sklearn.datasets.make_sparse_coded_signal¶
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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.
Parameters: - n_samples : int
number of samples to generate
- n_components : int,
number of components in the dictionary
- n_features : int
number of features of the dataset to generate
- n_nonzero_coefs : int
number of active (non-zero) coefficients in each sample
- random_state : int, RandomState instance or None (default)
Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.
Returns: - data : array of shape [n_features, n_samples]
The encoded signal (Y).
- dictionary : array of shape [n_features, n_components]
The dictionary with normalized components (D).
- code : array 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).
