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[WIP] DOC Description of X missing in KMeans.fit (#7772) #7775

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[WIP] DOC Description of X missing in KMeans.fit (#7772) #7775

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3d6c1c7
[MRG] Added explanation for X in fit( ) (#7722)
Erotemic Oct 27, 2016
7903abd
[MRG] Added explanation for X in fit( ) (#7722)
theendless219 Oct 28, 2016
1fdefef
[MRG] Added clearer explanation for X in both fit( ) (#7722)
theendless219 Oct 28, 2016
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6 changes: 6 additions & 0 deletions doc/whats_new.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,12 @@ New features
Enhancements
............

- :class:`cluster.MiniBatchKMeans` and :class:`cluster.KMeans`
now uses significantly less memory when assigning data points to their
nearest cluster center.
(`#7721 <https://github.com/scikit-learn/scikit-learn/pull/7721>`_)
By `Jon Crall`_.

- Added ``classes_`` attribute to :class:`model_selection.GridSearchCV`
that matches the ``classes_`` attribute of ``best_estimator_``. (`#7661
<https://github.com/scikit-learn/scikit-learn/pull/7661>`_) by `Alyssa
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25 changes: 12 additions & 13 deletions sklearn/cluster/k_means_.py
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Expand Up @@ -18,6 +18,7 @@

from ..base import BaseEstimator, ClusterMixin, TransformerMixin
from ..metrics.pairwise import euclidean_distances
from ..metrics.pairwise import pairwise_distances_argmin_min
from ..utils.extmath import row_norms, squared_norm, stable_cumsum
from ..utils.sparsefuncs_fast import assign_rows_csr
from ..utils.sparsefuncs import mean_variance_axis
Expand Down Expand Up @@ -552,17 +553,14 @@ def _labels_inertia_precompute_dense(X, x_squared_norms, centers, distances):

"""
n_samples = X.shape[0]
k = centers.shape[0]
all_distances = euclidean_distances(centers, X, x_squared_norms,
squared=True)
labels = np.empty(n_samples, dtype=np.int32)
labels.fill(-1)
mindist = np.empty(n_samples)
mindist.fill(np.infty)
for center_id in range(k):
dist = all_distances[center_id]
labels[dist < mindist] = center_id
mindist = np.minimum(dist, mindist)

# Breakup nearest neighbor distance computation into batches to prevent
# memory blowup in the case of a large number of samples and clusters.
# TODO: Once PR #7383 is merged use check_inputs=False in metric_kwargs.
labels, mindist = pairwise_distances_argmin_min(
X=X, Y=centers, metric='euclidean', metric_kwargs={'squared': True})
# cython k-means code assumes int32 inputs
labels = labels.astype(np.int32)
if n_samples == distances.shape[0]:
# distances will be changed in-place
distances[:] = mindist
Expand Down Expand Up @@ -876,6 +874,7 @@ def fit(self, X, y=None):
Parameters
----------
X : array-like or sparse matrix, shape=(n_samples, n_features)
Training instances to cluster
"""
random_state = check_random_state(self.random_state)
X = self._check_fit_data(X)
Expand Down Expand Up @@ -1310,8 +1309,8 @@ def fit(self, X, y=None):

Parameters
----------
X : array-like, shape = [n_samples, n_features]
Coordinates of the data points to cluster
X : array-like or sparse matrix, shape=(n_samples, n_features)
Training instances to cluster
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For now we can have it as

Training data to cluster.

A more general docstring which is standardized across the codebase will be addressed by fixing #3791 later...

"""
random_state = check_random_state(self.random_state)
X = check_array(X, accept_sparse="csr", order='C',
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