mojolearn.randomized_svd#

mojolearn.randomized_svd(M, n_components, *, n_oversamples=10, n_iter='auto', power_iteration_normalizer='auto', transpose='auto', flip_sign=True, random_state=None, numeric_mode=None)[source]#

Truncated SVD by randomized range finding.

Follows scikit-learn’s utils.extmath.randomized_svd (Halko et al.). Every power iteration re-orthonormalizes with a QR step whatever power_iteration_normalizer says (all choices span the same subspace), and the random test matrix comes from mojolearn’s own generator, so results differ slightly from scikit-learn’s for the same seed.

Parameters:

  • M (array-like or scipy.sparse matrix of shape (n_samples, n_features))

  • n_components (int) – Number of singular values and vectors; between 1 and min(n_samples, n_features).

  • n_oversamples (int, default 10) – Extra random vectors used to sample the range.

  • n_iter (int or “auto”, default “auto”) – Power iterations. "auto" uses 7 when n_components < 0.1 * min(M.shape), else 4.

  • power_iteration_normalizer ({“auto”, “QR”, “LU”, “none”}, default “auto”) – Accepted for scikit-learn compatibility; see above.

  • transpose (bool or “auto”, default “auto”) – Work on M.T; "auto" does so when M has more columns than rows.

  • flip_sign (bool, default True) – Make the largest-magnitude entry of each column of U positive, as scikit-learn’s svd_flip.

  • random_state (int or None, default None) – Seed of the random test matrix; None means 0.

  • numeric_mode ({“identical”, “fast”} or None, default None) – Numeric mode for this call. None uses the package default ("identical" unless changed). "identical" gives the same bits on NVIDIA and AMD GPUs; "fast" applies on Apple GPUs.

Returns:

  • U (ndarray of shape (n_samples, n_components), float32)

  • s (ndarray of shape (n_components,), float32) – Singular values, descending.

  • Vt (ndarray of shape (n_components, n_features), float32)

Raises:

ValueError – If n_components is out of range, power_iteration_normalizer is unknown, or M is numerically zero.

Examples

>>> import numpy as np, mojolearn
>>> X = np.random.default_rng(0).random((1000, 50), dtype=np.float32)
>>> U, s, Vt = mojolearn.randomized_svd(X, 5, random_state=0)
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