mvpa2.datasets.sources.skl_sparse_coded_signal

mvpa2.datasets.sources.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.

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).

Notes

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.