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"""
Machine Learning module in python
=================================
sklearn is a Python module integrating classical machine
learning algorithms in the tightly-knit world of scientific Python
packages (numpy, scipy, matplotlib).
It aims to provide simple and efficient solutions to learning problems
that are accessible to everybody and reusable in various contexts:
machine-learning as a versatile tool for science and engineering.
See http://scikit-learn.sourceforge.net for complete documentation.
"""
from . import check_build
from .base import clone
try:
from numpy.testing import nosetester
class NoseTester(nosetester.NoseTester):
""" Subclass numpy's NoseTester to add doctests by default
"""
def test(self, label='fast', verbose=1, extra_argv=['--exe'],
doctests=True, coverage=False):
"""Run the full test suite
Examples
--------
This will run the test suite and stop at the first failing
example
>>> from sklearn import test
>>> test(extra_argv=['--exe', '-sx']) #doctest: +SKIP
"""
return super(NoseTester, self).test(label=label, verbose=verbose,
extra_argv=extra_argv,
doctests=doctests, coverage=coverage)
try:
test = NoseTester(raise_warnings="release").test
except TypeError:
# Older versions of numpy do not have a raise_warnings argument
test = NoseTester().test
del nosetester
except:
pass
__all__ = ['check_build', 'cross_validation', 'cluster', 'covariance',
'datasets', 'decomposition', 'feature_extraction',
'feature_selection', 'semi_supervised',
'gaussian_process', 'grid_search', 'hmm', 'lda', 'linear_model',
'metrics', 'mixture', 'naive_bayes', 'neighbors', 'pipeline',
'preprocessing', 'qda', 'svm', 'test', 'clone', 'pls']
__version__ = '0.11'
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