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 DAEclass import DAE

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)

    data = omics

    input_dim = data.shape[1]

    encoding1_dim = 4096

    encoding2_dim = 1024

    middle_dim = 2

    dims = [encoding1_dim, encoding2_dim, middle_dim]

    # ae = AE(data, dims)

    # ae.train()

    # encoded_factors = ae.predict(data)

    noise_factor = 0.1

    dae = DAE(data, dims, noise_factor)

    dae.autoencoder.summary()

    dae.train()

    encoded_factors = dae.predict(data)

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

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

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