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 matrices Y, D and X such that Y = XD where X is of shape (n_samples, n_components), D is of shape (n_components, n_features), and each row of X has exactly n_nonzero_coefs non-zero elements.

Read more in the User Guide.

Parameters:

n_samplesint: Number of samples to generate.
n_componentsint: Number of components in the dictionary.
n_featuresint: Number of features of the dataset to generate.
n_nonzero_coefsint: Number of active (non-zero) coefficients in each sample.
random_stateint, RandomState instance or None, default=None: Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns:

datandarray of shape (n_samples, n_features): The encoded signal (Y).
dictionaryndarray of shape (n_components, n_features): The dictionary with normalized components (D).
codendarray of shape (n_samples, n_components): The sparse code such that each column of this matrix has exactly n_nonzero_coefs non-zero items (X).

Examples

>>> from sklearn.datasets import make_sparse_coded_signal
>>> data, dictionary, code = make_sparse_coded_signal(
...     n_samples=50,
...     n_components=100,
...     n_features=10,
...     n_nonzero_coefs=4,
...     random_state=0
... )
>>> data.shape
(50, 10)
>>> dictionary.shape
(100, 10)
>>> code.shape
(50, 100)

Gallery examples#

Orthogonal Matching Pursuit