SVM_Trainer.py 文件源码

def learn_best_param(self):
        C_range = np.logspace(-2, 10, 13)
        gamma_range = np.logspace(-9, 3, 13)
        param_grid = dict(gamma=gamma_range, C=C_range)
        cv = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=42)
        grid = GridSearchCV(SVC(), param_grid=param_grid, cv=cv)
        grid.fit(self.training_data, self.training_target)
        self.clf.set_params(C=grid.best_params_['C'], gamma=grid.best_params_['gamma'])
        print("The best parameters are %s with a score of %0.2f"
              % (grid.best_params_, grid.best_score_))
        self.draw_visualization_param_effect(grid, C_range, gamma_range)