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class sklearn.decomposition.SparseCoder(dictionary, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, split_sign=False, n_jobs=1)
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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:X ~= code * dictionary
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 defaultNumber of nonzero coefficients to target in each column of the solution. This is only used by
algorithm=?lars?
andalgorithm=?omp?
and is overridden byalpha
in theomp
case.transform_alpha : float, 1. by default
If
algorithm=?lasso_lars?
oralgorithm=?lasso_cd?
,alpha
is the penalty applied to the L1 norm. Ifalgorithm=?threshold?
,alpha
is the absolute value of the threshold below which coefficients will be squashed to zero. Ifalgorithm=?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,
number of parallel jobs to run
Attributes: components_ : array, [n_components, n_features]
The unchanged dictionary atoms
See also
DictionaryLearning
,MiniBatchDictionaryLearning
,SparsePCA
,MiniBatchSparsePCA
,sparse_encode
Methods
fit
(X[, y])Do nothing and return the estimator unchanged 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[, y])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=1)
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fit(X, y=None)
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Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
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fit_transform(X, y=None, **fit_params)
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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.
Returns: X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
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get_params(deep=True)
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Get parameters for this estimator.
Parameters: deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
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set_params(**params)
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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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transform(X, y=None)
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Encode the data as a sparse combination of the dictionary atoms.
Coding method is determined by the object parameter
transform_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.
Returns: X_new : array, shape (n_samples, n_components)
Transformed data
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decomposition.SparseCoder()
Examples using
2017-01-15 04:21:25
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