def test_model(fnn_model):
org_data, org_label = LoadData.get_method2_test()
X_train, X_test, y_train, y_test = train_test_split(org_data,
org_label,
test_size=0.3)
# Convert y_test(28 category to 6 category)
y_test = np.array([int(e[1:2]) for e in y_test])
print('<---Test Model Start--->')
output_list = np.array([])
for model in fnn_model:
fnn = FNN(fnn_input_size, fnn_membership_size, fnn_rule_size,
fnn_output_size, model.mean, model.stddev, model.weight,
fnn_lr, 1)
output = fnn.testing_model(X_test)
output_list = np.append(output_list, output)
# y_label = label_convert(y_test, build_hash_table())
output_list = output_list.reshape(-1, len(fnn_model))
# 不再做正規畫了試試
output_list = Normalize.normalization(output_list)
label_pred, count = label_encoding(output_list, build_hash_table())
# cnt = 0
# for x, y in zip(output_list[0:10], y_test[0:10]):
# print(x, ' ', y, ' ', label_pred[cnt])
# cnt += 1
for x, y in zip(y_test, label_pred):
print('correct', x, '<->', 'predict', y)
cnf_matrix = confusion_matrix(y_test, label_pred)
# 做confusion matrix 的圖
# plt.figure()
# ConfusionMatrix.plot_confusion_matrix(cnf_matrix, classes=list(set(y_test)),
# title='Confusion matrix(Final FNN Model)')
cnf_accuracy = np.sum(cnf_matrix.diagonal()) / np.sum(cnf_matrix)
print('FinalModel_Accuracy: ', accuracy_score(y_test, label_pred))
print('This is the confusion matrix(test_all_model)\n', cnf_matrix)
# print(C_matrix)
# print(C_accuracy)
print('<---Test Model Successfully--->')
print('<----------------------------------------------->')
return cnf_accuracy, count
fnn_label_size = 6
fnn_input_size = 3
fnn_membership_size = fnn_input_size * fnn_label_size
fnn_rule_size = 6
fnn_output_size = 1
fnn_lr = 0.001
fnn_epoch = 1
fnn_random_size = 1
fnn_threshold = [
0.2, 0.0, 0.1, 0.1, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1, 0.0, 0.2, 0.1, 0.1, 0.0,
0.2, 0.0, 0.1, 0.1, 0.2, 0.1, 0.0, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1
]
# Load the Test data
org_data, org_label = LoadData.get_method2_test()
# print('org_data.shape', org_data.shape)
# print('org_label.shape', org_label)
output_array = np.array([])
# Load the test data, forward, store
for nn in nn_category:
print('nn -> ', nn)
rel_path = '../Experiment/Method2/FNNModel/FNN/' + str(nn) + '.json'
abs_path = os.path.join(os.path.dirname(__file__), rel_path)
attribute = LoadData.load_fnn_weight(abs_path)
# print(attribute)
mean = np.asarray(attribute['Mean'])
stddev = np.asarray(attribute['Stddev'])
weight = np.asarray(attribute['Weight'])