d793f1f73e6f816458d8b307762675aa9fa84d22,ot/da.py,BaseTransport,fit,#BaseTransport#Any#Any#Any#Any#,1069

Before Change

                    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)

After Change

            // 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)

                // all the coefficients corresponding to a source sample
                // and a target sample :

Instances