ab7bfeb252d0c9cbc4cd44a50f7757a53dbd7ecb,lightning/dual_cd.py,DualSVC,fit,#DualSVC#Any#Any#,65

Before Change

                          self.C, self.loss, self.max_iter, rs, self.tol,
                          precomputed_kernel=True, verbose=self.verbose)
                    for i in range(Y.shape[1])]
        self.dual_coef_ = np.array(Alpha) * Y.T
        // FIXME: can trim the model
        self.X_train_ = X

After Change

        rs = check_random_state(self.random_state)
        self.label_binarizer_ = LabelBinarizer(neg_label=-1, pos_label=1)
        Y = self.label_binarizer_.fit_transform(y)
        n_vectors = Y.shape[1]
        K = pairwise_kernels(X, X, metric=self.kernel,
                             filter_params=True, n_jobs=self.n_jobs,
                             **self._kernel_params())
        self.dual_coef_ = np.zeros((n_vectors, n_samples), dtype=np.float64)
        for i in xrange(n_vectors):
            _dual_cd(self.dual_coef_[i], K, Y[:, i],
                     self.C, self.loss, self.max_iter, rs, self.tol,
                     precomputed_kernel=True, verbose=self.verbose)
        self.dual_coef_ *= Y.T
        // FIXME: can trim the model
        self.X_train_ = X

Instances