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CenterSpace.NMath.Core Namespace

 
Classes
 ClassDescription
Public classAbstractRandomNumberStream 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).
Public classActiveSetLineSearchSQP Class ActiveSetLineSearchSQP solves nonlinear programming problems using a Sequential Quadratic Programming (SQP) iterative algorithm.
Public classActiveSetLineSearchSQPOptions Contains the options available to the ActiveSetLineSearchSQP Nonlinear Program Solver (NLP).
Public classActiveSetQPSolver Class ActiveSetQPSolver solves convex quadratic programming (QP) problems.
Public classAnalysisFunctionsObsolete.
No longer used. Please use NMathFunctions.
Public classAnnealingHistory Class AnnealingHistory encapsulates all of the data generated during a series of steps through an annealing schedule.
Public classAnnealingHistoryStep Class AnnealingHistory.Step encapsulates all of the data associated with a step in an AnnealingHistory.
Public classAnnealingMinimizer Class AnnealingMinimizer minimizes a multivariable function using the simulated annealing method.
Public classAnnealingScheduleBase Class AnnealingScheduleBase is the abstract base class for annealing schedules.
Public classAnovaRegressionFactorParam Class AnovaRegressionFactorParam provides information about a regression parameter associated with a specific level of an ANOVA factor.
Public classAnovaRegressionInteractionParam 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.
Public classAnovaRegressionParameter Class AnovaRegressionParameter provides information about a regression parameter used to perform an analysis of variance by class TwoWayAnova.
Public classAnovaRegressionSubjectParam Class AnovaRegressionSubjectParam provides information about a regression parameter associated with a subject dummy regression variable.
Public classArnoldiEigenvalueOptions Options for solving symmetric eigenvalue problems using the shift and invert spectral transformation.
Public classArnoldiEigenvalueSolution Class contianing solution information for an Arnoldi iteration eigenvalue problem.
Public classArnoldiEigenvalueSolver Solve the generalized eigenvalue problem Ax = Mx(lambda) Where A is sparse symmetric and M is sparse symmetric semi position definite. Solve is accomplished using a shift and invert spectral transformation and implicitly restarted Arnoldi iteration.
Public classBairstowRootFinder Class implementing Bairstows method finds roots for polynomials of degree greater than 3.
Public classBairstowRootFinderSolveResult Class encapsulating information about the result of applying Bairstows method to a polynomial.
Public classBetaDistribution Class BetaDistribution represents the beta probability distribution.
Public classBinomialDistribution 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.
Public classBoundedMultiVariableFunctionFitterM Class MultiVariableFunctionFitter< M > fits a parameterized multivariable function to a set of points where the parameters have inequality constraints.
Public classBoundedOneVariableFunctionFitterM Class BoundedOneVariableFunctionFitter fits a parameterized one variable function to a set of points, where the functions parameters are constrained by upper and lower bounds.
Public classBoundedVariableProblem Abstract class for representing a problem with bounded variables.
Public classBoxCox Class for computing the Box-Cox 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 log-likelihood function and the value of lambda which maximizes it are provided.
Public classBoxCoxTransformation Class BoxCoxTransformation performs a Box-Cox power transformation, which can be used to make non-normal data resemble normally-distributed data.
Public classBracket Class Bracket searches in the downhill direction for two points that bracket a minimum of a univariate function.
Public classBrentMinimizer Class BrentMinimizer uses Brent's Method to minimize a function within an interval known to contain a minimum.
Public classCentralDifferenceHessianUpdater Class CentralDifferenceHessianUpdater updates the Hessian of the Lagrangian while solving a nonlinear programming problem using a Sequential Quadratic Programming algorithm.
Public classChiSquareDistribution Class ChiSquareDistribution represents the chi-square probability distribution.
Public classClampedCubicSpline Class ClampedCubicSpline represents a function determined by tabulated values. Function values are calculated using clamped cubic spline interpolation.
Public classClosedInterval Class ClosedInterval represents a numeric interval with inclusive lower and upper bounds.
Public classClosedOpenInterval Class ClosedOpenInterval represents a numeric interval with an inclusive lower bound and an exclusive upper bound.
Public classCode exampleClusterAnalysis Class ClusterAnalysis perform hierarchical cluster analysis.
Public classClusterSet Class ClusterSet represents a collection of objects assigned to a finite number of clusters.
Public classCode exampleCompressedSparseRowT Class CompressedSparseRow stores general sparse matrix data in compressed row format.
Public classConjugateGradientMinimizer Class ConjugateGradientMinimizer minimizes a multivariable function using the Polak-Ribiere variant of the Fletcher-Reeves conjugate gradient method.
Public classConnectivityMatrix Class ConnectivityMatrix represents a symmetric matrix of double-precision floating point values.
Public classConstantSQPStepSize Class ConstantSQPStepSize computes the step size for a Sequential Quadratic Programming solver. Simply returns a constant step size regardless of iteration values.
Public classConstrainedLeastSquares Class for solver constrained least squares problems.
Public classConstrainedLeastSquaresProblem Class that encapsulates a constrained least squares problem.
Public classConstrainedOptimizer Base class for linear Microsoft Solver Foundation based linear solvers.
Public classConstrainedOptimizerORTools Base class for linear Google OR-Tools based linear solvers.
Public classConstraint Class Constraint represents a constraint in a constrained optimization problem.
Public classConvolutionBase Abstract base class for all concrete convolution classes.
Public classCORegressionCalculation Class CORegressionCalculation computes linear regression parameters by the method of least squares using a complete orthogonal decomposition.
Public classCorrelationBase Abstract base class for all concrete correlation classes.
Public classCorrelationFilter The base correlation filter which provides basic correlation services.
Public classCubicSpline Class CublicSpline represents a function whose values are determined by cubic spline interpolation between tabulated values.
Public classCurveFitDataSet Class for aggregating data used in curve fitting. Contains x-values with their corresponding y-values along with with weights to be applied to the y-values during curve fitting.
Public classCustomAnnealingSchedule Class CustomAnnealingSchedule encapsulates a series of iterations and temperatures.
Public classDampedBFGSHessianUpdater Class DampedBFGSHessianUpdater updates the value of the Lagrangian Hessian based on iterate values using a quasi-Newton approximation.
Public classDataFrame Class DataFrame represents a two-dimensional data object consisting of a list of columns of the same length.
Public classDBrentMinimizer Class DBrentMinimizer minimizes a function using Brent's method as well as the first derivative.
Public classDFBoolColumn Class DFBoolColumn represents a column of logical data in a data frame.
Public classDFColumn Abstract base class for data frame column types.
Public classDFDateTimeColumn Class DFDataTimeColumn represents a column of DataTime data in a data frame.
Public classDFGenericColumn Class DFGenericColumn represents a column of generic data in a data frame.
Public classDFIntColumn Class DFIntColumn represents a column of integer data in a data frame.
Public classDFNumericColumn Class DFNumericColumn represents a column of numeric data in a data frame.
Public classDFStringColumn Class DFStringColumn represents a column of string data in a data frame.
Public classDiscreteDataIntegrator Integrates discrete data for either unit-spaced or arbitrarily spaced data.
Public classDiscreteWaveletTransform This abstract class represents all discrete wavelet transforms objects.
Public classDistance Class Distance provides functions for computing the distance between objects.
Public classDistancePowerDistance Class PowerDistance compute the power distance between two vectors.
Public classCode exampleDouble1DConvolution Double1DConvolution represents a 1D convolution, with a specified kernel and data length.
Public classCode exampleDouble1DCorrelation Double1DCorrelation represents a 1D correlation, with a specified kernel and data length.
Public classDoubleBandFact Class DoubleBandFact represents the factorization of a banded matrix of double-precision floating point numbers.
Public classDoubleBandMatrix Class DoubleBandMatrix represents a banded matrix of double-precision floating point values. A banded matrix is a matrix that has all its non-zero entries near the diagonal.
Public classDoubleBisquareWeightingFunction Class DoubleBisquareWeightingFunction implements the bisquare weighting function for Iteratively Reweighted Least Squares (IRLS).
Public classDoubleCholeskyLeastSq Class DoubleCholeskyLeastSq solves least square problems by using the Cholesky factorization to solve the normal equations.
Public classCode exampleDoubleComplex1DConvolution DoubleComplex1DConvolution represents a 1D convolution, with a specified kernel and data length.
Public classCode exampleDoubleComplex1DCorrelation DoubleComplex1DCorrelation represents a 1D correlation, with a specified kernel and data length.
Public classDoubleComplexBackward1DFFT DoubleComplexBackward1DFFT represents the backward discrete fourier transform of a 1D complex signal vector.
Public classDoubleComplexBackward2DFFT DoubleComplexBackward2DFFT represents the backward discrete fourier transform of a 2D complex signal vector.
Public classDoubleComplexBandFact Class DoubleComplexBandFact represents the factorization of a banded matrix of complex double-precision floating point numbers.
Public classDoubleComplexBandMatrix Class DoubleComplexBandMatrix represents a banded matrix of double-precision complex numbers. A banded matrix is a matrix that has all its non-zero entries near the diagonal.
Public classDoubleComplexCholeskyLeastSq Class DoubleComplexCholeskyLeastSq solves least square problems by using the Cholesky factorization to solve the normal equations.
Public classDoubleComplexCsrSparseMatrix Class DoubleComplexCsrSparseMatrix stores a general sparse matrix using Compressed Row (CSR) Storage format.
Public classDoubleComplexDataBlock 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.
Public classDoubleComplexEigDecomp Class DoubleComplexEigDecomp computes the eigenvalues and left and right eigenvectors of a general matrix, with preliminary balancing.
Public classDoubleComplexEigDecompServer 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.
Public classDoubleComplexForward1DFFT DoubleComplexForward1DFFT represents the forward discrete fourier transform of a 1D complex signal vector.
Public classDoubleComplexForward2DFFT DoubleComplexForward2DFFT represents the forward discrete fourier transform of a 2D complex signal vector.
Public classDoubleComplexGSVDecomp Class DoubleComplexGSVDecomp computes the generalized singular value decomposition (GSVD) of a pair of general rectangular matrices.
Public classDoubleComplexGSVDecompServer Class for serving up generalized singular value decompositions (GSVD) in the form of DoubleComplexGSVDecomp instances.
Public classDoubleComplexLeastSquares Class DoubleComplexLeastSquares computes the minimum-norm solution to a linear system Ax = y.
Public classDoubleComplexLowerTriMatrix Class DoubleComplexLowerTriMatrix represents a lower triangular matrix of double-precision complex numbers. A lower triangular matrix is a square matrix with all elements above the main diagonal equal to zero.
Public classDoubleComplexLUFact Class DoubleComplexLUFact represents the LU factorization of a matrix of DoubleComplex numbers.
Public classDoubleComplexMatrix Class DoubleComplexMatrix represents a general mathematical matrix class of DoubleComplex numbers. Methods are provided for performing algebraic operations, data manipulation, and slicing.
Public classDoubleComplexQRDecomp Class DoubleComplexQRDecomp represents the QR decomposition of a general matrix.
Public classDoubleComplexQRDecompServer Class DoubleComplexQRDecompServer allows control over how the pivoting is done in the creation of DoubleComplexQRDecomp objects.
Public classDoubleComplexQRLeastSq Class DoubleComplexQRLeastSq solves least squares problems by using a QR decomposition.
Public classDoubleComplexSchurDecomp Class DoubleComplexSchurDecomp represents the Schur decomposition of a general matrix.
Public classDoubleComplexSparseFact Class DoubleComplexSparseFact performs general sparse matrix factorizations.
Public classDoubleComplexSparseVector Class DoubleComplexSparseVector encapsulates a general sparse vector.
Public classDoubleComplexSVDecomp Class DoubleComplexSVDecomp represents the singular value decomposition (SVD) of a matrix.
Public classDoubleComplexSVDecompServer Class DoubleComplexSVDecompServer constructs instances of the DoubleComplexSVDecomp class.
Public classDoubleComplexSVDLeastSq Class DoubleComplexSVDLeastSq solves least squares problems by using a singular value decomposition.
Public classDoubleComplexTriDiagFact Class DoubleComplexTriDiagFact represents the LU factorization of a tridiagonal matrix of double-precision complex floating point numbers.
Public classDoubleComplexTriDiagMatrix Class DoubleComplexTriDiagMatrix represents a tridiagonal matrix of double-precision complex numbers. A tridiagonal matrix is a matrix which has all its non-zero entries on the main diagonal, the super diagonal, and the subdiagonal.
Public classDoubleComplexUpperTriMatrix Class DoubleComplexComplexUpperTriMatrix represents an upper triangular matrix of double-precision complex numbers. An upper triangular matrix is a square matrix with all elements below the main diagonal equal to zero.
Public classDoubleComplexVector Class DoubleComplexVector represents a mathematical vector of DoubleComplex numbers.
Public classDoubleCOWeightedLeastSq Class DoubleCOWeightedLeastSq solves weighted least squares problems by using a Complete Orthogonal (CO) decomposition technique.
Public classDoubleCsrSparseMatrix Class DoubleCsrSparseMatrix stores a general sparse matrix using Compressed Row (CSR) storage format.
Public classDoubleDataBlock The DoubleDataBlock struct defines a contiguous subset of an array of double-precision floating point numbers. A DoubleDataBlock instance contains a reference to an array and an offset into the array.
Public classDoubleDWT 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.
Public classDoubleEigDecomp Class DoubleEigDecomp computes the eigenvalues and left and right eigenvectors of a general matrix, with preliminary balancing.
Public classDoubleEigDecompServer 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.
Public classDoubleFactorAnalysisExtraction, Rotation Class
C#
DoubleFactorAnalysis
performs a factor analysis on a symmetric matrix of data, assumed to be either a correlation or covariance matrix, using specified factor extraction and rotation algorithms. The analysis consists of 2 steps: First, factors are extracted from the symmetric matrix data, second, the factors are rotated in order to maximize the relationship between the variables and some of the factors.
Public classDoubleFairWeightingFunction Class DoubleFairWeightingFunction implements the fair weighting function for Iteratively Reweighted Least Squares (IRLS).
Public classCode exampleDoubleForward1DFFT DoubleForward1DFFT represents the forward discrete fourier transform of a 1D real signal vector.
Public classDoubleForward2DFFT DoubleForward2DFFT represents the forward discrete fourier transform of a 2D real signal vector.
Public classDoubleFunctional Class DoubleFunctional represents a double precision functional.
Public classDoubleFunctionalDelegate Class DoubleFunctionalDelegate wraps a functional delegate specified by a delegate in a DoubleFunctional object.
Public classCode exampleDoubleGeneral1DFFT General 1D FFT class assuming the behavior of the provided FFT configuration instance.
Public classDoubleGSVDecomp Class DoubleGSVDecomp computes the generalized singular value decomposition (GSVD) of a pair of general rectangular matrices.
Public classDoubleGSVDecompServer Class for serving up generalized singular value decompositions (GSVD) in the form of DoubleGSVDecomp instances.
Public classDoubleHermCsrSparseMatrix Class DoubleHermCsrSparseMatrix stores a general sparse Hermitian matrix using the Compressed Row (CSR) storage format.
Public classDoubleHermitianBandMatrix Class DoubleHermitianBandMatrix represents an Hermitian banded matrix of double-precision floating point values. An Hermitian banded matrix is an Hermitian matrix that has all its non-zero entries near the diagonal.
Public classDoubleHermitianEigDecomp Class DoubleHermitianEigDecomp computes the eigenvalues and eigenvectors of a symmetrix matrix.
Public classDoubleHermitianEigDecompServer 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.
Public classDoubleHermitianFact Class DoubleHermitianFact represents the factorization of a Hermitian, matrix of complex double-precision floating point numbers.
Public classDoubleHermitianMatrix Class DoubleHermitianMatrix represents a matrix of double-precision floating point complex values.
Public classDoubleHermitianPDBandFact Class DoubleHermitianPDBandFact represents the factorization of a Hermitian, positive definite, banded matrix of complex double-precision floating point numbers.
Public classDoubleHermitianPDFact Class DoubleHermitianPDFact represents the Cholesky factorization of a Hermitian, positive definite, matrix of double-precision 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.
Public classDoubleHermPDTriDiagFact Class DoubleHermPDTriDiagFact represents the LDL' factorization of a Hermitian, positive definite, tridiagonal matrix of complex double-precision floating point numbers.
Public classDoubleIterativelyReweightedLeastSq Class DoubleIterativelyReweightedLeastSq solves a least squares problems by iteratively applying a weighted least squares fit.
Public classDoubleLeastSquares Class DoubleLeastSquares computes the minimum-norm solution to a linear system Ax = y.
Public classDoubleLeastSqWeightingFunction Abstract base class for least squares weighting functions used in the Iteratively Reweighted Least Squares algorithm.
Public classDoubleLowerTriMatrix Class DoubleLowerTriMatrix represents a lower triangular matrix of double-precision floating point values. A lower triangular matrix is a square matrix with all elements above the main diagonal equal to zero.
Public classDoubleLUFact Class DoubleLUFact represents the LU factorization of a matrix of double-precision floating point numbers.
Public classDoubleMatrix Class DoubleMatrix represents a general mathematical matrix class of double-precision floating point numbers. Methods are provided for performing algebraic operations, data manipulation, and slicing.
Public classDoubleMultiVariableFunction Abstract class for representing a multi-variable function.
Public classDoubleNonnegativeLeastSqResult Class containing the results of a nonnegative least squares solve attempt. Double precision version.
Public classDoubleNonnegativeLeastSquares Class DoubleNonnegativeLeastSquares computes the minimum-norm solution to a linear system Ax = y subject to the constraint that all the elements, x[i], are nonnegative.
Public classDoubleParameterizedDelegate Class which creates a DoubleParameterizedFunction instance from delegates.
Public classDoubleParameterizedFunction Abstract class representing a parameterized function.
Public classDoubleParameterizedFunctional Abstract class representing a parameterized functional.
Public classDoublePCA Class DoublePCA performs a principal component analysis on a given double-precision data matrix, or data frame.
Public classDoubleQRDecomp Class DoubleQRDecomp represents the QR decomposition of a general matrix.
Public classDoubleQRDecompServer Class DoubleQRDecompServer allows control over how the pivoting is done in the creation of DoubleQRDecomp objects.
Public classDoubleQRLeastSq Class DoubleQRLeastSq solves least squares problems by using a QR decomposition.
Public classDoubleRandomBetaDistribution Class DoubleRandomBetaDistribution generates random numbers from a beta distribution.
Public classDoubleRandomCauchyDistribution Class DoubleRandomCauchyDistribution generates random numbers from a Cauchy distribution.
Public classDoubleRandomExponentialDistribution Class DoubleRandomExponentialDistribution generates random numbers from an exponential distribution.
Public classDoubleRandomGammaDistribution Class DoubleRandomGammaDistribution generates random numbers from a gamma distribution.
Public classDoubleRandomGaussianDistribution Class DoubleRandomGaussianDistribution generates random numbers from a Gaussian distribution.
Public classDoubleRandomGumbelDistribution Class DoubleRandomGumbelDistribution generates random numbers from a Gumbel distribution.
Public classDoubleRandomLaplaceDistribution Class DoubleRandomLaplaceDistribution generates random numbers from a Laplace distribution.
Public classDoubleRandomLogNormalDistribution Class DoubleRandomLogNormalDistribution generates random numbers from a lognormal distribution.
Public classDoubleRandomRayleighDistribution Class DoubleRandomRayleighDistribution generates random numbers from an Rayleigh distribution.
Public classDoubleRandomUniformDistribution Class DoubleRandomUniformDistribution generates random numbers uniformly distributed over an interval.
Public classDoubleRandomWeibullDistribution Class DoubleRandomWeibullDistribution generates random numbers from a Weibull distribution.
Public classDoubleSchurDecomp Class DoubleSchurDecomp represents the Schur decomposition of a general matrix.
Public classDoubleSparseFact Class DoubleSparseFact performs general sparse matrix factorizations.
Public classDoubleSparseHermFact Class DoubleSparseHermFact performs Hermitian sparse matrix factorizations.
Public classDoubleSparseHermPDFact Class DoubleSparseHermPDFact performs sparse Hermitian Positive Definite matrix factorizations.
Public classDoubleSparseSymFact Class DoubleSparseSymFact performs sparse symmetric matrix factorizations.
Public classDoubleSparseSymPDFact Class DoubleSparseSymPDFact performs sparse positive definite symmetric matrix factorizations.
Public classDoubleSparseVector Class DoubleSparseVector encapsulates a general sparse vector.
Public classDoubleSVDecomp Class DoubleSVDecomp represents the singular value decomposition (SVD) of a matrix.
Public classDoubleSVDecompServer Class DoubleSVDecompServer constructs instances of the DoubleSVDecomp class.
Public classDoubleSVDLeastSq Class DoubleSVDLeastSq solves least squares problems by using a singular value decomposition.
Public classDoubleSWT The stationary wavelet transform (SWT) is an advanced development of the discrete wavelet transfrom (DWT). The DWT lacks signal translation-invariance. By using an up and down sampling strategy, often refered to by its french name of "algorithme à trous", the SWT gains translation-invariance.
Public classDoubleSymBandMatrix Class DoubleSymBandMatrix represents a symmetric banded matrix of double-precision floating point values. A symmetric banded matrix is a symmetric matrix that has all its non-zero entries near the diagonal.
Public classDoubleSymCsrSparseMatrix Class DoubleSymCsrSparseMatrix stores a sparse symmetric matrix using the CompreSsed Row (CSR) storage format.
Public classDoubleSymEigDecomp Class DoubleSymEigDecomp computes the eigenvalues and eigenvectors of a symmetrix matrix.
Public classDoubleSymEigDecompServer 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.
Public classDoubleSymFact Class DoubleSymFact represents the factorization of a symmetric, matrix of double-precision floating point numbers.
Public classCode exampleDoubleSymmetric2DSignalReader Provides symmetric complex conjugate signal unpacking services. Typically used for unpacking 2D FFT's of real signals.
Public classDoubleSymmetricBackward1DFFT DoubleSymmetricBackward1DFFT represents the backward discrete fourier transform of a 1D real signal vector, and inverses packed conjugate symmetric signals back to the real domain.
Public classDoubleSymmetricMatrix Class DoubleSymmetricMatrix represents a symmetric matrix of double-precision floating point values.
Public classCode exampleDoubleSymmetricSignalReader Provides symmetric complex conjugate signal unpacking services. Typically used for unpacking 1D FFT's of real signals.
Public classDoubleSymPDBandFact Class DoubleSymPDBandFact represents the factorization of a symmetric, positive definite, banded matrix of double-precision floating point numbers.
Public classDoubleSymPDFact Class DoubleSymPDFact represents the Cholesky factorization of a symmetric, positive definite, matrix of double-precision 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.
Public classDoubleSymPDTriDiagFact Class DoubleSymPDTriDiagFact represents the LDL' factorization of a symmetric, positive definite, tridiagonal matrix of double-precision floating point numbers.
Public classDoubleSymSemiPDFact Given a real symmetric, positive semidefinite matrix A, class DoubleSymSemiPDFact performs a Cholesky factorization with complete pivoting. In the following ' denotes matrix transposition. The form of the factorization is: P'AP = U'U if upper is specified, and P'AP = LL' if lower is specified. where P is a permutation matrix, and U and L are upper and lower triangular matrices, respectively. The algorithm does not attempt to check if the matrix A is positive semidefinite.
Public classDoubleTriDiagFact Class DoubleTriDiagFact represents the LU factorization of a tridiagonal matrix of double-precision floating point numbers.
Public classDoubleTriDiagMatrix Class DoubleTriDiagMatrix represents a tridiagonal matrix of double-precision floating point values. A tridiagonal matrix is a matrix which has all its non-zero entries on the main diagonal, the super diagonal, and the subdiagonal.
Public classDoubleUpperTriMatrix Class DoubleUpperTriMatrix represents an upper triangular matrix of double-precision floating point values. An upper triangular matrix is a square matrix with all elements below the main diagonal equal to zero.
Public classDoubleVector Class DoubleVector represents a mathematical vector of double-precision floating point numbers.
Public classDoubleVectorParameterizedDelegate Class DoubleVectorParameterizedDelegate creates a DoubleParameterizedFunctional instance from delegates.
Public classCode exampleDoubleWavelet 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 low-pass decimation filter parameters in the constructor.
Public classDownhillSimplexMinimizer Class DownhillSimplexMinimizer minimizes a multivariable function using the downhill simplex method of Nelder and Mead.
Public classDualSimplexSolverObsolete.
Class DualSimplexSolver is a class for solving linear programming prolems using the dual simplex method.
Public classDualSimplexSolverORTools Class DualSimplexSolverORTools is a class for solving linear programming prolems using the dual simplex method.
Public classDualSimplexSolverParamsObsolete.
Dual simplex algorithm parameters.
Public classEqualityConstrainedQPProblem 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.
Public classExponentialDistribution Class ExponentialDistribution represents the Exponential probability distribution.
Public classFactor Class Factor represents a categorical vector in which all elements are drawn from a finite number of factor levels.
Public classFactorAnalysisCorrelationExtraction, Rotation Class FactorAnalysisCorrelation performs a factor analysis on a set of case data using the correlation matrix and specified factor extraction and rotation algorithms.
Public classFactorAnalysisCovarianceExtraction, Rotation Class FactorAnalysisCovariance performs a factor analysis on a set of case data using the covariance matrix and specified factor extraction and rotation algorithms.
Public classFDistribution Class FDistribution represents the F probability distribution.
Public classFFT2DBase Abstract base class for all 2D discrete FFT transform classes. This class manages the setup and tear down of all discrete fourier resources.
Public classFFTBase Abstract base class for all 1D discrete FFT transform classes. This class manages the setup and tear down of all discrete fourier resources.
Public classFFTConfiguration 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.
Public classFFTKernelException Exception thrown when MKL or CUDA returns an error condition when computing a FFT
Public classCode exampleFloat1DConvolution Float1DConvolution represents a 1D convolution, with a specified kernel and data length.
Public classCode exampleFloat1DCorrelation Float1DCorrelation represents a 1D correlation, with a specified kernel and data length.
Public classFloatBandFact Class FloatBandFact represents the factorization of a banded matrix of single-precision floating point numbers.
Public classFloatBandMatrix Class FloatBandMatrix represents a banded matrix of single-precision floating point values. A banded matrix is a matrix that has all its non-zero entries near the diagonal.
Public classFloatCholeskyLeastSq Class FloatCholeskyLeastSq solves least square problems by using the Cholesky factorization to solve the normal equations.
Public classCode exampleFloatComplex1DConvolution FloatComplex1DConvolution represents a 1D convolution, with a specified kernel and data length.
Public classCode exampleFloatComplex1DCorrelation FloatComplex1DCorrelation represents a 1D correlation, with a specified kernel and data length.
Public classFloatComplexBackward1DFFT FloatComplexBackward1DFFT represents the backward discrete fourier transform of a 1D complex signal vector.
Public classFloatComplexBackward2DFFT FloatComplexBackward2DFFT represents the backward discrete fourier transform of a 2D complex signal vector.
Public classFloatComplexBandFact Class FloatComplexBandFact represents the factorization of a banded matrix of complex single-precision floating point numbers.
Public classFloatComplexBandMatrix Class FloatComplexBandMatrix represents a banded matrix of single-precision complex numbers. A banded matrix is a matrix that has all its non-zero entries near the diagonal.
Public classFloatComplexCholeskyLeastSq Class FloatComplexCholeskyLeastSq solves least square problems by using the Cholesky factorization to solve the normal equations.
Public classFloatComplexCsrSparseMatrix Class FloatComplexCsrSparseMatrix stores a general sparse matrix using Compressed Row (CSR) Storage format.
Public classFloatComplexDataBlock 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.
Public classFloatComplexEigDecomp Class FloatComplexEigDecomp computes the eigenvalues and left and right eigenvectors of a general matrix, with preliminary balancing.
Public classFloatComplexEigDecompServer 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.
Public classFloatComplexForward1DFFT FloatComplexForward1DFFT represents the forward discrete fourier transform of a 1D complex signal vector.
Public classFloatComplexForward2DFFT FloatComplexForward2DFFT represents the forward discrete fourier transform of a 2D complex signal vector.
Public classFloatComplexGSVDecomp Class FloatComplexGSVDecomp computes the generalized singular value decomposition (GSVD) of a pair of general rectangular matrices.
Public classFloatComplexGSVDecompServer Class for serving up generalized singular value decompositions (GSVD) in the form of FloatComplexGSVDecomp instances.
Public classFloatComplexLeastSquares Class FloatComplexLeastSquares computes the minimum-norm solution to a linear system Ax = y.
Public classFloatComplexLowerTriMatrix Class FloatComplexLowerTriMatrix represents a lower triangular matrix of single-precision complex numbers. A lower triangular matrix is a square matrix with all elements above the main diagonal equal to zero.
Public classFloatComplexLUFact Class FloatComplexFact represents the LU factorization of a matrix of FloatComplex numbers.
Public classFloatComplexMatrix Class FloatComplexMatrix represents a general mathematical matrix class of FloatComplex numbers. Methods are provided for performing algebraic operations, data manipulation, and slicing.
Public classFloatComplexQRDecomp Class FloatComplexQRDecomp represents the QR decomposition of a general matrix.
Public classFloatComplexQRDecompServer Class FloatComplexQRDecompServer allows control over how the pivoting is done in the creation of FloatComplexQRDecomp objects.
Public classFloatComplexQRLeastSq Class FloatComplexQRLeastSq solves least squares problems by using a QR decomposition.
Public classFloatComplexSchurDecomp Class FloatComplexSchurDecomp represents the Schur decomposition of a general matrix.
Public classFloatComplexSparseFact Class FloatComplexSparseFact performs general sparse matrix factorizations.
Public classFloatComplexSparseVector Class FloatComplexSparseVector encapsulates a general sparse vector.
Public classFloatComplexSVDecomp Class FloatComplexSVDecomp represents the singular value decomposition (SVD) of a matrix.
Public classFloatComplexSVDecompServer Class FloatComplexSVDecompServer constructs instances of the FloatComplexSVDecomp class.
Public classFloatComplexSVDLeastSq Class FloatComplexSVDLeastSq solves least squares problems by using a singular value decomposition.
Public classFloatComplexTriDiagFact Class FloatComplexTriDiagFact represents the LU factorization of a tridiagonal matrix of single-precision complex floating point numbers.
Public classFloatComplexTriDiagMatrix Class FloatComplexTriDiagMatrix represents a tridiagonal matrix of single-precision complex numbers. A tridiagonal matrix is a matrix which has all its non-zero entries on the main diagonal, the super diagonal, and the subdiagonal.
Public classFloatComplexUpperTriMatrix Class FloatComplexUpperTriMatrix represents an upper triangular matrix of single-precision complex numbers. An upper triangular matrix is a square matrix with all elements below the main diagonal equal to zero.
Public classFloatComplexVector Class FloatComplexVector represents a mathematical vector of FloatComplex numbers.
Public classFloatCsrSparseMatrix Class FloatCsrSparseMatrix stores a general sparse matrix using Compressed Row (CSR) storage format.
Public classFloatDataBlock 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.
Public classFloatDWT 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.
Public classFloatEigDecomp Class FloatEigDecomp computes the eigenvalues and left and right eigenvectors of a general matrix, with preliminary balancing.
Public classFloatEigDecompServer 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.
Public classCode exampleFloatForward1DFFT FloatForward1DFFT represents the forward discrete fourier transform of a 1D real signal vector.
Public classFloatForward2DFFT FloatForward2DFFT represents the forward discrete fourier transform of a 2D real signal vector.
Public classCode exampleFloatGeneral1DFFT General 1D FFT class assuming the behavior of the provided FFT configuration instance.
Public classFloatGSVDecomp Class FloatGSVDecomp computes the generalized singular value decomposition (GSVD) of a pair of general rectangular matrices.
Public classFloatGSVDecompServer Class for serving up generalized singular value decompositions (GSVD) in the form of FloatGSVDecomp instances.
Public classFloatHermCsrSparseMatrix Class FloatHermCsrSparseMatrix stores a general sparse Hermitian matrix using the Compressed Row (CSR) storage format.
Public classFloatHermitianBandMatrix Class FloatHermitianBandMatrix represents an Hermitian banded matrix of double-precision floating point values. An Hermitian banded matrix is an Hermitian matrix that has all its non-zero entries near the diagonal.
Public classFloatHermitianEigDecomp Class FloatHermitianEigDecomp computes the eigenvalues and eigenvectors of a symmetrix matrix.
Public classFloatHermitianEigDecompServer 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.
Public classFloatHermitianFact Class FloatHermitianFact represents the factorization of a Hermitian, matrix of complex single-precision floating point numbers.
Public classFloatHermitianMatrix Class FloatHermitianMatrix represents a matrix of single-precision floating point complex values.
Public classFloatHermitianPDBandFact Class FloatHermitianPDBandFact represents the factorization of a Hermitian, positive definite, banded matrix of complex single-precision floating point numbers.
Public classFloatHermitianPDFact Class FloatHermitianPDFact represents the Cholesky factorization of a Hermitian, positive definite, matrix of single-precision 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.
Public classFloatHermPDTriDiagFact Class FloatHermPDTriDiagFact represents the LDL' factorization of a Hermitian, positive definite, tridiagonal matrix of complex single-precision floating point numbers.
Public classFloatLeastSquares Class FloatLeastSquares computes the minimum-norm solution to a linear system Ax = y.
Public classFloatLowerTriMatrix Class FloatLowerTriMatrix represents a lower triangular matrix of single-precision floating point values. A lower triangular matrix is a square matrix with all elements above the main diagonal equal to zero.
Public classFloatLUFact Class FloatLUFact represents the LU factorization of a matrix of floating point numbers.
Public classFloatMatrix Class FloatMatrix represents a general mathematical matrix class of floating point numbers. Methods are provided for performing algebraic operations, data manipulation, and slicing.
Public classFloatNonnegativeLeastSqResult Class containing the results of a nonnegative least squares solve attempt. Single precision version.
Public classFloatNonnegativeLeastSquares Class FloatNonnegativeLeastSquares computes the minimum-norm solution to a linear system Ax = y subject to the constraint that all the elements, x[i], are nonnegative.
Public classFloatPCA Class FloatPCA performs a principal component analysis on a given single-precision data matrix.
Public classFloatQRDecomp Class FloatQRDecomp represents the QR decomposition of a general matrix.
Public classFloatQRDecompServer Class FloatQRDecompServer allows control over how the pivoting is done in the creation of FloatQRDecomp objects.
Public classFloatQRLeastSq Class FloatQRLeastSq solves least squares problems by using a QR decomposition.
Public classFloatRandomBetaDistribution Class FloatRandomBetaDistribution generates random numbers from a beta distribution.
Public classFloatRandomCauchyDistribution Class FloatRandomCauchyDistribution generates random numbers from a Cauchy distribution.
Public classFloatRandomExponentialDistribution Class FloatRandomExponentialDistribution generates random numbers from an exponential distribution.
Public classFloatRandomGammaDistribution Class FloatRandomGammaDistribution generates random numbers from a gamma distribution.
Public classFloatRandomGaussianDistribution Class FloatRandomGaussianDistribution generates random numbers from a Gaussian distribution.
Public classFloatRandomGumbelDistribution Class FloatRandomGumbelDistribution generates random numbers from a Gumbel distribution.
Public classFloatRandomLaplaceDistribution Class FloatRandomLaplaceDistribution generates random numbers from a Laplace distribution.
Public classFloatRandomLogNormalDistribution Class FloatRandomLogNormalDistribution generates random numbers from a lognormal distribution.
Public classFloatRandomRayleighDistribution Class FloatRandomRayleighDistribution generates random numbers from an Rayleigh distribution.
Public classFloatRandomUniformDistribution Class DoubleRandomUniformDistribution generates random numbers uniformly distributed over an interval.
Public classFloatRandomWeibullDistribution Class FloatRandomWeibullDistribution generates random numbers from a Weibull distribution.
Public classFloatSchurDecomp Class FloatSchurDecomp represents the Schur decomposition of a general matrix.
Public classFloatSparseFact Class FloatSparseFact performs general sparse matrix factorizations.
Public classFloatSparseHermFact Class FloatSparseHermFact performs Hermitian sparse matrix factorizations.
Public classFloatSparseHermPDFact Class FloatSparseHermPDFact performs sparse Hermitian Positive Definite matrix factorizations.
Public classFloatSparseSymFact Class FloatSparseSymFact performs sparse symmetric matrix factorizations.
Public classFloatSparseSymPDFact Class FloatSparseSymPDFact performs sparse positive definite symmetric matrix factorizations.
Public classFloatSparseVector Class FloatSparseVector encapsulates a general sparse vector.
Public classFloatSVDecomp Class FloatSVDecomp represents the singular value decomposition (SVD) of a matrix.
Public classFloatSVDecompServer Class FloatSVDecompServer constructs instances of the FloatSVDecomp class.
Public classFloatSVDLeastSq Class FloatSVDLeastSq solves least squares problems by using a singular value decomposition.
Public classFloatSWT The stationary wavelet transform (SWT) is an advanced development of the discrete wavelet transfrom (DWT). The DWT lacks signal translation-invariance. By using an up and down sampling strategy, often refered to by its french name of "algorithme à trous", the SWT gains translation-invariance.
Public classFloatSymBandMatrix Class FloatSymBandMatrix represents a symmetric banded matrix of single-precision floating point values. A symmetric banded matrix is a symmetric matrix that has all its non-zero entries near the diagonal.
Public classFloatSymCsrSparseMatrix Class FloatSymCsrSparseMatrix stores a sparse symmetric matrix using the CompreSsed Row (CSR) storage format.
Public classFloatSymEigDecomp Class FloatSymEigDecomp computes the eigenvalues and eigenvectors of a symmetrix matrix.
Public classFloatSymEigDecompServer 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.
Public classFloatSymFact Class FloatSymFact represents the factorization of a symmetric matrix of single-precision floating point numbers.
Public classCode exampleFloatSymmetric2DSignalReader Provides symmetric complex conjugate signal unpacking services. Typically used for unpacking 2D FFT's of real signals.
Public classFloatSymmetricBackward1DFFT 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.
Public classFloatSymmetricMatrix Class FloatSymmetricMatrix represents a symmetric matrix of float-precision floating point values.
Public classCode exampleFloatSymmetricSignalReader Provides symmetric complex conjugate signal unpacking services. Typically used for unpacking 1D FFT's of real signals.
Public classFloatSymPDBandFact Class FloatSymPDBandFact represents the factorization of a symmetric, positive definite, banded matrix of single-precision floating point numbers.
Public classFloatSymPDFact Class FloatSymPDFact represents the Cholesky factorization of a symmetric, positive definite, matrix of single-precision 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.
Public classFloatSymPDTriDiagFact Class FloatSymPDTriDiagFact represents the LDL' factorization of a symmetric, positive definite, tridiagonal matrix of single-precision floating point numbers.
Public classFloatTriDiagFact Class FloatTriDiagFact represents the LU factorization of a tridiagonal matrix of single-precision floating point numbers.
Public classFloatTriDiagMatrix Class FloatTriDiagMatrix represents a tridiagonal matrix of single-precision floating point values. A tridiagonal matrix is a matrix which has all its non-zero entries on the main diagonal, the super diagonal, and the subdiagonal.
Public classFloatUpperTriMatrix Class FloatUpperTriMatrix represents an upper triangular matrix of single-precision floating point values. An upper triangular matrix is a square matrix with all elements below the main diagonal equal to zero.
Public classFloatVector Class FloatVector represents a mathematical vector of floating point numbers.
Public classCode exampleFloatWavelet 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 low-pass decimation filter parameters in the constructor.
Public classFZero FZero finds a zero of the function in the given interval. Repeated roots are not found, FZero can find only bracketed single roots.
Public classGammaDistribution Class GammaDistribution represents the gamma probability distribution.
Public classGaussKronrod21Integrator Class GaussKronrod21Integrator calculates an approximation of the integral of a function over a finite interval using the Gauss 10-point and the Kronrod 21-point rule.
Public classGaussKronrod43Integrator Class GaussKronrod43Integrator calculates an approximation of the integral of a function over a finite interval using the Gauss 21-point and the Kronrod 43-point rule.
Public classGaussKronrod87Integrator Class GaussKronrod87Integrator calculates an approximation of the integral of a function over a finite interval using the Gauss 43-point and the Kronrod 87-point rule.
Public classGaussKronrodIntegrator Class GaussKronrodIntegrator calculates an approximation of the integral of a function over a finite interval using Gauss-Kronrod rules.
Public classGeometricDistribution Class GeometricDistribution represents the goemetric probability distribution.
Public classGlobalCurveFitAnova 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 F-statistic and p-value for the overall model, and degrees of freedom.
Public classGlobalCurveFitter 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.
Public classGlobalFitParameterInfo Contains information about a parameter in a global curve fit: name, description, shared or not shared. Only the sharing information is required.
Public classGlobalFittedParameter Class for providing fit statistics about a global fit parameter after the fit has been performed.
Public classGlobalFixedFitParameterInfo 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 non-shared. 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.
Public classGoldenMinimizer Class GoldenMinimizer performs a golden section search for a minimium of a function within an interval known to contain a minimum.
Public classGoodnessOfFit Class GoodnessOfFit tests goodness of fit for least squares model-fitting classes, such as LinearRegression, PolynomialLeastSquares, and OneVariableFunctionFitter.
Public classGoodnessOfFitParameter Class GoodnessOfFitParameter tests statistical hypotheses about estimated parameters in regression models.
Public classHistogram 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.
Public classIActiveSetQPSolver 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.
Public classIndependentRandomStreams Base class for creating streams of independent random numbers. Deriving classes must construct the streams_ array.
Public classIndexArray Class IndexArray presents 0-based indices to the user, but uses 1-based indices internally.
Public classIndexOutOfRangeException Exception thrown when an out of range index is passed to an NMath function.
Public classInputVariableCorrelator Instances of the InputVariableCorrelator class are used to induce a desired rank correlation among input variables.
Public classInteriorPointQPSolver Class for sovling quadratic programming (QP) problems using an interior point algorithm.
Public classInteriorPointQPSolverParams Parameters controlling the behavior of the interior point quadratic programming algorithm in the InteriorPointQPSolver class.
Public classInterval Class Interval represents a numeric interval with inclusive or exclusive lower and upper bounds.
Public classIntRandomBernoulliDistribution Class IntRandomBernoulliDistribution generates random numbers from a discrete binomial distribution.
Public classIntRandomBinomialDistribution Class IntRandomBinomialDistribution generates random numbers from a discrete binomial distribution.
Public classIntRandomGeometricDistribution Class IntRandomGeometricDistribution generates random numbers from a discrete geometric distribution.
Public classIntRandomHypergeometricDistribution Class IntRandomHypergeometricDistribution generates random numbers from a discrete hypergeometric distribution.
Public classIntRandomNegativeBinomialDistribution Class IntRandomNegativeBinomialDistribution generates random numbers from a discrete negative binomial distribution.
Public classIntRandomPoissonDistribution Class IntRandomPoissonDistribution generates random numbers from a discrete Poisson distribution.
Public classIntRandomPoissonVaryingMeanDistribution Class IntRandomPoissonVaryingMeanDistribution generates random numbers from a discrete Poisson distribution with varying mean.
Public classIntRandomUniformBitsDistribution Class IntRandomUniformBitsDistribution generates integer values with uniform bit distribution.
Public classIntRandomUniformDistribution Class IntRandomUniformDistribution generates random numbers uniformly distributed over an interval.
Public classInvalidArgumentException Exception thrown when an invalid argument is passed to an NMath function.
Public classInvalidBinBdryException Exception thrown when a histogram operation results in invalid bin boundaries.
Public classIPLS1Calc Interface for performing a Partial Least Squares (PLS) calculation.
Public classIPLS2Calc Interface for performing a Partial Least Squares (PLS) calculation.
Public classJohnsonDistribution Class JohnsonDistribution represents the Johnson system of distributions.
Public classKFoldsSubsets Class KFoldsSubsets generates k-fold subsets for cross validation.
Public classCode exampleKMeansClustering Class KMeansClustering performs k-means clustering on a set of data points.
Public classKruskalWallisTable Class KruskalWallisTable summarizes the information of Kruskal-Wallis rank sum test.
Public classKruskalWallisTest Class KruskalWallisTest performs a Kruskal-Wallis rank sum test.
Public classL1MeritStepSize Class L1MeritStepSize computes the step size for a Sequential Quadratic Programming solver based on sufficient decrease in the L1 merit function.
Public classLagrangianFunction Class LagrangianFunction represents the Lagrangian function associated with a nonlinear programming problem.
Public classLagrangianFunctionLagrangianGradientFunction Class LagrangianGradientFunction derives from DoubleMultiVariableFunction for evaluating the gradient of the Lagrangian functions.
Public classLeapfrogRandomStreams Class LeapfrogRandomStreams creates several independent streams of random numbers using the method know as leapfrogging.
Public classLeapfrogStream Class LeapfrogStream represents a single leapfrog stream.
Public classLeaveOneOutSubsets Class LeaveOneOutSubsets generates the index subsets for a leave-one-out cross validations calculation.
Public classLevenbergMarquardtMinimizer Class for minimizing the L2 norm of a function using the Levenberg Marquardt algorithm.
Public classLikelihoodRatioStatistic Class LikelihoodRatioStatistic computes the Likelihood Ratio Statistic values for a logistic regression.
Public classLinearAnnealingSchedule Class LinearAnnealingSchedule encapsulates the linear descent of a starting temperature to zero. Each step has a specified number of iterations.
Public classLinearConstrainedProblem Abstract class for representing an optimization problem with linear constraints.
Public classLinearConstraint Class LinearConstraint represents a linear constraint for a constrained optimization problem.
Public classLinearProgrammingProblem Class LinearProgrammingProblem encapsulates a Linear programming problem.
Public classLinearRegression Class LinearRegression computes a multiple linear regression from an input matrix of independent variable values and vector of dependent variable values.
Public classLinearRegressionAnova Class LinearRegressionAnova tests overall model significance for linear regressions computed by class LinearRegression.
Public classLinearRegressionParameter Class LinearRegressionParameter tests statistical hypotheses about estimated parameters in linear regressions computed by class LinearRegression.
Public classLinearSpline Class LinearSpline represents a function whose values are determined by linear interpolation between tabulated values.
Public classLinkage Class Linkage provides functions for computing the distance between clusters of objects.
Public classLogisticDistribution Class LogisticDistribution represents the logistic probability distribution with a specifed location (mean) and scale.
Public classLogisticRegressionParameterCalc Class for performing a binomial logistic regression.
Public classLogisticRegressionAuxiliaryStatsParameterCalc Class LogisticRegressionAuxiliaryStats computes pseudo R-squared metrics for a logistic regression, and odds ratios for the computed coefficients.
Public classLogisticRegressionFitAnalysisParameterCalc Class for for calculating "goodness of fit" statistics for a logistic regression model.
Public classLogisticRegressionFitAnalysisParameterCalcHosmerLemeshowGroup Class representing a group used in computing the Hosmer Lemeshow statistic for a logistic regression model.
Public classLogisticRegressionFitAnalysisParameterCalcHosmerLemeshowStatistic Class containing the attributes of the Hosmer Lemeshow statistic for a logistic regression model.
Public classLogisticRegressionFitAnalysisParameterCalcPearsonChiSqrStatistic Class containing the attributes of the Pearson chi-square statistic associated with a logistic regression model.
Public classLogisticRegressionFitAnalysisParameterCalcPearsonResidual Class containing Pearson Residual attributes. The Pearson Residual is calculated for each covariate pattern.
Public classLogisticRegressionParameterParameterCalc Class LogisticRegressionParameter tests statistical hypotheses about estimated parameters in linear regressions computed by class LogisticRegression.
Public classLognormalDistribution Class LognormalDistribution represents the lognormal probability distribution.
Public classMarginalEffect Class containing marginal effect values and statistics for a model parameter
Public classMarginalEffectsParameterCalc Marginal effects are use a logistic regression model to predict how changing the value of a predictor, or covariate, effects the predicted outcome. It is the slope of the regression surface with respect to a given covariate and communicates the rate at which the outcome computed by the regression model changes at a given point in covariate space, with respect to one covariate dimension and holding all covariate values constant. Marginal effects for categorical covariates, including binary covariates, are straightforward - it is simply the difference in the predicted outcomes as the the design or dummy variable's value changes from 0 to 1. For continuous valued covariates it is the derivate dy/dx where y is the models prediction function and x is the covariate we are computing the marginal effects for. Note that the marginal effect for a predictor is computed at specific observation.
Public classMatrixFunctionsObsolete.
No longer used. Please use NMathFunctions.
Public classMatrixNotSquareException Exception thrown when a matrix operation requiring a square matrix is presented with a non-square one.
Public classMinimizerBase Class MinimizerBase is the abstract base class for classes that perform function minimization.
Public classCode exampleMismatchedSizeException Exception thrown when an operation is performed with operands whose sizes are incompatible with the operation.
Public classMixedIntegerLinearProgrammingProblem Class MixedIntegerLinearProgrammingProblem encapsulates a Linear programming problem which may contain integral constraints.
Public classMixedIntegerNonlinearProgrammingProblem Class MixedIntegerNonlinearProgrammingProblem represents a nonlinear programming problem.
Public classModifiedLevenbergMarquardtMinimizer Class for minimizing the L2 norm of a function using the a modified Levenberg Marquardt algorithm.
Public classMovingWindowFilter 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 Savitzky-Golay smoothing filter.
Public classMultipleCurveFit 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 parameter beta(1) being the same for both datasets.
Public classMultipleCurveFitFunction Function for simulatenous fitting of multiple data sets with shared fitting parameters. MultipleCurveFit
Public classMultipleCurveFitResidual Calculates the residuals of a MultipleCurveFitFunction with a given set of parameters and data sets.
Public classMultipleFitCurveInfo 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
Public classMultiVariableFunctionObsolete.
Class MultiVariableFunction represents multivariate functions.
Public classMultiVariableFunctionFitterM Class MultiVariableFunctionFitter fits a generalized multivariable function to a set of points.
Protected classMultiVariableFunctionFitterMResidualFunction Residual function. This is the function that is minimized to produce the parameters for the best fit.
Public classNaturalCubicSpline Class NaturalCubicSpline represents a function determined by tabulated values. Function values are calculated using natural cubic spline interpolation.
Public classNegativeBinomialDistribution 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.
Public classNewtonRaphsonParameterCalc 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.
Public classNewtonRaphsonRootFinder Class NewtonRaphsonRootFinder finds roots of univariate functions using the Newton-Raphson algorithm.
Public classNiederreiterQuasiRandomGenerator Class NiederreiterQuasiRandomGenerator is a quasi-random number generator which can be used for generating sequences of quasi-random point in n-dimensional space.
Public classNMathConfiguration Class NMathConfiguration provides properties for controlling the loading of the NMath kernel assembly and native library, and specifying license keys.
Public classNMathException Base class for exceptions thrown by the NMath product suite.
Public classCode exampleNMathFormatException Exception thrown when a method encounters a faulty text representation.
Public classNMathFunctions 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.
Public classNMathFunctionsFiveParameterLogisticFtn Computes the 5-parameter logistic (5PL) function, using the given vector of function parameters, at the specified point.
Public classNMathFunctionsFourParameterLogisticFtn Computes the 4-parameter logistic (4PL) function, using the given vector of function parameters, at the specified point.
Public classNMathFunctionsThreeParameterExponentialFtn Evaluates the three parameter exponential function for the given parameter values at the given point.
Public classNMathFunctionsThreeParameterSineFtn Computes the three parameter sine function, using the given vector of function parameters, at the specified point.
Public classNMathFunctionsTwoParameterAsymptoticFtn Computes the asymptotic function, using the given vector of function parameters, at the specified point.
Public classNMathSettings Class NMathSettings contains global settings for NMath classes.
Public classNMFact Class NMFact performs non-negative matrix factorization.
Public classNMFAlsUpdate Class NMFAlsUpdate encapsulates the Alternating Least Squares (ALS) update algorithm.
Public classNMFClusteringAlg Class NMFClustering performs a Non-negative Matrix Factorization (NMF) of a given matrix.
Public classNMFConsensusMatrixAlg Class NMFConsensusMatrix uses a non-negative matrix factorization to cluster samples.
Public classNMFDivergenceUpdate Class NMFDivergenceUpdate encapulates an NMF update algorithm which minimizes a divergence functional.
Public classNMFGdClsUpdate Class NMFGdClsUpdate encapsulates the Gradient Descent - Constrained Least Squares (GDCLS) algorithm for Nonnegative Matrix Facotorization (NMF).
Public classNMFMultiplicativeUpdate Class NMFMultiplicativeUpdate encapsulates a multiplicative update algorithm for Nonnegative Matrix Factorization (NMF).
Public classNMFNonsmoothUpdate 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.
Public classNoncentralTDistribution Class NoncentralTDistribution represents a generalized Student's t-distribution with the specified degrees of freedom and noncentrality parameter.
Public classNonlinearConstraint Class NonlinearConstraint represents a nonlinear constraint in an optimization problem.
Public classNonlinearProgrammingProblem Class NonlinearProgrammingProblem represents a nonlinear programming problem.
Public classNonModifiableElementException Exception thrown when an attempt is made to change the value of an element in a structured matrix that cannot be changed.
Public classNormalDistribution Class NormalDistribution represents the normal (Gaussian) probability distribution with a specifed mean and variance.
Public classNoRotation Used as a class type parameter value to factor analysis classes when no factor rotation is desired.
Public classNumberOfFactors The
C#
NumberOfFactors
class contains static methods for creating function objects suitable for use as "number of factors" functors used in the factor extraction step of a factor analysis. These functions take as parameters the eigenvalues and eigenvectors values computed during factor extraction and return the number of factors to "keep".
Public classOdeSolverBase Base class for ODE solvers which use a Runge-Kutta order 5 algorithm. Includes enums and functions for incorporating mass matrices into ODE's.
Protected classOdeSolverBaseConstMassMatrixOdeFcn 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.
Protected classOdeSolverBaseMassMatrixOdeFcn When solving ODE's of the form y' = M(t,y)*f(t,y) where M is a time-state 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).
Public classOneSampleAndersonDarlingTest Class OneSampleAndersonDarlingTest performs a Anderson-Darling test of the distribution of one sample.
Public classOneSampleKSTest Class OneSampleKSTest performs a Kolmogorov-Smirnov test of the distribution of one sample.
Public classOneSampleTTest Class OneSampleTTest compares a single sample mean to an expected mean from a normal distribution with an unknown standard deviation.
Public classOneSampleZTest Class OneSampleZTest compares a single sample mean to an expected mean from a normal distribution with known standard deviation.
Public classOneVariableFunction Class OneVariableFunction represents functions of one variable.
Public classOneVariableFunctionFitterM Class OneVariableFunctionFitter fits a parameterized one variable function to a set of points.
Public classOneVariableFunctionFitterMCurveFitResidualFunction Class representing the residual function for the curve fit.
Public classOneWayAnova Class OneWayAnova computes and summarizes a traditional one-way (single factor) Analysis of Variance (ANOVA).
Public classOneWayAnovaTable Class OneWayAnovaTable summarizes the information of a traditional one-way Analysis of Variance (ANOVA) table.
Public classOneWayRanova Class OneWayRanova summarizes the information of a one-way repeated measures Analysis of Variance (RANOVA).
Public classOneWayRanovaTable Class OneWayRanovaTable summarizes the information of a traditional one-way repeated measures Analysis of Variance (RANOVA) table.
Public classOpenClosedInterval Class OpenClosedInterval represents a numeric interval with an exclusive lower bound and an inclusive upper bound.
Public classOpenInterval Class OpenInterval represents a numeric interval with exclusive lower and upper bounds.
Public classOrderedConnectivityMatrix 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.
Public classParameterizedMultivariableFunction Abstract class representing multi-variable a parameterized function.
Public classPCFactorExtraction 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.
Public classPeakFinderBase Abstract base class for all peak finding algorithms. The class is an enumerable collection of all found peaks.
Public classPeakFinderRuleBased 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. Non-infinity end points are excluded as a peak.
Public classCode examplePeakFinderSavitzkyGolay Class PeakFinderSavitzkyGolay uses smooth Savitzky-Golay derivatives to find peaks in data and acts as a collection for the found peaks.
Public classPearsonsChiSquareTest Class PearsonsChiSquareTest tests whether the frequency distribution of experimental outcomes are consistant with a particular theoretical distribution.
Public classPLS1 Class PLS1 performs a Partial Least Squares (PLS) regression calculation on a set of predictive and one-dimensional response values. The result is used to predict response variable values.
Public classPLS1Anova Class PLS1Anova performs a standard ANalysis Of VAriance (ANOVA) for a Partial Least Squares 1 (PLS1) regression model.
Public classPLS1CrossValidation Class PLS1CrossValidation performs an evaluation of a PLS (Partial Least Squares) model.
Public classPLS1CrossValidationData Class PLS1CrossValidationData divides Partial Least Squares - one dimensional response variable,(PLS1), data into training and testing subsets.
Public classPLS1CrossValidationResult Class PLS2CrossValidationResult performs a Partial Least Squares - one dimensional response variable, (PLS1), cross validation calculation.
Public classPLS1NipalsAlgorithm Class PLS1NipalsAlgorithm encapsulates the Nonlinear Iterative PArtial Least Squares (NIPALS) algorithm for computing partial least squares regression components.
Public classPLS2 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.
Public classPLS2Anova Class PLS2Anova performs a standard ANalysis Of VAriance (ANOVA) for a Partial Least Squares (PLS) regression model.
Public classPLS2CrossValidation Class PLS2CrossValidation performs an evaluation of a PLS (Partial Least Squares) model.
Public classPLS2CrossValidationData Class PLS2CrossValidationData divides Partial Least Squares (PLS) data into training and testing subsets.
Public classPLS2CrossValidationResult Class PLS2CrossValidationResult performs a Partial Least Squares (PLS) cross validation calculation.
Public classPLS2CrossValidationWithJackknife Class PLS2CrossValidationWithJackknife performs an evaluation of a PLS (Partial Least Squares) model with model coefficient variance estimates and confidence intervals.
Public classPLS2NipalsAlgorithm Class PLS2NipalsAlgorithm encapsulates the Nonlinear Iterative PArtial Least Squares (NIPALS) algorithm for computing partial least squares regression components.
Public classPLS2SimplsAlgorithm Class PLS2SimplsAlgorithm encapsulates the Straightforward IMplementation of Partial Least Squares, or SIMPLS, algorithm (de Jong, 1993) for computing partial least squares regression components.
Public classPoissonDistribution 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.
Public classPolynomial Class Polynomial represents a polynomial function as a vector of coefficients.
Public classPolynomialDifferentiator Class PolynomialDifferentiator encapsulates exact differentiation of polynomials.
Public classPolynomialIntegrator Class PolynomialIntegration encapsulates exact integration of polynomials.
Public classPolynomialLeastSquares Class PolynomialLeastSquares performs a least squares fit of a polynomial to the data.
Public classPowellMinimizer Class PowellMinimizer minimizes a multivariable function using Powell's Method.
Public classPowerMethod Class for computing the dominant eigenvalue and eigenvector of a square matrix using the iterative power method.
Public classPrimalSimplexSolverObsolete.
Class PrimalSimplexSolver is a class for solving linear programming prolems using the primal simplex method.
Public classPrimalSimplexSolverORTools Class PrimalSimplexSolver is a class for solving linear programming prolems using the primal simplex method.
Public classPrimalSimplexSolverParamsObsolete.
Primal simplex algorithm parameters.
Public classProbabilityDistribution Class ProbabilityDistribution is the abstract base class for classes that represent distributions of random variables.
Public classProcessCapability Computes the process capability parameters Cp, Cpm, Cp for normally distributed data. If the data is not normal the Box-Cox transform can be used.
Public classProcessPerformance Computes process performance parameters Pp and Ppk for normally distributed data. If the data is not normal the Box-Cox transform can be used.
Public classQRRegressionCalculation Class QRRegressionCalculation computes linear regression parameters by the method of least squares using a QR decomposition.
Public classQuadraticProgrammingProblem Class QuadraticProgrammingProblem encapsulates a quadratic programming (QP) problem.
Public classQuasiRandomNumberGenerator Abstract base class for generating sequences of quasirandom points. A quasirandom sequence is a sequence of n-tuples that fills n-space more uniformly than uncorrelated random points.
Public classRandGenBeta Class RandGenBeta generates random numbers from a beta distribution.
Public classRandGenBinomial Class RandGenBinomial generates random numbers from a binomial distribution.
Public classRandGenExponential Class RandGenExponential generates random numbers from an exponential distribution.
Public classRandGenGamma Class RandGenGamma generates random numbers from an gamma distribution.
Public classRandGenGeometric Class RandGenGeometric generaties random numbers from a Geometric distribution.
Public classRandGenJohnson Class RandGenJohnson generates random numbers from a Johnson distribution.
Public classRandGenLogNormal Class RandGenLogNormal generates random numbers from a lognormal distribution.
Public classRandGenMTwist Class RandGenMTwist generates random numbers from a uniform distribution using the Mersenne Twister algorithm.
Public classRandGenNormal Class RandGenNormal generates random numbers from a normal distribution.
Public classRandGenPareto Class RandGenPareto generates random numbers from a Pareto distribution.
Public classRandGenPoisson Class RandGenPoisson generates random numbers from an Poisson distribution.
Public classRandGenTriangular Class RandGenTriangular generates random numbers from a triangular distribution.
Public classRandGenUniform Class RandGenUniform generates random numbers from a uniform distribution.
Public classRandGenWeibull Class RandGenWeibull generates random numbers from a Weibull distribution.
Public classRandomNumberGenerator Abstract base class for NMath random number generators.
Public classRandomNumbersT, D Class RandomNumbers is an adapter for the RandomNumberStream class to give the same behavior as a scalar-type random number generator.
Public classRandomNumberStream Class RandomNumberStream is a vectorized random number generator which yields a stream of random numbers from various probability distributions.
Public classRange 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.
Public classReducedVarianceInputCorrelator Instances of the ReducedVarianceInputCorrelator class are used to induce a desired rank correlation among input variables.
Public classRegressionBase Base class for linear and logistic regression.
Public classRegressionFactorScores Class implementing the
C#
IFactorScores
interface for computing factor scores using the regression algorithm. The regression algorithm uses a least squares regression approach to predict factor scores. Specifically this method computes the solution X to the matrix equation RX = B, where R = covariance matrix, B = factor matrix, X = factor scores.
Public classRiddersDifferentiator Class RidderDifferentiator encapsulates numerical differentiation of functions.
Public classRiddersRootFinder Class RiddersRootFinder finds roots of univariate functions using Ridders' Method.
Public classRombergIntegrator Class RombergIntegrator approximates integrals of functions over a given interval using the Romberg method.
Public classRootFinderBase Abstract base class for classes that perform root finding on univariate functions.
Public classRungeKutta45OdeSolver Class RungeKutta45OdeSolver solves an initial value, Ordinary Differential Equation (ODE) using an explicit Runge-Kutta (4,5) formula known as the Dormand-Prince pair.
Public classRungeKutta45OdeSolverOptions User settable options for RungeKutta45OdeSolver.
Public classRungeKutta45OdeSolverSolutionYtype Data structor contiaing solution values an statistics for an ODE solve.
Public classRungeKutta5OdeSolver Class RungeKutta5OdeSolver solves an initial value, Ordinary Differential Equation (ODE) using a non-adaptive explicit Runge-Kutta formula of order 5.
Public classRungeKutta5OdeSolverOptions User settable options for RungeKutta5OdeSolver.
Public classCode exampleSavitzkyGolay Class generates the Savitzy-Golay filter coefficients for smoothing data or computing smoothed derivatives. Smoothed derivatives can be found up to the order of the fitting polynomial.
Public classCode exampleSavitzkyGolayFilter Class SavitzkyGolayFilter is a correlation filter specialized for filtering with Savitzky-Golay coefficients with additional boundary options for better edge continuity. The default boundary option ShiftFilterCenter provides data smoothing all the way boundary ends.
Public classSecantRootFinder Class SecantRootFinder finds roots of univariate functions using the secant method.
Public classSequentialQuadraticProgrammingSolver Base class for sequential quadratic programming solvers.
Public classSequentialQuadraticProgrammingSolverIteration Data structure containing various values for an iteration of a Sequential Quadratic Programming algorithm.
Public classShapiroWilkTest Class ShapiroWilkTest tests the null hypothesis that the sample comes from a normally distributed population.
Public classSimplexSolverBaseObsolete.
Class SimplexSolverBase is an abstract base class for classes solving mixed integer linear programming prolems using variants of the simplex method.
Public classSimplexSolverBaseORTools Class SimplexSolverBase is an abstract base class for classes solving mixed integer linear programming prolems using variants of the simplex method.
Public classSimplexSolverMixedIntParamsObsolete.
Class containing parameters for simplex based mixed integer linear programming solvers.
Public classSimplexSolverParamsBaseObsolete.
Base class for simplex solver parameters.
Public classSingularMatrixException Exception thrown when a matrix operation requiring a non-singular matrix is presented with a singular one.
Public classSkipAheadRandomStreams Class for creating several independent streams of random numbers using the method know as skip-ahead, or block-splitting.
Public classSkipAheadStream Class SkipAheadStream represents a single skip-ahead stream.
Public classCode exampleSlice 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.
Public classSmoothCubicSpline Class SmoothCubicSpline calculates smoothing splines.
Public classSobolQuasiRandomGenerator Class SobolQuasiRandomGenerator is quasi-random number generator which can be used for generating sequences of quasi-random point in n-dimensional space.
Public classSparseConstraintCoefficients Class implementing the ILinearConstraintCoefficients for sparse linear constraint coefficients. Only the non-zero coefficients are stored.
Public classSparseMatrixBuilderT 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 matrix-like row/column indexing for setting and retrieving values. Only nonzero values are stored.
Public classSparseMatrixDataStorage, Type Class SparseMatrixData stores general sparse matrix data.
Public classSparseMatrixFactT Abstract base class for sparse matrix factorizations using the Parallel Direct Sparse Solver Interface (PARDISO).
Public classSparsePls 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.
Public classSparsePlsDa 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. PLS-DA is then run as if Y was a continuous matrix.
Public classSparsePLSDACrossValidation Class SparsePLSDACrossValidation performs an evaluation of a PLS (Partial Least Squares) model.
Public classSparseVectorDataT Class SparseVectorData stores sparse vector data.
Public classSpecialFunctions This class contains a collection of special functions.
Public classStatsFunctionsObsolete.
No longer used. Please use NMathFunctions.
Public classStatsSettings Class StatsSettings contains global settings for NMath Stats classes.
Public classStochasticHillClimbingParameters Parameter class for the StochasticHillClimbingSolver class.
Public classStochasticHillClimbingSolver 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.
Public classSubset Class Subset represents a collection of indices that can be used to view a subset of data from another data structure.
Public classSVDRegressionCalculation Class SVDRegressionCalculation computes linear regression parameters by the method of least squares using a singular value decomposition.
Public classTabulatedFunction Class TabulatedFunction is an abstract class representing a function determined by tabulated values.
Public classTDistribution Class TDistribution represents Student's t-distribution with the specified degrees of freedom.
Public classTriangularDistribution Class TriangularDistribution represents the triangular probability distribution.
Public classTrustRegionMinimizer Class TrustRegionMinimizer solves both constrained and unconstrained nonlinear least squares problems using the Trust Region method.
Public classTrustRegionParameterCalc 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
Public classTwoSampleFTest Class TwoSampleFTest tests if the variances of two populations are equal.
Public classTwoSampleKSTest Class TwoSampleKSTest performs a two-sample Kolmogorov-Smirnov test to compare the distributions of values in two data sets.
Public classTwoSamplePairedTTest Class TwoSamplePairedTTest tests if two paired sets of observed values differ from each other in a significant way.
Public classTwoSampleUnpairedTTest Class TwoSampleUnpairedTTest tests the null hypothesis that the two population means corresponding to two random samples are equal.
Public classTwoSampleUnpairedUnequalTTest Class TwoSampleUnpairedUnequalTTest tests the null hypothesis that the two population means corresponding to two random samples are equal.
Public classTwoVariableIntegrator Class TwoVariableIntegrator integrates functions of two variables.
Public classTwoWayAnova Class TwoWayAnova performs a balanced two-way analysis of variance.
Public classTwoWayAnovaBase Base class for both balanced and unbalanced two way ANOVA.
Public classTwoWayAnovaTable Class TwoWayAnovaTable summarizes the information of a traditional two-way Analysis of Variance (ANOVA) table.
Public classTwoWayAnovaTypeI 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.
Public classTwoWayAnovaTypeII 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.
Public classTwoWayAnovaTypeIII 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).
Public classTwoWayAnovaUnbalanced 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.
Public classTwoWayAnovaUnbalancedOrderedLinearRegression 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.
Public classTwoWayAnovaUnbalancedParameterSlices 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.
Public classTwoWayAnovaUnbalancedTable Class TwoWayAnovaUnbalancedTable summarizes the information of a traditional two-way Analysis of Variance (ANOVA) table.
Public classTwoWayRanova Class TwoWayRanova performs a balanced two-way analysis of variance with repeated measures on one factor.
Public classTwoWayRanovaTable Class TwoWayRanovaTable summarizes the information of a traditional two-way Analysis of Variance (RANOVA) table.
Public classTwoWayRanovaTwo Class TwoWayRanovaTwo performs a balanced two-way analysis of variance with repeated measures on both factors.
Public classTwoWayRanovaTwoTable Class TwoWayRanovaTwoTable summarizes the information of a traditional two-way Analysis of Variance, with repeated measures on both factors, table,
Public classUniformDistribution Class UniformDistribution represents the Uniform probability distribution.
Public classVariableBounds Class specifying a variable ID, lower and upper bounds, and a tolerance used in testing bounds compliance.
Public classVariableMetricMinimizer Class VariableMetricMinimizer uses the Broyden-Fletcher-Goldfarb-Shanno variable metric algorithm to minimize multivariable functions.
Public classVariableOrderOdeSolver Solver for stiff and non-stiff ordinary differential equations. The algorithm uses higher order methods and smaller step size when the solution varies rapidly.
Public classVariableOrderOdeSolverOptions Class containing available options for the VariableOrderOdeSolver.
Public classVariableOrderOdeSolverSolutionYtype Data structor contianing solution values and statistics for an ODE solve.
Public classVarimaxRotation 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 (1e-12) is used.
Public classWavelet This abstract class represents a wavelet. There are fives types of built in wavelets avaiable: Harr, Daubechies, Least Asymmetric, Best Localized, and Coiflet.
Public classWeibullDistribution Class WeibullDistribution represents the Weibull probability distribution.
Public classWeibullParameterFunction Distribution function.
Public classWeightedRegressionAnova Class for performing an Analysis Of Variance on a weighted linear least squares fit.
Public classWilcoxonSignedRankTest Class WilcoxonSignedRankTest tests if two paired sets of observed values differ from each other in a significant way.
Public classZBenchNormal Computes the ZBench for normally distributed data, percent defective, and the parts per million defective.
Structures
 StructureDescription
Protected structureDiscreteWaveletTransformLevelT Represents one level of a DWT decomposition.
Public structureDoubleComplex The DoubleComplex struct represents a complex number, consisting of a real part and an imaginary part.
Public structureExtrema Represents an imutable extrema of a function in two dimensions.
Public structureFloatComplex The FloatComplex struct represents a complex number, consisting of a real part and an imaginary part.
Public structureIntPair Class IntPair represents a pair of integers.
Interfaces
 InterfaceDescription
Public interfaceIArnoldiShiftInvertOperations Eigenvalue Arnoldi solver shift invert operations.
Public interfaceIBoundedNonlinearLeastSqMinimizer Interface for nonlinear least squares minimizer where the solution is constrained by upper and lower bounds.
Public interfaceICrossValidationSubsets Interface for generating subsets of data to be used in a cross validation process.
Public interfaceIDFColumn Interface for data frame column types.
Public interfaceIDifferentiator Interface for classes that perform differentiation of a function at a point.
Public interfaceIDoubleComplexEnumerator 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.
Public interfaceIDoubleEnumerator 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.
Public interfaceIDoubleLeastSqWeightingFunction Interface for least squares weighting functions.
Public interfaceIFactorExtraction Interface for factor extration algorithms used in factor analysis.
Public interfaceIFactorRotation Interface for factor analysis factor rotation algorithms. Factors are rotated in order to maximize the relationship between the variables and some of the factors.
Public interfaceIFactorScores Interface for factor score computation in a factor analysis.
Public interfaceIFloatComplexEnumerator 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.
Public interfaceIFloatEnumerator 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.
Public interfaceIIntegrator Interface for classes that perform integration of a function over an interval.
Public interfaceILinearConstraintCoefficients Interface for the coefficients for a linear constraint in a non-linear 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.
Public interfaceILogisticRegressionCalc Interface class for calculating the parameters of a logistic regression model.
Public interfaceIMultiVariableDMinimizer Interface for classes that perform minimization of multivariate functions using derivative information.
Public interfaceIMultiVariableMinimizer Interface for classes that perform minimization of multivariate functions.
Public interfaceINMFUpdateAlgorithm Interface to be implemented by all Non-negative Matrix Factorization (NMF) update algorithms used by the NMFact class.
Public interfaceINonlinearLeastSqMinimizer Interface for nonlinear least squares minimizer.
Public interfaceIOneVariableDMinimizer Interface for classes that perform minimization of univariate functions using derivative information.
Public interfaceIOneVariableDRootFinder Interface for classes that find roots of univariate functions using derivative information.
Public interfaceIOneVariableMinimizer Interface for classes that perform minimization of univariate functions.
Public interfaceIOneVariableRootFinder Interface for classes that find roots of univariate functions using only function evaluations.
Public interfaceIRandomNumberDistributionT Interface for random number distributions.
Public interfaceIRandomVariableMoments Interface implemented by probablility distributions.
Public interfaceIRegressionCalculation Interface for classes used by class LinearRegression to calculate regression parameters.
Public interfaceISliceableT Implement this interface to indicate support of range slicing in a vector.
Public interfaceISparseMatrixStorageT Interface for general sparse matrix storage formats.
Public interfaceISpecialFunctions 
Public interfaceSequentialQuadraticProgrammingSolverILagrangianHessianUpdater 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.
Public interfaceSequentialQuadraticProgrammingSolverIStepSizeCalculator Computes a step size alphak for performing the update xk+1 = xk + alphak*pk, where pk is the step direction vector.
Delegates
 DelegateDescription
Public delegateDistanceFunction Functor that takes two vectors and returns a measure of the distance (similarity) between them.
Public delegateDoubleIterativelyReweightedLeastSqToleranceMetFunction Tolerance met function delegate.
Public delegateLinkageFunction Functor that computes the linkage (similarity) between two groups.
Public delegateOrderedConnectivityMatrixElementDistance 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.
Public delegateRandomNumberGeneratorUniformRandomNumber Functor for generating uniform deviates between zero and one.
Enumerations
 EnumerationDescription
Public enumerationActiveSetLineSearchSQPTerminationStatus Enum for possible algorithm termination reasons.
Public enumerationArnoldiSolveStatus Result of eigenvalue problem solve attempt using implicitly restarted Arnoldi iteration.
Public enumerationBairstowRootFinderSolveResultStatus Status values indicating why iteration was terminated.
Public enumerationBalanceOption Enumeration for specifying balancing options in eigenvalue decompositions.
Public enumerationBiasType Enumeration for specifying a biased or unbiased estimator.
Public enumerationConjugateTransposeOption Enum specifying a particualr conjugate transpose option.
Public enumerationConstrainedOptimizerSolveResult Enum whose value indicate the status of the solution.
Public enumerationConstrainedOptimizerORToolsSolveResult Enum whose value indicate the status of the solution.
Public enumerationConstraintType Enumeration for specifying constraint types possible for the Constraint class and other constrained optimization classes.>
Public enumerationControlLimits Emum specifics types of control limits.
Public enumerationConvolutionBaseWindowing Options for handling the various convolution/correlation result boundaries.
Public enumerationConvolutionMode Controls internal funtion of convolution class.
Public enumerationCorrelationBaseWindowing Options for handling the various convolution/correlation result boundaries.
Public enumerationCorrelationMode 
Public enumerationDiscreteDataIntegratorStrategy Discrete data integration strategies.
Public enumerationDiscreteWaveletTransformThresholdMethod Details thresholding methods.
Public enumerationDiscreteWaveletTransformThresholdPolicy Thresholding policy to apply to details thresholding.
Public enumerationDiscreteWaveletTransformWaveletCoefficientType The two types of wavelet coefficients.
Public enumerationDiscreteWaveletTransformWaveletMode Method for padding the signal edges for improved DWT accuracy near signal edges.
Public enumerationDoubleRandomBetaDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationDoubleRandomExponentialDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationDoubleRandomGammaDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationDoubleRandomGaussianDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationDoubleRandomLogNormalDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationDoubleRandomRayleighDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationDoubleRandomUniformDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationDoubleRandomWeibullDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationDualSimplexCostingObsolete.
Possible values for the dual simplex solver costing parameter. These values specify the pivoting strategy used by the solver.
Public enumerationFFTDirection Direction of FFT. Used for building FFTConfiguration types.
Public enumerationFFTDomain Forward Domain of FFT. Used for building FFTConfiguration types.
Public enumerationFFTPrecision Precision of FFT transform. Used for building FFTConfiguration types.
Public enumerationFloatRandomBetaDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationFloatRandomExponentialDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationFloatRandomGammaDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationFloatRandomGaussianDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationFloatRandomLogNormalDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationFloatRandomRayleighDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationFloatRandomUniformDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationFloatRandomWeibullDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationHypothesisType Enumeration for specifying the form of an alternative hypothesis in a hypothesis test.
Public enumerationIActiveSetQPSolverAlgorithmStatus Enum whose value indicate the status of the solution.
Public enumerationInteriorPointQPSolverParamsKktFormOption KKT form options for the interior point quadratic programming solver.
Public enumerationInteriorPointQPSolverParamsPresolveLevelOption Presolve options for the interior point quadratic programming solver.
Public enumerationInteriorPointQPSolverParamsSymbolicOrderingOption Options for symbolic ordering for the interior point quadratic programming solver.
Public enumerationIntervalType An enumeration representing the possible interval types classified according to whether or not the endpoints are included in the interval.
Public enumerationIntRandomPoissonDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationIntRandomUniformDistributionGenerationMethod Enumeration specifying different methods of random number generation.
Public enumerationJohnsonTransformationType Enumeration for specifying the type of transformation of a normal random variate in the Johnson system.
Public enumerationKMeansClusteringStart An enumeration representing methods used to choose the initial cluster centers.
Public enumerationMovingWindowFilterBoundaryOption Options for handling the boundaries in a moving window filter.
Public enumerationNonnegLeastSqTermination Enumeration of possible nonnegative least squares algorithm termination states.
Public enumerationNormType Enumeration for specifying different types of norms.
Public enumerationOdeSolverBaseOutputFunctionFlag 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.
Public enumerationParameterSharing Enum for indicating whether a parameter is shared or not in a global curve fit calculation.
Public enumerationPeakFinderBasePeakSortOrder Enumeration for specifying sorting order.
Public enumerationPeakFinderRuleBasedRules Rules to filter peaks.
Public enumerationPosition Enumeration for specifying different view positions of underlying data.
Public enumerationPrimalSimplexCosting Costing algorithms supported by the primal simplex algorithm.
Public enumerationProductTransposeOption Enum for specifying transpose operations to be performed on the operands of a matrix-matrix multiply operation.
Public enumerationRandomNumberStreamBasicRandGenType Enumeration for the various algorithms available for generating random numbers uniformly dstributed in the interval [0, 1]
Public enumerationRandomNumberStreamStreamStatus Enum indicating the status of a random number stream.
Public enumerationSavitzkyGolayFilterSavitzyGolayBoundaryOptions Enumeration specifying Savitsky-Golay boundary options.
Public enumerationSimplexSolverMixedIntParamsBranchingStrategies Enumeration of branching strategies used in the branch-and-bound algorithm.
Public enumerationSimplexSolverMixedIntParamsSearchStrategies Enumeration of options for search strategies for the mixed integer linear programming solver.
Public enumerationSortingType Enumeration for specifying different sorting types, such as ascending or descending order.
Public enumerationSparseMatrixFactTError Enumeration for specifying possible return values for errors.
Protected enumerationSparseMatrixFactTMatrixType Enumeration for specifying the types of matrices that can be factored.
Public enumerationSparsePLSMode Partial Least Squares Mode.
Public enumerationStorageType Enumeration for specifying the storage scheme of a matrix.
Public enumerationTriangularMatrixTypes Enumeration for specifying the storage scheme of a matrix.
Public enumerationTrustRegionMinimizerCheck Options for checking the arrays passed into the solver as input parameters. If an array contains any INF or NaN values an exception will be thrown.
Public enumerationTrustRegionMinimizerCriterion Enumeration for specifying the stop criterion.
Public enumerationTwoWayAnovaUnbalancedParameterOrder Enumeration indicating the factors and their order in a sum of squares computation.
Public enumerationWaveletWavelets Built-in wavelets organized by short name. The first letter abbreviates the wavelet family name, and number that follows, the wavelet length.