# get the predicted values
ca_pred = ca_model.predict(ca_X_test)[:, 0]
print('Lowpass coefficient estimation finish.')
mu, sigma_2, cd_pred = generateData(cd[0:train_row],
outputnum=len(cd) - 24 - train_row)
# cd_pred = cd_model.predict(cd_X_test)[:,0]
print('Highpass coefficient estimation finish.')
#print(np.shape(ca_pred),np.shape(cd_pred))
predicted_values = idwt((ca_pred + shifted_value_ca) * 10, cd_pred)
#predicted_values = idwt(ca_pred+ca_shifted_value, cd_pred+cd_shifted_value)
print('IDWT finish.')
# mape = statistics.mape([y_test_true[i]*1000 for i in range(0,len(y_test_true))],(predicted_values)*1000
print(len(y_test_true), len(predicted_values))
mape = statistics.mape([(y_test_true[i] + shifted_value) * 1000
for i in range(0, len(y_test_true))],
(predicted_values + shifted_value) * 1000)
print('MAPE is ', mape)
mae = statistics.mae([(y_test_true[i] + shifted_value) * 1000
for i in range(0, len(y_test_true))],
(predicted_values + shifted_value) * 1000)
print('MAE is ', mae)
mse = statistics.meanSquareError([(y_test_true[i] + shifted_value) * 1000
for i in range(0, len(y_test_true))],
(predicted_values + shifted_value) * 1000)
print('MSE is ', mse)
rmse = math.sqrt(mse)
print('RMSE is ', rmse)
nrmse = statistics.normRmse([(y_test_true[i] + shifted_value) * 1000
for i in range(0, len(y_test_true))],
(predicted_values + shifted_value) * 1000)
names = []
names.append('true')
for x in range(len(dimensions)):
s = "d=" + str(dimensions[x]) + ",h=" + str(neurons[x])
names.append(s)
preds = []
preds.append(y_test)
for x in range(len(dimensions)):
i = 0
# Perform dimensionality reduction on the feature vectors
pca = PCA(n_components=dimensions[x])
pca.fit(X_train)
xTrainRed = pca.transform(X_train)
xTestRed = pca.transform(X_test)
predicted_values = fit_predict(xTrainRed, y_train, xTestRed, 40, neurons[x])
mape = statistics.mape((y_test + shifted_value) * 1000, (predicted_values + shifted_value) * 1000)
print('MAPE is ', mape)
mae = statistics.mae((y_test + shifted_value) * 1000, (predicted_values + shifted_value) * 1000)
print('MAE is ', mae)
mse = statistics.meanSquareError((y_test + shifted_value) * 1000, (predicted_values + shifted_value) * 1000)
print('MSE is ', mse)
rmse = math.sqrt(mse)
print('RMSE is ', rmse)
nrmse = statistics.normRmse((y_test + shifted_value) * 1000, (predicted_values + shifted_value) * 1000)
print('NRMSE is ', nrmse)
print (i+1)
preds.append(predicted_values)
# show
fig = plt.figure()
colors = ["g","r","b","c","m","y","k","w"]
legendVars = []
# save model
model.save('../model/seq2seq_code.h5')
model = load_model('../model/seq2seq_code.h5')
# evaluate on some new patterns
result = model.predict(X_test, batch_size=50, verbose=0)
# calculate error,evaluate the result
predicted = one_hot_decode(result, series_min, series_max, n_unique)
# plot the results
fig = plt.figure()
plt.plot(test[:, n_in:] + shifted_value, label="$true$", c='green')
plt.plot(predicted + shifted_value, label="$predict$", c='red')
plt.xlabel('Hour')
plt.ylabel('Electricity load')
plt.legend()
plt.show()
fig.savefig('../result/seq2seq_code_result.jpg', bbox_inches='tight')
# evaluation
mape = statistics.mape(test[:, n_in:] + shifted_value,
predicted + shifted_value)
print('MAPE is ', mape)
nrmse = statistics.normRmse(test[:, n_in:] + shifted_value,
predicted + shifted_value)
print('NRMSE is ', nrmse)
mae = statistics.mae(test[:, n_in:] + shifted_value,
predicted + shifted_value)
print('MAE is ', mae)
mse = statistics.mse(test[:, n_in:] + shifted_value,
predicted + shifted_value)
print('MSE is ', mse)
rmse = sqrt(mse)
print('RMSE is ', rmse)
