpoint rule.
MilsteinSDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete
Milstein scheme is a first-order approximation to a continuous-time SDE.
MilsteinSDE(SDE) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.MilsteinSDE
Discretize a continuous-time 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.
MinimaDistribution - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate
The distribution of \(M\), where \(M=\min(x_1,x_2,...,x_n)\) and \(x_i\)'s are iid samples drawn from of a random variable \(X\) with cdf \(F(x)\).
MinimaDistribution(ProbabilityDistribution, int) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
 
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.NelderMeadMinimizer.Solution
 
minimizer() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
 
minimizer() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer.Solution
 
minimizer() - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearchMinimizer.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.NelderMeadMinimizer.Solution
 
minimum() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
 
minimum() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer.Solution
 
minimum() - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearchMinimizer.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.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(SparseVector) - 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(GenericFieldMatrix<F>) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericFieldMatrix
 
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, entry-by-entry.
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(DenseVector) - 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.
MixtureBaumWelch - 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.
MixtureBaumWelch(double[], MixtureHiddenMarkovModel, double, int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureBaumWelch
Construct a mixture HMM model by training an initial model using the Baum-Welch algorithm.
MixtureBaumWelch.TrainedModel - Class in com.numericalmethod.suanshu.stats.hmm.mixture
the result of the Baum-Welch algorithm
MixtureBetaDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
The HMM states use the Beta distribution to model the observations.
MixtureBetaDistribution(MixtureBetaDistribution.Lambda[], boolean, boolean, double, int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureBetaDistribution
Construct a Beta distribution for each state in the HMM model.
MixtureBetaDistribution(MixtureBetaDistribution.Lambda[], int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureBetaDistribution
Construct a Beta distribution for each state in the HMM model.
MixtureBetaDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
the Beta distribution parameters
MixtureBetaDistribution.Lambda(double, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureBetaDistribution.Lambda
Store the Beta distribution parameters.
MixtureBinomialDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
The HMM states use the Binomial distribution to model the observations.
MixtureBinomialDistribution(MixtureBinomialDistribution.Lambda[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureBinomialDistribution
Construct a Binomial distribution for each state in the HMM model.
MixtureBinomialDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
the Binomial distribution parameters
MixtureBinomialDistribution.Lambda(int, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureBinomialDistribution.Lambda
Store the Binomial distribution parameters.
MixtureDistribution - 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.
MixtureExponentialDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
The HMM states use the Exponential distribution to model the observations.
MixtureExponentialDistribution(Double[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureExponentialDistribution
Construct an Exponential distribution for each state in the HMM model.
MixtureForwardBackward - 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.
MixtureForwardBackward(MixtureHiddenMarkovModel) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureForwardBackward
Construct an instance of HmmForwardBackward to compute the forward and backward probabilities and the log-likelihood.
MixtureGammaDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
The HMM states use the Gamma distribution to model the observations.
MixtureGammaDistribution(MixtureGammaDistribution.Lambda[], boolean, boolean, double, int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureGammaDistribution
Construct a Gamma distribution for each state in the HMM model.
MixtureGammaDistribution(MixtureGammaDistribution.Lambda[], int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureGammaDistribution
Construct a Gamma distribution for each state in the HMM model.
MixtureGammaDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
the Gamma distribution parameters
MixtureGammaDistribution.Lambda(double, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureGammaDistribution.Lambda
Store the Gamma distribution parameters.
MixtureHiddenMarkovModel - Class in com.numericalmethod.suanshu.stats.hmm.mixture
This is the mixture hidden Markov model (HMM).
MixtureHiddenMarkovModel(Vector, Matrix, MixtureDistribution) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureHiddenMarkovModel
Construct a mixture hidden Markov model.
MixtureHiddenMarkovModel(MixtureHiddenMarkovModel) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureHiddenMarkovModel
Copy constructor.
MixtureLogNormalDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
The HMM states use the Log-Normal distribution to model the observations.
MixtureLogNormalDistribution(MixtureLogNormalDistribution.Lambda[], boolean, boolean) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureLogNormalDistribution
Construct a Log-Normal distribution for each state in the HMM model.
MixtureLogNormalDistribution(MixtureLogNormalDistribution.Lambda[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureLogNormalDistribution
Construct a Log-Normal distribution for each state in the HMM model.
MixtureLogNormalDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
the Log-Normal distribution parameters
MixtureLogNormalDistribution.Lambda(double, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureLogNormalDistribution.Lambda
Construct a Log-Normal distribution.
MixtureNormalDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
The HMM states use the Normal distribution to model the observations.
MixtureNormalDistribution(MixtureNormalDistribution.Lambda[], boolean, boolean) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureNormalDistribution
Construct a Normal distribution for each state in the HMM model.
MixtureNormalDistribution(MixtureNormalDistribution.Lambda[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureNormalDistribution
Construct a Normal distribution for each state in the HMM model.
MixtureNormalDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
the Normal distribution parameters
MixtureNormalDistribution.Lambda(double, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureNormalDistribution.Lambda
Construct a Normal distribution.
MixturePoissonDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
The HMM states use the Poisson distribution to model the observations.
MixturePoissonDistribution(Double[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixturePoissonDistribution
Construct a Poisson distribution for each state in the HMM model.
MixtureViterbi - 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.
MixtureViterbi(MixtureHiddenMarkovModel) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureViterbi
Construct an Viterbi algorithm for an HMM.
ML() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticRegression
Get the maximum log-likelihood.
MMAModel - Class in com.numericalmethod.suanshu.stats.evt.timeseries
This is equivalent to MARMA(0, q).
MMAModel(double[]) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MMAModel
Create an instance with the MA coefficients, using FrechetDistribution as the GEV distribution.
MMAModel(double[], UnivariateEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MMAModel
Create an instance with the MA coefficients, and a GEV distribution for generating innovations.
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.MixtureBaumWelch.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
 
MOLAR_GAS_R - Static variable in class com.numericalmethod.suanshu.PhysicalConstants
The molar gas constant \(R\) in joule per kelvin mole (J mol-1 K-1).
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.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
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.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.evt.evd.univariate.GeneralizedEVD
 
moment(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
 
moment(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
 
moment(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
 
moment(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution
 
moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated. 
moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated. 
moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated. 
moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
Deprecated. 
moment(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
Deprecated. 
moment(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
Deprecated. 
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 available types of moving average filtering
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.MixtureNormalDistribution.Lambda
the mean
mu() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.GLMFitting
Get μ as in E(Y) = μ = g-1(Xβ)
mu() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
 
mu() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
 
mu() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateSDE
Get the drift: \(\mu(t,X_t,Z_t,...)\).
mu() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
Get the overall mean.
mu() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
Get the intercept (constant) vector.
mu() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
Get the intercept vector.
mu() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
Get the intercept (constant) term.
MultiCubicSpline - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
Implementation of natural cubic spline interpolation for an arbitrary number of dimensions.
MultiCubicSpline() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultiCubicSpline
Create an instance with CubicSpline as the implementation of the univariate cubic spline interpolation algorithm.
MultiDimensionalArray<T> - Class in com.numericalmethod.suanshu.datastructure
A generic multi-dimensional array, with an arbitrary number of dimensions.
MultiDimensionalArray(int...) - Constructor for class com.numericalmethod.suanshu.datastructure.MultiDimensionalArray
Create an instance with the specified size along each dimension.
MultiDimensionalArray(MultiDimensionalCollection<T>) - Constructor for class com.numericalmethod.suanshu.datastructure.MultiDimensionalArray
A copy constructor that constructs a shallow copy of the given collection of instances.
MultiDimensionalCollection<T> - Interface in com.numericalmethod.suanshu.datastructure
A generic collection with an arbitrary number of dimensions.
MultiDimensionalGrid - Class in com.numericalmethod.suanshu.datastructure
An arbitrary dimensional grid.
MultiDimensionalGrid(Double[]...) - Constructor for class com.numericalmethod.suanshu.datastructure.MultiDimensionalGrid
Construct a multi-dimensional grid of points.
MultiDimensionalGrid(double[]...) - Constructor for class com.numericalmethod.suanshu.datastructure.MultiDimensionalGrid
Construct a multi-dimensional grid of points.
MultiDimensionalGrid(MultiDimensionalGrid.Discretization...) - Constructor for class com.numericalmethod.suanshu.datastructure.MultiDimensionalGrid
Construct a multi-dimensional grid of points.
MultiDimensionalGrid.Discretization - Class in com.numericalmethod.suanshu.datastructure
Specify the discretization of an interval.
MultiDimensionalGrid.Discretization(double, double, double) - Constructor for class com.numericalmethod.suanshu.datastructure.MultiDimensionalGrid.Discretization
Construct a discretization of an interval.
MultiLinearInterpolation - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
Implementation of linear interpolation for an arbitrary number of dimensions.
MultiLinearInterpolation() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultiLinearInterpolation
Create an instance with LinearInterpolation as the implementation of the univariate linear interpolation algorithm.
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[]) - 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.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(SparseVector) - 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(GenericFieldMatrix<F>) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericFieldMatrix
 
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(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
 
multiply(double[], double[]) - Method in interface com.numericalmethod.suanshu.number.doublearray.DoubleArrayOperation
Multiply one double array to another, entry-by-entry.
multiply(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.ParallelDoubleArrayOperation
 
multiply(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.SimpleDoubleArrayOperation
 
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
 
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.
MultivariateArrayGrid - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
Implementation of MultivariateGrid, backed by the given MultiDimensionalCollection instance.
MultivariateArrayGrid(MultiDimensionalCollection<Double>, double[]...) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
Create a new instance where the dependent variable is specified by a MultiDimensionalCollection and the independent variables form the specified grid.
MultivariateAutoCorrelationFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate
This is the auto-correlation function of a multi-dimensional time series {Xt}.
MultivariateAutoCorrelationFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.MultivariateAutoCorrelationFunction
 
MultivariateAutoCovarianceFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate
This is the auto-covariance function of a multi-dimensional time series {Xt}, \[ K(i, j) = E((X_i - \mu_i) \times (X_j - \mu_j)') \] For a stationary process, the auto-covariance depends only on the lag, |i - j|.
MultivariateAutoCovarianceFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.MultivariateAutoCovarianceFunction
 
MultivariateBrownianRRG - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random
This is the Random Walk construction of a multivariate Brownian motion.
MultivariateBrownianRRG(int, TimeGrid, Vector) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateBrownianRRG
Construct a random realization generator to produce multi-dimensional Brownian paths at time points specified.
MultivariateBrownianRRG(int, TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateBrownianRRG
Construct a random realization generator to produce multi-dimensional Brownian paths at time points specified.
MultivariateBrownianRRG(int, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateBrownianRRG
Construct a random realization generator to produce multi-dimensional Brownian paths at evenly spaced time points [0, 1, ...].
MultivariateBrownianSDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete
A multivariate Brownian motion is a stochastic process with the following properties.
MultivariateBrownianSDE(int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
Construct a standard multi-dimensional Brownian motion.
MultivariateBrownianSDE(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
Construct a multi-dimensional Brownian motion.
MultivariateDiscreteSDE - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete
This interface represents the discrete approximation of a multivariate SDE.
MultivariateDLM - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification.
MultivariateDLM(Vector, Matrix, MultivariateObservationEquation, MultivariateStateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLM
Construct a (multivariate) controlled dynamic linear model.
MultivariateDLM(MultivariateDLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLM
Copy constructor.
MultivariateDLMSeries - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
This is a simulator for a multivariate controlled dynamic linear model process.
MultivariateDLMSeries(int, MultivariateDLM, MultivariateIntTimeTimeSeries, NormalRVG) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSeries
Simulate a multivariate controlled dynamic linear model process.
MultivariateDLMSeries(int, MultivariateDLM, MultivariateIntTimeTimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSeries
Simulate a multivariate controlled dynamic linear model process.
MultivariateDLMSeries(int, MultivariateDLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSeries
Simulate a multivariate controlled dynamic linear model process.
MultivariateDLMSeries.Entry - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
This is the TimeSeries.Entry for a multivariate DLM time series.
MultivariateDLMSim - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
This is a simulator for a multivariate controlled dynamic linear model process.
MultivariateDLMSim(MultivariateDLM, NormalRVG) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSim
Simulate a multivariate controlled dynamic linear model process.
MultivariateDLMSim.Innovation - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
a simulated innovation
MultivariateEulerSDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete
The Euler scheme is the first order approximation of an SDE.
MultivariateEulerSDE(MultivariateSDE) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
Discretize a multivariate, continuous-time SDE using the Euler scheme.
MultivariateFiniteDifference - 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.
MultivariateFiniteDifference(RealScalarFunction, int[]) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.MultivariateFiniteDifference
Construct the partial derivative of a multi-variable function.
MultivariateFt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
This represents the concept 'Filtration', the information available at time t.
MultivariateFt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFt
Construct an empty filtration (no information).
MultivariateFt(MultivariateFt) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFt
Copy constructor.
MultivariateFtWt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
This is a filtration implementation that includes the path-dependent information, Wt.
MultivariateFtWt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFtWt
Construct an empty filtration (no information).
MultivariateFtWt(MultivariateFtWt) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFtWt
Copy constructor.
MultivariateGenericTimeTimeSeries<T extends java.lang.Comparable<? super T>> - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate
This is a multivariate time series indexed by some notion of time.
MultivariateGenericTimeTimeSeries(T[], Vector[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
Construct a multivariate time series from timestamps and vectors.
MultivariateGenericTimeTimeSeries(T[], double[][]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
Construct a multivariate time series from timestamps and vectors.
MultivariateGenericTimeTimeSeries(T[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
Construct a multivariate time series from timestamps and vectors.
MultivariateGrid - Interface in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
A multivariate rectilinear (not necessarily uniform) grid of double values.
MultivariateGridInterpolation - Interface in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
Interpolation on a rectilinear multi-dimensional grid.
MultivariateInnovationAlgorithm - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess
The innovation algorithm is an efficient way to obtain a one step least square linear predictor for a multivariate linear time series with known auto-covariance and these properties (not limited to ARMA processes): {xt} can be non-stationary. E(xt) = 0 for all t.
MultivariateInnovationAlgorithm(MultivariateIntTimeTimeSeries, MultivariateAutoCovarianceFunction) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm
Construct an instance of InnovationAlgorithm for a multivariate time series with known auto-covariance structure.
MultivariateIntTimeTimeSeries - Interface in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime
This is a multivariate time series indexed by integers.
MultivariateIntTimeTimeSeries.Entry - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime
This is the TimeSeries.Entry for an integer -indexed multivariate time series.
MultivariateIntTimeTimeSeries.Entry(int, Vector) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateIntTimeTimeSeries.Entry
Construct an instance of Entry.
MultivariateLinearKalmanFilter - 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.
MultivariateLinearKalmanFilter(MultivariateDLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
Construct a Kalman filter from a multivariate controlled dynamic linear model.
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.
MultivariateObservationEquation - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
This is the observation equation in a controlled dynamic linear model.
MultivariateObservationEquation(R1toMatrix, R1toMatrix, NormalRVG) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
Construct an observation equation.
MultivariateObservationEquation(R1toMatrix, R1toMatrix) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
Construct an observation equation.
MultivariateObservationEquation(Matrix, Matrix, NormalRVG) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
Construct a time-invariant an observation equation.
MultivariateObservationEquation(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
Construct a time-invariant an observation equation.
MultivariateObservationEquation(ObservationEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
Construct a multivariate observation equation from a univariate observation equation.
MultivariateObservationEquation(MultivariateObservationEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
Copy constructor.
MultivariatePredictionErrorCovariances - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess
This class implements the part of the innovation algorithm that computes the prediction error covariances, V and prediction coefficients Θ.
MultivariatePredictionErrorCovariances(int, MultivariateAutoCovarianceFunction) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.MultivariatePredictionErrorCovariances
Run the Innovation Algorithm to compute the prediction parameters, V and Θ.
MultivariateRandomProcess - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random
This interface represents a multivariate random process a.k.a.
MultivariateRandomProcess(int, TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomProcess
Construct a multivariate random process.
MultivariateRandomRealizationGenerator - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random
This interface defines a generator to construct random realizations from a multivariate stochastic process.
MultivariateRandomRealizationOfRandomProcess - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random
This class generates random realizations from a multivariate random/stochastic process.
MultivariateRandomRealizationOfRandomProcess(MultivariateRandomProcess, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
Construct a random realization generator from a multivariate random/stochastic process.
MultivariateRandomRealizationOfRandomProcess(MultivariateDiscreteSDE, TimeGrid, Vector) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
Construct a random realization generator from a multivariate discrete SDE.
MultivariateRandomRealizationOfRandomProcess(MultivariateSDE, TimeGrid, Vector) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
Construct a random realization generator from a multivariate SDE.
MultivariateRandomRealizationOfRandomProcess(MultivariateSDE, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
Construct a random realization generator from a multivariate SDE.
MultivariateRandomWalk - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random
This is the Random Walk construction of a multivariate stochastic process per SDE specification.
MultivariateRandomWalk(MultivariateDiscreteSDE, TimeGrid, Vector) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomWalk
Construct a multivariate stochastic process from an SDE.
MultivariateRealization - Interface in com.numericalmethod.suanshu.stats.timeseries.datastructure.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.datastructure.multivariate.realtime
This is the TimeSeries.Entry for a real number -indexed multivariate time series.
MultivariateRealization.Entry(double, Vector) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.MultivariateRealization.Entry
Construct an instance of TimeSeries.Entry.
MultivariateRegularGrid - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
A regular grid is a tessellation of n-dimensional Euclidean space by congruent parallelotopes (e.g.
MultivariateRegularGrid(MultiDimensionalCollection<Double>, MultivariateRegularGrid.EquallySpacedVariable...) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
Create a new instance where the dependent variable is specified by a MultiDimensionalCollection and the independent variables form the specified grid.
MultivariateRegularGrid.EquallySpacedVariable - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
Specify the positioning and spacing along one dimension.
MultivariateRegularGrid.EquallySpacedVariable(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid.EquallySpacedVariable
Create a new instance which specifies the position of the first element and the spacing along a dimension as the given values.
MultivariateSDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
This class represents a multi-dimensional, continuous-time Stochastic Differential Equation (SDE) of this form: \[ dX_t = \mu(t,X_t,Z_t,...)*dt + \sigma(t, X_t, Z_t, ...)*dB_t \]
MultivariateSDE(DriftVector, DiffusionMatrix, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateSDE
Construct a multi-dimensional diffusion type stochastic differential equation.
MultivariateSimpleTimeSeries - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime
This simple multivariate time series has its vectored values indexed by integers.
MultivariateSimpleTimeSeries(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
Construct an instance of MultivariateSimpleTimeSeries.
MultivariateSimpleTimeSeries(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
Construct an instance of MultivariateSimpleTimeSeries.
MultivariateSimpleTimeSeries(Vector...) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
Construct an instance of MultivariateSimpleTimeSeries.
MultivariateSimpleTimeSeries(IntTimeTimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
Construct an instance of MultivariateSimpleTimeSeries from a univariate time series.
MultivariateStateEquation - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
This is the state equation in a controlled dynamic linear model.
MultivariateStateEquation(R1toMatrix, R1toMatrix, R1toMatrix, NormalRVG) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
Construct a state equation.
MultivariateStateEquation(R1toMatrix, R1toMatrix, R1toMatrix) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
Construct a state equation.
MultivariateStateEquation(R1toMatrix, R1toMatrix) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
Construct a state equation without control variables.
MultivariateStateEquation(Matrix, Matrix, Matrix, NormalRVG) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
Construct a time-invariant state equation.
MultivariateStateEquation(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
Construct a time-invariant state equation without control variables.
MultivariateStateEquation(StateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
Construct a multivariate state equation from a univariate state equation.
MultivariateStateEquation(MultivariateStateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
Copy constructor.
MultivariateTimeSeries<T extends java.lang.Comparable,E extends MultivariateTimeSeries.Entry<T>> - Interface in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate
A multivariate time series is a sequence of vectors indexed by some notion of time.
MultivariateTimeSeries.Entry<T> - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate
This is the TimeSeries.Entry for a multivariate time series.
MultivariateTimeSeries.Entry(T, Vector) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
Construct an instance of TimeSeries.Entry.
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].
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SuanShu, a Java numerical and statistical library
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