dislib.math

dislib.kron(a, b, block_size=None)[source]

Kronecker product of two ds-arrays.

Parameters:
  • a, b (ds-arrays) – Input ds-arrays.
  • block_size (tuple of two ints, optional) – Block size of the resulting array. Defaults to the block size of b.
Returns:

out

Return type:

ds-array

Raises:

NotImplementedError – If a or b are sparse.

dislib.svd(a, compute_uv=True, sort=True, copy=True, eps=1e-09)[source]

Performs singular value decomposition of a ds-array via the one-sided block Jacobi algorithm described in Arbenz and Slapnicar [1] and Dongarra et al. [2].

Singular value decomposition is a factorization of the form A = USV’, where U and V are unitary matrices and S is a rectangular diagonal matrix.

Parameters:
  • a (ds-array, shape=(m, n)) – Input matrix (m >= n). Needs to be partitioned in two column blocks at least due to the design of the block Jacobi algorithm.
  • compute_uv (boolean, optional (default=True)) – Whether or not to compute u and v in addition to s.
  • sort (boolean, optional (default=True)) – Whether to return sorted u, s and v. Sorting requires a significant amount of additional computation.
  • copy (boolean, optional (default=True)) – Whether to create a copy of a or to apply transformations on a directly. Only valid if a is regular (i.e., top left block is of regular shape).
  • eps (float, optional (default=1e-9)) – Tolerance for the convergence criterion.
Returns:

  • u (ds-array, shape=(m, n)) – U matrix. Only returned if compute_uv is True.
  • s (ds-array, shape=(1, n)) – Diagonal entries of S.
  • v (ds-array, shape=(n, n)) – V matrix. Only returned if compute_uv is True.

Raises:

ValueError – If a has less than 2 column blocks or m < n.

References

[1]Arbenz, P. and Slapnicar, A. (1995). An Analysis of Parallel Implementations of the Block-Jacobi Algorithm for Computing the SVD. In Proceedings of the 17th International Conference on Information Technology Interfaces ITI (pp. 13-16).
[2]Dongarra, J., Gates, M., Haidar, A. et al. (2018). The singular value decomposition: Anatomy of optimizing an algorithm for extreme scale. In SIAM review, 60(4) (pp. 808-865).

Examples

>>> import numpy as np
>>> import dislib as ds
>>> x = ds.random_array((10, 6), (2, 2), random_state=7)
>>> u, s, v = ds.svd(x)
>>> u = u.collect()
>>> s = np.diag(s.collect())
>>> v = v.collect()
>>> print(np.allclose(a, u @ s @ v.T))