9e62f772ee6e7e1b346640533f5122154f102b5a,code/traditional/MEDA/MEDA.py,,,#,140

Before Change

                src_domain, tar_domain = scipy.io.loadmat(src), scipy.io.loadmat(tar)
                Xs, Ys, Xt, Yt = src_domain["feas"], src_domain["label"], tar_domain["feas"], tar_domain["label"]
                meda = MEDA(Xs, Ys, Xt, Yt, kernel_type="rbf", dim=20, lamb=10, rho=1.0, eta=0.1, p=10, gamma=1, T=10)
                acc, ypre, list_acc = meda.fit_predict()
                print(acc)

After Change

                src_domain, tar_domain = scipy.io.loadmat(src), scipy.io.loadmat(tar)
                Xs, Ys, Xt, Yt = src_domain["feas"], src_domain["label"], tar_domain["feas"], tar_domain["label"]
                meda = MEDA(kernel_type="rbf", dim=20, lamb=10, rho=1.0, eta=0.1, p=10, gamma=1, T=10)
                acc, ypre, list_acc = meda.fit_predict(Xs, Ys, Xt, Yt)
                print(acc)

Instances