def fit(self, x, y, grp=[], n_jobs=-1):
"""Run the sequence and get the selected features.
x : array-like
The data to fit. x should have a shape of (n trials x n features)
y : array-like
The target variable to try to predict in the case of
supervised learning.
grp : list/array, optionnal, [def: []]
The grp parameter can be used to define groups of features. If grp
is not an empty list, the feature will not be applied on single
features but on group of features.
n_jobs : integer, optional, default : 1
The number of CPUs to use to do the computation. -1 means all CPUs
"""
return _sequence(x, y, self._clf, self._direction, grp, n_jobs,
self._display, self._cwi)
def fit(self, x, y, grp=[], n_jobs=-1):
"""Run the sequence and get the selected features.
x : array-like
The data to fit. x should have a shape of (n trials x n features)
y : array-like
The target variable to try to predict in the case of
supervised learning.
grp : list/array, optionnal, [def: []]
The grp parameter can be used to define groups of features. If grp
is not an empty list, the feature will not be applied on single
features but on group of features.
n_jobs : integer, optional, default : 1
The number of CPUs to use to do the computation. -1 means all CPUs
"""
return _sequence(x, y, self._clf, self._direction, grp, n_jobs,
self._display, self._cwi)
def MFmeth(x, GRP):
return _sequence(x, y, clf, direction, GRP, 1, display, False)
