sklearn.datasets.make_sparse_coded_signal

sklearn.datasets.make_sparse_coded_signal(n_samples, n_components, n_features, n_nonzero_coefs, random_state=None)

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