CenterSpace.NMath.Core Namespace 
Class  Description  

AbstractRandomNumberStream 
Class AbstractRandomNumberStream may be used to construct a
RandomNumberStream object which uses a user defined function
for the generation of uniformly distributed random numbers in the
interval (0, 1).
 
ActiveSetLineSearchSQP 
Class ActiveSetLineSearchSQP solves nonlinear programming problems using a
Sequential Quadratic Programming (SQP) iterative algorithm.
 
ActiveSetLineSearchSQPOptions 
Contains the options available to the ActiveSetLineSearchSQP
Nonlinear Program Solver (NLP).
 
ActiveSetQPSolver 
Class ActiveSetQPSolver solves convex quadratic programming (QP) problems.
 
AnalysisFunctions  Obsolete.
No longer used. Please use NMathFunctions.
 
AnnealingHistory 
Class AnnealingHistory encapsulates all of the data generated during a series
of steps through an annealing schedule.
 
AnnealingHistoryStep 
Class AnnealingHistory.Step encapsulates all of the data associated with a
step in an AnnealingHistory.
 
AnnealingMinimizer 
Class AnnealingMinimizer minimizes a multivariable function using
the simulated annealing method.
 
AnnealingScheduleBase 
Class AnnealingScheduleBase is the abstract base class for annealing schedules.
 
AnovaRegressionFactorParam 
Class AnovaRegressionFactorParam provides information about a regression
parameter associated with a specific level of an ANOVA factor.
 
AnovaRegressionInteractionParam 
Class AnovaRegressionInteractionParam provides information about a
regression parameter associated with the interaction between the
level of one ANOVA factor and the level of another ANOVA factor.
 
AnovaRegressionParameter 
Class AnovaRegressionParameter provides information about a
regression parameter used to perform an analysis of variance by class
TwoWayAnova.
 
AnovaRegressionSubjectParam 
Class AnovaRegressionSubjectParam provides information about a regression
parameter associated with a subject dummy regression variable.
 
ArnoldiEigenvalueOptions 
Options for solving symmetric eigenvalue problems using the shift
and invert spectral transformation.
 
ArnoldiEigenvalueSolution 
Class contianing solution information for an Arnoldi iteration eigenvalue
problem.
 
BairstowRootFinder 
Class implementing Bairstows method finds roots for polynomials of degree
greater than 3.
 
BairstowRootFinderSolveResult 
Class encapsulating information about the result of applying
Bairstows method to a polynomial.
 
BetaDistribution 
Class BetaDistribution represents the beta probability distribution.
 
BinomialDistribution 
Class BinomialDistribution represents the discrete probability distribution of obtaining
exactly n successes in N trials where the probability of success on each
trial is P.
 
BoundedMultiVariableFunctionFitterM 
Class MultiVariableFunctionFitter< M > fits a parameterized multivariable function to a set of points where
the parameters have inequality constraints.
 
BoundedOneVariableFunctionFitterM 
Class BoundedOneVariableFunctionFitter fits a parameterized one variable function to a set of points,
where the functions parameters are constrained by upper and lower bounds.
 
BoundedVariableProblem 
Abstract class for representing a problem with bounded variables.
 
BoxCox 
Class for computing the BoxCox power tranformations defined for a set of data
points, {yi}, and parameter value lambda by
yi(lambda) = (yi^lambda  1)/lambda.
In addition methods for computing the corresponding loglikelihood function and
the value of lambda which maximizes it are provided.
 
BoxCoxTransformation 
Class BoxCoxTransformation performs a BoxCox power transformation, which can be
used to make nonnormal data resemble normallydistributed data.
 
Bracket 
Class Bracket searches in the downhill direction for two points that
bracket a minimum of a univariate function.
 
BrentMinimizer 
Class BrentMinimizer uses Brent's Method to minimize a function within an interval
known to contain a minimum.
 
CentralDifferenceHessianUpdater 
Class CentralDifferenceHessianUpdater updates the Hessian of the Lagrangian while solving a nonlinear
programming problem using a Sequential Quadratic Programming algorithm.
 
ChiSquareDistribution 
Class ChiSquareDistribution represents the chisquare probability distribution.
 
ClampedCubicSpline 
Class ClampedCubicSpline represents a function determined by tabulated values.
Function values are calculated using clamped cubic spline interpolation.
 
ClosedInterval 
Class ClosedInterval represents a numeric interval with inclusive
lower and upper bounds.
 
ClosedOpenInterval 
Class ClosedOpenInterval represents a numeric interval with an inclusive
lower bound and an exclusive upper bound.
 
ClusterAnalysis 
Class ClusterAnalysis perform hierarchical cluster analysis.
 
ClusterSet 
Class ClusterSet represents a collection of objects assigned to a
finite number of clusters.
 
CompressedSparseRowT 
Class CompressedSparseRow stores general sparse matrix data in compressed row format.
 
ConjugateGradientMinimizer 
Class ConjugateGradientMinimizer minimizes a multivariable function using
the PolakRibiere variant of the FletcherReeves conjugate gradient method.
 
ConnectivityMatrix 
Class ConnectivityMatrix represents a symmetric matrix of doubleprecision
floating point values.
 
ConstantSQPStepSize 
Class ConstantSQPStepSize computes the step size for a Sequential Quadratic Programming solver. Simply returns
a constant step size regardless of iteration values.
 
ConstrainedLeastSquares 
Class for solver constrained least squares problems.
 
ConstrainedLeastSquaresProblem 
Class that encapsulates a constrained least squares problem.
 
ConstrainedOptimizer 
Base class for linear Microsoft Solver Foundation based linear solvers.
 
ConstrainedOptimizerORTools 
Base class for linear Google ORTools based linear solvers.
 
Constraint 
Class Constraint represents a constraint in a constrained optimization
problem.
 
ConvolutionBase 
Abstract base class for all concrete convolution classes.
 
CORegressionCalculation 
Class CORegressionCalculation computes linear regression parameters by
the method of least squares using a complete orthogonal decomposition.
 
CorrelationBase 
Abstract base class for all concrete correlation classes.
 
CorrelationFilter 
The base correlation filter which provides basic correlation services.
 
CubicSpline 
Class CublicSpline represents a function whose values are determined
by cubic spline interpolation between tabulated values.
 
CurveFitDataSet 
Class for aggregating data used in curve fitting. Contains xvalues with
their corresponding yvalues along with with weights to be applied to the
yvalues during curve fitting.
 
CustomAnnealingSchedule 
Class CustomAnnealingSchedule encapsulates a series of iterations and
temperatures.
 
DampedBFGSHessianUpdater 
Class DampedBFGSHessianUpdater updates the value of the Lagrangian Hessian based
on iterate values using a quasiNewton approximation.
 
DataFrame 
Class DataFrame represents a twodimensional data object consisting of
a list of columns of the same length.
 
DBrentMinimizer 
Class DBrentMinimizer minimizes a function using Brent's method as well
as the first derivative.
 
DFBoolColumn 
Class DFBoolColumn represents a column of logical data in a data frame.
 
DFColumn 
Abstract base class for data frame column types.
 
DFDateTimeColumn 
Class DFDataTimeColumn represents a column of DataTime data in a data frame.
 
DFGenericColumn 
Class DFGenericColumn represents a column of generic data in a data frame.
 
DFIntColumn 
Class DFIntColumn represents a column of integer data in a data frame.
 
DFNumericColumn 
Class DFNumericColumn represents a column of numeric data in a data frame.
 
DFStringColumn 
Class DFStringColumn represents a column of string data in a data frame.
 
DiscreteDataIntegrator 
Integrates discrete data for either unitspaced or arbitrarily spaced data.
 
DiscreteWaveletTransform 
This abstract class represents all discrete wavelet transforms objects.
 
Distance 
Class Distance provides functions for computing the distance between objects.
 
DistancePowerDistance 
Class PowerDistance compute the power distance between two vectors.
 
Double1DConvolution 
Double1DConvolution represents a 1D convolution, with a specified kernel and data length.
 
Double1DCorrelation 
Double1DCorrelation represents a 1D correlation, with a specified kernel and data length.
 
DoubleBandFact 
Class DoubleBandFact represents the factorization of a banded matrix of
doubleprecision floating point numbers.
 
DoubleBandMatrix 
Class DoubleBandMatrix represents a banded matrix of doubleprecision floating
point values. A banded matrix is a matrix that has all its nonzero entries near
the diagonal.
 
DoubleBisquareWeightingFunction 
Class DoubleBisquareWeightingFunction implements the bisquare weighting function
for Iteratively Reweighted Least Squares (IRLS).
 
DoubleCholeskyLeastSq 
Class DoubleCholeskyLeastSq solves least square problems by using
the Cholesky factorization to solve the normal equations.
 
DoubleComplex1DConvolution 
DoubleComplex1DConvolution represents a 1D convolution, with a specified kernel and data length.
 
DoubleComplex1DCorrelation 
DoubleComplex1DCorrelation represents a 1D correlation, with a specified kernel and data length.
 
DoubleComplexBackward1DFFT 
DoubleComplexBackward1DFFT represents the backward discrete fourier transform of a 1D complex signal vector.
 
DoubleComplexBackward2DFFT 
DoubleComplexBackward2DFFT represents the backward discrete fourier transform of a 2D complex signal vector.
 
DoubleComplexBandFact 
Class DoubleComplexBandFact represents the factorization of a banded matrix of
complex doubleprecision floating point numbers.
 
DoubleComplexBandMatrix 
Class DoubleComplexBandMatrix represents a banded matrix of doubleprecision
complex numbers. A banded matrix is a matrix that has all its nonzero
entries near the diagonal.
 
DoubleComplexCholeskyLeastSq 
Class DoubleComplexCholeskyLeastSq solves least square problems by using
the Cholesky factorization to solve the normal equations.
 
DoubleComplexCsrSparseMatrix 
Class DoubleComplexCsrSparseMatrix stores a general sparse matrix using
Compressed Row (CSR) Storage format.
 
DoubleComplexDataBlock 
The DoubleComplexDataBlock struct defines a contiguous subset of an array
of DoubleComplex numbers. A DoubleComplexDataBlock instance contains a reference
to an array and an offset into the array.
 
DoubleComplexEigDecomp 
Class DoubleComplexEigDecomp computes the eigenvalues and left and right eigenvectors
of a general matrix, with preliminary balancing.
 
DoubleComplexEigDecompServer 
Class DoubleComplexEigDecompServer creates eigenvalue decompositions.
A server instance can be configured to perform preliminary balancing,
and to compute left eigenvectors, right eigenvectors, both, or
neither.
 
DoubleComplexForward1DFFT 
DoubleComplexForward1DFFT represents the forward discrete fourier transform of a 1D complex signal vector.
 
DoubleComplexForward2DFFT 
DoubleComplexForward2DFFT represents the forward discrete fourier transform of a 2D complex signal vector.
 
DoubleComplexGSVDecomp 
Class DoubleComplexGSVDecomp computes the generalized singular value
decomposition (GSVD) of a pair of general rectangular matrices.
 
DoubleComplexGSVDecompServer 
Class for serving up generalized singular value
decompositions (GSVD) in the form of DoubleComplexGSVDecomp
instances.
 
DoubleComplexLeastSquares 
Class DoubleComplexLeastSquares computes the minimumnorm solution to a linear
system Ax = y.
 
DoubleComplexLowerTriMatrix 
Class DoubleComplexLowerTriMatrix represents a lower triangular matrix of doubleprecision
complex numbers. A lower triangular matrix is a square matrix with all elements
above the main diagonal equal to zero.
 
DoubleComplexLUFact 
Class DoubleComplexLUFact represents the LU factorization of a matrix
of DoubleComplex numbers.
 
DoubleComplexMatrix 
Class DoubleComplexMatrix represents a general mathematical matrix class
of DoubleComplex numbers. Methods are provided for performing algebraic
operations, data manipulation, and slicing.
 
DoubleComplexQRDecomp 
Class DoubleComplexQRDecomp represents the QR decomposition of a general matrix.
 
DoubleComplexQRDecompServer 
Class DoubleComplexQRDecompServer allows control over how the pivoting is done
in the creation of DoubleComplexQRDecomp objects.
 
DoubleComplexQRLeastSq 
Class DoubleComplexQRLeastSq solves least squares problems by using
a QR decomposition.
 
DoubleComplexSchurDecomp 
Class DoubleComplexSchurDecomp represents the Schur decomposition of a general matrix.
 
DoubleComplexSparseFact 
Class DoubleComplexSparseFact performs general sparse matrix factorizations.
 
DoubleComplexSparseVector 
Class DoubleComplexSparseVector encapsulates a general sparse vector.
 
DoubleComplexSVDecomp 
Class DoubleComplexSVDecomp represents the singular value decomposition (SVD)
of a matrix.
 
DoubleComplexSVDecompServer 
Class DoubleComplexSVDecompServer constructs instances of the DoubleComplexSVDecomp class.
 
DoubleComplexSVDLeastSq 
Class DoubleComplexSVDLeastSq solves least squares problems by using
a singular value decomposition.
 
DoubleComplexTriDiagFact 
Class DoubleComplexTriDiagFact represents the LU factorization of a tridiagonal matrix of
doubleprecision complex floating point numbers.
 
DoubleComplexTriDiagMatrix 
Class DoubleComplexTriDiagMatrix represents a tridiagonal matrix of doubleprecision
complex numbers. A tridiagonal matrix is a matrix which has all its nonzero
entries on the main diagonal, the super diagonal, and the subdiagonal.
 
DoubleComplexUpperTriMatrix 
Class DoubleComplexComplexUpperTriMatrix represents an upper triangular matrix of doubleprecision
complex numbers. An upper triangular matrix is a square matrix with all elements
below the main diagonal equal to zero.
 
DoubleComplexVector 
Class DoubleComplexVector represents a mathematical vector of
DoubleComplex numbers.
 
DoubleCOWeightedLeastSq 
Class DoubleCOWeightedLeastSq solves weighted least squares problems
by using a Complete Orthogonal (CO) decomposition technique.
 
DoubleCsrSparseMatrix 
Class DoubleCsrSparseMatrix stores a general sparse matrix using Compressed Row (CSR)
storage format.
 
DoubleDataBlock 
The DoubleDataBlock struct defines a contiguous subset of an array
of doubleprecision floating point numbers. A DoubleDataBlock instance
contains a reference to an array and an offset into the array.
 
DoubleDWT 
This class represents a double precision discrete wavelet transform. It supports both
single step forward and reverse DWT's and multilevel signal deconstruction and reconstruction.
Details thresholding at any level and threshold calculations are also supported.
 
DoubleEigDecomp 
Class DoubleEigDecomp computes the eigenvalues and left and right eigenvectors
of a general matrix, with preliminary balancing.
 
DoubleEigDecompServer 
Class DoubleEigDecompServer creates eigenvalue decompositions.
A server instance can be configured to perform preliminary balancing,
and to compute left eigenvectors, right eigenvectors, both, or
neither.
 
DoubleFactorAnalysisExtraction, Rotation 
Class DoubleFactorAnalysis  
DoubleFairWeightingFunction 
Class DoubleFairWeightingFunction implements the fair weighting function
for Iteratively Reweighted Least Squares (IRLS).
 
DoubleForward1DFFT 
DoubleForward1DFFT represents the forward discrete fourier transform of a 1D real signal vector.
 
DoubleForward2DFFT 
DoubleForward2DFFT represents the forward discrete fourier transform of a 2D real signal vector.
 
DoubleFunctional 
Class DoubleFunctional represents a double precision functional.
 
DoubleFunctionalDelegate 
Class DoubleFunctionalDelegate wraps a functional delegate specified by a delegate in
a DoubleFunctional object.
 
DoubleGeneral1DFFT 
General 1D FFT class assuming the behavior of the provided FFT configuration instance.
 
DoubleGSVDecomp 
Class DoubleGSVDecomp computes the generalized singular value
decomposition (GSVD) of a pair of general rectangular matrices.
 
DoubleGSVDecompServer 
Class for serving up generalized singular value
decompositions (GSVD) in the form of DoubleGSVDecomp
instances.
 
DoubleHermCsrSparseMatrix 
Class DoubleHermCsrSparseMatrix stores a general sparse Hermitian matrix using
the Compressed Row (CSR) storage format.
 
DoubleHermitianBandMatrix 
Class DoubleHermitianBandMatrix represents an Hermitian banded matrix of
doubleprecision floating point values. An Hermitian banded matrix is an
Hermitian matrix that has all its nonzero entries near the diagonal.
 
DoubleHermitianEigDecomp 
Class DoubleHermitianEigDecomp computes the eigenvalues and eigenvectors
of a symmetrix matrix.
 
DoubleHermitianEigDecompServer 
Class DoubleHermitianEigDecompServer creates eigenvalue decompositions.
A server instance can be configured to compute eigenvalues only, or
both eigenvalues and eigenvectors. In addition, the server can be
configured to compute only the eigenvalues in a given range. A
tolerance for the convergence of the algorithm may also be specified.
 
DoubleHermitianFact 
Class DoubleHermitianFact represents the factorization of a Hermitian,
matrix of complex doubleprecision floating point numbers.
 
DoubleHermitianMatrix 
Class DoubleHermitianMatrix represents a matrix of doubleprecision
floating point complex values.
 
DoubleHermitianPDBandFact 
Class DoubleHermitianPDBandFact represents the factorization of a Hermitian,
positive definite, banded matrix of
complex doubleprecision floating point numbers.
 
DoubleHermitianPDFact 
Class DoubleHermitianPDFact represents the Cholesky factorization of a Hermitian,
positive definite, matrix of doubleprecision complex floating point numbers. In
a Cholesky factorization a Hermitian, positive definite matrix A is factored
as
A = UU'
where U is upper triangular and U' is the conjugate transpose of U.
 
DoubleHermPDTriDiagFact 
Class DoubleHermPDTriDiagFact represents the LDL' factorization of a Hermitian,
positive definite, tridiagonal matrix of complex doubleprecision floating point numbers.
 
DoubleIterativelyReweightedLeastSq 
Class DoubleIterativelyReweightedLeastSq solves a least squares problems by iteratively
applying a weighted least squares fit.
 
DoubleLeastSquares 
Class DoubleLeastSquares computes the minimumnorm solution to a linear
system Ax = y.
 
DoubleLeastSqWeightingFunction 
Abstract base class for least squares weighting functions used in the
Iteratively Reweighted Least Squares algorithm.
 
DoubleLowerTriMatrix 
Class DoubleLowerTriMatrix represents a lower triangular matrix of doubleprecision
floating point values. A lower triangular matrix is a square matrix with all elements
above the main diagonal equal to zero.
 
DoubleLUFact 
Class DoubleLUFact represents the LU factorization of a matrix of
doubleprecision floating point numbers.
 
DoubleMatrix 
Class DoubleMatrix represents a general mathematical matrix class of
doubleprecision floating point numbers. Methods are provided for
performing algebraic operations, data manipulation, and slicing.
 
DoubleMultiVariableFunction 
Abstract class for representing a multivariable function.
 
DoubleNonnegativeLeastSqResult 
Class containing the results of a nonnegative least squares solve attempt.
Double precision version.
 
DoubleNonnegativeLeastSquares 
Class DoubleNonnegativeLeastSquares computes the minimumnorm solution to a linear
system Ax = y subject to the constraint that all the elements, x[i],
are nonnegative.
 
DoubleParameterizedDelegate 
Class which creates a DoubleParameterizedFunction instance from delegates.
 
DoubleParameterizedFunction 
Abstract class representing a parameterized function.
 
DoubleParameterizedFunctional 
Abstract class representing a parameterized functional.
 
DoublePCA 
Class DoublePCA performs a principal component analysis on a given
doubleprecision data matrix, or data frame.
 
DoubleQRDecomp 
Class DoubleQRDecomp represents the QR decomposition of a general matrix.
 
DoubleQRDecompServer 
Class DoubleQRDecompServer allows control over how the pivoting is done
in the creation of DoubleQRDecomp objects.
 
DoubleQRLeastSq 
Class DoubleQRLeastSq solves least squares problems by using
a QR decomposition.
 
DoubleRandomBetaDistribution 
Class DoubleRandomBetaDistribution generates random numbers from a beta distribution.
 
DoubleRandomCauchyDistribution 
Class DoubleRandomCauchyDistribution generates random numbers from a Cauchy distribution.
 
DoubleRandomExponentialDistribution 
Class DoubleRandomExponentialDistribution generates random numbers from an exponential distribution.
 
DoubleRandomGammaDistribution 
Class DoubleRandomGammaDistribution generates random numbers from a gamma distribution.
 
DoubleRandomGaussianDistribution 
Class DoubleRandomGaussianDistribution generates random numbers from a Gaussian distribution.
 
DoubleRandomGumbelDistribution 
Class DoubleRandomGumbelDistribution generates random numbers from a Gumbel distribution.
 
DoubleRandomLaplaceDistribution 
Class DoubleRandomLaplaceDistribution generates random numbers from a Laplace distribution.
 
DoubleRandomLogNormalDistribution 
Class DoubleRandomLogNormalDistribution generates random numbers from a lognormal distribution.
 
DoubleRandomRayleighDistribution 
Class DoubleRandomRayleighDistribution generates random numbers from an Rayleigh distribution.
 
DoubleRandomUniformDistribution 
Class DoubleRandomUniformDistribution generates random numbers uniformly distributed over an interval.
 
DoubleRandomWeibullDistribution 
Class DoubleRandomWeibullDistribution generates random numbers from a Weibull distribution.
 
DoubleSchurDecomp 
Class DoubleSchurDecomp represents the Schur decomposition of a general matrix.
 
DoubleSparseFact 
Class DoubleSparseFact performs general sparse matrix factorizations.
 
DoubleSparseHermFact 
Class DoubleSparseHermFact performs Hermitian sparse matrix factorizations.
 
DoubleSparseHermPDFact 
Class DoubleSparseHermPDFact performs sparse Hermitian Positive Definite matrix factorizations.
 
DoubleSparseSymFact 
Class DoubleSparseSymFact performs sparse symmetric matrix factorizations.
 
DoubleSparseSymPDFact 
Class DoubleSparseSymPDFact performs sparse positive definite symmetric matrix factorizations.
 
DoubleSparseVector 
Class DoubleSparseVector encapsulates a general sparse vector.
 
DoubleSVDecomp 
Class DoubleSVDecomp represents the singular value decomposition (SVD)
of a matrix.
 
DoubleSVDecompServer 
Class DoubleSVDecompServer constructs instances of the DoubleSVDecomp class.
 
DoubleSVDLeastSq 
Class DoubleSVDLeastSq solves least squares problems by using
a singular value decomposition.
 
DoubleSymBandMatrix 
Class DoubleSymBandMatrix represents a symmetric banded matrix of doubleprecision
floating point values. A symmetric banded matrix is a symmetric matrix that has all its
nonzero entries near the diagonal.
 
DoubleSymCsrSparseMatrix 
Class DoubleSymCsrSparseMatrix stores a sparse symmetric matrix using the
CompreSsed Row (CSR) storage format.
 
DoubleSymEigDecomp 
Class DoubleSymEigDecomp computes the eigenvalues and eigenvectors
of a symmetrix matrix.
 
DoubleSymEigDecompServer 
Class DoubleSymEigDecompServer creates eigenvalue decompositions.
A server instance can be configured to compute eigenvalues only, or
both eigenvalues and eigenvectors. In addition, the server can be
configured to compute only the eigenvalues in a given range. A
tolerance for the convergence of the algorithm may also be specified.
 
DoubleSymFact 
Class DoubleSymFact represents the factorization of a symmetric,
matrix of doubleprecision floating point numbers.
 
DoubleSymmetric2DSignalReader 
Provides symmetric complex conjugate signal unpacking services. Typically used for unpacking 2D FFT's of real
signals.
 
DoubleSymmetricBackward1DFFT 
DoubleSymmetricBackward1DFFT represents the backward discrete fourier transform of a 1D real signal vector, and
inverses packed conjugate symmetric signals back to the real domain.
 
DoubleSymmetricMatrix 
Class DoubleSymmetricMatrix represents a symmetric matrix of doubleprecision
floating point values.
 
DoubleSymmetricSignalReader 
Provides symmetric complex conjugate signal unpacking services. Typically used for unpacking 1D FFT's of real
signals.
 
DoubleSymPDBandFact 
Class DoubleSymPDBandFact represents the factorization of a symmetric,
positive definite, banded matrix of
doubleprecision floating point numbers.
 
DoubleSymPDFact 
Class DoubleSymPDFact represents the Cholesky factorization of a symmetric,
positive definite, matrix of doubleprecision floating point numbers. In
a Cholesky factorization a symmetric, positive definite matrix A is factored
as
A = UU'
where U is upper triangular and U' is the transpose of U.
 
DoubleSymPDTriDiagFact 
Class DoubleSymPDTriDiagFact represents the LDL' factorization of a symmetric,
positive definite, tridiagonal matrix of doubleprecision floating point numbers.
 
DoubleTriDiagFact 
Class DoubleTriDiagFact represents the LU factorization of a tridiagonal matrix of
doubleprecision floating point numbers.
 
DoubleTriDiagMatrix 
Class DoubleTriDiagMatrix represents a tridiagonal matrix of doubleprecision
floating point values. A tridiagonal matrix is a matrix which has all its nonzero
entries on the main diagonal, the super diagonal, and the subdiagonal.
 
DoubleUpperTriMatrix 
Class DoubleUpperTriMatrix represents an upper triangular matrix of doubleprecision
floating point values. An upper triangular matrix is a square matrix with all elements
below the main diagonal equal to zero.
 
DoubleVector 
Class DoubleVector represents a mathematical vector of doubleprecision
floating point numbers.
 
DoubleVectorParameterizedDelegate 
Class DoubleVectorParameterizedDelegate creates a DoubleParameterizedFunctional instance from delegates.
 
DoubleWavelet 
This class represents a double precision wavelet. There are fives types of built in wavelets
avaiable: Harr, Daubechies, Least Asymmetric, Best Localized, and Coiflet. User
generated wavelets can be created by provided the lowpass decimation filter
parameters in the constructor.
 
DownhillSimplexMinimizer 
Class DownhillSimplexMinimizer minimizes a multivariable function using the downhill
simplex method of Nelder and Mead.
 
DualSimplexSolver  Obsolete.
Class DualSimplexSolver is a class for solving linear
programming prolems using the dual simplex method.
 
DualSimplexSolverORTools 
Class DualSimplexSolverORTools is a class for solving linear
programming prolems using the dual simplex method.
 
DualSimplexSolverParams  Obsolete.
Dual simplex algorithm parameters.
 
EqualityConstrainedQPProblem 
Class representing an equality constrained Quadratic Programming
problem.
Minimize 0.5 * x'Hx + x'c
Subject to
Ax = b
where x is a vector of unknows, H a symmetric matrix, and A matrix.
 
ExponentialDistribution 
Class ExponentialDistribution represents the Exponential probability distribution.
 
Factor 
Class Factor represents a categorical vector in which all elements are drawn from
a finite number of factor levels.
 
FactorAnalysisCorrelationExtraction, Rotation 
Class FactorAnalysisCorrelation performs a factor analysis
on a set of case data using the correlation matrix and specified
factor extraction and rotation algorithms.
 
FactorAnalysisCovarianceExtraction, Rotation 
Class FactorAnalysisCovariance performs a factor analysis
on a set of case data using the covariance matrix and specified
factor extraction and rotation algorithms.
 
FDistribution 
Class FDistribution represents the F probability distribution.
 
FFT2DBase 
Abstract base class for all 2D discrete FFT transform classes.
This class manages the setup and tear down of all discrete fourier resources.
 
FFTBase 
Abstract base class for all 1D discrete FFT transform classes.
This class manages the setup and tear down of all discrete fourier resources.
 
FFTConfiguration 
FFTConfiguration contains all of the FFT configuration state to efficiently compute a FFT. This class is
typically used in conjunction with the GeneralxDFFT set of classes to configure FFT's with offset and
strided signal data.
 
FFTKernelException 
Exception thrown when MKL or CUDA returns an error condition when computing a FFT
 
Float1DConvolution 
Float1DConvolution represents a 1D convolution, with a specified kernel and data length.
 
Float1DCorrelation 
Float1DCorrelation represents a 1D correlation, with a specified kernel and data length.
 
FloatBandFact 
Class FloatBandFact represents the factorization of a banded matrix of
singleprecision floating point numbers.
 
FloatBandMatrix 
Class FloatBandMatrix represents a banded matrix of singleprecision floating point
values. A banded matrix is a matrix that has all its nonzero entries near the diagonal.
 
FloatCholeskyLeastSq 
Class FloatCholeskyLeastSq solves least square problems by using
the Cholesky factorization to solve the normal equations.
 
FloatComplex1DConvolution 
FloatComplex1DConvolution represents a 1D convolution, with a specified kernel and data length.
 
FloatComplex1DCorrelation 
FloatComplex1DCorrelation represents a 1D correlation, with a specified kernel and data length.
 
FloatComplexBackward1DFFT 
FloatComplexBackward1DFFT represents the backward discrete fourier transform of a 1D complex signal vector.
 
FloatComplexBackward2DFFT 
FloatComplexBackward2DFFT represents the backward discrete fourier transform of a 2D complex signal vector.
 
FloatComplexBandFact 
Class FloatComplexBandFact represents the factorization of a banded matrix of
complex singleprecision floating point numbers.
 
FloatComplexBandMatrix 
Class FloatComplexBandMatrix represents a banded matrix of singleprecision
complex numbers. A banded matrix is a matrix that has all its nonzero entries
near the diagonal.
 
FloatComplexCholeskyLeastSq 
Class FloatComplexCholeskyLeastSq solves least square problems by using
the Cholesky factorization to solve the normal equations.
 
FloatComplexCsrSparseMatrix 
Class FloatComplexCsrSparseMatrix stores a general sparse matrix using
Compressed Row (CSR) Storage format.
 
FloatComplexDataBlock 
The FloatComplexDataBlock struct defines a contiguous subset of an array
of FloatComplex numbers. A FloatComplexDataBlock instance contains a reference
to an array and an offset into the array.
 
FloatComplexEigDecomp 
Class FloatComplexEigDecomp computes the eigenvalues and left and right eigenvectors
of a general matrix, with preliminary balancing.
 
FloatComplexEigDecompServer 
Class FloatComplexEigDecompServer creates eigenvalue decompositions.
A server instance can be configured to perform preliminary balancing,
and to compute left eigenvectors, right eigenvectors, both, or
neither.
 
FloatComplexForward1DFFT 
FloatComplexForward1DFFT represents the forward discrete fourier transform of a 1D complex signal vector.
 
FloatComplexForward2DFFT 
FloatComplexForward2DFFT represents the forward discrete fourier transform of a 2D complex signal vector.
 
FloatComplexGSVDecomp 
Class FloatComplexGSVDecomp computes the generalized singular value
decomposition (GSVD) of a pair of general rectangular matrices.
 
FloatComplexGSVDecompServer 
Class for serving up generalized singular value
decompositions (GSVD) in the form of FloatComplexGSVDecomp
instances.
 
FloatComplexLeastSquares 
Class FloatComplexLeastSquares computes the minimumnorm solution to a linear
system Ax = y.
 
FloatComplexLowerTriMatrix 
Class FloatComplexLowerTriMatrix represents a lower triangular matrix of singleprecision
complex numbers. A lower triangular matrix is a square matrix with all elements
above the main diagonal equal to zero.
 
FloatComplexLUFact 
Class FloatComplexFact represents the LU factorization of a matrix
of FloatComplex numbers.
 
FloatComplexMatrix 
Class FloatComplexMatrix represents a general mathematical matrix class
of FloatComplex numbers. Methods are provided for
performing algebraic operations, data manipulation, and slicing.
 
FloatComplexQRDecomp 
Class FloatComplexQRDecomp represents the QR decomposition of a general matrix.
 
FloatComplexQRDecompServer 
Class FloatComplexQRDecompServer allows control over how the pivoting is done
in the creation of FloatComplexQRDecomp objects.
 
FloatComplexQRLeastSq 
Class FloatComplexQRLeastSq solves least squares problems by using
a QR decomposition.
 
FloatComplexSchurDecomp 
Class FloatComplexSchurDecomp represents the Schur decomposition of a general matrix.
 
FloatComplexSparseFact 
Class FloatComplexSparseFact performs general sparse matrix factorizations.
 
FloatComplexSparseVector 
Class FloatComplexSparseVector encapsulates a general sparse vector.
 
FloatComplexSVDecomp 
Class FloatComplexSVDecomp represents the singular value decomposition (SVD)
of a matrix.
 
FloatComplexSVDecompServer 
Class FloatComplexSVDecompServer constructs instances of the FloatComplexSVDecomp class.
 
FloatComplexSVDLeastSq 
Class FloatComplexSVDLeastSq solves least squares problems by using
a singular value decomposition.
 
FloatComplexTriDiagFact 
Class FloatComplexTriDiagFact represents the LU factorization of a tridiagonal matrix of
singleprecision complex floating point numbers.
 
FloatComplexTriDiagMatrix 
Class FloatComplexTriDiagMatrix represents a tridiagonal matrix of singleprecision
complex numbers. A tridiagonal matrix is a matrix which has all its nonzero
entries on the main diagonal, the super diagonal, and the subdiagonal.
 
FloatComplexUpperTriMatrix 
Class FloatComplexUpperTriMatrix represents an upper triangular matrix of singleprecision
complex numbers. An upper triangular matrix is a square matrix with all elements
below the main diagonal equal to zero.
 
FloatComplexVector 
Class FloatComplexVector represents a mathematical vector of
FloatComplex numbers.
 
FloatCsrSparseMatrix 
Class FloatCsrSparseMatrix stores a general sparse matrix using Compressed Row (CSR)
storage format.
 
FloatDataBlock 
The FloatDataBlock struct defines a contiguous subset of an array
of floating point numbers. A FloatDataBlock instance contains a
reference to an array, and an offset into the array.
 
FloatDWT 
This class represents a single precision discrete wavelet transform. It supports both
single step forward and reverse DWT's and multilevel signal deconstruction and reconstruction.
Details thresholding at any level and threshold calculations are also supported.
 
FloatEigDecomp 
Class FloatEigDecomp computes the eigenvalues and left and right eigenvectors
of a general matrix, with preliminary balancing.
 
FloatEigDecompServer 
Class FloatEigDecompServer creates eigenvalue decompositions.
A server instance can be configured to perform preliminary balancing,
and to compute left eigenvectors, right eigenvectors, both, or
neither.
 
FloatForward1DFFT 
FloatForward1DFFT represents the forward discrete fourier transform of a 1D real signal vector.
 
FloatForward2DFFT 
FloatForward2DFFT represents the forward discrete fourier transform of a 2D real signal vector.
 
FloatGeneral1DFFT 
General 1D FFT class assuming the behavior of the provided FFT configuration instance.
 
FloatGSVDecomp 
Class FloatGSVDecomp computes the generalized singular value
decomposition (GSVD) of a pair of general rectangular matrices.
 
FloatGSVDecompServer 
Class for serving up generalized singular value
decompositions (GSVD) in the form of FloatGSVDecomp
instances.
 
FloatHermCsrSparseMatrix 
Class FloatHermCsrSparseMatrix stores a general sparse Hermitian matrix using
the Compressed Row (CSR) storage format.
 
FloatHermitianBandMatrix 
Class FloatHermitianBandMatrix represents an Hermitian banded matrix of
doubleprecision floating point values. An Hermitian banded matrix is an
Hermitian matrix that has all its nonzero entries near the diagonal.
 
FloatHermitianEigDecomp 
Class FloatHermitianEigDecomp computes the eigenvalues and eigenvectors
of a symmetrix matrix.
 
FloatHermitianEigDecompServer 
Class FloatHermitianEigDecompServer creates eigenvalue decompositions.
A server instance can be configured to compute eigenvalues only, or
both eigenvalues and eigenvectors. In addition, the server can be
configured to compute only the eigenvalues in a given range. A
tolerance for the convergence of the algorithm may also be specified.
 
FloatHermitianFact 
Class FloatHermitianFact represents the factorization of a Hermitian,
matrix of complex singleprecision floating point numbers.
 
FloatHermitianMatrix 
Class FloatHermitianMatrix represents a matrix of singleprecision
floating point complex values.
 
FloatHermitianPDBandFact 
Class FloatHermitianPDBandFact represents the factorization of a Hermitian,
positive definite, banded matrix of
complex singleprecision floating point numbers.
 
FloatHermitianPDFact 
Class FloatHermitianPDFact represents the Cholesky factorization of a Hermitian,
positive definite, matrix of singleprecision complex floating point numbers. In
a Cholesky factorization a Hermitian, positive definite matrix A is factored
as
A = UU'
where U is upper triangular and U' is the conjugate transpose of U.
 
FloatHermPDTriDiagFact 
Class FloatHermPDTriDiagFact represents the LDL' factorization of a Hermitian,
positive definite, tridiagonal matrix of complex singleprecision floating point numbers.
 
FloatLeastSquares 
Class FloatLeastSquares computes the minimumnorm solution to a linear
system Ax = y.
 
FloatLowerTriMatrix 
Class FloatLowerTriMatrix represents a lower triangular matrix of singleprecision
floating point values. A lower triangular matrix is a square matrix with all elements
above the main diagonal equal to zero.
 
FloatLUFact 
Class FloatLUFact represents the LU factorization of a matrix of floating point
numbers.
 
FloatMatrix 
Class FloatMatrix represents a general mathematical matrix class of
floating point numbers. Methods are provided for performing algebraic
operations, data manipulation, and slicing.
 
FloatNonnegativeLeastSqResult 
Class containing the results of a nonnegative least squares solve attempt.
Single precision version.
 
FloatNonnegativeLeastSquares 
Class FloatNonnegativeLeastSquares computes the minimumnorm solution to a linear
system Ax = y subject to the constraint that all the elements, x[i],
are nonnegative.
 
FloatPCA 
Class FloatPCA performs a principal component analysis on a given singleprecision
data matrix.
 
FloatQRDecomp 
Class FloatQRDecomp represents the QR decomposition of a general matrix.
 
FloatQRDecompServer 
Class FloatQRDecompServer allows control over how the pivoting is done
in the creation of FloatQRDecomp objects.
 
FloatQRLeastSq 
Class FloatQRLeastSq solves least squares problems by using
a QR decomposition.
 
FloatRandomBetaDistribution 
Class FloatRandomBetaDistribution generates random numbers from a beta distribution.
 
FloatRandomCauchyDistribution 
Class FloatRandomCauchyDistribution generates random numbers from a Cauchy distribution.
 
FloatRandomExponentialDistribution 
Class FloatRandomExponentialDistribution generates random numbers from an exponential distribution.
 
FloatRandomGammaDistribution 
Class FloatRandomGammaDistribution generates random numbers from a gamma distribution.
 
FloatRandomGaussianDistribution 
Class FloatRandomGaussianDistribution generates random numbers from a Gaussian distribution.
 
FloatRandomGumbelDistribution 
Class FloatRandomGumbelDistribution generates random numbers from a Gumbel distribution.
 
FloatRandomLaplaceDistribution 
Class FloatRandomLaplaceDistribution generates random numbers from a Laplace distribution.
 
FloatRandomLogNormalDistribution 
Class FloatRandomLogNormalDistribution generates random numbers from a lognormal distribution.
 
FloatRandomRayleighDistribution 
Class FloatRandomRayleighDistribution generates random numbers from an Rayleigh distribution.
 
FloatRandomUniformDistribution 
Class DoubleRandomUniformDistribution generates random numbers uniformly distributed over an interval.
 
FloatRandomWeibullDistribution 
Class FloatRandomWeibullDistribution generates random numbers from a Weibull distribution.
 
FloatSchurDecomp 
Class FloatSchurDecomp represents the Schur decomposition of a general matrix.
 
FloatSparseFact 
Class FloatSparseFact performs general sparse matrix factorizations.
 
FloatSparseHermFact 
Class FloatSparseHermFact performs Hermitian sparse matrix factorizations.
 
FloatSparseHermPDFact 
Class FloatSparseHermPDFact performs sparse Hermitian Positive Definite matrix factorizations.
 
FloatSparseSymFact 
Class FloatSparseSymFact performs sparse symmetric matrix factorizations.
 
FloatSparseSymPDFact 
Class FloatSparseSymPDFact performs sparse positive definite symmetric matrix factorizations.
 
FloatSparseVector 
Class FloatSparseVector encapsulates a general sparse vector.
 
FloatSVDecomp 
Class FloatSVDecomp represents the singular value decomposition (SVD)
of a matrix.
 
FloatSVDecompServer 
Class FloatSVDecompServer constructs instances of the FloatSVDecomp class.
 
FloatSVDLeastSq 
Class FloatSVDLeastSq solves least squares problems by using
a singular value decomposition.
 
FloatSymBandMatrix 
Class FloatSymBandMatrix represents a symmetric banded matrix of singleprecision
floating point values. A symmetric banded matrix is a symmetric matrix that has all its
nonzero entries near the diagonal.
 
FloatSymCsrSparseMatrix 
Class FloatSymCsrSparseMatrix stores a sparse symmetric matrix using the
CompreSsed Row (CSR) storage format.
 
FloatSymEigDecomp 
Class FloatSymEigDecomp computes the eigenvalues and eigenvectors
of a symmetrix matrix.
 
FloatSymEigDecompServer 
Class FloatSymEigDecompServer creates eigenvalue decompositions.
A server instance can be configured to compute eigenvalues only, or
both eigenvalues and eigenvectors. In addition the server can be
configured to compute only the eigenvalues in a given range. A
tolerance for the convergence of the algorithm may also be specified.
 
FloatSymFact 
Class FloatSymFact represents the factorization of a symmetric
matrix of singleprecision floating point numbers.
 
FloatSymmetric2DSignalReader 
Provides symmetric complex conjugate signal unpacking services. Typically used for unpacking 2D FFT's of real
signals.
 
FloatSymmetricBackward1DFFT 
FloatSymmetricBackward1DFFT represents the backward discrete fourier transform of a 1D real signal vector. This class
and inverts packed conjugate symmetric signals in the freqency domain back to the real domain.
 
FloatSymmetricMatrix 
Class FloatSymmetricMatrix represents a symmetric matrix of floatprecision
floating point values.
 
FloatSymmetricSignalReader 
Provides symmetric complex conjugate signal unpacking services. Typically used for unpacking 1D FFT's of real
signals.
 
FloatSymPDBandFact 
Class FloatSymPDBandFact represents the factorization of a symmetric,
positive definite, banded matrix of
singleprecision floating point numbers.
 
FloatSymPDFact 
Class FloatSymPDFact represents the Cholesky factorization of a symmetric,
positive definite, matrix of singleprecision floating point numbers. In
a Cholesky factorization a symmetric, positive definite matrix A is factored
as
A = UU'
where U is upper triangular and U' is the transpose of U.
 
FloatSymPDTriDiagFact 
Class FloatSymPDTriDiagFact represents the LDL' factorization of a symmetric,
positive definite, tridiagonal matrix of singleprecision floating point numbers.
 
FloatTriDiagFact 
Class FloatTriDiagFact represents the LU factorization of a tridiagonal matrix of
singleprecision floating point numbers.
 
FloatTriDiagMatrix 
Class FloatTriDiagMatrix represents a tridiagonal matrix of singleprecision
floating point values. A tridiagonal matrix is a matrix which has all its nonzero
entries on the main diagonal, the super diagonal, and the subdiagonal.
 
FloatUpperTriMatrix 
Class FloatUpperTriMatrix represents an upper triangular matrix of singleprecision
floating point values. An upper triangular matrix is a square matrix with all elements
below the main diagonal equal to zero.
 
FloatVector 
Class FloatVector represents a mathematical vector of floating point
numbers.
 
FloatWavelet 
This class represents a single precision wavelet. There are fives types of built in wavelets
avaiable: Harr, Daubechies, Least Asymmetric, Best Localized, and Coiflet. User
generated wavelets can be created by provided the lowpass decimation filter
parameters in the constructor.
 
FZero 
FZero finds a zero of the function in the given interval. Repeated roots are not found, FZero can find only bracketed single roots.
 
GammaDistribution 
Class GammaDistribution represents the gamma probability distribution.
 
GaussKronrod21Integrator 
Class GaussKronrod21Integrator calculates an approximation of the integral
of a function over a finite interval using the Gauss 10point and the
Kronrod 21point rule.
 
GaussKronrod43Integrator 
Class GaussKronrod43Integrator calculates an approximation of the integral
of a function over a finite interval using the Gauss 21point and the
Kronrod 43point rule.
 
GaussKronrod87Integrator 
Class GaussKronrod87Integrator calculates an approximation of the integral
of a function over a finite interval using the Gauss 43point and the
Kronrod 87point rule.
 
GaussKronrodIntegrator 
Class GaussKronrodIntegrator calculates an approximation of the integral
of a function over a finite interval using GaussKronrod rules.
 
GeometricDistribution 
Class GeometricDistribution represents the goemetric probability distribution.
 
GlobalCurveFitAnova 
Class GlobalCurveFitAnova computes ANOVA style statistics for the least squares
model fit that result from a global curve fit.
Available statistics include the residual standard error, the coefficient of determination (R2 and "adjusted" R2),
the Fstatistic and pvalue for the overall model, and degrees of freedom.
 
GlobalCurveFitter 
Global fitting in involves fitting multiple datasets with the same fitting
function. Parameters in the fitting function can optionally be shared amongst
all datasets. If a parameter is shared, the fitting procedure will yield the
same value for that parameter for all datasets. If a parameter is not shared,
the fitting procedure will yield a unique value for that parameter for
each dataset.
 
GlobalFitParameterInfo 
Contains information about a parameter in a global curve fit:
name, description, shared or not shared. Only the sharing
information is required.
 
GlobalFittedParameter 
Class for providing fit statistics about a global fit parameter
after the fit has been performed.
 
GlobalFixedFitParameterInfo 
Contains information about a parameter with fixed value(s) in a global curve fit:
name, description, shared or not shared, and the fixed value(s).
If the parameter is not shared, then its values may be fixed for specific
datasets. In this case the fixed values must be specified in using
an IDictionary parameter.
For example if the dictionary contained entries
{ 1, 1.0 }, { 3, 1.2 }
this would mean the parameter has a fixed value of 1.0 in second
dataset (zero based indexing) and a fixed value of 1.2 in the
fourth dataset.
If the parameter info object is constructed with a dictionary
of fixed values it is assumed to be nonshared. If it is
constructed with a single fixed value it is assumed to be shared.
If a GlobalFixedFitParameterInfo has multiple fixed values
and its sharing property is set to ParameterSharing.Shared,
The first entry in the dictionary will be taken as the shared
fixed value.
 
GoldenMinimizer 
Class GoldenMinimizer performs a golden section search for a minimium of a function
within an interval known to contain a minimum.
 
GoodnessOfFit 
Class GoodnessOfFit tests goodness of fit for least squares modelfitting classes, such as LinearRegression,
PolynomialLeastSquares, and OneVariableFunctionFitter.
 
GoodnessOfFitParameter 
Class GoodnessOfFitParameter tests statistical hypotheses about
estimated parameters in regression models.
 
Histogram 
Class Histogram constructs and maintains a histogram of input data.
Input data is sorted into bins and a count is kept of how many data
points fall into each bin.
 
IActiveSetQPSolver 
Class IActiveSetQPSolver is an interface for classes that solver convex
quadratic programming (QP) problems.
In particular, classes that implement this abstract class may be used
in the Active Set Sequential Quadratic Programming solver for general linear
programming problems.
 
IndependentRandomStreams 
Base class for creating streams of independent random numbers. Deriving
classes must construct the streams_ array.
 
IndexArray 
Class IndexArray presents 0based indexices to the user, but uses
1based indices internally.
 
IndexOutOfRangeException 
Exception thrown when an out of range index is passed to an NMath function.
 
InputVariableCorrelator 
Instances of the InputVariableCorrelator class are used to induce
a desired rank correlation among input variables.
 
InteriorPointQPSolver 
Class for sovling quadratic programming (QP) problems using an interior point
algorithm.
 
InteriorPointQPSolverParams 
Parameters controlling the behavior of the interior point quadratic programming
algorithm in the InteriorPointQPSolver class.
 
Interval 
Class Interval represents a numeric interval with inclusive or exclusive
lower and upper bounds.
 
IntRandomBernoulliDistribution 
Class IntRandomBernoulliDistribution generates random numbers from a discrete binomial distribution.
 
IntRandomBinomialDistribution 
Class IntRandomBinomialDistribution generates random numbers from a discrete binomial distribution.
 
IntRandomGeometricDistribution 
Class IntRandomGeometricDistribution generates random numbers from a discrete geometric distribution.
 
IntRandomHypergeometricDistribution 
Class IntRandomHypergeometricDistribution generates random numbers from a discrete hypergeometric distribution.
 
IntRandomNegativeBinomialDistribution 
Class IntRandomNegativeBinomialDistribution generates random numbers from a discrete
negative binomial distribution.
 
IntRandomPoissonDistribution 
Class IntRandomPoissonDistribution generates random numbers from a discrete Poisson distribution.
 
IntRandomPoissonVaryingMeanDistribution 
Class IntRandomPoissonVaryingMeanDistribution generates random numbers from a discrete Poisson
distribution with varying mean.
 
IntRandomUniformBitsDistribution 
Class IntRandomUniformBitsDistribution generates integer values with uniform bit distribution.
 
IntRandomUniformDistribution 
Class IntRandomUniformDistribution generates random numbers uniformly distributed over an interval.
 
InvalidArgumentException 
Exception thrown when an invalid argument is passed to an NMath function.
 
InvalidBinBdryException 
Exception thrown when a histogram operation results in invalid
bin boundaries.
 
IPLS1Calc 
Interface for performing a Partial Least Squares (PLS) calculation.
 
IPLS2Calc 
Interface for performing a Partial Least Squares (PLS) calculation.
 
JohnsonDistribution 
Class JohnsonDistribution represents the Johnson system of distributions.
 
KFoldsSubsets 
Class KFoldsSubsets generates kfold subsets for cross validation.
 
KMeansClustering 
Class KMeansClustering performs kmeans clustering on a set of data points.
 
KruskalWallisTable 
Class KruskalWallisTable summarizes the information of KruskalWallis rank sum test.
 
KruskalWallisTest 
Class KruskalWallisTest performs a KruskalWallis rank sum test.
 
L1MeritStepSize 
Class L1MeritStepSize computes the step size for a Sequential Quadratic Programming solver based on
sufficient decrease in the L1 merit function.
 
LagrangianFunction 
Class LagrangianFunction represents the Lagrangian function associated with a nonlinear programming problem.
 
LagrangianFunctionLagrangianGradientFunction 
Class LagrangianGradientFunction derives from DoubleMultiVariableFunction for evaluating the
gradient of the Lagrangian functions.
 
LeapfrogRandomStreams 
Class LeapfrogRandomStreams creates several independent streams of random numbers using
the method know as leapfrogging.
 
LeapfrogStream 
Class LeapfrogStream represents a single leapfrog stream.
 
LeaveOneOutSubsets 
Class LeaveOneOutSubsets generates the index subsets for a leaveoneout cross validations
calculation.
 
LevenbergMarquardtMinimizer 
Class for minimizing the L2 norm of a function using the Levenberg Marquardt
algorithm.
 
LikelihoodRatioStatistic 
Class LikelihoodRatioStatistic computes the Likelihood Ratio Statistic values for a
logistic regression.
 
LinearAnnealingSchedule 
Class LinearAnnealingSchedule encapsulates the linear descent of a starting
temperature to zero. Each step has a specified number of iterations.
 
LinearConstrainedProblem 
Abstract class for representing an optimization problem with linear
constraints.
 
LinearConstraint 
Class LinearConstraint represents a linear constraint for a constrained optimization
problem.
 
LinearProgrammingProblem 
Class LinearProgrammingProblem encapsulates a Linear programming problem.
 
LinearRegression 
Class LinearRegression computes a multiple linear regression from an input
matrix of independent variable values and vector of dependent variable
values.
 
LinearRegressionAnova 
Class LinearRegressionAnova tests overall model significance for linear
regressions computed by class LinearRegression.
 
LinearRegressionParameter 
Class LinearRegressionParameter tests statistical hypotheses about
estimated parameters in linear regressions computed by class
LinearRegression.
 
LinearSpline 
Class LinearSpline represents a function whose values are determined
by linear interpolation between tabulated values.
 
Linkage 
Class Linkage provides functions for computing the distance between clusters
of objects.
 
LogisticDistribution 
Class LogisticDistribution represents the logistic probability distribution
with a specifed location (mean) and scale.
 
LogisticRegressionParameterCalc 
Class for performing a binomial logistic regression.
 
LogisticRegressionAuxiliaryStatsParameterCalc 
Class LogisticRegressionAuxiliaryStats computes pseudo Rsquared metrics for a logistic regression,
and odds ratios for the computed coefficients.
 
LogisticRegressionFitAnalysisParameterCalc 
Class for for calculating "goodness of fit" statistics for a logistic
regression model.
 
LogisticRegressionFitAnalysisParameterCalcHosmerLemeshowGroup 
Class representing a group used in computing the Hosmer Lemeshow
statistic for a logistic regression model.
 
LogisticRegressionFitAnalysisParameterCalcHosmerLemeshowStatistic 
Class containing the attributes of the Hosmer Lemeshow statistic for
a logistic regression model.
 
LogisticRegressionFitAnalysisParameterCalcPearsonChiSqrStatistic 
Class containing the attributes of the Pearson chisquare statistic
associated with a logistic regression model.
 
LogisticRegressionFitAnalysisParameterCalcPearsonResidual 
Class containing Pearson Residual attributes. The Pearson Residual
is calculated for each covariate pattern.
 
LogisticRegressionParameterParameterCalc 
Class LogisticRegressionParameter tests statistical hypotheses about
estimated parameters in linear regressions computed by class
LogisticRegression.
 
LognormalDistribution 
Class LognormalDistribution represents the lognormal probability distribution.
 
MatrixFunctions  Obsolete.
No longer used. Please use NMathFunctions.
 
MatrixNotSquareException 
Exception thrown when a matrix operation requiring a square matrix is
presented with a nonsquare one.
 
MinimizerBase 
Class MinimizerBase is the abstract base class for classes that perform
function minimization.
 
MismatchedSizeException 
Exception thrown when an operation is performed with operands whose
sizes are incompatible with the operation.
 
MixedIntegerLinearProgrammingProblem 
Class MixedIntegerLinearProgrammingProblem encapsulates a Linear programming problem
which may contain integral constraints.
 
MixedIntegerNonlinearProgrammingProblem 
Class MixedIntegerNonlinearProgrammingProblem represents a nonlinear programming problem.
 
ModifiedLevenbergMarquardtMinimizer 
Class for minimizing the L2 norm of a function using the a modified Levenberg Marquardt
algorithm.
 
MovingWindowFilter 
Class implementing data filtering by replacing data points f(i) with a
linear combination of the data points immediately to the left and right
of f(i). The user provides the coefficients to use in the linear combination.
Static class methods are provided for generating coefficients to implement a
moving average filter and a SavitzkyGolay smoothing filter.
 
MultipleCurveFit 
Class for performing simulatenous fitting of multiple data sets with
shared fitting parameters.
As an example suppose we have two sets of data points
dataset 1 = { (x11,y11), (x12,y12),...,(x1n,y1n) }
dataset 2 = { (x21,y21), (x22,y22),...,(x2n,y2m) }
And suppose we have two parameterized functions
f1(beta,x) = cos(2*pi*beta(0)*x)*exp(x/beta(1))
f2(beta,x) = beta(3) + beta(2)*exp((x/beta(1)))
where beta is a vector of four parameters. The MultiCurveFit
class solves the problem of finding the parameter values beta
that best fit, in the least squares sense, the function f1 to dataset 1
and the function f2 to dataset 2 with the paramter beta(1) being the same for
both datasets.
 
MultipleCurveFitFunction 
Function for simulatenous fitting of multiple data sets with shared
fitting parameters.
MultipleCurveFit  
MultipleCurveFitResidual 
Calculates the residuals of a MultipleCurveFitFunction
with a given set of parameters and data sets.
 
MultipleFitCurveInfo 
Class for pairing a parameterized function with a data set for use in
performing a simulatenous fitting of multiple data sets with shared
fitting parameters.
MultipleCurveFit  
MultiVariableFunction  Obsolete.
Class MultiVariableFunction represents multivariate functions.
 
MultiVariableFunctionFitterM 
Class MultiVariableFunctionFitter fits a generalized multivariable function to a set of points.
 
MultiVariableFunctionFitterMResidualFunction 
Residual function. This is the function that is minimized to produce the parameters for
the best fit.
 
NaturalCubicSpline 
Class NaturalCubicSpline represents a function determined by tabulated values.
Function values are calculated using natural cubic spline interpolation.
 
NegativeBinomialDistribution 
Class NegativeBinomialDistribution represents the discrete probability distribution
of obtaining N successes in a series of x trials, where the probability of
success on each trial is P.
 
NewtonRaphsonParameterCalc 
Parameter calculation for a logistic regression model. The parameters are
computed to maximize the log likelihood function for the model, using
the Newton Raphson algorithm to compute the zeros of the first order
partial derivaties of the log likelihood function.
 
NewtonRaphsonRootFinder 
Class NewtonRaphsonRootFinder finds roots of univariate functions using the
NewtonRaphson algorithm.
 
NiederreiterQuasiRandomGenerator 
Class NiederreiterQuasiRandomGenerator is a quasirandom number generator which can be
used for generating sequences of quasirandom point in ndimensional space.
 
NMathConfiguration 
Class NMathConfiguration provides properties for controlling the loading of the NMath kernel assembly and
native library, and specifying license keys.
 
NMathException 
Base class for exceptions thrown by the NMath product suite.
 
NMathFormatException 
Exception thrown when a method encounters a faulty text representation.
 
NMathFunctions 
Class NMathFunctions provides standard mathematical functions for NMath types.
Trigonometric functions, exponents, logarithms, powers, and square roots are
provided for vector, matrix, and complex number types.
 
NMathFunctionsFiveParameterLogisticFtn 
Computes the 5parameter logistic (5PL) function, using the given vector of function parameters,
at the specified point.
 
NMathFunctionsFourParameterLogisticFtn 
Computes the 4parameter logistic (4PL) function, using the given vector of function parameters,
at the specified point.
 
NMathFunctionsThreeParameterExponentialFtn 
Evaluates the three parameter exponential function for the given parameter values at the
given point.
 
NMathFunctionsThreeParameterSineFtn 
Computes the three parameter sine function, using the given vector of function parameters, at the specified point.
 
NMathFunctionsTwoParameterAsymptoticFtn 
Computes the asymptotic function, using the given vector of function parameters, at the specified point.
 
NMathSettings 
Class NMathSettings contains global settings for NMath classes.
 
NMFact 
Class NMFact performs nonnegative matrix factorization.
 
NMFAlsUpdate 
Class NMFAlsUpdate encapsulates the Alternating Least Squares (ALS) update algorithm.
 
NMFClusteringAlg 
Class NMFClustering performs a Nonnegative Matrix Factorization (NMF) of
a given matrix.
 
NMFConsensusMatrixAlg 
Class NMFConsensusMatrix uses a nonnegative matrix factorization to
cluster samples.
 
NMFDivergenceUpdate 
Class NMFDivergenceUpdate encapulates an NMF update algorithm which
minimizes a divergence functional.
 
NMFGdClsUpdate 
Class NMFGdClsUpdate encapsulates the Gradient Descent  Constrained
Least Squares (GDCLS) algorithm for Nonnegative Matrix Facotorization (NMF).
 
NMFMultiplicativeUpdate 
Class NMFMultiplicativeUpdate encapsulates a multiplicative update algorithm
for Nonnegative Matrix Factorization (NMF).
 
NMFNonsmoothUpdate 
Class NMFNonsmoothUpdate encapulates an NMF update algorithm which
minimizes a cost functional designed to explicitly represent sparseness,
in the form on nonsmoothness, which is controlled by a single parameter.
 
NoncentralTDistribution 
Class NoncentralTDistribution represents a generalized Student's tdistribution with
the specified degrees of freedom and noncentrality parameter.
 
NonlinearConstraint 
Class NonlinearConstraint represents a nonlinear constraint in an optimization problem.
 
NonlinearProgrammingProblem 
Class NonlinearProgrammingProblem represents a nonlinear programming problem.
 
NonModifiableElementException 
Exception thrown when an attempt is made to change the value of an
element in a structured matrix that cannot be changed.
 
NormalDistribution 
Class NormalDistribution represents the normal (Gaussian) probability distribution
with a specifed mean and variance.
 
NoRotation 
Used as a class type parameter value to factor analysis classes when no
factor rotation is desired.
 
NumberOfFactors 
The NumberOfFactors  
OdeSolverBase 
Base class for ODE solvers which use a RungeKutta order 5 algorithm.
Includes enums and functions for incorporating mass matrices into ODE's.
 
OdeSolverBaseConstMassMatrixOdeFcn 
When solving ODE's of the form
y' = M*f(t,y)
where M is a constant "mass" matrix, this class provides a function
g(t,y) for the right hand side of the above equation which incorporates
the mass matrix M.
 
OdeSolverBaseMassMatrixOdeFcn 
When solving ODE's of the form
y' = M(t,y)*f(t,y)
where M is a timestate dependent "mass" matrix, this class provides a function
g(t,y) for the right hand side of the above equation which incorporates
the mass matrix function M(t,y).
 
OneSampleAndersonDarlingTest 
Class OneSampleAndersonDarlingTest performs a AndersonDarling test of the distribution of
one sample.
 
OneSampleKSTest 
Class OneSampleKSTest performs a KolmogorovSmirnov test of the distribution of
one sample.
 
OneSampleTTest 
Class OneSampleTTest compares a single sample mean to an expected mean
from a normal distribution with an unknown standard deviation.
 
OneSampleZTest 
Class OneSampleZTest compares a single sample mean to an expected mean
from a normal distribution with known standard deviation.
 
OneVariableFunction 
Class OneVariableFunction represents functions of one variable.
 
OneVariableFunctionFitterM 
Class OneVariableFunctionFitter fits a parameterized one variable function to a set of points.
 
OneVariableFunctionFitterMCurveFitResidualFunction 
Class representing the residual function for the curve fit.
 
OneWayAnova 
Class OneWayAnova computes and summarizes a traditional oneway (single
factor) Analysis of Variance (ANOVA).
 
OneWayAnovaTable 
Class OneWayAnovaTable summarizes the information of a traditional oneway
Analysis of Variance (ANOVA) table.
 
OneWayRanova 
Class OneWayRanova summarizes the information of a
oneway repeated measures Analysis of Variance (RANOVA).
 
OneWayRanovaTable 
Class OneWayRanovaTable summarizes the information of a traditional oneway
repeated measures Analysis of Variance (RANOVA) table.
 
OpenClosedInterval 
Class OpenClosedInterval represents a numeric interval with an exclusive
lower bound and an inclusive upper bound.
 
OpenInterval 
Class OpenInterval represents a numeric interval with exclusive
lower and upper bounds.
 
OrderedConnectivityMatrix 
Class OrderedConnectivityMatrix reorders the rows and columns of an
connectivity matrix so that the most affiliated elements appear as clusters
of higher values along the diagonal.
 
ParameterizedMultivariableFunction 
Abstract class representing multivariable a parameterized function.
 
PCFactorExtraction 
Class implementing the principle components (PC) algorithm for factor
extraction when performing factor analysis.
Used as a class type parameter for the factor analysis classes.
 
PeakFinderBase 
Abstract base class for all peak finding algorithms. The class
is an enumerable collection of all found peaks.
 
PeakFinderRuleBased 
Class PeakFinderRuleBased simply returns all of the peaks subject to
rules about peak height and peak separation. A peak is defined as
a point which is higher that both neighbors or infinity. Noninfinity
end points are excluded as a peak.
 
PeakFinderSavitzkyGolay 
Class PeakFinderSavitzkyGolay uses smooth SavitzkyGolay
derivatives to find peaks in data and acts as a collection for the
found peaks.
 
PearsonsChiSquareTest 
Class PearsonsChiSquareTest tests whether the frequency distribution of experimental outcomes are
consistant with a particular theoretical distribution.
 
PLS1 
Class PLS1 performs a Partial Least Squares (PLS) regression calculation on a
set of predictive and onedimensional response values. The result is used to
predict response variable values.
 
PLS1Anova 
Class PLS1Anova performs a standard ANalysis Of VAriance (ANOVA) for
a Partial Least Squares 1 (PLS1) regression model.
 
PLS1CrossValidation 
Class PLS1CrossValidation performs an evaluation of a PLS (Partial Least
Squares) model.
 
PLS1CrossValidationData 
Class PLS1CrossValidationData divides Partial Least Squares  one
dimensional response variable,(PLS1), data into training and testing
subsets.
 
PLS1CrossValidationResult 
Class PLS2CrossValidationResult performs a Partial Least Squares  one
dimensional response variable, (PLS1), cross validation calculation.
 
PLS1NipalsAlgorithm 
Class PLS1NipalsAlgorithm encapsulates the Nonlinear Iterative PArtial Least
Squares (NIPALS) algorithm for computing partial least squares regression components.
 
PLS2 
Class PLS2 performs a Partial Least Squares (PLS) regression calculation
on a set of predictive and response values. The result is used to predict
response variable values.
 
PLS2Anova 
Class PLS2Anova performs a standard ANalysis Of VAriance (ANOVA) for
a Partial Least Squares (PLS) regression model.
 
PLS2CrossValidation 
Class PLS2CrossValidation performs an evaluation of a PLS (Partial Least
Squares) model.
 
PLS2CrossValidationData 
Class PLS2CrossValidationData divides Partial Least Squares (PLS) data
into training and testing subsets.
 
PLS2CrossValidationResult 
Class PLS2CrossValidationResult performs a Partial Least Squares (PLS)
cross validation calculation.
 
PLS2CrossValidationWithJackknife 
Class PLS2CrossValidationWithJackknife performs an evaluation of a PLS (Partial Least
Squares) model with model coefficient variance estimates and confidence intervals.
 
PLS2NipalsAlgorithm 
Class PLS2NipalsAlgorithm encapsulates the Nonlinear Iterative PArtial Least
Squares (NIPALS) algorithm for computing partial least squares regression
components.
 
PLS2SimplsAlgorithm 
Class PLS2SimplsAlgorithm encapsulates the Straightforward IMplementation
of Partial Least Squares, or SIMPLS, algorithm (de Jong, 1993) for
computing partial least squares regression components.
 
PoissonDistribution 
Class PoissonDistribution represents a poisson distribution with a specified lambda, which is
both the mean and the variance of the distribution. The poisson distribution a discrete
distribution representing the probability of obtaining exactly n successes in
N trials.
 
Polynomial 
Class Polynomial represents a polynomial function as a vector of
coefficients.
 
PolynomialDifferentiator 
Class PolynomialDifferentiator encapsulates exact differentiation of
polynomials.
 
PolynomialIntegrator 
Class PolynomialIntegration encapsulates exact integration of polynomials.
 
PolynomialLeastSquares 
Class PolynomialLeastSquares performs a least squares fit of a polynomial to
the data.
 
PowellMinimizer 
Class PowellMinimizer minimizes a multivariable function using Powell's Method.
 
PowerMethod 
Class for computing the dominant eigenvalue and eigenvector of a square
matrix using the iterative power method.
 
PrimalSimplexSolver  Obsolete.
Class PrimalSimplexSolver is a class for solving linear
programming prolems using the primal simplex method.
 
PrimalSimplexSolverORTools 
Class PrimalSimplexSolver is a class for solving linear
programming prolems using the primal simplex method.
 
PrimalSimplexSolverParams  Obsolete.
Primal simplex algorithm parameters.
 
ProbabilityDistribution 
Class ProbabilityDistribution is the abstract base class for classes that
represent distributions of random variables.
 
ProcessCapability 
Computes the process capability parameters Cp, Cpm, Cp for normally distributed data. If the data
is not normal the BoxCox transform can be used.
 
ProcessPerformance 
Computes process performance parameters Pp and Ppk for normally distributed data. If the data
is not normal the BoxCox transform can be used.
 
QRRegressionCalculation 
Class QRRegressionCalculation computes linear regression parameters by
the method of least squares using a QR decomposition.
 
QuadraticProgrammingProblem 
Class QuadraticProgrammingProblem encapsulates a quadratic programming (QP) problem.
 
QuasiRandomNumberGenerator 
Abstract base class for generating sequences of quasirandom points.
A quasirandom sequence is a sequence of ntuples that fills nspace
more uniformly than uncorrelated random points.
 
RandGenBeta 
Class RandGenBeta generates random numbers from a beta distribution.
 
RandGenBinomial 
Class RandGenBinomial generates random numbers from a binomial distribution.
 
RandGenExponential 
Class RandGenExponential generates random numbers from an exponential distribution.
 
RandGenGamma 
Class RandGenGamma generates random numbers from an gamma distribution.
 
RandGenGeometric 
Class RandGenGeometric generaties random numbers from a Geometric distribution.
 
RandGenJohnson 
Class RandGenJohnson generates random numbers from a Johnson distribution.
 
RandGenLogNormal 
Class RandGenLogNormal generates random numbers from a lognormal
distribution.
 
RandGenMTwist 
Class RandGenMTwist generates random numbers from a uniform distribution using
the Mersenne Twister algorithm.
 
RandGenNormal 
Class RandGenNormal generates random numbers from a normal distribution.
 
RandGenPareto 
Class RandGenPareto generates random numbers from a Pareto distribution.
 
RandGenPoisson 
Class RandGenPoisson generates random numbers from an Poisson distribution.
 
RandGenTriangular 
Class RandGenTriangular generates random numbers from a triangular distribution.
 
RandGenUniform 
Class RandGenUniform generates random numbers from a uniform distribution.
 
RandGenWeibull 
Class RandGenWeibull generates random numbers from a Weibull distribution.
 
RandomNumberGenerator 
Abstract base class for NMath random number generators.
 
RandomNumbersT, D 
Class RandomNumbers is an adapter for the RandomNumberStream class to give the same behavior as a scalartype
random number generator.
 
RandomNumberStream 
Class RandomNumberStream is a vectorized random number generator which yields
a stream of random numbers from various probability distributions.
 
Range 
Class Range represents a collection of indices that can be used to view a subset of
data from another data structure. A Range is defined by a starting index, an ending
index or enumerated Position value, and a step increment called the stride.
 
ReducedVarianceInputCorrelator 
Instances of the ReducedVarianceInputCorrelator class are used to induce
a desired rank correlation among input variables.
 
RegressionBase 
Base class for linear and logistic regression.
 
RegressionFactorScores 
Class implementing the IFactorScores  
RiddersDifferentiator 
Class RidderDifferentiator encapsulates numerical differentiation of
functions.
 
RiddersRootFinder 
Class RiddersRootFinder finds roots of univariate functions using
Ridders' Method.
 
RombergIntegrator 
Class RombergIntegrator approximates integrals of functions over a
given interval using the Romberg method.
 
RootFinderBase 
Abstract base class for classes that perform root finding on
univariate functions.
 
RungeKutta45OdeSolver 
Class RungeKutta45OdeSolver solves an initial value, Ordinary Differential
Equation (ODE) using an explicit RungeKutta (4,5) formula known as the DormandPrince pair.
 
RungeKutta45OdeSolverOptions 
User settable options for RungeKutta45OdeSolver.
 
RungeKutta45OdeSolverSolutionYtype 
Data structor contiaing solution values an statistics for an ODE
solve.
 
RungeKutta5OdeSolver 
Class RungeKutta5OdeSolver solves an initial value, Ordinary Differential
Equation (ODE) using a nonadaptive explicit RungeKutta formula of order 5.
 
RungeKutta5OdeSolverOptions 
User settable options for RungeKutta5OdeSolver.
 
SavitzkyGolay 
Class generates the SavitzyGolay filter coefficients for smoothing data
or computing smoothed derivatives. Smoothed derivatives can be found
up to the order of the fitting polynomial.
 
SavitzkyGolayFilter 
Class SavitzkyGolayFilter is a correlation filter specialized for filtering with SavitzkyGolay
coefficients with additional boundary options for better edge continuity.
The default boundary option ShiftFilterCenter provides
data smoothing all the way boundary ends.
 
SecantRootFinder 
Class SecantRootFinder finds roots of univariate functions using the
secant method.
 
SequentialQuadraticProgrammingSolver 
Base class for sequential quadratic programming solvers.
 
SequentialQuadraticProgrammingSolverIteration 
Data structure containing various values for an iteration of a
Sequential Quadratic Programming algorithm.
 
ShapiroWilkTest 
Class ShapiroWilkTest tests the null hypothesis that the sample comes from a normally distributed population.
 
SimplexSolverBase  Obsolete.
Class SimplexSolverBase is an abstract base class for classes solving mixed
integer linear programming prolems using variants of the simplex method.
 
SimplexSolverBaseORTools 
Class SimplexSolverBase is an abstract base class for classes solving mixed
integer linear programming prolems using variants of the simplex method.
 
SimplexSolverMixedIntParams  Obsolete.
Class containing parameters for simplex based mixed integer linear
programming solvers.
 
SimplexSolverParamsBase  Obsolete.
Base class for simplex solver paramters.
 
SingularMatrixException 
Exception thrown when a matrix operation requiring a nonsingular
matrix is presented with a singular one.
 
SkipAheadRandomStreams 
Class for creating several independent streams of random numbers using
the method know as skipahead, or blocksplitting.
 
SkipAheadStream 
Class SkipAheadStream represents a single skipahead stream.
 
Slice 
Class Slice represents a collection of indices that can be used to view
a subset of data from another data structure. A slice is defined by a starting
index, a total number of elements, and a step increment called the stride.
 
SmoothCubicSpline 
Class SmoothCubicSpline calculates smoothing splines.
 
SobolQuasiRandomGenerator 
Class SobolQuasiRandomGenerator is quasirandom number generator which can be used
for generating sequences of quasirandom point in ndimensional space.
 
SparseConstraintCoefficients 
Class implementing the ILinearConstraintCoefficients for sparse linear
constraint coefficients. Only the nonzero coefficients are stored.
 
SparseMatrixBuilderT 
Class SparseMatrixBuilder{T} serves as the base class for the sparse
matrix builder classes of specific types. This class implements the interface
System.Collections.Generic.IDictionary{IntPair,T}, providing matrixlike
row/column indexing for setting and retrieving values. Only nonzero values are stored.
 
SparseMatrixDataStorage, Type 
Class SparseMatrixData stores general sparse matrix data.
 
SparseMatrixFactT 
Abstract base class for sparse matrix factorizations using the Parallel
Direct Sparse Solver Interface (PARDISO).
 
SparsePls 
Class SparsePls performs a Partial Least Squares (PLS) calculation
for the model X ~ Y with variable selection.
The LASSO penalization is used on the pairs of loading
vectors.
SparsePls allows matrices with missing values in them by using
the NIPALS algorithm to estimate them. Missing values are represented
as Double.NaN.
 
SparsePlsDa 
Class for performing Discriminant Analysis (DA) using sparse Partial Least
Squares (sPLS). This is a classical sPLS regression, but where the
response variable is catagorical. The response vector Y is qualitative and
is recoded as a dummy block matrix where each of the response categories
are coded via an indicator variable. PLSDA is then run as if Y was a
continuous matrix.
 
SparsePLSDACrossValidation 
Class SparsePLSDACrossValidation performs an evaluation of a PLS (Partial Least
Squares) model.
 
SparseVectorDataT 
Class SparseVectorData stores sparse vector data.
 
SpecialFunctions 
This class contains a collection of special functions.
 
StatsFunctions  Obsolete.
No longer used. Please use NMathFunctions.
 
StatsSettings 
Class StatsSettings contains global settings for NMath Stats classes.
 
StochasticHillClimbingParameters 
Parameter class for the StochasticHillClimbingSolver class.
 
StochasticHillClimbingSolver 
Nonlinear programming solver that uses a stocastic hill climbing algorithm.
It starts from a random set of decision values, and repeatedly tries
small random changes to the decision values. It keeps changes that
make the answer better, and rejects changes that make it worse.
 
Subset 
Class Subset represents a collection of indices that can be used to view
a subset of data from another data structure.
 
SVDRegressionCalculation 
Class SVDRegressionCalculation computes linear regression parameters by
the method of least squares using a singular value decomposition.
 
TabulatedFunction 
Class TabulatedFunction is an abstract class representing a function
determined by tabulated values.
 
TDistribution 
Class TDistribution represents Student's tdistribution with the specified
degrees of freedom.
 
TriangularDistribution 
Class TriangularDistribution represents the triangular probability distribution.
 
TrustRegionMinimizer 
Class TrustRegionMinimizer solves both constrained and unconstrained nonlinear least
squares problems using the Trust Region method.
 
TrustRegionParameterCalc 
Parameter calculation for a logistic regression model. The parameters are
computed to maximize the log likelihood function for the model, using
a trust region optimization algorithm to compute the zeros of the first order
partial derivaties of the log likelihood function.
The minimization is performed by an instance of the class
CenterSpace.NMath.Core.TrustRegionMinimizer and algorithms
parameters may be controlled through this object. It is accessible
through the Minimizer class property, and a TrustRegionParameterCalc
instace may be constructed with a give TrustRegionMinimizer object which
has the desired properties.
TrustRegionMinimizer  
TwoSampleFTest 
Class TwoSampleFTest tests if the variances of two populations
are equal.
 
TwoSampleKSTest 
Class TwoSampleKSTest performs a twosample KolmogorovSmirnov test to compare
the distributions of values in two data sets.
 
TwoSamplePairedTTest 
Class TwoSamplePairedTTest tests if two paired sets of observed values differ
from each other in a significant way.
 
TwoSampleUnpairedTTest 
Class TwoSampleUnpairedTTest tests the null hypothesis that the two population
means corresponding to two random samples are equal.
 
TwoSampleUnpairedUnequalTTest 
Class TwoSampleUnpairedUnequalTTest tests the null hypothesis that the two population
means corresponding to two random samples are equal.
 
TwoVariableIntegrator 
Class TwoVariableIntegrator integrates functions of two variables.
 
TwoWayAnova 
Class TwoWayAnova performs a balanced twoway analysis of variance.
 
TwoWayAnovaBase 
Base class for both balanced and unbalanced two way ANOVA.
 
TwoWayAnovaTable 
Class TwoWayAnovaTable summarizes the information of a traditional twoway
Analysis of Variance (ANOVA) table.
 
TwoWayAnovaTypeI 
Class for performing a Type I ANOVA on unbalanced data. See the description
of the base class TwoWayAnovaUnbalanced for a description of
the notation.
Type I, also called "sequential" sum of squares:
SS(A) for factor A.
SS(B  A) for factor B.
SS(AB  B, A) for interaction AB.
This tests the main effect of factor A, followed by the main effect of
factor B after the main effect of A, followed by the interaction effect
AB after the main effects.
 
TwoWayAnovaTypeII 
Class for performing a Type II ANOVA on unbalanced data. See the description
of the base class TwoWayAnovaUnbalanced for a description of
the notation.
SS(A  B) for factor A.
SS(B  A) for factor B.
SS(AB  A, B) for interaction.
This type tests for each main effect after the other main effect.
Note that no significant interaction is assumed and you should look
look at the test for interaction first (SS(AB  A, B)) and only if
interaction after the main effects AB is not significant continue
with the analysis for main effects.
 
TwoWayAnovaTypeIII 
Class for performing a Type III ANOVA on unbalanced data. See the description
of the base class TwoWayAnovaUnbalanced for a description of
the notation.
SS(A  B, AB) for factor A.
SS(B  A, AB) for factor B.
SS(AB  A, B) for interaction.
This type tests for the presence of a main effect after the other main effect and
interaction. This approach is therefore valid in the presence of significant interactions.
However, it is often not interesting to interpret a main effect if interactions are
present (generally speaking, if a significant interaction is present, the main effects
should not be further analysed).
 
TwoWayAnovaUnbalanced 
Class TwoWayAnovaUnbalanced is the base class for performing a
two way ANOVA when the number of observations in each cell is not the
same (an unbalanced design). In this case the main and interaction effects are
interdependent, and we must obtain the marginal sum of squares associated with
each factor after all the other factors have already been included in the model
(the marginal sum of squares for each variable equals the incremental sum
of squares for that variable when it is entered into the equation last).
In terms of a regression approach to ANOVA, the marginal sum of squares due
to a factor is the sum of squares for the set of dummy variables associated
with that factor when those dummy variables are entered into the model last,
after all other dummy variables.
Classes deriving from TwoWayAnovaUnbalanced provide the ordering of
dummy regression variables and use the base class to compute the resulting
regressions and sums of squares.
 
TwoWayAnovaUnbalancedOrderedLinearRegression 
Class containing the linear regression object computed from a particular
ordering of factor and interaction parameters.
Note that a factor parameter or interaction parameters may be
absent depending on which type of sum of squares is being computed.
 
TwoWayAnovaUnbalancedParameterSlices 
As long as the dummy regression parameters are the same, we may use
the same regression matrix to compute the various marginal sums of
squares. Since the dummy variables for a given factor, or the interaction
parameters, will always be contiguous columns in the regression
matrix, their location can be specified with slices. The ParameterSlices
class specifies the slices where factor A, factor B, and interaction
dummy variables are located in the regression matrix.
The following terminology is used throughout:
Regression matrix  the matrix of regression parameters include the
intercept parameter, which is expressed as a leading column of 1's
in the matrix.
Predictor matrix  The matrix of regression parameters excluding the
intercept parameter.
Thus regression parameters start at column 1 in the regression matrix and
column 0 in the predictor matrix.
 
TwoWayAnovaUnbalancedTable 
Class TwoWayAnovaUnbalancedTable summarizes the information of a traditional twoway
Analysis of Variance (ANOVA) table.
 
TwoWayRanova 
Class TwoWayRanova performs a balanced twoway analysis of variance with
repeated measures on one factor.
 
TwoWayRanovaTable 
Class TwoWayRanovaTable summarizes the information of a traditional twoway
Analysis of Variance (RANOVA) table.
 
TwoWayRanovaTwo 
Class TwoWayRanovaTwo performs a balanced twoway analysis of variance with
repeated measures on both factors.
 
TwoWayRanovaTwoTable 
Class TwoWayRanovaTwoTable summarizes the information of a traditional twoway
Analysis of Variance, with repeated measures on both factors, table,
 
UniformDistribution 
Class UniformDistribution represents the Uniform probability distribution.
 
VariableBounds 
Class specifying a variable ID, lower and upper bounds, and a tolerance
used in testing bounds compliance.
 
VariableMetricMinimizer 
Class VariableMetricMinimizer uses the BroydenFletcherGoldfarbShanno variable
metric algorithm to minimize multivariable functions.
 
VariableOrderOdeSolver 
Solver for stiff and nonstiff ordinary differential equations. The algorithm
uses higher order methods and smaller step size when the solution
varies rapidly.
 
VariableOrderOdeSolverOptions 
Class containing available options for the VariableOrderOdeSolver.
 
VariableOrderOdeSolverSolutionYtype 
Data structor contianing solution values and statistics for an ODE
solve.
 
VarimaxRotation 
Class for computing the varimax rotation of the factor from a factor analysis.
Rotates the coordinates to maximize the sum of the variances of the squared
loadings. Kaiser normalization is optionally performed, and the default stopping
tolerance (1e12) is used.
 
Wavelet 
This abstract class represents a wavelet. There are fives types of built in wavelets
avaiable: Harr, Daubechies, Least Asymmetric, Best Localized, and Coiflet.
 
WeibullDistribution 
Class WeibullDistribution represents the Weibull probability distribution.
 
WeibullParameterFunction  
WeightedRegressionAnova 
Class for performing an Analysis Of Variance on a weighted linear least
squares fit.
 
WilcoxonSignedRankTest 
Class WilcoxonSignedRankTest tests if two paired sets of observed values differ
from each other in a significant way.
 
ZBenchNormal 
Computes the ZBench for normally distributed data, percent defective, and the parts per million defective.

Structure  Description  

DiscreteWaveletTransformLevelT 
Represents one level of a DWT decomposition.
 
DoubleComplex 
The DoubleComplex struct represents a complex number, consisting of a
real part and an imaginary part.
 
Extrema 
Represents an imutable extrema of a function in two dimensions.
 
FloatComplex 
The FloatComplex struct represents a complex number, consisting of a
real part and an imaginary part.
 
IntPair 
Class IntPair represents a pair of integers.

Interface  Description  

IArnoldiShiftInvertOperations 
Eigenvalue Arnoldi solver shift invert operations.
 
IBoundedNonlinearLeastSqMinimizer 
Interface for nonlinear least squares minimizer where the solution
is constrained by upper and lower bounds.
 
ICrossValidationSubsets 
Interface for generating subsets of data to be used in a cross validation
process.
 
IDFColumn 
Interface for data frame column types.
 
IDifferentiator 
Interface for classes that perform differentiation of a function at a point.
 
IDoubleComplexEnumerator 
Classes that implement the IDoubleComplexEnumerator interface behave like regular
IEnumerator implementations except that they return a DoubleComplex instead
of an object. They avoid casting and are therefore much faster.
 
IDoubleEnumerator 
Classes that implement the IDoubleEnumerator interface behave like regular IEnumerator
implementations except that they return a double instead of an object.
They avoid casting and are therefore much faster.
 
IDoubleLeastSqWeightingFunction 
Interface for least squares weighting functions.
 
IFactorExtraction 
Interface for factor extration algorithms used in factor analysis.
 
IFactorRotation 
Interface for factor analysis factor rotation algorithms. Factors are
rotated in order to maximize the relationship between the variables and
some of the factors.
 
IFactorScores 
Interface for factor score computation in a factor analysis.
 
IFloatComplexEnumerator 
Classes that implement the IFloatComplexEnumerator interface behave like regular
IEnumerator implementations except that they return a FloatComplex instead
of an object. They avoid casting and are therefore much faster.
 
IFloatEnumerator 
Classes that implement the IFloatEnumerator interface behave like regular
IEnumerator implementations except that they return a float instead
of an object. They avoid casting and are therefore much faster.
 
IIntegrator 
Interface for classes that perform integration of a function over an interval.
 
ILinearConstraintCoefficients 
Interface for the coefficients for a linear constraint in a nonlinear optimization
problem. The interface is an abstraction of a vector of real numbers representing the
coefficients of a linear constraint and thus defines the indexer property
[int index]. This interface allows for both sparse and dense implementations.
 
ILogisticRegressionCalc 
Interface class for calculating the parameters of a logistic regression
model.
 
IMultiVariableDMinimizer 
Interface for classes that perform minimization of multivariate functions
using derivative information.
 
IMultiVariableMinimizer 
Interface for classes that perform minimization of multivariate functions.
 
INMFUpdateAlgorithm 
Interface to be implemented by all Nonnegative Matrix Factorization (NMF)
update algorithms used by the NMFact class.
 
INonlinearLeastSqMinimizer 
Interface for nonlinear least squares minimizer.
 
IOneVariableDMinimizer 
Interface for classes that perform minimization of univariate functions
using derivative information.
 
IOneVariableDRootFinder 
Interface for classes that find roots of univariate functions using
derivative information.
 
IOneVariableMinimizer 
Interface for classes that perform minimization of univariate functions.
 
IOneVariableRootFinder 
Interface for classes that find roots of univariate functions using
only function evaluations.
 
IRandomNumberDistributionT 
Interface for random number distributions.
 
IRandomVariableMoments 
Interface implemented by probablility distributions.
 
IRegressionCalculation 
Interface for classes used by class LinearRegression to calculate regression
parameters.
 
ISliceableT 
Implement this interface to indicate support of range slicing in a vector.
 
ISparseMatrixStorageT 
Interface for general sparse matrix storage formats.
 
ISpecialFunctions  
SequentialQuadraticProgrammingSolverILagrangianHessianUpdater 
Interface for classes which provide a method for obtaining the updated value of the Hessian
of the Lagrangian based on the current iteration data for a Sequentail Quadratic
Programming algorithm.
 
SequentialQuadraticProgrammingSolverIStepSizeCalculator 
Computes a step size alphak for performing the update
xk+1 = xk + alphak*pk, where pk is the step direction
vector.

Delegate  Description  

DistanceFunction 
Functor that takes two vectors and returns a measure of the distance
(similarity) between them.
 
DoubleIterativelyReweightedLeastSqToleranceMetFunction 
Tolerance met function delegate.
 
LinkageFunction 
Functor that computes the linkage (similarity) between two groups.
 
OrderedConnectivityMatrixElementDistance 
Given an entry aij in the connectivity matrix A, this delegate must return
the distance between the elements i and j to be used for performing the
hierarchical cluster analysis.
 
RandomNumberGeneratorUniformRandomNumber 
Functor for generating uniform deviates between zero and one.

Enumeration  Description  

ActiveSetLineSearchSQPTerminationStatus 
Enum for possible algorithm termination reasons.
 
ArnoldiSolveStatus 
Result of eigenvalue problem solve attempt using implicitly
restarted Arnoldi iteration.
 
BairstowRootFinderSolveResultStatus 
Status values indicating why iteration was terminated.
 
BalanceOption 
Enumeration for specifying balancing options in eigenvalue decompositions.
 
BiasType 
Enumeration for specifying a biased or unbiased estimator.
 
ConjugateTransposeOption 
Enum specifying a particualr conjugate transpose option.
 
ConstrainedOptimizerSolveResult 
Enum whose value indicate the status of the solution.
 
ConstrainedOptimizerORToolsSolveResult 
Enum whose value indicate the status of the solution.
 
ConstraintType 
Enumeration for specifying constraint types possible for the Constraint
class and other constrained optimization classes.>
 
ControlLimits 
Emum specifics types of control limits.
 
ConvolutionBaseWindowing 
Options for handling the various convolution/correlation result boundaries.
 
ConvolutionMode 
Controls internal funtion of convolution class.
 
CorrelationBaseWindowing 
Options for handling the various convolution/correlation result boundaries.
 
CorrelationMode  
DiscreteDataIntegratorStrategy 
Discrete data integration strategies.
 
DiscreteWaveletTransformThresholdMethod 
Details thresholding methods.
 
DiscreteWaveletTransformThresholdPolicy 
Thresholding policy to apply to details thresholding.
 
DiscreteWaveletTransformWaveletCoefficientType 
The two types of wavelet coefficients.
 
DiscreteWaveletTransformWaveletMode 
Method for padding the signal edges for improved DWT accuracy near signal edges.
 
DoubleRandomBetaDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
DoubleRandomExponentialDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
DoubleRandomGammaDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
DoubleRandomGaussianDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
DoubleRandomLogNormalDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
DoubleRandomRayleighDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
DoubleRandomUniformDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
DoubleRandomWeibullDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
DualSimplexCosting  Obsolete.
Possible values for the dual simplex solver costing
parameter. These values specify the pivoting strategy
used by the solver.
 
FFTDirection 
Direction of FFT. Used for building FFTConfiguration types.
 
FFTDomain 
Forward Domain of FFT. Used for building FFTConfiguration types.
 
FFTPrecision 
Precision of FFT transform. Used for building FFTConfiguration types.
 
FloatRandomBetaDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
FloatRandomExponentialDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
FloatRandomGammaDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
FloatRandomGaussianDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
FloatRandomLogNormalDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
FloatRandomRayleighDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
FloatRandomUniformDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
FloatRandomWeibullDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
HypothesisType 
Enumeration for specifying the form of an alternative hypothesis in a
hypothesis test.
 
IActiveSetQPSolverAlgorithmStatus 
Enum whose value indicate the status of the solution.
 
InteriorPointQPSolverParamsKktFormOption 
KKT form options for the interior point quadratic programming solver.
 
InteriorPointQPSolverParamsPresolveLevelOption 
Presolve options for the interior point quadratic programming solver.
 
InteriorPointQPSolverParamsSymbolicOrderingOption 
Options for symbolic ordering for the interior point quadratic programming
solver.
 
IntervalType 
An enumeration representing the possible interval types classified
according to whether or not the endpoints are included in the interval.
 
IntRandomPoissonDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
IntRandomUniformDistributionGenerationMethod 
Enumeration specifying different methods of random number generation.
 
JohnsonTransformationType 
Enumeration for specifying the type of transformation of a normal
random variate in the Johnson system.
 
KMeansClusteringStart 
An enumeration representing methods used to choose the initial cluster centers.
 
MovingWindowFilterBoundaryOption 
Options for handling the boundaries in a moving window filter.
 
NonnegLeastSqTermination 
Enumeration of possible nonnegative least squares algorithm
termination states.
 
NormType 
Enumeration for specifying different types of norms.
 
OdeSolverBaseOutputFunctionFlag 
Output functions are functions supplied by the user to derived ODE
solvers. These functions are called by solver during
1. Initialization. Before the first integration step is performed.
2. After each integration step.
3. When the solver is complete.
Output functions are called with an OutputFunctionFlag parameter
indicating under which of the above conditions it is being invoked.
 
ParameterSharing 
Enum for indicating whether a parameter is shared or not in a
global curve fit calculation.
 
PeakFinderBasePeakSortOrder 
Enumeration for specifying sorting order.
 
PeakFinderRuleBasedRules 
Rules to filter peaks.
 
Position 
Enumeration for specifying different view positions of underlying data.
 
PrimalSimplexCosting 
Costing algorithms supported by the primal simplex algorithm.
 
ProductTransposeOption 
Enum for specifying transpose operations to be performed on the operands
of a matrixmatrix multiply operation.
 
RandomNumberStreamBasicRandGenType 
Enumeration for the various algorithms available for generating random
numbers uniformly dstributed in the interval [0, 1]
 
RandomNumberStreamStreamStatus 
Enum indicating the status of a random number stream.
 
SavitzkyGolayFilterSavitzyGolayBoundaryOptions 
Enumeration specifying SavitskyGolay boundary options.
 
SimplexSolverMixedIntParamsBranchingStrategies 
Enumeration of branching strategies used in the branchandbound algorithm.
 
SimplexSolverMixedIntParamsSearchStrategies 
Enumeration of options for search strategies for the mixed integer
linear programming solver.
 
SortingType 
Enumeration for specifying different sorting types, such as ascending
or descending order.
 
SparseMatrixFactTError 
Enumeration for specifying possible return values for errors.
 
SparseMatrixFactTMatrixType 
Enumeration for specifying the types of matrices that can be factored.
 
SparsePLSMode 
Partial Least Squares Mode.
 
StorageType 
Enumeration for specifying the storage scheme of a matrix.
 
TrustRegionMinimizerCriterion 
Enumeration for specifying the stop criterion.
 
TwoWayAnovaUnbalancedParameterOrder 
Enumeration indicating the factors and their order in a sum of squares
computation.
 
WaveletWavelets 
Builtin wavelets organized by short name. The first letter abbreviates
the wavelet family name, and number that follows, the wavelet length.
