svd
Performs a singular value decomposition of a matrix.
Signatures
- svd( matrix matA, matrix& matU, matrix& matW, matrix& matV )
Details
Applies a singular value decomposition to the matrix. This means, that the given matrix A is expressed in the form
A = U * W * VT
with matrices U, W and V. Matrix W is diagonal and contains the singular values as diagonal elements.
Example
matrix A = <[ <[ 1, 2, 3 ]>,
<[ 4, 5, 6 ]>,
<[ 7, 8, 9 ]> ]>
matrix U
matrix W
matrix V
svd( A, U, W, V )
echo( U * W * V.t() )
Output
<[<[1,2,3]>,<[4,5,6]>,<[7,8,9]>]>
Parameters
- matA
-
The input matrix A.
- matU
-
Returns the matrix U.
- matW
-
Returns the matrix W.
- matV
-
Returns the matrix V.
