(self, X)
return pred
r"""Predict the probability of being 1 for each label.

        Parameters
        ----------
        X : array-like, shape=(n_samples, n_features)
            Feature vector.

        Returns
        -------
        pred : numpy array, shape=(n_samples, n_labels)
            Predicted probability of each label.
r"""Predict the probability of being 1 for each label.
r Predict the probability of being 1 for each label .
def predict_proba(self, X):
        r"""Predict the probability of being 1 for each label.

        Parameters
        ----------
        X : array-like, shape=(n_samples, n_features)
            Feature vector.

        Returns
        -------
        pred : numpy array, shape=(n_samples, n_labels)
            Predicted probability of each label.
        """
        X = np.asarray(X)
        if self.clfs_ is None:
            raise ValueError("Train before prediction")
        if X.shape[1] != self.n_features_:
            raise ValueError('given feature size does not match')

        pred = np.zeros((X.shape[0], self.n_labels_))
        for i in range(self.n_labels_):
            pred[:, i] = self.clfs_[i].predict_proba(X)[:, 1]
        return pred
def predict_proba ( self , X ) : X = np . asarray ( X ) if self . clfs_ is None : raise ValueError ( "Train before prediction" ) if X . shape [ 1 ] != self . n_features_ : raise ValueError ( 'given feature size does not match' ) pred = np . zeros ( ( X . shape [ 0 ] , self . n_labels_ ) ) for i in range ( self . n_labels_ ) : pred [ : , i ] = self . clfs_ [ i ] . predict_proba ( X ) [ : , 1 ] return pred