LinearAlgebra

Namespace: Deedle.Math

Linear algebra on frame using MathNet.Numerics library.

Static members

Static member	Description
`LinearAlgebra.cholesky(df)` Signature: df:Frame<'R,'C> -> Cholesky<float> Type parameters: 'R, 'C	Cholesky decomposition
`LinearAlgebra.condition(df)` Signature: df:Frame<'R,'C> -> float Type parameters: 'R, 'C	Matrix condition
`LinearAlgebra.conjugate(df)` Signature: df:Frame<'R,'C> -> Matrix<float> Type parameters: 'R, 'C	Conjugate
`LinearAlgebra.conjugateTranspose(df)` Signature: df:Frame<'R,'C> -> Matrix<float> Type parameters: 'R, 'C	Conjugate tranpose
`LinearAlgebra.determinant(df)` Signature: df:Frame<'R,'C> -> float Type parameters: 'R, 'C	Matrix determinant
`LinearAlgebra.eigen(df)` Signature: df:Frame<'R,'C> -> Evd<float> Type parameters: 'R, 'C	Eigen values and eigen vectors of matrix
`LinearAlgebra.inverse(df)` Signature: df:Frame<'R,'C> -> Matrix<float> Type parameters: 'R, 'C	Inverse
`LinearAlgebra.isHermitian(df)` Signature: df:Frame<'R,'C> -> bool Type parameters: 'R, 'C	Check whether it is Hermitian matrix
`LinearAlgebra.isSymmetric(df)` Signature: df:Frame<'R,'C> -> bool Type parameters: 'R, 'C	Check whether it is symmetric matrix
`LinearAlgebra.kernel(df)` Signature: df:Frame<'R,'C> -> Vector<float> [] Type parameters: 'R, 'C	Matrix kernel
`LinearAlgebra.lu(df)` Signature: df:Frame<'R,'C> -> LU<float> Type parameters: 'R, 'C	LU decomposition
`LinearAlgebra.norm(df)` Signature: df:Frame<'R,'C> -> float Type parameters: 'R, 'C	Norm
`LinearAlgebra.normCols(df)` Signature: df:Frame<'R,'C> -> Vector<float> Type parameters: 'R, 'C	Norm of columns
`LinearAlgebra.normRows(df)` Signature: df:Frame<'R,'C> -> Vector<float> Type parameters: 'R, 'C	Norm of rows
`LinearAlgebra.nullity(df)` Signature: df:Frame<'R,'C> -> int Type parameters: 'R, 'C	Matrix nullity
`LinearAlgebra.pseudoInverse(df)` Signature: df:Frame<'R,'C> -> Matrix<float> Type parameters: 'R, 'C	Pseudo-inverse of matrix
`LinearAlgebra.qr(df)` Signature: df:Frame<'R,'C> -> QR<float> Type parameters: 'R, 'C	QR decomposition
`LinearAlgebra.range(df)` Signature: df:Frame<'R,'C> -> Vector<float> [] Type parameters: 'R, 'C	Matrix range
`LinearAlgebra.rank(df)` Signature: df:Frame<'R,'C> -> int Type parameters: 'R, 'C	Matrix rank
`LinearAlgebra.svd(df)` Signature: df:Frame<'R,'C> -> Svd<float> Type parameters: 'R, 'C	SVD decomposition
`LinearAlgebra.trace(df)` Signature: df:Frame<'R,'C> -> float Type parameters: 'R, 'C	Matrix trace
`LinearAlgebra.transpose(df)` Signature: df:Frame<'R,'C> -> Frame<'C,'R> Type parameters: 'R, 'C	Transpose. Performance is faster than generic Frame.transpose as it only applies to frame of float values