Skip to content

[MRG] Add dictionary learning examples #16907

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 1 commit into from
Closed

[MRG] Add dictionary learning examples #16907

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
34 changes: 32 additions & 2 deletions sklearn/decomposition/_dict_learning.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1057,7 +1057,7 @@ class DictionaryLearning(SparseCodingMixin, BaseEstimator):

Solves the optimization problem::

(U^*,V^*) = argmin 0.5 || Y - U V ||_2^2 + alpha * || U ||_1
(U^*,V^*) = argmin 0.5 || X - U V ||_2^2 + alpha * || U ||_1
(U,V)
with || V_k ||_2 = 1 for all 0 <= k < n_components

Expand Down Expand Up @@ -1170,6 +1170,21 @@ class DictionaryLearning(SparseCodingMixin, BaseEstimator):
n_iter_ : int
Number of iterations run.

Examples
--------
>>> import numpy as np
>>> from sklearn.datasets import make_sparse_coded_signal
>>> from sklearn.decomposition import DictionaryLearning
>>>
>>> np.random.seed(42)
>>> X, dictionary, code = make_sparse_coded_signal(
... n_samples=100, n_components=15, n_features=20, n_nonzero_coefs=10)
>>> dico = DictionaryLearning(n_components=15,
... transform_algorithm='lasso_lars')
>>> X_transformed = dico.fit_transform(X)
>>> np.mean(X_transformed == 0) # represents data using a sparse code
0.87

Notes
-----
**References:**
Expand Down Expand Up @@ -1255,7 +1270,7 @@ class MiniBatchDictionaryLearning(SparseCodingMixin, BaseEstimator):

Solves the optimization problem::

(U^*,V^*) = argmin 0.5 || Y - U V ||_2^2 + alpha * || U ||_1
(U^*,V^*) = argmin 0.5 || X - U V ||_2^2 + alpha * || U ||_1
(U,V)
with || V_k ||_2 = 1 for all 0 <= k < n_components

Expand Down Expand Up @@ -1375,6 +1390,21 @@ class MiniBatchDictionaryLearning(SparseCodingMixin, BaseEstimator):
RandomState instance that is generated either from a seed, the random
number generattor or by `np.random`.

Examples
--------
>>> import numpy as np
>>> from sklearn.datasets import make_sparse_coded_signal
>>> from sklearn.decomposition import MiniBatchDictionaryLearning
>>>
>>> np.random.seed(42)
>>> X, dictionary, code = make_sparse_coded_signal(
... n_samples=100, n_components=15, n_features=20, n_nonzero_coefs=10)
>>> dico = MiniBatchDictionaryLearning(n_components=15,
... transform_algorithm='lasso_lars')
>>> X_transformed = dico.fit_transform(X)
>>> np.mean(X_transformed == 0) # represents data using a sparse code
0.83...

Notes
-----
**References:**
Expand Down