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')