sklearn.datasets.make_sparse_coded_signal()

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 or RandomState instance, optional (default=None) seed used by the pseudo random number generator
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).

Examples using sklearn.datasets.make_sparse_coded_signal

Orthogonal Matching Pursuit