import numpy
import pandas as pd
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import accuracy_score
# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back):
        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)
                dataX.append(xset)
                dataY.append(dataset[i + look_back,0])
        return numpy.array(dataX), numpy.array(dataY)
# fix random seed for reproducibility
numpy.random.seed(1)
# load the dataset
file='test123456.xlsx'
xl=pd.ExcelFile(file)
dataframe = xl.parse('Sheet1')
dataset = dataframe.values
dataset = dataset.astype('float32')
# normalize the dataset
scaler = MinMaxScaler(feature_range=(0,1))
dataset = scaler.fit_transform(dataset)
# split into train and test sets
train_size = int(len(dataset) * 0.75)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:],dataset[train_size:len(dataset),:]
# reshape into X=t and Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0],1,trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0],1,testX.shape[1]))                            
# create and fit the LSTM network
model = Sequential()
model.add(LSTM(8, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='Adam')
model.fit(trainX, trainY, epochs=10000, batch_size=1, verbose=2)
# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# invert predictions
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
#
print("X=%s, Predicted=%s" % (testPredict[-1],testX[-1]))
print("X=%s, Predicted=%s" % (testPredict[0],testX[0]))