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.
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).
