LinearAlgebra (Deedle)

LinearAlgebra Type

Namespace: Deedle.MathNetNumerics
Assembly: Deedle.MathNetNumerics.dll
Base Type: obj

Linear algebra on frame using MathNet.Numerics library. Linear Algebra

Static members

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