def frog2_classifier(): result = np.empty((5,8)) for i in range(5): with open('mfcc/frog_data/experiment2/train_mfcc'+str(i)+'.pkl', 'rb') as train_pkl: train_data = pickle.load(train_pkl) with open('mfcc/frog_data/experiment2/test_mfcc'+str(i)+'.pkl', 'rb') as test_pkl: test_data = pickle.load(test_pkl) result[i] = linear_svm(train_data, test_data) all_score = np.sum(result, axis=0) for i in range(4, 8): all_score[i] = all_score[i] / 5 result = np.vstack((result, all_score)) np_excel(result, 'mfcc/predict/result_experiment2.xlsx')
def experiment4_classifier(): result = np.empty((5,8)) for num in range(5): with open('mfcc/frog_data/experiment4/experiment4_train_mfcc_'+str(num)+'.pkl', 'rb') as train_pkl: train_data = pickle.load(train_pkl) with open('mfcc/frog_data/experiment2/experiment2_test_mfcc_'+str(num)+'.pkl', 'rb') as test_pkl: test_data = pickle.load(test_pkl) result[num] = linear_svm(train_data, test_data) print('finished:'+str(num)) all_score = np.sum(result, axis=0) for i in range(4, 8): all_score[i] = all_score[i] / 5 result = np.vstack((result, all_score)) np_excel(result, 'mfcc/predict/result_experiment4.xlsx')