def main(test_X, test_y):
m = SVR(kernel='rbf', gamma=0.01, C=100.0, degree=3)
# 6. 预测
m.fit(train_X, train_y)
yhat = m.predict(test_X)
test_X = test_X.reshape((test_X.shape[0], TIME_STEPS * INPUT_DIMS))
inv_yhat = inverse_trans(yhat, test_X, scaler, N_TRAIN_WEEKS, INPUT_DIMS)
inv_y = inverse_trans(test_y, test_X, scaler, N_TRAIN_WEEKS, INPUT_DIMS)
# # 计算RMSE误差值
mae_array.append(mean_absolute_error(inv_y, inv_yhat))
rmse_array.append(sqrt(mean_squared_error(inv_y, inv_yhat)))
def main(test_X, test_y):
m = MLPRegressor(hidden_layer_sizes=24,
learning_rate_init=0.1,
max_iter=500)
# 6. 预测
m.fit(train_X, train_y)
yhat = m.predict(test_X)
test_X = test_X.reshape((test_X.shape[0], TIME_STEPS * INPUT_DIMS))
inv_yhat = inverse_trans(yhat, test_X, scaler, N_TRAIN_WEEKS, INPUT_DIMS)
inv_y = inverse_trans(test_y, test_X, scaler, N_TRAIN_WEEKS, INPUT_DIMS)
mae_array.append(mean_absolute_error(inv_y, inv_yhat))
rmse_array.append(sqrt(mean_squared_error(inv_y, inv_yhat)))
def main(test_X, test_y):
m = rnn_model(TIME_STEPS, INPUT_DIMS)
optimizer = tf.optimizers.Adam(learning_rate=0.001)
m.compile(optimizer=optimizer, loss='mae')
history = m.fit([train_X], train_y, epochs=EPOCHS,
batch_size=BATCH_SIZE, validation_data=(test_X, test_y))
# 6. 预测
yhat = m.predict(test_X, verbose=0)
print("rmse", sqrt(mean_squared_error(test_y, yhat)))
print("mae", mean_absolute_error(test_y, yhat))
print("mape", mape(test_y, yhat))
test_X = test_X.reshape((test_X.shape[0], TIME_STEPS*INPUT_DIMS))
inv_yhat = inverse_trans(yhat, test_X, scaler, N_TRAIN_WEEKS, INPUT_DIMS)
inv_y = inverse_trans(test_y, test_X, scaler, N_TRAIN_WEEKS, INPUT_DIMS)
# # 计算RMSE误差值
mae_array.append(mean_absolute_error(inv_y, inv_yhat))
rmse_array.append(sqrt(mean_squared_error(inv_y, inv_yhat)))
def main(test_X, test_y):
# 对pH进行预测，若需要对NH3-N进行预测时，需要讲函数更改为cnn_lstm_ph2，以及在data_processing中将第73行的0改为3.
m = cnn_lstm_ph3(TIME_STEPS, INPUT_DIMS)
optimizer = tf.optimizers.Adam(learning_rate=0.001)
m.compile(optimizer=optimizer, loss='mae')
# t0 = time.time()
history = m.fit([train_X],
train_y,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
validation_data=(test_X, test_y))
# 6. 预测
yhat = m.predict(test_X, verbose=0)
print("rmse", sqrt(mean_squared_error(test_y, yhat)))
print("mae", mean_absolute_error(test_y, yhat))
test_X = test_X.reshape((test_X.shape[0], TIME_STEPS * INPUT_DIMS))
inv_yhat = inverse_trans(yhat, test_X, scaler, N_TRAIN_WEEKS, INPUT_DIMS)
inv_y = inverse_trans(test_y, test_X, scaler, N_TRAIN_WEEKS, INPUT_DIMS)
# # 计算RMSE误差值
mae_array.append(mean_absolute_error(inv_y, inv_yhat))
rmse_array.append(sqrt(mean_squared_error(inv_y, inv_yhat)))