dask.array.linalg.svd
dask.array.linalg.svd¶
- dask.array.linalg.svd(a, coerce_signs=True)[source]¶
Compute the singular value decomposition of a matrix.
- Parameters
- a(M, N) Array
- coerce_signsbool
Whether or not to apply sign coercion to singular vectors in order to maintain deterministic results, by default True.
- Returns
- u(M, K) Array, unitary / orthogonal
Left-singular vectors of a (in columns) with shape (M, K) where K = min(M, N).
- s(K,) Array, singular values in decreasing order (largest first)
Singular values of a.
- v(K, N) Array, unitary / orthogonal
Right-singular vectors of a (in rows) with shape (K, N) where K = min(M, N).
Warning
SVD is only supported for arrays with chunking in one dimension. This requires that all inputs either contain a single column of chunks (tall-and-skinny) or a single row of chunks (short-and-fat). For arrays with chunking in both dimensions, see da.linalg.svd_compressed.
See also
np.linalg.svd
Equivalent NumPy Operation
da.linalg.svd_compressed
Randomized SVD for fully chunked arrays
dask.array.linalg.tsqr
QR factorization for tall-and-skinny arrays
dask.array.utils.svd_flip
Sign normalization for singular vectors
Examples
>>> u, s, v = da.linalg.svd(x)