def execute(self):
# Pre processing
x_data = util.normalizeData(self.x_data)
x_data = util.insertBias(x_data)
y_data = self.y_data
for i in range(self.realizations):
self.initWeigths()
x_data_aux, y_data_aux = util.shuffleData(x_data, y_data)
x_train, x_test, y_train, y_test = util.splitData(
x_data_aux, y_data_aux, self.train_size)
self.train(x_train, y_train)
acc, tpr, spc, ppv = self.test(x_test, y_test)
self.hit_rate.append(acc)
self.tpr.append(tpr)
self.spc.append(spc)
self.ppv.append(ppv)
#util.plotColorMap(x_train, x_test, y_train, self.predict)
self.acc = np.mean(self.hit_rate)
self.std = np.std(self.hit_rate)
self.tpr = np.mean(self.tpr)
self.spc = np.mean(self.spc)
self.ppv = np.mean(self.ppv)
print('Hit rate: {}'.format(self.hit_rate))
print('Accuracy: {:.2f}'.format(self.acc * 100))
print('Minimum: {:.2f}'.format(np.amin(self.hit_rate) * 100))
print('Maximum: {:.2f}'.format(np.amax(self.hit_rate) * 100))
print('Standard Deviation: {:.2f}'.format(self.std))
print('Sensitivity: {:.2f}'.format(self.tpr * 100))
print('Specificity: {:.2f}'.format(self.spc * 100))
print('Precision: {:.2f}'.format(self.ppv * 100))
def execute(self):
x_data = util.insertBias(self.x_data)
y_data = self.y_data
for i in range(self.realizations):
x_data_aux, y_data_aux = util.shuffleData(x_data, y_data)
x_train, x_test, y_train, y_test = util.splitData(x_data_aux, y_data_aux, self.train_size)
best_hidden_layer = self.hidden_layer
params = self.train(x_train, y_train, best_hidden_layer)
mse, rmse = self.test(x_test, y_test, params)
self.mse.append(mse)
self.rmse.append(rmse)
print('{} Realizations'.format(self.realizations))
print('MSE: {}'.format(np.mean(self.mse)))
print('Std MSE: {}'.format(np.std(self.mse)))
print('RMSE: {}'.format(np.mean(self.rmse)))
print('Std RMSE: {}'.format(np.std(self.rmse)))
def execute(self):
x_data = util.normalizeData(self.x_data)
x_data = util.insertBias(x_data)
y_data = self.y_data
for i in range(self.realizations):
x_data_aux, y_data_aux = util.shuffleData(x_data, y_data)
x_train, x_test, y_train, y_test = util.splitData(
x_data_aux, y_data_aux, self.train_size)
if self.g_search:
best_n_centers, best_width = self.grid_search(x_train, y_train)
print('Best N Centers: ', best_n_centers)
print('Best Width: ', best_width)
else:
best_n_centers = self.n_centers
best_width = self.width
params = self.train(x_train, y_train, best_n_centers, best_width)
acc, tpr, spc, ppv = self.test(x_test, y_test, params,
best_n_centers, best_width)
self.hit_rate.append(acc)
self.tpr.append(tpr)
self.spc.append(spc)
self.ppv.append(ppv)
self.acc = np.mean(self.hit_rate)
self.std = np.std(self.hit_rate)
self.tpr = np.mean(self.tpr)
self.spc = np.mean(self.spc)
self.ppv = np.mean(self.ppv)
print('Hit rate: {}'.format(self.hit_rate))
print('Accuracy: {:.2f}'.format(self.acc * 100))
print('Minimum: {:.2f}'.format(np.amin(self.hit_rate) * 100))
print('Maximum: {:.2f}'.format(np.amax(self.hit_rate) * 100))
print('Standard Deviation: {:.2f}'.format(self.std))
print('Sensitivity: {:.2f}'.format(self.tpr * 100))
print('Specificity: {:.2f}'.format(self.spc * 100))
print('Precision: {:.2f}'.format(self.ppv * 100))
def adaline(self):
# Pre processing
x_data = util.normalizeData(self.x_data)
x_data = util.insertBias(x_data)
y_data = self.y_data
for i in range(self.realizations):
self.initWeigths()
x_data_aux, y_data_aux = util.shuffleData(x_data, y_data)
x_train, x_test, y_train, y_test = util.splitData(
x_data_aux, y_data_aux, self.train_size)
self.train(x_train, y_train)
mse, rmse = self.test(x_test, y_test)
self.mse.append(mse)
self.rmse.append(rmse)
print('{} Realizations'.format(self.realizations))
print('MSE: {}'.format(np.mean(self.mse)))
print('Std MSE: {}'.format(np.std(self.mse)))
print('RMSE: {}'.format(np.mean(self.rmse)))
print('Std RMSE: {}'.format(np.std(self.rmse)))
