mvpa2.datasets.sources.skl_data.skl_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.


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


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).


This function has been auto-generated by wrapping make_sparse_coded_signal() from the sklearn package. The documentation of this function has been kept verbatim. Consequently, the actual return value is not as described in the documentation, but the data is returned as a PyMVPA dataset.