make_sparse_coded_signal (original) (raw)

sklearn.datasets.make_sparse_coded_signal(n_samples, *, n_components, n_features, n_nonzero_coefs, random_state=None)[source]#

Generate a signal as a sparse combination of dictionary elements.

Returns matrices Y, D and X such that Y = XD where X is of shape(n_samples, n_components), D is of shape (n_components, n_features), and each row of X has exactly n_nonzero_coefs non-zero elements.

Read more in the User Guide.

Parameters:

n_samplesint

Number of samples to generate.

n_componentsint

Number of components in the dictionary.

n_featuresint

Number of features of the dataset to generate.

n_nonzero_coefsint

Number of active (non-zero) coefficients in each sample.

random_stateint, RandomState instance or None, default=None

Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns:

datandarray of shape (n_samples, n_features)

The encoded signal (Y).

dictionaryndarray of shape (n_components, n_features)

The dictionary with normalized components (D).

codendarray of shape (n_samples, n_components)

The sparse code such that each column of this matrix has exactly n_nonzero_coefs non-zero items (X).

Examples

from sklearn.datasets import make_sparse_coded_signal data, dictionary, code = make_sparse_coded_signal( ... n_samples=50, ... n_components=100, ... n_features=10, ... n_nonzero_coefs=4, ... random_state=0 ... ) data.shape (50, 10) dictionary.shape (100, 10) code.shape (50, 100)

Gallery examples#