Matrix Methods

The Matrix type exposes the following members.

Methods

	Name	Description
	Add(Double)	Adds the scalar to this matrix and returns the result.
	Add(Matrix)	Adds the given matrix to this one and returns the result.
	Add(Double, Matrix)	Adds the scalar to the matrix and writes the result in another matrix.
	Add(Matrix, Matrix)	Adds another matrix to this one and writes result to the given matrix.
	AddInPlace(Double)	Adds the scalar to the matrix.
	AddInPlace(Matrix)	Adds another matrix to this one.
	Assign(Double)	Sets all elements of the matrix to the given value. This is the same as SetValues() call.
	Assign(Double)	Copies the elements of the matrix from 2D array. This is the same as SetValues() call.
	BilinearForm	Computes the value of bilinear form *x' A * y**.
	Clear	Sets all elements of the matrix to zero.
	Clone	Creates a copy of this matrix.
	ConditionNumber	Computes the condition number of the matrix using SVD.
	CopyTo(Matrix)	Copies this matrix into another one.
	CopyTo(Matrix, Int32, Int32, Int32, Int32, Int32, Int32)	Copies part of this matrix to another one.
	DelayedClone	Returns a mirror of this matrix.
	Determinant	Computes the determinant of the matrix using LU factorization.
	Diagonal(Int32, Vector)	Creates square matrix with given elements on the main diagonal.
	Diagonal(Int32, Int32, Vector)	Creates rectangular matrix with given elements on the main diagonal.
	DiagonalStack(Matrix, Matrix)	Diagonally stacks two given matrices. The new matrix is a M-by-N diagonal block matrix, where Copy 1M = topLeft.Rows + bottomRight.Rows and Copy 1N = topLeft.Columns + 2 bottomRight.Columns . The values of the off-diagonal blocks are unchanged.
	DiagonalStack(Matrix, Matrix, Matrix)	Diagonally stacks two given matrices. The new matrix is a M-by-N diagonal block matrix, where Copy 1M = topLeft.Rows + bottomRight.Rows and Copy 1N = topLeft.Columns + 2 bottomRight.Columns . The values of the off-diagonal blocks are unchanged.
	Divide(Double)	Divides this matrix by given scalar and writes result into another matrix.
	Divide(Double, Matrix)	Divides this matrix by given scalar and writes result into another matrix.
	DivideInPlace	Divides each element of the matrix by given scalar.
	Equals(Object)	Determines whether the specified Object is equal to the current Object. (Overrides ObjectEquals(Object).)
	Equals(Matrix)	Indicates whether the current matrix is equal to another.
	FrobeniusNorm	Computes the Frobenius norm.
	GeneralMatrixMatrixMultiply	Computes the matrix-matrix product in general form. This method corresponds to the GEMM routine from BLAS Level 3. It multiplies this matrix by scalar beta and adds the product of matrices A and B scaled by scalar alpha.
	GetColumn(Int32)	Returns the vector that stores the elements of particular column of this matrix.
	GetColumn(Int32, Vector)	Copies the elements of particular column of this matrix to given vector.
	GetColumn(Int32, Double)	Copies the elements of particular column of this matrix to given vector.
	GetDiagonalValues	Creates the vector of diagonal elements of this matrix.
	GetDiagonalValues(Vector)	Copies the diagonal elements of this matrix into the vector.
	GetHashCode	Serves as a hash function for a particular type. (Overrides ObjectGetHashCode.)
	GetInverse	Computes the Moore-Penrose pseudoinverse matrix. This routine uses advanced algorithm that performs much faster for most matrices.
	GetInverse(Matrix)	Computes the Moore-Penrose pseudoinverse matrix. This routine uses advanced algorithm that performs much faster for most matrices.
	GetLowerTrapezoid	Returns a new matrix of the same size containing the lower trapezoid of this matrix.
	GetLowerTrapezoid(Matrix)	Copies the lower trapezoid of this matrix into another matrix of the same size.
	GetLowerTrapezoid(Matrix, Boolean)	Copies the lower trapezoid of this matrix into another matrix of the same size.
	GetLowerTriangle	Returns a new square matrix containing the lower triangle of this matrix.
	GetLowerTriangle(Matrix)	Copies the lower triangle of this matrix into another square matrix.
	GetLowerTriangle(Matrix, Boolean)	Copies the lower triangle of this matrix into another square matrix.
	GetLUInverse	Computes the inverse of this matrix.
	GetLUInverse(Matrix)	Computes the inverse of this matrix.
	GetNormalized(MatrixNormType)	Computes normalized matrix (with unity norm).
	GetNormalized(MatrixNormType, Matrix)	Computes normalized matrix (with unity norm).
	GetPseudoInverse	Computes the Moore-Penrose pseudoinverse matrix.
	GetPseudoInverse(Matrix)	Computes the Moore-Penrose pseudoinverse matrix.
	GetRow(Int32)	Returns the vector that stores the elements of the particular row.
	GetRow(Int32, Vector)	Copies the elements of the particular row to the given vector.
	GetRow(Int32, Double)	Copies the elements of the particular row to the given vector.
	GetStrictlyLowerTrapezoid	Returns a new matrix of the same size containing the strictly lower trapezoid of this matrix.
	GetStrictlyLowerTrapezoid(Matrix)	Copies the strictly lower trapezoid of this matrix into another matrix of the same size.
	GetStrictlyLowerTrapezoid(Matrix, Boolean)	Copies the strictly lower trapezoid of this matrix into another matrix of the same size.
	GetStrictlyLowerTriangle	Returns a new square matrix containing the strictly lower triangle of this matrix.
	GetStrictlyLowerTriangle(Matrix)	Copies the strictly lower triangle of this matrix into another square matrix.
	GetStrictlyLowerTriangle(Matrix, Boolean)	Copies the strictly lower triangle of this matrix into another square matrix.
	GetStrictlyUpperTrapezoid	Returns a new matrix of the same size containing the strictly upper trapezoid of this matrix.
	GetStrictlyUpperTrapezoid(Matrix)	Copies the strictly upper trapezoid of this matrix into another matrix of the same size.
	GetStrictlyUpperTrapezoid(Matrix, Boolean)	Copies the strictly upper trapezoid of this matrix into another matrix of the same size.
	GetStrictlyUpperTriangle	Returns a new square matrix containing the strictly upper triangle of this matrix.
	GetStrictlyUpperTriangle(Matrix)	Copies the strictly upper triangle of this matrix into another square matrix.
	GetStrictlyUpperTriangle(Matrix, Boolean)	Copies the strictly upper triangle of this matrix into another square matrix.
	GetSubMatrix(Int32, Int32, Matrix)	Copies submatrix to another matrix.
	GetSubMatrix(Int32, Int32, Int32, Int32)	Creates a matrix that contains the values from the requested sub-matrix.
	GetTransposed	Computes the transpose of this matrix.
	GetTransposed(Matrix)	Computes the transpose of this matrix.
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	GetUpperTrapezoid	Returns a new matrix of the same size containing the upper trapezoid of this matrix.
	GetUpperTrapezoid(Matrix)	Copies the upper trapezoid of this matrix into another matrix of the same size.
	GetUpperTrapezoid(Matrix, Boolean)	Copies the upper trapezoid of this matrix into another matrix of the same size.
	GetUpperTriangle	Returns a new square matrix containing the upper triangle of this matrix.
	GetUpperTriangle(Matrix)	Copies the upper triangle of this matrix into another square matrix.
	GetUpperTriangle(Matrix, Boolean)	Copies the upper triangle of this matrix into another square matrix.
	GetValues	Returns the 2D array with matrix' elements.
	GetValues(Double)	Copies the elements of the matrix into 2D array.
	HadamardProduct(Matrix, Matrix)	Computes the Hadamard product (also known as pointwise product or Shur product) of two matrices.
	HadamardProduct(Matrix, Matrix, Matrix)	Computes the Hadamard product (also known as pointwise product or Shur product) of two matrices.
	HasAbnormal	Checks whether the matrix contains abnormal numbers: NaN, NegativeInfinity, or PositiveInfinity.
	HasInfinity	Checks whether the matrix contains NegativeInfinity or PositiveInfinity.
	HasNaN	Checks whether the matrix contains NaN.
	HasNegativeInfinity	Checks whether the matrix contains NegativeInfinity.
	HasPositiveInfinity	Checks whether the matrix contains PositiveInfinity.
	HorizontalStack(Matrix, Matrix)	Stacks the given matrices horizontally. The new matrix is M-by-N matrix, where Copy 1M = left.Rows and Copy 1N = left.Columns + right.Columns .
	HorizontalStack(Matrix, Matrix, Matrix)	Stacks the given matrices horizontally. The new matrix is M-by-N matrix, where Copy 1M = left.Rows and Copy 1N = left.Columns + right.Columns .
	Identity(Int32)	Creates identity matrix of the given order.
	Identity(Int32, Int32)	Creates rectangular matrix with unit diagonal elements.
	InfinityNorm	Computes the infinity norm (induced by infinity vector norm).
	Invert	Computes the Moore-Penrose pseudoinverse matrix in place. This routine uses advanced algorithm that performs much faster for most matrices.
	KroneckerProduct(Matrix, Matrix)	Computes the Kronecker product of two matrices.
	KroneckerProduct(Matrix, Matrix, Matrix)	Computes the Kronecker product of two matrices.
	L1Norm	Computes the L1 norm (induced by L1 vector norm).
	L2Norm	Computes the L2 norm (induced by L2 vector norm).
	LeftMultiply(Matrix)	Multiplies this matrix by another one from the left and returns the result.
	LeftMultiply(Vector)	Multiplies this matrix by the row vector from the left and returns the result.
	LeftMultiply(Matrix, Matrix)	Multiplies this matrix by another one from the left and writes the result into another matrix.
	LeftMultiply(Vector, Vector)	Multiplies this matrix by the row vector from the left and writes the result into another row vector.
	LeftSubtract(Double)	Subtracts this matrix from the given scalar and returns the result.
	LeftSubtract(Matrix)	Subtracts this matrix from the given one and returns the result.
	LeftSubtract(Double, Matrix)	Subtracts this matrix from the given scalar and writes the result into another matrix.
	LeftSubtract(Matrix, Matrix)	Subtracts this matrix from another one and writes result into the given matrix.
	LeftSubtractInPlace(Double)	Subtracts this matrix from the given result.
	LeftSubtractInPlace(Matrix)	Subtracts this matrix from another one.
	Load(String)	Loads Matrix from CSV file.
	Load(String, Boolean, Char)	Loads Matrix from CSV file.
	LUInvert	Overwrites this matrix with its inverse.
	MaxNorm	Computes the max norm (maximal absolute value of matrix' elements).
	Minus	Returns the negated copy of this matrix.
	Minus(Matrix)	Writes the negated copy of this matrix into another matrix.
	MinusInPlace	Negates this matrix.
	Multiply(Double)	Multiplies this matrix by the given scalar and returns the result.
	Multiply(Matrix)	Multiplies this matrix by the given matrix from the right and returns the result.
	Multiply(Vector)	Multiplies this matrix by the column vector from the right and returns the result.
	Multiply(Double, Matrix)	Multiplies this matrix by the given scalar and writes the result in another matrix.
	Multiply(Matrix, Matrix)	Multiplies this matrix by the given matrix from the right and writes the result into another matrix.
	Multiply(Vector, Vector)	Multiplies this matrix by the column vector from the right and writes the result into another vector.
	MultiplyInPlace	Multiplies the matrix by given scalar.
	Negate	Negates all elements of the matrix.
	Norm	Computes matrix norm.
	Normalize	Normalizes this matrix (with unity norm).
	PermuteColumns	Permutes columns of the matrix.
	PermuteRows	Permutes rows of the matrix.
	Plus	Returns the copy of this matrix.
	Plus(Matrix)	Copies this matrix into another one.
	PlusInPlace	Does nothing.
	PointwiseAdd(Double)	Adds the given scalar to each element of the matrix and returns the result.
	PointwiseAdd(Matrix)	Adds each element of the given matrix to the corresponding element of this matrix and returns the result.
	PointwiseAdd(Double, Matrix)	Adds the given scalar to each element of the matrix and writes the result into another one.
	PointwiseAdd(Matrix, Matrix)	Adds each element of the given matrix to the corresponding element of this matrix and writes the result into another one.
	PointwiseAddInPlace(Double)	Adds given scalar to each element of the matrix.
	PointwiseAddInPlace(Matrix)	Adds each element of the given matrix to the corresponding element of this matrix.
	PointwiseColumnAddInPlace	Adds the elements of each matrix column with the input vector elements.
	PointwiseColumnDivideInPlace	Divide the elements of each matrix column by the input vector elements.
	PointwiseColumnMultiplyInPlace	Multiply the elements of each matrix column by the input vector elements.
	PointwiseColumnSubtractInPlace	Subtract the elements of each matrix column by the input vector elements.
	PointwiseDivide(Double)	Divides each element of this matrix by given scalar and returns the result.
	PointwiseDivide(Matrix)	Divides each element of this matrix by the corresponding element of the given one and returns the result.
	PointwiseDivide(Double, Matrix)	Divides each element of this matrix by given scalar and writes the result into another matrix.
	PointwiseDivide(Matrix, Matrix)	Divides each element of this matrix by the corresponding element of the given one and writes the result into another matrix.
	PointwiseDivideInPlace(Double)	Divides each element of this matrix by given scalar.
	PointwiseDivideInPlace(Matrix)	Divides each element of this matrix by the corresponding element of the given one.
	PointwiseLeftDivide(Double)	Divides the given scalar by each element of the matrix and returns the result.
	PointwiseLeftDivide(Matrix)	Divides each element of the given matrix by the corresponding element of this one and returns the result.
	PointwiseLeftDivide(Double, Matrix)	Divides the given scalar by each element of the matrix and writes the result into another matrix.
	PointwiseLeftDivide(Matrix, Matrix)	Divides each element of the given matrix by the corresponding element of this one and writes the result into another matrix.
	PointwiseLeftDivideInPlace(Double)	Divides the given scalar by each element of the matrix.
	PointwiseLeftDivideInPlace(Matrix)	Divides each element of the given matrix by the corresponding element of this one.
	PointwiseLeftSubtract(Double)	Subtracts each element of the matrix from the given scalar and returns the result.
	PointwiseLeftSubtract(Matrix)	Subtracts each element of this matrix from the corresponding element of the given matrix and returns the result.
	PointwiseLeftSubtract(Double, Matrix)	Subtracts each element of the matrix from the given scalar and writes the result into another matrix.
	PointwiseLeftSubtract(Matrix, Matrix)	Subtracts each element of this matrix from the corresponding element of the given matrix and writes the result into antother matrix.
	PointwiseLeftSubtractInPlace(Double)	Subtracts each element of the matrix from the given scalar.
	PointwiseLeftSubtractInPlace(Matrix)	Subtracts each element of this matrix from the corresponding element of the given matrix.
	PointwiseMultiply(Double)	Multiplies each element of the matrix by given scalar and returns the result.
	PointwiseMultiply(Matrix)	Multiplies each element of this matrix by the corresponding element of the given matrix and returns the result.
	PointwiseMultiply(Double, Matrix)	Multiplies each elementof the matrix by given scalar and writes the result into another matrix.
	PointwiseMultiply(Matrix, Matrix)	Multiplies each element of this matrix by the corresponding element of the given matrix and writes the result into another matrix.
	PointwiseMultiplyInPlace(Double)	Multiplies each element of the matrix by given scalar.
	PointwiseMultiplyInPlace(Matrix)	Multiplies each element of this matrix by the corresponding element of the given matrix.
	PointwiseRowAddInPlace	Adds the elements of each matrix row with the input vector elements.
	PointwiseRowDivideInPlace	Divide the elements of each matrix row by the input vector elements.
	PointwiseRowMultiplyInPlace	Multiply the elements of each matrix row by the input vector elements.
	PointwiseRowSubtractInPlace	Subtract the elements of each matrix row by the input vector elements.
	PointwiseSubtract(Double)	Subtracts given scalar from each element of the matrix and returns the result.
	PointwiseSubtract(Matrix)	Subtracts each element of the given matrix from the corresponding element of this matrix and returns the result.
	PointwiseSubtract(Double, Matrix)	Subtracts given scalar from each element of the matrix and writes the result into another one.
	PointwiseSubtract(Matrix, Matrix)	Subtracts each element of the given matrix from the corresponding element of this matrix and writes the result into another matrix.
	PointwiseSubtractInPlace(Double)	Subtracts given scalar from each element of the matrix.
	PointwiseSubtractInPlace(Matrix)	Subtracts each element of the given matrix from the corresponding element of this matrix.
	Power	Raises square matrix to a specified positive integer power.
	PseudoInvert	Overwrites current matrix with its Moore-Penrose pseudoinverse.
	QuadraticForm	Computes the value of quadratic form *x' A * x**.
	Random(Int32)	Creates square matrix with elements which are samples from the continuous uniform [0;1] distribution.
	Random(CMDistribution)	Creates rectangular matrix with elements which are samples from the specified matrix-variate distribution. Matrix size determined by distribution.
	Random(Int32, CUDistribution)	Creates square matrix with elements which are samples from the specified distribution.
	Random(Int32, RandomGenerator)	Creates rectangular matrix with elements uniformly distributed in [0; 1].
	Random(Int32, Int32)	Creates rectangular matrix with elements which are samples from the continuous uniform [0;1] distribution.
	Random(Int32, Random)	Creates rectangular matrix with elements uniformly distributed in [0; 1].
	Random(Int32, Int32, CUDistribution)	Creates rectangular matrix with elements which are samples from the specified distribution.
	Random(Int32, Int32, RandomGenerator)	Creates rectangular matrix with elements uniformly distributed in [0; 1].
	Random(Int32, Int32, Random)	Creates rectangular matrix with elements uniformly distributed in [0; 1].
	Rank	Computes the rank of the matrix using SVD.
	RankOneUpdate	Performs in-place rank-1 update of this matrix (adds scaled outer product of two vectors).
	Save	Saves Matrix into CSV file.
	ScaleColumns	Scales each column of the matrix by corresponding factor.
	ScaleRows	Scales each row of the matrix by corresponding factor.
	SetColumn(Int32, Vector)	Sets elements of the particular column.
	SetColumn(Int32, Double)	Sets elements of the particular column.
	SetDiagonalValues	Copies the diagonal elements of this matrix from the vector.
	SetRow(Int32, Vector)	Sets elements of particular row.
	SetRow(Int32, Double)	Sets elements of particular row.
	SetSubMatrix	Sets submatrix.
	SetValues(Double)	Sets all elements of the matrix to the given value. This is the same as Assign() call.
	SetValues(Double)	Copies the elements of the matrix from 2D array. This is the same as Assign() call.
	Subtract(Double)	Subtracts the given scalar from the matrix and returns the result.
	Subtract(Matrix)	Subtracts the given matrix from this one and returns the result.
	Subtract(Double, Matrix)	Subtracts the given scalar from the matrix and writes the result in another matrix.
	Subtract(Matrix, Matrix)	Subtracts the given matrix from this one and writes the result into the given matrix.
	SubtractInPlace(Double)	Subtracts the scalar from the matrix.
	SubtractInPlace(Matrix)	Subtracts another matrix from this one.
	ToArray	Converts this matrix to 2D array of its values.
	ToColumnWiseArray	Writes all elements of matrix to 1D array by columns.
	ToRowWiseArray	Writes all elements of matrix to 1D array by rows.
	ToString	Returns a String that represents the current Matrix. (Overrides ObjectToString.)
	ToString(IFormatProvider)	Formats the value of the current instance using the specified format.
	ToString(String)	Formats the value of the current instance using the specified format.
	ToString(String, IFormatProvider)	Formats the value of the current instance using the specified format.
	Trace	Computes the trace of the matrix (sum of diagonal elements).
	Transpose	Overwrites this matrix with its transpose.
	TransposedMultiply(Matrix)	Multiplies transposed matrix by another one.
	TransposedMultiply(Vector)	Multiplies transposed matrix by given vector.
	TransposedMultiply(Matrix, Matrix)	Multiplies transposed matrix by another one and writes the result to the given matrix.
	TransposedMultiply(Vector, Vector)	Multiplies transposed matrix by given vector and writes the result to another one.
	VerticalStack(Matrix, Matrix)	Stacks the given matrices vertically. The new matrix is M-by-N matrix, where Copy 1M = top.Rows + bottom.Rows and Copy 1N = top.Columns .
	VerticalStack(Matrix, Matrix, Matrix)	Stacks the given matrices vertically. The new matrix is M-by-N matrix, where Copy 1M = top.Rows + bottom.Rows and Copy 1N = top.Columns .

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Extension Methods

	Name	Description
	CalculateMetric(MetricType)	Overloaded. Calculate metric. (Defined by MetricsCalculator.)
	CalculateMetric(Matrix, MetricType)	Overloaded. Calculate metric. (Defined by MetricsCalculator.)
	Center	Overloaded. Center each series in specified data matrix. Columns corresponds to variables Rows corresponds to observations. (Defined by DescriptiveStatisticsMatrix.)
	Center(Vector)	Overloaded. Center each series in specified data matrix. Columns corresponds to variables Rows corresponds to observations. (Defined by DescriptiveStatisticsMatrix.)
	CholeskyWhite	Overloaded. White series in specified data matrix. I.e. center apply linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation use inversed Cholesky factor. (Defined by DescriptiveStatisticsMatrix.)
	CholeskyWhite(Matrix)	Overloaded. White series in specified data matrix. Use user specified covariance matrix instead of actual calculation. I.e. center apply linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation use inversed Cholesky factor. (Defined by DescriptiveStatisticsMatrix.)
	CholeskyWhite(Matrix, Matrix)	Overloaded. White series in specified data matrix. Use user specified covariance matrix instead of actual calculation. I.e. center apply linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation use inversed Cholesky factor. (Defined by DescriptiveStatisticsMatrix.)
	Correlation	Returns correlation. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Correlation (Defined by DescriptiveStatisticsMatrix.)
	CorrelationAndMean	Calculates mean and correlation for data matrix. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Correlation (Defined by DescriptiveStatisticsMatrix.)
	CosineSimilarity	Overloaded. Cosine Similarity Distance between set of objects. http://en.wikipedia.org/wiki/Cosine_similarity (Defined by MetricsCalculator.)
	CosineSimilarity(Matrix)	Overloaded. Cosine Similarity Distance between set of objects. http://en.wikipedia.org/wiki/Cosine_similarity (Defined by MetricsCalculator.)
	Covariance	Returns population covariance. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Covariance (Defined by DescriptiveStatisticsMatrix.)
	CovarianceAndMean	Calculates mean and population covariance for data matrix. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Covariance (Defined by DescriptiveStatisticsMatrix.)
	CovarianceAndMeanPopulation	Calculates mean and population covariance for data matrix. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Covariance (Defined by DescriptiveStatisticsMatrix.)
	CovarianceAndMeanSample	Calculates mean and estimation of sample covariance for data matrix. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Covariance (Defined by DescriptiveStatisticsMatrix.)
	CovariancePopulation	Returns population covariance. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Covariance (Defined by DescriptiveStatisticsMatrix.)
	CovarianceSample	Returns estimation of sample covariance. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Covariance (Defined by DescriptiveStatisticsMatrix.)
	CrossCorrelation	Calculates mean and cross-correlation between two vector sets. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Correlation (Defined by DescriptiveStatisticsMatrix.)
	CrossCorrelationAndMean	Calculates mean and cross-correlation between two vector sets. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Correlation (Defined by DescriptiveStatisticsMatrix.)
	CrossCovariance	Calculates mean and population cross-covariance between two vector sets. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Covariance (Defined by DescriptiveStatisticsMatrix.)
	CrossCovarianceAndMean	Calculates mean and population cross-covariance between two vector sets. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Covariance (Defined by DescriptiveStatisticsMatrix.)
	CrossCovarianceAndMeanPopulation	Calculates mean and population cross-covariance between two vector sets. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Covariance (Defined by DescriptiveStatisticsMatrix.)
	CrossCovarianceAndMeanSample	Calculates mean and sample cross-covariance between two vector sets. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Covariance (Defined by DescriptiveStatisticsMatrix.)
	CrossCovariancePopulation	Calculates mean and population cross-covariance between two vector sets. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Covariance (Defined by DescriptiveStatisticsMatrix.)
	CrossCovarianceSample	Calculates mean and sample cross-covariance between two vector sets. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Covariance (Defined by DescriptiveStatisticsMatrix.)
	EnsureBoundaries	Values below leftBoundary will be replaced with leftBoundary value. Values above rightBoundary will be replaced with rightBoundary value. (Defined by DescriptiveStatisticsMatrix.)
	EnsureLeftBoundary	Values below boundary will be replaced with boundary value. (Defined by DescriptiveStatisticsMatrix.)
	EnsureRightBoundary	Values above boundary will be replaced with boundary value. (Defined by DescriptiveStatisticsMatrix.)
	EuclideanDistance	Overloaded. Euclidean Distance between set of objects. http://en.wikipedia.org/wiki/Euclidean_distance (Defined by MetricsCalculator.)
	EuclideanDistance(Matrix)	Overloaded. Cosine Similarity Distance between set of objects. http://en.wikipedia.org/wiki/Cosine_similarity (Defined by MetricsCalculator.)
	GetCentered	Overloaded. Get centered copy of data matrix. Columns corresponds to variables Rows corresponds to observations. (Defined by DescriptiveStatisticsMatrix.)
	GetCentered(Matrix)	Overloaded. Get centered copy of data matrix. Columns corresponds to variables Rows corresponds to observations. (Defined by DescriptiveStatisticsMatrix.)
	GetCentered(Vector)	Overloaded. Get centered copy of data matrix. Columns corresponds to variables Rows corresponds to observations. (Defined by DescriptiveStatisticsMatrix.)
	GetCentered(Matrix, Vector)	Overloaded. Get centered copy of data matrix. Columns corresponds to variables Rows corresponds to observations. (Defined by DescriptiveStatisticsMatrix.)
	GetCholeskyWhited	Overloaded. Get whited copy of data matrix. I.e. centered and after applying linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation use inversed Cholesky factor. (Defined by DescriptiveStatisticsMatrix.)
	GetCholeskyWhited(Matrix)	Overloaded. Get whited copy of data matrix. I.e. centered and after applying linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation use inversed Cholesky factor. (Defined by DescriptiveStatisticsMatrix.)
	GetCholeskyWhited(Matrix, Matrix)	Overloaded. Get whited copy of data matrix. Use user specified covariance matrix instead of actual calculation. I.e. centered and after applying linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation use inversed Cholesky factor. (Defined by DescriptiveStatisticsMatrix.)
	GetCholeskyWhited(Matrix, Matrix, Matrix)	Overloaded. Get whited copy of data matrix. Use user specified covariance matrix instead of actual calculation. I.e. centered and after applying linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation use inversed Cholesky factor. (Defined by DescriptiveStatisticsMatrix.)
	GetEnsuredBoundaries(Double, Double)	Overloaded. Returns copy of matrix. Values below leftBoundary will be replaced with leftBoundary value. Values above rightBoundary will be replaced with rightBoundary value. (Defined by DescriptiveStatisticsMatrix.)
	GetEnsuredBoundaries(Double, Double, Matrix)	Overloaded. Store copy of matrix. Values below leftBoundary will be replaced with leftBoundary value. Values above rightBoundary will be replaced with rightBoundary value. (Defined by DescriptiveStatisticsMatrix.)
	GetEnsuredLeftBoundary(Double)	Overloaded. Returns copy of matrix. Values below boundary will be replaced with boundary value. (Defined by DescriptiveStatisticsMatrix.)
	GetEnsuredLeftBoundary(Double, Matrix)	Overloaded. Store copy of matrix. Values below boundary will be replaced with boundary value. (Defined by DescriptiveStatisticsMatrix.)
	GetEnsuredRightBoundary(Double)	Overloaded. Returns copy of matrix. Values above boundary will be replaced with boundary value. (Defined by DescriptiveStatisticsMatrix.)
	GetEnsuredRightBoundary(Double, Matrix)	Overloaded. Store copy of matrix. Values above boundary will be replaced with boundary value. (Defined by DescriptiveStatisticsMatrix.)
	GetStandardized	Overloaded. Get standardized copy of data matrix. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Standardize (Defined by DescriptiveStatisticsMatrix.)
	GetStandardized(Matrix)	Overloaded. Get standardized copy of data matrix. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Standardize (Defined by DescriptiveStatisticsMatrix.)
	GetWhited	Overloaded. Get whited copy of data matrix. I.e. centered and after applying linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation power singular values of covariance matrix. http://en.wikipedia.org/wiki/Whitening_transformation (Defined by DescriptiveStatisticsMatrix.)
	GetWhited(Matrix)	Overloaded. Get whited copy of data matrix. I.e. centered and after applying linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation power singular values of covariance matrix. http://en.wikipedia.org/wiki/Whitening_transformation (Defined by DescriptiveStatisticsMatrix.)
	GetWhited(Matrix, Matrix)	Overloaded. Get whited copy of data matrix. Use user specified covariance matrix instead of actual calculation. I.e. centered and after applying linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation power singular values of covariance matrix. http://en.wikipedia.org/wiki/Whitening_transformation (Defined by DescriptiveStatisticsMatrix.)
	GetWhited(Matrix, Matrix, Matrix)	Overloaded. Get whited copy of data matrix. Use user specified covariance matrix instead of actual calculation. I.e. centered and after applying linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation power singular values of covariance matrix. http://en.wikipedia.org/wiki/Whitening_transformation (Defined by DescriptiveStatisticsMatrix.)
	HammingMetric	Overloaded. Hamming Distance between set of objects. http://en.wikipedia.org/wiki/Hamming_distance (Defined by MetricsCalculator.)
	HammingMetric(Matrix)	Overloaded. Hamming Distance between set of objects. http://en.wikipedia.org/wiki/Hamming_distance (Defined by MetricsCalculator.)
	InfinityDistance	Overloaded. Maximum Distance between set of objects. http://en.wikipedia.org/wiki/Uniform_norm (Defined by MetricsCalculator.)
	InfinityDistance(Matrix)	Overloaded. Maximum Distance between set of objects. http://en.wikipedia.org/wiki/Uniform_norm (Defined by MetricsCalculator.)
	ManhattanDistance	Overloaded. Manhattan Distance between set of objects. http://en.wikipedia.org/wiki/Manhattan_distance (Defined by MetricsCalculator.)
	ManhattanDistance(Matrix)	Overloaded. Manhattan Distance between set of objects. http://en.wikipedia.org/wiki/Manhattan_distance (Defined by MetricsCalculator.)
	MantegnaMetric	Overloaded. Mantegna Metric between set of objects. Mantegna - Metric which convert correlation into distance. (Defined by MetricsCalculator.)
	MantegnaMetric(Matrix)	Overloaded. Mantegna Metric between set of objects. Mantegna - Metric which convert correlation into distance. (Defined by MetricsCalculator.)
	Maximum	Overloaded. Returns maximum value for each data series. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Maxima_and_minima (Defined by DescriptiveStatisticsMatrix.)
	Maximum(Vector)	Overloaded. Calculates maximum value for each data series. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Maxima_and_minima (Defined by DescriptiveStatisticsMatrix.)
	Mean	Overloaded. Returns mean for each data series. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Mean (Defined by DescriptiveStatisticsMatrix.)
	Mean(Vector)	Overloaded. Calculates mean for each data series. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Mean (Defined by DescriptiveStatisticsMatrix.)
	Minimum	Overloaded. Returns minimum value for each data series. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Maxima_and_minima (Defined by DescriptiveStatisticsMatrix.)
	Minimum(Vector)	Overloaded. Calculates minimum value for each data series. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Maxima_and_minima (Defined by DescriptiveStatisticsMatrix.)
	MinimumMaximum	Overloaded. Returns minimum and maximum values for each data series. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Maxima_and_minima (Defined by DescriptiveStatisticsMatrix.)
	MinimumMaximum(IListDescriptiveStatisticsMinimumMaximumValuesDouble)	Overloaded. Calculates minimum and maximum values for each data series. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Maxima_and_minima (Defined by DescriptiveStatisticsMatrix.)
	SquaredEuclideanDistance	Overloaded. Squared Euclidean Distance between set of objects. It is just square of Euclidean distance. (Defined by MetricsCalculator.)
	SquaredEuclideanDistance(Matrix)	Overloaded. Squared Euclidean Distance between set of objects. It is just square of Euclidean distance. (Defined by MetricsCalculator.)
	Standardize	Standardize all series in data matrix. Columns corresponds to variables Rows corresponds to observations. http://en.wikipedia.org/wiki/Standardize (Defined by DescriptiveStatisticsMatrix.)
	Sum	Overloaded. Calculates sum of rows. (Defined by DescriptiveStatisticsMatrix.)
	Sum(Vector)	Overloaded. Calculates sum of rows. (Defined by DescriptiveStatisticsMatrix.)
	SumSquares	Overloaded. Calculates sum squared values in rows. (Defined by DescriptiveStatisticsMatrix.)
	SumSquares(Vector)	Overloaded. Calculates sum squared values in rows. (Defined by DescriptiveStatisticsMatrix.)
	White	Overloaded. White series in specified data matrix. I.e. center apply linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation power singular values of covariance matrix. http://en.wikipedia.org/wiki/Whitening_transformation (Defined by DescriptiveStatisticsMatrix.)
	White(Matrix)	Overloaded. White series in specified data matrix. Use user specified covariance matrix instead of actual calculation. I.e. center apply linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation power singular values of covariance matrix. http://en.wikipedia.org/wiki/Whitening_transformation (Defined by DescriptiveStatisticsMatrix.)
	White(Matrix, Matrix)	Overloaded. White series in specified data matrix. Use user specified covariance matrix instead of actual calculation. I.e. center apply linear transformation which intends to make covariance matrix unit. Columns corresponds to variables Rows corresponds to observations. This transformation power singular values of covariance matrix. http://en.wikipedia.org/wiki/Whitening_transformation (Defined by DescriptiveStatisticsMatrix.)

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Reference

Matrix Class

FinMath.LinearAlgebra Namespace