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, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

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