62b40a9993e9ccca27d1677aa1294fff6246e904,ot/da.py,BaseTransport,fit,#BaseTransport#Any#Any#Any#Any#,1069

Before Change

        // pairwise distance
        self.Cost = dist(Xs, Xt, metric=self.metric)

        if self.mode == "semisupervised":

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

                // assumes labeled source samples occupy the first rows
                // and labeled target samples occupy the first columns
                classes = np.unique(ys)
                for c in classes:
                    ids = np.where(ys == c)
                    idt = np.where(yt == c)

                    // all the coefficients corresponding to a source sample
                    // and a target sample with the same label gets a 0
                    // transport cost
                    for j in idt[0]:
                        self.Cost[ids[0], j] = 0
            else:
                print("Warning: using unsupervised mode\
                       \nto use semisupervised mode, please provide ys and yt")
                pass

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

        // store arrays of samples

After Change

        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 = np.unique(ys)
            for c in classes:
                idx_s = np.where((ys != c) & (ys != -1))
                idx_t = np.where(yt == c)

Instances