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sklearn.decomposition.sparse_encode

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=1, check_input=True, verbose=0) [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.

Parameters:

Parameters:	X: array of shape (n_samples, n_features) : Data matrix dictionary: array 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. gram: array, shape=(n_components, n_components) : Precomputed Gram matrix, dictionary * dictionary’ cov: array, shape=(n_components, n_samples) : Precomputed covariance, dictionary’ * X algorithm: {‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’} : 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 alpha from the projection dictionary * X’ *n_nonzero_coefs: int, 0.1 n_features by default : 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. alpha: float, 1. by default : 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`. init: array of shape (n_samples, n_components) : Initialization value of the sparse codes. Only used if `algorithm=’lasso_cd’`. max_iter: int, 1000 by default : Maximum number of iterations to perform if `algorithm=’lasso_cd’`. copy_cov: boolean, optional : Whether to copy the precomputed covariance matrix; if False, it may be overwritten. n_jobs: int, optional : Number of parallel jobs to run. check_input: boolean, optional : If False, the input arrays X and dictionary will not be checked. verbose** : int, optional Controls the verbosity; the higher, the more messages. Defaults to 0.
Returns:	code: array of shape (n_samples, n_components) : The sparse codes

X: array of shape (n_samples, n_features) :

Data matrix

dictionary: array 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.

gram: array, shape=(n_components, n_components) :

Precomputed Gram matrix, dictionary * dictionary’

cov: array, shape=(n_components, n_samples) :

Precomputed covariance, dictionary’ * X

algorithm: {‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’} :

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 alpha from the projection dictionary * X’

n_nonzero_coefs: int, 0.1 * n_features by default :

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.

alpha: float, 1. by default :

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.

init: array of shape (n_samples, n_components) :

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

max_iter: int, 1000 by default :

Maximum number of iterations to perform if algorithm=’lasso_cd’.

copy_cov: boolean, optional :

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

n_jobs: int, optional :

Number of parallel jobs to run.

check_input: boolean, optional :

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

verbose : int, optional

Controls the verbosity; the higher, the more messages. Defaults to 0.

Returns:

code: array of shape (n_samples, n_components) :

The sparse codes