RBFTrainer.py 文件源码

def grid_retrain_in_f(self, n_dim=500):
        rbf_map = RBFSampler(n_dim, random_state=1)
        fourier_approx_svm = pipeline.Pipeline([("mapper", rbf_map),
                                                ("svm", LinearSVC())])

        # C_range = np.logspace(-5, 15, 21, base=2)
        # gamma_range = np.logspace(-15, 3, 19, base=2)
        # param_grid = dict(mapper__gamma=gamma_range, svm__C=C_range)
        # cv = StratifiedShuffleSplit(Y, n_iter=5, test_size=0.2, random_state=42)
        # grid = GridSearchCV(fourier_approx_svm, param_grid=param_grid, cv=cv)
        # grid.fit(X, Y)
        #
        # rbf_svc2 = grid.best_estimator_

        rbf_svc2 = fourier_approx_svm
        rbf_svc2.fit(self.X_ex, self.y_ex)

        self.set_clf2(rbf_svc2)
        return self.benchmark()