def __init__(self, sd=0, distribution='rdist', fpp=None, nbins=400, **kwargs):
"""L2-Norm the values, convert them to p-values of a given distribution.
Parameters
----------
sd : int
Samples dimension (if len(x.shape)>1) on which to operate
distribution : string
Which distribution to use. Known are: 'rdist' (later normal should
be there as well)
fpp : float
At what p-value (both tails) if not None, to control for false
positives. It would iteratively prune the tails (tentative real positives)
until empirical p-value becomes less or equal to numerical.
nbins : int
Number of bins for the iterative pruning of positives
WARNING: Highly experimental/slow/etc: no theoretical grounds have been
presented in any paper, nor proven
"""
externals.exists('scipy', raise_=True)
ClassWithCollections.__init__(self, **kwargs)
self.sd = sd
if not (distribution in ['rdist']):
raise ValueError, "Actually only rdist supported at the moment" \
" got %s" % distribution
self.distribution = distribution
self.fpp = fpp
self.nbins = nbins
def __init__(self, mode='discard', **kwargs):
"""
Parameters
----------
mode : {'discard', 'select'}
Decides whether to `select` or to `discard` features.
"""
ClassWithCollections.__init__(self, **kwargs)
self._set_mode(mode)
"""Flag whether to select or to discard elements."""
def __init__(self, tail='both', **kwargs):
"""
Parameters
----------
tail : {'left', 'right', 'any', 'both'}
Which tail of the distribution to report. For 'any' and 'both'
it chooses the tail it belongs to based on the comparison to
p=0.5. In the case of 'any' significance is taken like in a
one-tailed test.
"""
ClassWithCollections.__init__(self, **kwargs)
self._set_tail(tail)
def __init__(self, space=None, postproc=None, **kwargs):
"""
Parameters
----------
space: str, optional
Name of the 'processing space'. The actual meaning of this argument
heavily depends on the sub-class implementation. In general, this is
a trigger that tells the node to compute and store information about
the input data that is "interesting" in the context of the
corresponding processing in the output dataset.
postproc : Node instance, optional
Node to perform post-processing of results. This node is applied
in `__call__()` to perform a final processing step on the to be
result dataset. If None, nothing is done.
"""
ClassWithCollections.__init__(self, **kwargs)
self.set_space(space)
self.set_postproc(postproc)
def __init__(self, clf, labels=None, train=True, **kwargs):
"""Initialization.
Parameters
----------
clf : Classifier
Either trained or untrained classifier
labels : list
if provided, should be a set of labels to add on top of the
ones present in testdata
train : bool
unless train=False, classifier gets trained if
trainingdata provided to __call__
"""
ClassWithCollections.__init__(self, **kwargs)
self.__clf = clf
self._labels = labels
"""Labels to add on top to existing in testing data"""
self.__train = train
"""Either to train classifier if trainingdata is provided"""
def __init__(self, **kwargs):
ClassWithCollections.__init__(self, **kwargs)
def __init__(self, **kwargs):
# XXX make such example when we actually need to invoke
# constructor
# TestClassProper.__init__(self, **kwargs)
ClassWithCollections.__init__(self, **kwargs)
def __init__(self, *args, **kwargs):
"""Base Kernel class has no parameters
"""
ClassWithCollections.__init__(self, *args, **kwargs)
self._k = None
"""Implementation specific version of the kernel"""
