"""
Machine Learning module in python
=================================

scikits.learn is a Python module integrating classique machine
learning algorithms in the tightly-nit 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 .base import clone
from . import cross_val
from . import ball_tree
from . import cluster
from . import covariance
from . import gmm
from . import glm
from . import logistic
from . import lda
from . import metrics
from . import svm
from . import feature_selection

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):
            return super(NoseTester, self).test(label=label, verbose=verbose,
                                    extra_argv=extra_argv,
                                    doctests=doctests, coverage=coverage)
        
    test = NoseTester().test
    del nosetester
except:
    pass

__all__ = ['cross_val', 'ball_tree', 'cluster', 'covariance', 'gmm', 'glm',
           'logistic', 'lda', 'metrics', 'svm', 'features', 'clone', 
           'test']

__version__ = '0.6.git'


"""
Machine Learning module in python
=================================

scikits.learn is a Python module integrating classique machine
learning algorithms in the tightly-nit 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 .base import clone
from . import cross_val
from . import ball_tree
from . import cluster
from . import covariance
from . import datasets
from . import gmm
from . import glm
from . import logistic
from . import lda
from . import metrics
from . import svm
from . import feature_selection

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):
            return super(NoseTester, self).test(label=label, verbose=verbose,
                                    extra_argv=extra_argv,
                                    doctests=doctests, coverage=coverage)
        
    test = NoseTester().test
    del nosetester
except:
    pass

__all__ = ['cross_val', 'ball_tree', 'cluster', 'covariance', 'datasets', 'gmm', 'glm',
           'logistic', 'lda', 'metrics', 'svm', 'features', 'clone', 
           'test']

__version__ = '0.6.git'


Add datasets to __init__ file.