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 ZVAEclass import ZVAE

import os

from keras import backend as K

def get_EM(datapath,resultpath):

    omics1 = np.loadtxt('{}/log_exp_omics.txt'.format(datapath))

    omics1 = np.transpose(omics1)

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

    dim1=omics1.shape[1]

    print(omics1.shape)

    omics2 = np.loadtxt('{}/log_mirna_omics.txt'.format(datapath))

    omics2 = np.transpose(omics2)

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

    dim2=omics2.shape[1]

    print(omics2.shape)

    omics3 = np.loadtxt('{}/methy_omics.txt'.format(datapath))

    omics3 = np.transpose(omics3)

    omics3 = normalize(omics3, axis=0, norm='max')

    dim3=omics3.shape[1]

    print(omics3.shape)

    # omics = np.concatenate((omics1, omics2, omics3), axis=1)

    # print(omics.shape)

    # data = omics

    # input_dim = data.shape[1]

    # encoding1_dim = 3000

    # encoding2_dim = 300

    # middle_dim = 10

    # noise_factor = 0.1

    omics=[omics1,omics2,omics3]

    dims = [dim1, dim2, dim3]

    vae = ZVAE(dims)

    #vae.autoencoder.summary()

    vae.autoencoder.fit(omics, omics, epochs=100,verbose=1, batch_size=16, shuffle=True)

    encoded_factors = vae.encoder.predict(omics)

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

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

    #np.savetxt("{}/MMDVAE_EM_5.txt".format(resultpath), encoded_factors)

    np.savetxt("{}/MMDVAE_EM_10.txt".format(resultpath), encoded_factors)

    #np.savetxt("{}/MMDVAE_EM_15.txt".format(resultpath), encoded_factors)

    K.clear_session()

if __name__ == '__main__':

    data_dir_list = []

    result_dir_list = []

    data_path = r"data/cancer"

    result_path = r"result/cancer"

    dir_or_files = os.listdir(data_path)

    for dir_file in dir_or_files:

        # 获取目录或者文件的路径

        data_dir_file_path = os.path.join(data_path, dir_file)

        result_dir_file_path = os.path.join(result_path, dir_file)

        # 判断该路径为文件还是路径

        if os.path.isdir(data_dir_file_path):

            data_dir_list.append(data_dir_file_path)

            if not os.path.exists(result_dir_file_path):

                os.makedirs(result_dir_file_path)

            result_dir_list.append(result_dir_file_path)

    #print(data_dir_list)

    #print(result_dir_list)

    #data_dir_list=['data/cancer/breast', 'data/cancer/kidney', 'data/cancer/lung', 'data/cancer/liver']

    #result_dir_list=['result/cancer/breast', 'result/cancer/kidney', 'result/cancer/lung', 'result/cancer/liver']

    for datapath,resultpath in zip(data_dir_list,result_dir_list):

        get_EM(datapath, resultpath)

    # datapath='data/cancer/liver'

    # resultpath='result/cancer/liver'  

    # get_EM(datapath, resultpath)