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

Generate a signal as a sparse combination of dictionary elements.

Returns a matrix Y = DX, such that D is of shape (n_features, n_components), X is of shape (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.

data_transposedbool, default=False

By default, Y, D and X are not transposed.

New in version 1.1.

Changed in version 1.3: Default value changed from True to False.

Deprecated since version 1.3: data_transposed is deprecated and will be removed in 1.5.

datandarray of shape (n_features, n_samples) or (n_samples, n_features)

The encoded signal (Y). The shape is (n_samples, n_features) if data_transposed is False, otherwise it’s (n_features, n_samples).

dictionaryndarray of shape (n_features, n_components) or (n_components, n_features)

The dictionary with normalized components (D). The shape is (n_components, n_features) if data_transposed is False, otherwise it’s (n_features, n_components).

codendarray of shape (n_components, n_samples) or (n_samples, n_components)

The sparse code such that each column of this matrix has exactly n_nonzero_coefs non-zero items (X). The shape is (n_samples, n_components) if data_transposed is False, otherwise it’s (n_components, n_samples).

Examples using sklearn.datasets.make_sparse_coded_signal

Orthogonal Matching Pursuit

Orthogonal Matching Pursuit