10000 Merge pull request #3 from ogrisel/jakevdp-neighbors-refactor · ogrisel/scikit-learn@f75ee72 · GitHub
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Merge pull request #3 from ogrisel/jakevdp-neighbors-refactor
Jakevdp neighbors refactor
2 parents 0d4fa9e + db4f4e4 commit f75ee72

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‎AUTHORS.rst

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* Robert Layton
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* Gilles Louppe
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* `Gilles Louppe <http://www.montefiore.ulg.ac.be/~glouppe>`_
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If I forgot anyone, do not hesitate to send me an email to
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fabian.pedregosa@inria.fr and I'll include you in the list.

‎scikits/learn/cross_val.py

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import warnings
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warnings.warn('scikits.learn namespace is deprecated, please use sklearn instead')
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from sklearn.cross_val import *

‎scikits/learn/datasets/base.py

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import warnings
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warnings.warn('scikits.learn is deprecated, please use sklearn')
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from sklearn.datasets.base import *

‎scikits/learn/feature_extraction/__init__.py

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‎scikits/learn/feature_extraction/image.py

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import warnings
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warnings.warn('scikits.learn namespace is deprecated, please use sklearn instead')
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from sklearn.feature_extraction.image import *
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‎scikits/learn/feature_extraction/text.py

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import warnings
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warnings.warn('scikits.learn namespace is deprecated, please use sklearn instead')
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from sklearn.feature_extraction.text import *
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‎sklearn/datasets/base.py

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@@ -31,6 +31,7 @@ def __init__(self, **kwargs):
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dict.__init__(self, kwargs)
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self.__dict__ = self
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def get_data_home(data_home=None):
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"""Return the path of the scikit-learn data dir.
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makedirs(data_home)
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return data_home
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def clear_data_home(data_home=None):
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"""Delete all the content of the data home cache."""
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data_home = get_data_home(data_home)
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shutil.rmtree(data_home)
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def load_files(container_path, description=None, categories=None,
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load_content=True, shuffle=True, random_state=None):
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"""Load text files with categories as subfolder names.
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target=target,
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DESCR=description)
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def load_iris():
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"""Load and return the iris dataset (classification).
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feature_names=['sepal length (cm)', 'sepal width (cm)',
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'petal length (cm)', 'petal width (cm)'])
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def load_digits(n_class=10):
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"""Load and return the digits dataset (classification).
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images=images,
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DESCR=descr)
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def load_diabetes():
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"""Load and return the diabetes dataset (regression).
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target = np.loadtxt(join(base_dir, 'diabetes_target.csv.gz'))
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return Bunch(data=data, target=target)
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def load_linnerud():
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"""Load and return the linnerud dataset (multivariate regression).
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header_physiological=header_physiological,
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DESCR=fdescr.read())
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def load_boston():
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"""Load and return the boston house-prices dataset (regression).
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feature_names=feature_names,
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DESCR=fdescr.read())
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def load_sample_images():
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"""Load sample images for image manipulation.
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filenames=filenames,
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DESCR=descr)
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def load_sample_image(image_name):
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"""Load the numpy array of a single sample image
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‎sklearn/datasets/twenty_newsgroups.py

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import logging
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import tarfile
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import pickle
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import warnings
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import shutil
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import numpy as np

‎sklearn/linear_model/stochastic_gradient.py

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multi-class problems) computation. -1 means 'all CPUs'. Defaults
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to 1.
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learning_rate : int
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learning_rate : string, optional
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The learning rate:
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constant: eta = eta0
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optimal: eta = 1.0/(t+t0) [default]

‎sklearn/neighbors/base.py

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# License: BSD, (C) INRIA, University of Amsterdam
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import warnings
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import re
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import numpy as np
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from scipy.sparse import csr_matrix, issparse
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from scipy.spatial.ckdtree import cKDTree
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def _check_weights(weights):
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"""
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check to make sure weights are valid
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"""
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"""Check to make sure weights are valid"""
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if weights in (None, 'uniform', 'distance'):
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return weights
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elif callable(weights):
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def _get_weights(dist, weights):
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"""
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get the weights from an array of distances and a parameter ``weights``,
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which can be either a string or an executable
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"""Get the weights from an array of distances and a parameter ``weights``
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``weights`` can be either a string or an executable.
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returns ``weights_arr``, an array of the same size as ``dist``
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if ``weights == 'uniform'``, then returns None

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