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Nca temp #1

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010a211
minor corrections in docstring
Oct 27, 2017
9f1157d
minor corrections in docstring
Oct 27, 2017
22838c5
remove comment
Oct 27, 2017
8a9ff5b
Add verbose during iterations
Oct 30, 2017
76f3544
Update code according to code review:
Oct 31, 2017
0cee29d
Remove _make_masks and use OneHotEncoder instead
Oct 31, 2017
507b763
precise that distances are squared
Oct 31, 2017
f6d0973
simplify tests and remove None parameter in nca
Oct 31, 2017
4c0494d
remove useless None
Oct 31, 2017
fb8cefa
simplify tests
Oct 31, 2017
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12 changes: 6 additions & 6 deletions sklearn/neighbors/nca.py
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Expand Up @@ -27,7 +27,7 @@ class NeighborhoodComponentAnalysis(BaseEstimator, TransformerMixin):

Parameters
----------
n_features_out: int, optional (default=None)
n_features_out : int, optional (default=None)
Preferred dimensionality of the embedding.

init : string or numpy array, optional (default='pca')
Expand Down Expand Up @@ -87,10 +87,10 @@ class NeighborhoodComponentAnalysis(BaseEstimator, TransformerMixin):
Attributes
----------
transformation_ : array, shape (n_features_out, n_features)
The linear transformation learned during fitting.
The linear transformation learned during fitting.

n_iter_ : int
Counts the number of iterations performed by the optimizer.
Counts the number of iterations performed by the optimizer.

opt_result_ : scipy.optimize.OptimizeResult (optional)
A dictionary of information representing the optimization result.
Expand Down Expand Up @@ -121,9 +121,9 @@ class NeighborhoodComponentAnalysis(BaseEstimator, TransformerMixin):
Notes
-----
Neighborhood Component Analysis (NCA) is a machine learning algorithm for
metric learning. It learns a linear transformation of the space in a
supervised fashion to improve the classification accuracy of a
stochastic nearest neighbors rule in this new space.
metric learning. It learns a linear transformation in a supervised fashion
to improve the classification accuracy of a stochastic nearest neighbors
rule in the new space.
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transformed space

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Done


.. warning::

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