sklearn.datasets.make_low_rank_matrix(n_samples=100, n_features=100, effective_rank=10, tail_strength=0.5, random_state=None)
Generate a mostly low rank matrix with bell-shaped singular values
Most of the variance can be explained by a bell-shaped 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).
Read more in the User Guide.
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.