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)