class sklearn.decomposition.MiniBatchSparsePCA(n_components=None, alpha=1, ridge_alpha=0.01, n_iter=100, callback=None, batch_size=3, verbose=False, shuffle=True, n_jobs=None, method=’lars’, random_state=None, normalize_components=False)
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Minibatch Sparse Principal Components Analysis
Finds the set of sparse components that can optimally reconstruct the data. The amount of sparseness is controllable by the coefficient of the L1 penalty, given by the parameter alpha.
Read more in the User Guide.
Parameters: 


Attributes: 

See also
>>> import numpy as np >>> from sklearn.datasets import make_friedman1 >>> from sklearn.decomposition import MiniBatchSparsePCA >>> X, _ = make_friedman1(n_samples=200, n_features=30, random_state=0) >>> transformer = MiniBatchSparsePCA(n_components=5, ... batch_size=50, ... normalize_components=True, ... random_state=0) >>> transformer.fit(X) MiniBatchSparsePCA(...) >>> X_transformed = transformer.transform(X) >>> X_transformed.shape (200, 5) >>> # most values in the components_ are zero (sparsity) >>> np.mean(transformer.components_ == 0) 0.94
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. 
set_params (**params)  Set the parameters of this estimator. 
transform (X[, ridge_alpha])  Least Squares projection of the data onto the sparse components. 
__init__(n_components=None, alpha=1, ridge_alpha=0.01, n_iter=100, callback=None, batch_size=3, verbose=False, shuffle=True, n_jobs=None, method=’lars’, random_state=None, normalize_components=False)
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fit(X, y=None)
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Fit the model from data in X.
Parameters: 


Returns: 

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: 


Returns: 

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


Returns: 

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: 


transform(X, ridge_alpha=’deprecated’)
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Least Squares projection of the data onto the sparse components.
To avoid instability issues in case the system is underdetermined, regularization can be applied (Ridge regression) via the ridge_alpha
parameter.
Note that Sparse PCA components orthogonality is not enforced as in PCA hence one cannot use a simple linear projection.
Parameters: 


Returns: 

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