def train_fnn(nn):
accuracy = 0.0
matrix = np.array([])
fnn_copy = FNN()
all_nn_accuracy = np.array([])
org_data, org_label = LoadData.get_method2_fnn_train(nn)
org_label = np.array([1 if label == nn else 0 for label in org_label])
X_train, X_test, y_train, y_test = train_test_split(org_data,
org_label,
test_size=0.3)
# print(X_train, X_train.shape)
# print(y_train, y_train.shape)
print('<---Train the FNN ' + nn + ' Start--->')
for i in range(fnn_random_size):
# Random Generate the mean, standard deviation
mean = np.array(
[np.random.uniform(-1, 1) for _ in range(fnn_membership_size)])
stddev = np.array(
[np.random.uniform(0, 1) for _ in range(fnn_membership_size)])
weight = np.array(
[np.random.uniform(-1, 1) for _ in range(fnn_rule_size)])
fnn = FNN(fnn_input_size, fnn_membership_size, fnn_rule_size,
fnn_output_size, mean, stddev, weight, fnn_lr, 1)
fnn.training_model(fnn_epoch, X_train, y_train)
test_output = fnn.testing_model(X_test)
label_pred = [
1 if values >= fnn_threshold else 0 for values in test_output
]
C_matrix = confusion_matrix(y_test, label_pred)
C_accuracy = np.sum(C_matrix.diagonal()) / np.sum(C_matrix)
all_nn_accuracy = np.append(all_nn_accuracy, C_accuracy)
# print(C_matrix)
# print(C_accuracy)
if C_accuracy > accuracy:
fnn_copy = copy.deepcopy(fnn)
accuracy = copy.deepcopy(C_accuracy)
matrix = copy.deepcopy(C_matrix)
print('swap')
print('<---Train the FNN ' + nn + ' Successfully--->')
print('<----------------------------------------------->')
# rel_path = 'Experiment/Graph/method2/Best_FNN_' + nn + '_error_trend.png'
# abs_path = os.path.join(os.path.dirname(__file__), rel_path)
# ErrorPlot.error_trend(
# 'Best_FNN_' + str(nn) + '_error_trend', len(fnn_copy.error_list), fnn_copy.error_list, abs_path)
#
# rel_path = 'Experiment/Graph/method2/Accuracy vs FNN' + str(nn) + '.png'
# abs_path = os.path.join(os.path.dirname(__file__), rel_path)
# AccuracyPlot.build_accuracy_plot(
# 'Accuracy vs FNN'+str(nn), np.array([i for i in range(1, len(all_nn_accuracy) + 1, 1)]),
# all_nn_accuracy, abs_path)
return fnn_copy, accuracy, matrix
all_label = ['C1', 'C2', 'C3', 'C4', 'C5', 'C6']
# cluster_num = {'C1': 6, 'C2': 5, 'C3': 5, 'C4': 5, 'C5': 5, 'C6': 4}
cluster_num = {'C1': 2, 'C2': 2, 'C3': 2, 'C4': 2, 'C5': 2, 'C6': 2}
nn_category = np.array([])
for element in all_label:
if cluster_num[element] == 0:
nn_category = np.append(nn_category, element)
else:
for num in range(cluster_num[element]):
nn_category = np.append(nn_category, element + '_' + str(num))
print('nn_category', nn_category)
# Run the experiment from one dimension to five dimension
for nn in nn_category:
# Read file LNN_Train_data.xlsx'
org_data, org_label = LoadData.get_method2_fnn_train(nn)
# print('org_data', org_data)
# print('org_label', org_label)
data1, data2 = (np.array([]) for _ in range(2))
for element, stamp in zip(org_data, org_label):
print(element, stamp)
if stamp == nn:
data1 = np.append(data1, element)
else:
data2 = np.append(data2, element)
# Make graph
fig = plt.figure(figsize=(8, 6), dpi=100)
ax = Axes3D(fig)
