8000 [MRG+1] DOC manifold examples added to docstrings by adrinjalali · Pull Request #11823 · scikit-learn/scikit-learn · GitHub
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[MRG+1] DOC manifold examples added to docstrings #11823

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Merged
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Aug 16, 2018
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[MRG+1] DOC manifold examples added to docstrings #11823

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manifold examples added to docstrings
Aug 15, 2018
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triggering travis-ci
Aug 15, 2018
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change to digits data
Aug 15, 2018
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12 changes: 12 additions & 0 deletions sklearn/manifold/isomap.py
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Expand Up @@ -80,6 +80,18 @@ class Isomap(BaseEstimator, TransformerMixin):
dist_matrix_ : array-like, shape (n_samples, n_samples)
Stores the geodesic distance matrix of training data.

Examples
--------
>>> from sklearn.datasets import load_digits
>>> from sklearn.manifold import Isomap
>>> X, _ = load_digits(return_X_y=True)
>>> X.shape
(1797, 64)
>>> embedding = Isomap(n_components=2)
>>> X_transformed = embedding.fit_transform(X[:100])
>>> X_transformed.shape
(100, 2)

References
----------

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12 changes: 12 additions & 0 deletions sklearn/manifold/locally_linear.py
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Expand Up @@ -598,6 +598,18 @@ class LocallyLinearEmbedding(BaseEstimator, TransformerMixin):
Stores nearest neighbors instance, including BallTree or KDtree
if applicable.

Examples
--------
>>> from sklearn.datasets import load_digits
>>> from sklearn.manifold import LocallyLinearEmbedding
>>> X, _ = load_digits(return_X_y=True)
>>> X.shape
(1797, 64)
>>> embedding = LocallyLinearEmbedding(n_components=2)
>>> X_transformed = embedding.fit_transform(X[:100])
>>> X_transformed.shape
(100, 2)

References
----------

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11 changes: 11 additions & 0 deletions sklearn/manifold/mds.py
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Expand Up @@ -340,6 +340,17 @@ class MDS(BaseEstimator):
The final value of the stress (sum of squared distance of the
disparities and the distances for all constrained points).

Examples
--------
>>> from sklearn.datasets import load_digits
>>> from sklearn.manifold import MDS
>>> X, _ = load_digits(return_X_y=True)
>>> X.shape
(1797, 64)
>>> embedding = MDS(n_components=2)
>>> X_transformed = embedding.fit_transform(X[:100])
>>> X_transformed.shape
(100, 2)

References
----------
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12 changes: 12 additions & 0 deletions sklearn/manifold/spectral_embedding_.py
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Expand Up @@ -395,6 +395,18 @@ class SpectralEmbedding(BaseEstimator):
affinity_matrix_ : array, shape = (n_samples, n_samples)
Affinity_matrix constructed from samples or precomputed.

Examples
--------
>>> from sklearn.datasets import load_digits
>>> from sklearn.manifold import SpectralEmbedding
>>> X, _ = load_digits(return_X_y=True)
>>> X.shape
(1797, 64)
>>> embedding = SpectralEmbedding(n_components=2)
>>> X_transformed = embedding.fit_transform(X[:100])
>>> X_transformed.shape
(100, 2)

References
----------

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