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 AEclass import AE

import os

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):

        print(datapath)

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

        omics1 = np.transpose(omics1)

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

        print(omics1.shape)

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

        omics2 = np.transpose(omics2)

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

        print(omics2.shape)

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

        omics3 = np.transpose(omics3)

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

        print(omics3.shape)

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

        print(omics.shape)

        print('*'*100)

        # data = omics

        # input_dim = data.shape[1]

        # encoding1_dim = 3000

        # encoding2_dim = 300

        # middle_dim = 10

        # dims = [encoding1_dim, encoding2_dim, middle_dim]

        # ae = AE(data, dims)

        # #ae.autoencoder.summary()

        # ae.train()

        # encoded_factors = ae.predict(data)

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

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

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