`svd_dgesvd`¶

`isComplexType`(t)
`svd_dgesvd`(a[, full_matrices, compute_uv])	Singular Value Decomposition.

isComplexType¶

tikreg.svd_dgesvd.isComplexType(t)¶

svd_dgesvd¶

tikreg.svd_dgesvd.svd_dgesvd(a, full_matrices=0, compute_uv=1)¶

Singular Value Decomposition.

Factorizes the matrix a into two unitary matrices, U and Vh, and a 1-dimensional array of singular values, s (real, non-negative), such that a == U S Vh, where S is the diagonal matrix np.diag(s).

Parameters:	a : array_like, shape (M, N) Matrix to decompose full_matrices : boolean, optional If True (default), `U` and `Vh` are shaped `(M,M)` and `(N,N)`. Otherwise, the shapes are `(M,K)` and `(K,N)`, where `K = min(M,N)`. compute_uv : boolean Whether to compute `U` and `Vh` in addition to `s`. True by default.
Returns:	U : ndarray, shape (M, M) or (M, K) depending on full_matrices Unitary matrix. s : ndarray, shape (K,) where `K = min(M, N)` The singular values, sorted so that `s[i] >= s[i+1]`. Vh : ndarray, shape (N,N) or (K,N) depending on full_matrices Unitary matrix.
Raises:	LinAlgError If SVD computation fails. For details see dgesvd.f and dbdsqr.f of LAPACK