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@tizuo
Created August 8, 2017 07:50
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scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
xset = []
for j in range(dataset.shape[1]):
a = dataset[i:(i+look_back), j]
xset.append(a)
dataY.append(dataset[i + look_back, 0])
dataX.append(xset)
return numpy.array(dataX), numpy.array(dataY)
look_back = 12
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], trainX.shape[2]))
testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], testX.shape[2]))
model = Sequential()
model.add(LSTM(4, input_shape=(testX.shape[1], look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=1000, batch_size=1, verbose=2)
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
pad_col = numpy.zeros(dataset.shape[1]-1)
def pad_array(val):
return numpy.array([numpy.insert(pad_col, 0, x) for x in val])
trainPredict = scaler.inverse_transform(pad_array(trainPredict))
trainY = scaler.inverse_transform(pad_array(trainY))
testPredict = scaler.inverse_transform(pad_array(testPredict))
testY = scaler.inverse_transform(pad_array(testY))
trainScore = math.sqrt(mean_squared_error(trainY[:,0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[:,0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
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