(self, X, Y)
return self
r"""Train model with given feature.

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

        y : array-like, shape=(n_samples, n_labels)
            Target labels.

        Attributes
        ----------
        clfs\_ : list of :py:mod:`libact.models` object instances
            Classifier instances.

        Returns
        -------
        self : object
            Retuen self.
r"""Train model with given feature.
r Train model with given feature .
def train(self, X, Y):
        r"""Train model with given feature.

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

        y : array-like, shape=(n_samples, n_labels)
            Target labels.

        Attributes
        ----------
        clfs\_ : list of :py:mod:`libact.models` object instances
            Classifier instances.

        Returns
        -------
        self : object
            Retuen self.
        """
        # X = np.array(X)
        # Y = np.array(y)

        self.n_labels_ = np.shape(Y)[1]
        self.n_features_ = np.shape(X)[1]

        self.clfs_ = []
        for i in range(self.n_labels_):
            # TODO should we handle it here or we should handle it before calling
            if len(np.unique(Y[:, i])) == 1:
                clf = DummyClf()
            else:
                clf = copy.deepcopy(self.base_clf)
            clf.fit(X, Y[:, i])
            self.clfs_.append(clf)

        return self
def train ( self , X , Y ) : # X = np.array(X) # Y = np.array(y) self . n_labels_ = np . shape ( Y ) [ 1 ] self . n_features_ = np . shape ( X ) [ 1 ] self . clfs_ = [ ] for i in range ( self . n_labels_ ) : # TODO should we handle it here or we should handle it before calling if len ( np . unique ( Y [ : , i ] ) ) == 1 : clf = DummyClf ( ) else : clf = copy . deepcopy ( self . base_clf ) clf . fit ( X , Y [ : , i ] ) self . clfs_ . append ( clf ) return self