mojolearn.sparse_encode#

mojolearn.sparse_encode(X, dictionary, *, algorithm='lasso_lars', n_nonzero_coefs=None, alpha=None, init=None, max_iter=1000, positive=False, numeric_mode=None, **_ignored)[source]#

Sparse-code each row of X against a fixed dictionary.

Follows scikit-learn’s sparse_encode. "lasso_lars" and "lasso_cd" solve the same Lasso problem (both by coordinate descent; the optimum does not depend on the path). Other scikit-learn keywords (such as n_jobs or check_input) are accepted and ignored.

Parameters:

  • X (array-like of shape (n_samples, n_features))

  • dictionary (array-like of shape (n_components, n_features)) – Dictionary atoms, one per row.

  • algorithm ({“lasso_lars”, “lasso_cd”, “lars”, “omp”, “threshold”}, default “lasso_lars”) – Coding method.

  • n_nonzero_coefs (int or None, default None) – Nonzero coefficients per row for "lars" and "omp". None uses min(max(n_features / 10, 1), n_components).

  • alpha (float or None, default None) – Penalty for the Lasso methods, or threshold for "threshold". None uses 1.0.

  • init (array-like of shape (n_samples, n_components) or None, default None) – Starting code for "lasso_cd".

  • max_iter (int, default 1000) – Maximum iterations for the Lasso methods.

  • positive (bool, default False) – Force the code to be non-negative (Lasso methods and "threshold").

  • 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:

ndarray of shape (n_samples, n_components), float32

Raises:

ValueError – For an unknown algorithm.

Examples

>>> import numpy as np, mojolearn
>>> rng = np.random.default_rng(0)
>>> D = rng.random((5, 8), dtype=np.float32)
>>> code = mojolearn.sparse_encode(rng.random((100, 8), dtype=np.float32), D, algorithm="omp", n_nonzero_coefs=2)
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