Build a coupling matrix from source and target sets of samples
        (Xs, ys) and (Xt, yt)

        Parameters
        ----------
        Xs : array-like, shape (n_source_samples, n_features)
            The training input samples.
        ys : array-like, shape (n_source_samples,)
            The class labels
        Xt : array-like, shape (n_target_samples, n_features)
            The training input samples.
        yt : array-like, shape (n_target_samples,)
            The class labels. If some target samples are unlabeled, fill the
            yt's elements with -1.

            Warning: Note that, due to this convention -1 cannot be used as a
            class label

        Returns
        -------
        self : object
            Returns self.

def fit(self, Xs=None, ys=None, Xt=None, yt=None):
        """Build a coupling matrix from source and target sets of samples
        (Xs, ys) and (Xt, yt)

        Parameters
        ----------
        Xs : array-like, shape (n_source_samples, n_features)
            The training input samples.
        ys : array-like, shape (n_source_samples,)
            The class labels
        Xt : array-like, shape (n_target_samples, n_features)
            The training input samples.
        yt : array-like, shape (n_target_samples,)
            The class labels. If some target samples are unlabeled, fill the
            yt's elements with -1.

            Warning: Note that, due to this convention -1 cannot be used as a
            class label

        Returns
        -------
        self : object
            Returns self.
        """

        # check the necessary inputs parameters are here
        if check_params(Xs=Xs, Xt=Xt):

            # pairwise distance
            self.cost_ = dist(Xs, Xt, metric=self.metric)
            self.cost_ = cost_normalization(self.cost_, self.norm)

            if (ys is not None) and (yt is not None):

                if self.limit_max != np.infty:
                    self.limit_max = self.limit_max * np.max(self.cost_)

                # assumes labeled source samples occupy the first rows
                # and labeled target samples occupy the first columns
                classes = [c for c in np.unique(ys) if c != -1]
                for c in classes:
                    idx_s = np.where((ys != c) & (ys != -1))
                    idx_t = np.where(yt == c)

                    # all the coefficients corresponding to a source sample
                    # and a target sample :
                    # with different labels get a infinite
                    for j in idx_t[0]:
                        self.cost_[idx_s[0], j] = self.limit_max

            # distribution estimation
            self.mu_s = self.distribution_estimation(Xs)
            self.mu_t = self.distribution_estimation(Xt)

            # store arrays of samples
            self.xs_ = Xs
            self.xt_ = Xt

        return self