LinearAlgebraManaged (FSharp.Stats)

LinearAlgebraManaged Module

Namespace: FSharp.Stats.Algebra
Assembly: FSharp.Stats.dll

This module is for internal use only.

Types

Type	Description
range

Functions and values

Function or value	Description
`Cholesky a` Full Usage: `Cholesky a` Parameters: a : `matrix` Returns: `Matrix<float>`	a : `matrix` Returns: `Matrix<float>`
`Condition A` Full Usage: `Condition A` Parameters: A : `'a` Returns: `'b`	A : `'a` Returns: `'b`
`EigenSpectrum A` Full Usage: `EigenSpectrum A` Parameters: A : `'a` Returns: `'b`	A : `'a` Returns: `'b`
`Hessenberg A` Full Usage: `Hessenberg A` Parameters: A : `matrix` Returns: `matrix * Matrix<float>`	A : `matrix` Returns: `matrix * Matrix<float>`
`HouseholderTransform A i` Full Usage: `HouseholderTransform A i` Parameters: A : `matrix` - i : `int` - Returns: `Vector<float>`	This method computes and performs a Householder reflection. It will change the input matrix and return the reflection vector. A : `matrix` i : `int` Returns: `Vector<float>` Example
`Inverse A` Full Usage: `Inverse A` Parameters: A : `Matrix<float>` Returns: `Matrix<float>`	A : `Matrix<float>` Returns: `Matrix<float>`
`LU A` Full Usage: `LU A` Parameters: A : `Matrix<float>` - Returns: `(int -> int) * Matrix<float> * Matrix<float>`	For a matrix A, the LU factorization is a pair of lower triangular matrix L and upper triangular matrix U so that A = LU. The pivot function encode a permutation operation such for a matrix P PA = LU. A : `Matrix<float>` Returns: `(int -> int) Matrix<float> * Matrix<float>` Example
`NYI ()` Full Usage: `NYI ()` Parameters: () : `unit` Returns: `'a`	() : `unit` Returns: `'a`
`QR A` Full Usage: `QR A` Parameters: A : `matrix` - Returns: `matrix * matrix`	Matches the union type of the matrix and invokes the according QR factorization function A : `matrix` Returns: `matrix * matrix` Example
`QRDense A` Full Usage: `QRDense A` Parameters: A : `matrix` - Returns: `matrix * Matrix<float>`	QR factorization function for dense matrices A : `matrix` Returns: `matrix * Matrix<float>` Example
`QRSparse A` Full Usage: `QRSparse A` Parameters: A : `SparseMatrix<float>` - Returns: `matrix * matrix`	QR factorization function for sparse matrices, returns Q as a product of givens rotations and R as upper triangular A : `SparseMatrix<float>` Returns: `matrix * matrix` Example
`SVD a` Full Usage: `SVD a` Parameters: a : `matrix` Returns: `vector * matrix * matrix`	a : `matrix` Returns: `vector * matrix * matrix`
`SolveLinearSystem A b` Full Usage: `SolveLinearSystem A b` Parameters: A : `matrix` - b : `vector` - Returns: `Vector<float>`	Solves a system of linear equations, AX = B, with A LU factorized. A : `matrix` b : `vector` Returns: `Vector<float>` Example
`SolveLinearSystems A B` Full Usage: `SolveLinearSystems A B` Parameters: A : `matrix` - B : `matrix` - Returns: `Matrix<float>`	Solves a system of linear equations, Ax = b, with A LU factorized. A : `matrix` B : `matrix` Returns: `Matrix<float>` Example
`SolveTriangularLinearSystem K v isLower` Full Usage: `SolveTriangularLinearSystem K v isLower` Parameters: K : `Matrix<float>` v : `Vector<float>` isLower : `bool` Returns: `Vector<float>`	K : `Matrix<float>` v : `Vector<float>` isLower : `bool` Returns: `Vector<float>`
`SolveTriangularLinearSystems K B isLower` Full Usage: `SolveTriangularLinearSystems K B isLower` Parameters: K : `Matrix<float>` B : `Matrix<float>` isLower : `bool` Returns: `Matrix<float>`	K : `Matrix<float>` B : `Matrix<float>` isLower : `bool` Returns: `Matrix<float>`
`eigenvalues m` Full Usage: `eigenvalues m` Parameters: m : `'c` Returns: `'d`	m : `'c` Returns: `'d`
`eigenvectors m` Full Usage: `eigenvectors m` Parameters: m : `'c` Returns: `'d`	m : `'c` Returns: `'d`
`hatMatrix designMatrix` Full Usage: `hatMatrix designMatrix` Parameters: designMatrix : `Matrix<float>` - Returns: `Matrix<float>`	computes the hat matrix by the QR decomposition of the designmatrix used in ordinary least squares approaches designMatrix : `Matrix<float>` Returns: `Matrix<float>` Example
`leastSquares A b` Full Usage: `leastSquares A b` Parameters: A : `Matrix<float>` b : `vector` Returns: `Vector<float>`	A : `Matrix<float>` b : `vector` Returns: `Vector<float>`
`leastSquaresCholesky A b` Full Usage: `leastSquaresCholesky A b` Parameters: A : `Matrix<float>` b : `Vector<float>` Returns: `Vector<float>`	A : `Matrix<float>` b : `Vector<float>` Returns: `Vector<float>`
`leverage designMatrix` Full Usage: `leverage designMatrix` Parameters: designMatrix : `Matrix<float>` - Returns: `vector`	computes the leverage directly by QR decomposition of the designmatrix used in ordinary least squares approaches and computing of the diagnonal entries of the Hat matrix, known as the leverages of the regressors designMatrix : `Matrix<float>` Returns: `vector` Example
`leverageBy hatMatrix` Full Usage: `leverageBy hatMatrix` Parameters: hatMatrix : `Matrix<float>` - Returns: `Vector<float>`	computes the leverages of every dataPoint of a dataSet given by the diagonal of the hat matrix. hatMatrix : `Matrix<float>` Returns: `Vector<float>` Example
`pseudoInvers matrix` Full Usage: `pseudoInvers matrix` Parameters: matrix : `Matrix<float>` - Returns: `Matrix<float>`	Calculates the pseudo inverse of the matrix matrix : `Matrix<float>` Returns: `Matrix<float>` Example
`sign f` Full Usage: `sign f` Parameters: f : `float` - Returns: `float`	Computes the sign of a floating point number. f : `float` Returns: `float` Example
`sumfR f (arg2, arg3)` Full Usage: `sumfR f (arg2, arg3)` Parameters: f : `int -> float` arg1 : `int` arg2 : `int` Returns: `float` Modifiers: inline	f : `int -> float` arg1 : `int` arg2 : `int` Returns: `float`
`symmetricEigenspectrum a` Full Usage: `symmetricEigenspectrum a` Parameters: a : `matrix` Returns: `matrix * Vector<float>`	a : `matrix` Returns: `matrix * Vector<float>`
`symmetricEigenvalues a` Full Usage: `symmetricEigenvalues a` Parameters: a : `'c` Returns: `'d`	a : `'c` Returns: `'d`
`symmetricEigenvectors a` Full Usage: `symmetricEigenvectors a` Parameters: a : `'a` Returns: `'b`	a : `'a` Returns: `'b`
`unitV k` Full Usage: `unitV k` Parameters: k : `int` - Returns: `Vector<float>`	Generates a unit vector [1 0 .. 0 ]. k : `int` Returns: `Vector<float>` Example

LinearAlgebraManaged Module

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