class sklearn.decomposition.MiniBatchDictionaryLearning(n_components=None, alpha=1, n_iter=1000, fit_algorithm=’lars’, n_jobs=None, batch_size=3, shuffle=True, dict_init=None, transform_algorithm=’omp’, transform_n_nonzero_coefs=None, transform_alpha=None, verbose=False, split_sign=False, random_state=None, positive_code=False, positive_dict=False)
[source]
Minibatch dictionary learning
Finds a dictionary (a set of atoms) that can best be used to represent data using a sparse code.
Solves the optimization problem:
(U^*,V^*) = argmin 0.5  Y  U V _2^2 + alpha *  U _1 (U,V) with  V_k _2 = 1 for all 0 <= k < n_components
Read more in the User Guide.
Parameters: 


Attributes: 

See also
SparseCoder
, DictionaryLearning
, SparsePCA
, MiniBatchSparsePCA
References:
J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009: Online dictionary learning for sparse coding (http://www.di.ens.fr/sierra/pdfs/icml09.pdf)
fit (X[, y])  Fit the model from data in X. 
fit_transform (X[, y])  Fit to data, then transform it. 
get_params ([deep])  Get parameters for this estimator. 
partial_fit (X[, y, iter_offset])  Updates the model using the data in X as a minibatch. 
set_params (**params)  Set the parameters of this estimator. 
transform (X)  Encode the data as a sparse combination of the dictionary atoms. 
__init__(n_components=None, alpha=1, n_iter=1000, fit_algorithm=’lars’, n_jobs=None, batch_size=3, shuffle=True, dict_init=None, transform_algorithm=’omp’, transform_n_nonzero_coefs=None, transform_alpha=None, verbose=False, split_sign=False, random_state=None, positive_code=False, positive_dict=False)
[source]
fit(X, y=None)
[source]
Fit the model from data in X.
Parameters: 


Returns: 

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: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

partial_fit(X, y=None, iter_offset=None)
[source]
Updates the model using the data in X as a minibatch.
Parameters: 


Returns: 

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.
Returns: 


transform(X)
[source]
Encode the data as a sparse combination of the dictionary atoms.
Coding method is determined by the object parameter transform_algorithm
.
Parameters: 


Returns: 

sklearn.decomposition.MiniBatchDictionaryLearning
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.html