sklearn.decomposition.sparse_encode(X, dictionary, *, gram=None, cov=None, algorithm='lasso_lars', n_nonzero_coefs=None, alpha=None, copy_cov=True, init=None, max_iter=1000, n_jobs=None, check_input=True, verbose=0, positive=False)[source]#

Sparse coding.

Each row of the result is the solution to a sparse coding problem. The goal is to find a sparse array code such that:

X ~= code * dictionary

Read more in the User Guide.

Xarray-like of shape (n_samples, n_features)

Data matrix.

dictionaryarray-like of shape (n_components, n_features)

The dictionary matrix against which to solve the sparse coding of the data. Some of the algorithms assume normalized rows for meaningful output.

gramarray-like of shape (n_components, n_components), default=None

Precomputed Gram matrix, dictionary * dictionary'.

covarray-like of shape (n_components, n_samples), default=None

Precomputed covariance, dictionary' * X.

algorithm{‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’}, default=’lasso_lars’

The algorithm used:

  • 'lars': uses the least angle regression method (linear_model.lars_path);

  • 'lasso_lars': uses Lars to compute the Lasso solution;

  • 'lasso_cd': uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). lasso_lars will be faster if the estimated components are sparse;

  • 'omp': uses orthogonal matching pursuit to estimate the sparse solution;

  • 'threshold': squashes to zero all coefficients less than regularization from the projection dictionary * data'.

n_nonzero_coefsint, default=None

Number of nonzero coefficients to target in each column of the solution. This is only used by algorithm='lars' and algorithm='omp' and is overridden by alpha in the omp case. If None, then n_nonzero_coefs=int(n_features / 10).

alphafloat, default=None

If algorithm='lasso_lars' or algorithm='lasso_cd', alpha is the penalty applied to the L1 norm. If algorithm='threshold', alpha is the absolute value of the threshold below which coefficients will be squashed to zero. If algorithm='omp', alpha is the tolerance parameter: the value of the reconstruction error targeted. In this case, it overrides n_nonzero_coefs. If None, default to 1.

copy_covbool, default=True

Whether to copy the precomputed covariance matrix; if False, it may be overwritten.

initndarray of shape (n_samples, n_components), default=None

Initialization value of the sparse codes. Only used if algorithm='lasso_cd'.

max_iterint, default=1000

Maximum number of iterations to perform if algorithm='lasso_cd' or 'lasso_lars'.

n_jobsint, default=None

Number of parallel jobs to run. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

check_inputbool, default=True

If False, the input arrays X and dictionary will not be checked.

verboseint, default=0

Controls the verbosity; the higher, the more messages.

positivebool, default=False

Whether to enforce positivity when finding the encoding.

Added in version 0.20.

codendarray of shape (n_samples, n_components)

The sparse codes.

See also


Compute Least Angle Regression or Lasso path using LARS algorithm.


Solves Orthogonal Matching Pursuit problems.


Train Linear Model with L1 prior as regularizer.


Find a sparse representation of data from a fixed precomputed dictionary.


>>> import numpy as np
>>> from sklearn.decomposition import sparse_encode
>>> X = np.array([[-1, -1, -1], [0, 0, 3]])
>>> dictionary = np.array(
...     [[0, 1, 0],
...      [-1, -1, 2],
...      [1, 1, 1],
...      [0, 1, 1],
...      [0, 2, 1]],
...    dtype=np.float64
... )
>>> sparse_encode(X, dictionary, alpha=1e-10)
array([[ 0.,  0., -1.,  0.,  0.],
       [ 0.,  1.,  1.,  0.,  0.]])