def fit(self, X, C):
        """
        Fit one weighted classifier per class
        
        Parameters
        ----------
        X : array (n_samples, n_features)
            The data on which to fit a cost-sensitive classifier.
        C : array (n_samples, n_classes)
            The cost of predicting each label for each observation (more means worse).
        """
        X, C = _check_fit_input(X, C)
        C = np.asfortranarray(C)
        self.nclasses = C.shape[1]
        self.classifiers = [deepcopy(self.base_classifier) for i in range(self.nclasses)]
        if not self.weight_simple_diff:
            C = WeightedAllPairs._calculate_v(self, C)

        Parallel(n_jobs=self.njobs, verbose=0, require="sharedmem")(delayed(self._fit)(c, X, C) for c in range(self.nclasses))
        return self
def fit ( self , X , C ) : X , C = _check_fit_input ( X , C ) C = np . asfortranarray ( C ) self . nclasses = C . shape [ 1 ] self . classifiers = [ deepcopy ( self . base_classifier ) for i in range ( self . nclasses ) ] if not self . weight_simple_diff : C = WeightedAllPairs . _calculate_v ( self , C ) Parallel ( n_jobs = self . njobs , verbose = 0 , require = "sharedmem" ) ( delayed ( self . _fit ) ( c , X , C ) for c in range ( self . nclasses ) ) return self
Fit one weighted classifier per class
        
        Parameters
        ----------
        X : array (n_samples, n_features)
            The data on which to fit a cost-sensitive classifier.
        C : array (n_samples, n_classes)
            The cost of predicting each label for each observation (more means worse).