def ANNforecasting(dataset,
inputDim,
hiddenNum=50,
outputDim=1,
epoch=20,
batchSize=30):
# 归一化数
#dataset = dataset.reshape(-1, 1)
scaler = MinMaxScaler(feature_range=(0.0, 1.0))
dataset = scaler.fit_transform(dataset)
# 分割序列为样本,此处不整理成RNN形式，采用标准形式
train, test = util.divideTrainTest(dataset)
trainX, trainY = util.createSamples(train, inputDim, RNN=False)
testX, testY = util.createSamples(test, inputDim, RNN=False)
print("trainX shape is", trainX.shape)
print("trainY shape is", trainY.shape)
# 构建模型并训练
ANNModel = ANN.ANNModel(inputDim, hiddenNum, outputDim)
t1 = time.time()
ANNModel.train(trainX, trainY, epoch, batchSize)
t2 = time.time() - t1
print("train time is", t2)
# 预测
trainPred = ANNModel.predict(trainX)
testPred = ANNModel.predict(testX)
# 还原数据
trainPred = scaler.inverse_transform(trainPred)
trainY = scaler.inverse_transform(trainY)
testPred = scaler.inverse_transform(testPred)
testY = scaler.inverse_transform(testY)
dataset = scaler.inverse_transform(dataset)
# 评估指标
# MAE = eval.calcMAE(trainY, trainPred)
# print ("train MAE",MAE)
# MRSE = eval.calcRMSE(trainY, trainPred)
# print ("train MRSE",MRSE)
# MAPE = eval.calcMAPE(trainY, trainPred)
# print ("train MAPE",MAPE)
MAE = eval.calcMAE(testY, testPred)
print("test MAE", MAE)
MRSE = eval.calcRMSE(testY, testPred)
print("test RMSE", MRSE)
#MAPE = eval.calcMAPE(testY,testPred)
#print ("test MAPE",MAPE)
SMAPE = eval.calcSMAPE(testY, testPred)
print("test SMAPE", SMAPE)
util.plot(trainPred, trainY, testPred, testY)
return trainPred, testPred, MAE, MRSE, SMAPE
