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 a matrix Y = DX, such as D is (n_features, n_components), X is (n_components, n_samples) and each column of X has exactly n_nonzero_coefs non-zero elements.

Read more in the User Guide.


Number of samples to generate


Number of components in the dictionary


Number of features of the dataset to generate


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.

datandarray of shape (n_features, n_samples)

The encoded signal (Y).

dictionaryndarray of shape (n_features, n_components)

The dictionary with normalized components (D).

codendarray of shape (n_components, n_samples)

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

Examples using sklearn.datasets.make_sparse_coded_signal