from keras.layers import Input, Dense

from keras.models import Model

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.cluster import KMeans

from sklearn.cluster import k_means

from sklearn.metrics import silhouette_score, davies_bouldin_score

from sklearn.preprocessing import normalize

import time

from sklearn import metrics

from myUtils import *

from MMDVAEclass import VAE

#from AEclass import AE

import os

if __name__ == '__main__':

    datapath = 'data/single-cell/'

    resultpath = 'result/single-cell/'

    # groundtruth = np.loadtxt('{}/c.txt'.format(datapath))

    # groundtruth = list(np.int_(groundtruth))

    omics = np.loadtxt('{}/omics.txt'.format(datapath))

    omics = np.transpose(omics)

    omics1 = omics[0:206]

    omics2 = omics[206:412]

    omics1 = normalize(omics1, axis=0, norm='max')

    omics2 = normalize(omics2, axis=0, norm='max')

    #omics = np.concatenate((omics1, omics2), axis=1)

    encoding1_dim1 = 2048

    encoding2_dim1 = 512

    middle_dim1 = 1

    dims1 = [encoding1_dim1, encoding2_dim1, middle_dim1]

    ae1 = VAE(omics1, dims1)

    ae1.train()

    ae1.autoencoder.summary()

    encoded_factor1 = ae1.predict(omics1)

    encoding1_dim2 = 2048

    encoding2_dim2 = 512

    middle_dim2 = 1

    dims2 = [encoding1_dim2, encoding2_dim2, middle_dim2]

    ae2 = VAE(omics2, dims2)

    ae2.train()

    ae2.autoencoder.summary()

    encoded_factor2 = ae2.predict(omics2)

    encoded_factors = np.concatenate((encoded_factor1, encoded_factor2), axis=1)

    if not os.path.exists("{}/MMDVAE_FAETC_EM.txt".format(resultpath)):

        os.mknod("{}/MMDVAE_FAETC_EM.txt".format(resultpath))

    np.savetxt("{}/MMDVAE_FAETC_EM.txt".format(resultpath), encoded_factors)