sklearn.decomposition.SparseCoder — scikit-learn 0.20.4 documentation (original) (raw)
class sklearn.decomposition. SparseCoder(dictionary, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, split_sign=False, n_jobs=None, positive_code=False)[source]¶
Sparse coding
Finds a sparse representation of data against a fixed, precomputed dictionary.
Each row of the result is the solution to a sparse coding problem. The goal is to find a sparse array code such that:
Read more in the User Guide.
| Parameters: | dictionary : array, [n_components, n_features] The dictionary atoms used for sparse coding. Lines are assumed to be normalized to unit norm. transform_algorithm : {‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’} Algorithm used to transform the data: 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' transform_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. transform_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 overridesn_nonzero_coefs. split_sign : bool, False by default Whether to split the sparse feature vector into the concatenation of its negative part and its positive part. This can improve the performance of downstream classifiers. 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 Glossaryfor more details. positive_code : bool Whether to enforce positivity when finding the code. New in version 0.20. |
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| Attributes: | components_ : array, [n_components, n_features] The unchanged dictionary atoms |
Methods
| fit(X[, y]) | Do nothing and return the estimator unchanged |
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| fit_transform(X[, y]) | Fit to data, then transform it. |
| get_params([deep]) | Get parameters for this estimator. |
| set_params(**params) | Set the parameters of this estimator. |
| transform(X) | Encode the data as a sparse combination of the dictionary atoms. |
__init__(dictionary, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, split_sign=False, n_jobs=None, positive_code=False)[source]¶
Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
| Parameters: | X : Ignored y : Ignored |
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| Returns: | self : object Returns the object itself |
fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
| Parameters: | X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values. |
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| Returns: | X_new : numpy array of shape [n_samples, n_features_new] Transformed array. |
get_params(deep=True)[source]¶
Get parameters for this estimator.
| Parameters: | deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. |
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| Returns: | params : mapping of string to any Parameter names mapped to their values. |
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form<component>__<parameter> so that it’s possible to update each component of a nested object.
| Returns: | self |
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Encode the data as a sparse combination of the dictionary atoms.
Coding method is determined by the object parametertransform_algorithm.
| Parameters: | X : array of shape (n_samples, n_features) Test data to be transformed, must have the same number of features as the data used to train the model. |
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| Returns: | X_new : array, shape (n_samples, n_components) Transformed data |