mars.learn.datasets.make_low_rank_matrix¶

mars.learn.datasets.
make_low_rank_matrix
(n_samples=100, n_features=100, effective_rank=10, tail_strength=0.5, random_state=None, chunk_size=None)[source]¶ Generate a mostly low rank matrix with bellshaped singular values
Most of the variance can be explained by a bellshaped curve of width effective_rank: the low rank part of the singular values profile is:
(1  tail_strength) * exp(1.0 * (i / effective_rank) ** 2)
The remaining singular values’ tail is fat, decreasing as:
tail_strength * exp(0.1 * i / effective_rank).
The low rank part of the profile can be considered the structured signal part of the data while the tail can be considered the noisy part of the data that cannot be summarized by a low number of linear components (singular vectors).
 This kind of singular profiles is often seen in practice, for instance:
gray level pictures of faces
TFIDF vectors of text documents crawled from the web
Read more in the User Guide.
 Parameters
n_samples (int, optional (default=100)) – The number of samples.
n_features (int, optional (default=100)) – The number of features.
effective_rank (int, optional (default=10)) – The approximate number of singular vectors required to explain most of the data by linear combinations.
tail_strength (float between 0.0 and 1.0, optional (default=0.5)) – The relative importance of the fat noisy tail of the singular values profile.
random_state (int, RandomState instance or None (default)) – Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.
chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension
 Returns
X – The matrix.
 Return type
array of shape [n_samples, n_features]