import numpy as np

import pandas as pd

import random

from keras.models import Sequential

from keras.layers.core import Dense, Activation

from keras.layers.recurrent import LSTM

from keras.layers import Input, LSTM, RepeatVector, Masking, TimeDistributed, Lambda

from keras.losses import mse, binary_crossentropy, mean_squared_error

from keras.models import Model

from keras import backend as K

#define variables

latent_dim=3

Intermediate_dim=6

nb_epoch=1000

batch_size=100

optimizer='adadelta'

#X is the data matrix

#encoder LSTM

inputs = Input(shape=(7, 1), name='InputTimeSeries')  #(timesteps, input_dim)

encoded = LSTM(Intermediate_dim, name='EncoderLSTM')(inputs) # intermediate dimension

#Creating mean and sigma vectors

z_mean = Dense(latent_dim, name='MeanVector' )(encoded)

z_log_sigma = Dense(latent_dim,name='SigmaVector')(encoded)

#latent vector sampling

def sampling(args):

    z_mean, z_log_sigma = args

    batch = K.shape(z_mean)[0]

    dim = K.int_shape(z_mean)[1]

    epsilon = K.random_normal(shape=(batch, dim))

    return z_mean + K.exp(0.5 * z_log_sigma) * epsilon

z = Lambda(sampling, name='LatentVector', output_shape=(latent_dim,))([z_mean, z_log_sigma])  

#VAE Loss

def vae_loss(inputs, decoded):

    xent_loss = K.sum(K.binary_crossentropy(inputs, decoded), axis=1)

    kl_loss = - 0.5 * K.sum(1 + z_log_sigma - K.square(z_mean) - K.exp(z_log_sigma), axis=-1) 

    return K.mean(xent_loss + kl_loss)

#decoder LSTM

decoded = RepeatVector(7, name='EmbeddingtoTimeSeries')(z) #timesteps

decoded = LSTM(Intermediate_dim,name='DecoderLSTM1', return_sequences=True)(decoded) #intermediate dimensions

decoded = LSTM(1,name='DecoderLSTM2', return_sequences=True)(decoded) #input_dim

#decoded=TimeDistributed(Dense(1, name='Wrapper'), name='TimeDistributed')(decoded)  

v_autoencoder = Model(inputs, decoded)

encoder = Model(inputs, z_mean)  

#v_autoencoder.summary()

v_autoencoder.compile(optimizer=optimizer, loss=vae_loss)

v_autoencoder.fit(X,X,nb_epoch=nb_epoch,batch_size=batch_size)