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:
X ~= code * dictionary
Read more in the User Guide.
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See also
DictionaryLearning
, MiniBatchDictionaryLearning
, SparsePCA
, MiniBatchSparsePCA
, sparse_encode
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) | 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]
fit(X, y=None)
[source]
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)
[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.
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get_params(deep=True)
[source]
Get parameters for this estimator.
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set_params(**params)
[source]
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.
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transform(X)
[source]
Encode the data as a sparse combination of the dictionary atoms.
Coding method is determined by the object parameter transform_algorithm
.
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Returns: |
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sklearn.decomposition.SparseCoder
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.SparseCoder.html