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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=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.

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`. `copy_cov : boolean, optional` Whether to copy the precomputed covariance matrix; if False, it may be overwritten. `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’`. `n_jobs : int or None, optional (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_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. `positive : boolean, optional` Whether to enforce positivity when finding the encoding. New in version 0.20.
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.
copy_cov : boolean, optional: Whether to copy the precomputed covariance matrix; if False, it may be overwritten.
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’.
n_jobs : int or None, optional (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_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.
positive : boolean, optional: Whether to enforce positivity when finding the encoding.

New in version 0.20.

Returns:

code : array of shape (n_samples, n_components): The sparse codes