TMDetection.py 文件源码

def support_vector_machine(self, sensors_set):
        features = list(self.dataset.get_sensors_set_features(sensors_set))
        print("SUPPORT VECTOR MACHINE.....")
        print("CLASSIFICATION BASED ON THESE SENSORS: ", self.dataset.get_remained_sensors(sensors_set))
        print("NUMBER OF FEATURES: ", len(features))
        train_features, train_classes, test_features, test_classes = self.__get_sets_for_classification(
            self.dataset.get_train, self.dataset.get_test, features)
        train_features_scaled, test_features_scaled = util.scale_features(train_features, test_features)

        classifier_svm = SVC(C=const.PAR_SVM_C[sensors_set], gamma=const.PAR_SVM_GAMMA[sensors_set], verbose=False)
        classifier_svm.fit(train_features_scaled, train_classes)
        test_prediction = classifier_svm.predict(test_features_scaled)
        acc = accuracy_score(test_classes, test_prediction)
        print("ACCURACY : " + str(acc))
        print("END SUPPORT VECTOR MACHINE.....")

        if not os.path.exists(const.DIR_RESULTS):
            os.makedirs(const.DIR_RESULTS)
        file_content = "acc\n" + str(acc)
        with open(const.DIR_RESULTS + "/" + str(sensors_set) + const.FILE_SUPPORT_VECTOR_MACHINE_RESULTS, 'w') as f:
            f.write(file_content)

    # use different algorithms changing target classes, try all combination of two target classes