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"""