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1 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
2 # vi: set ft=python sts=4 ts=4 sw=4 et:
3 ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
4 #
5 # See COPYING file distributed along with the PyMVPA package for the
6 # copyright and license terms.
7 #
8 ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
9 """PyMVPA measures.
10
11 Module Description
12 ==================
13
14 Provide some measures given a dataset. Most of the time, derivatives of
15 `FeaturewiseDatasetMeasure` are used, such as
16
17 * `OneWayAnova`
18 * `CorrCoef`
19 * `IterativeRelief`
20 * `NoisePerturbationSensitivity`
21
22 Also many classifiers natively provide sensitivity estimators via the call to
23 `getSensitivityAnalyzer` method
24 """
25
26 __docformat__ = 'restructuredtext'
27
28 if __debug__:
29 from mvpa.base import debug
30 debug('INIT', 'mvpa.measures')
31
32 if __debug__:
33 debug('INIT', 'mvpa.measures end')
34
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