--- a
+++ b/python-scripts/runCancerMMDVAE.py
@@ -0,0 +1,91 @@
+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)
+
+
+
+
+
+
+
+
+