--- a
+++ b/python-scripts/runCancerSVAE2.py
@@ -0,0 +1,104 @@
+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 SVAEclass import VAE
+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')
+    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)
+
+
+
+    encoding1_dim1 = 1000
+    encoding2_dim1 = 100
+    middle_dim1 = 4
+    dims1 = [encoding1_dim1, encoding2_dim1, middle_dim1]
+    ae1 = VAE(omics1, dims1)
+    ae1.train()
+    ae1.autoencoder.summary()
+    encoded_factor1 = ae1.predict(omics1)
+
+    encoding1_dim2 = 500
+    encoding2_dim2 = 50
+    middle_dim2 = 2
+    dims2 = [encoding1_dim2, encoding2_dim2, middle_dim2]
+    ae2 = VAE(omics2, dims2)
+    ae2.train()
+    ae2.autoencoder.summary()
+    encoded_factor2 = ae2.predict(omics2)
+
+    encoding1_dim3 = 1000
+    encoding2_dim3 = 100
+    middle_dim3 = 4
+    dims3 = [encoding1_dim3, encoding2_dim3, middle_dim3]
+    ae3 = VAE(omics3, dims3)
+    ae3.autoencoder.summary()
+    ae3.train()
+    encoded_factor3 = ae3.predict(omics3)
+
+    encoded_factors = np.concatenate((encoded_factor1, encoded_factor2, encoded_factor3), axis=1)
+    if not os.path.exists("{}/SVAE_FAETC_EM.txt".format(resultpath)):
+        os.mknod("{}/SVAE_FAETC_EM.txt".format(resultpath))
+    np.savetxt("{}/SVAE_FAETC_EM.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/gbm', 'data/cancer/ovarian', 'data/cancer/sarcoma', 'data/cancer/lung', 'data/cancer/liver']
+    # result_dir_list=['result/cancer/breast', 'result/cancer/gbm', 'result/cancer/ovarian', 'result/cancer/sarcoma', 'result/cancer/lung', 'result/cancer/liver']
+
+    for datapath,resultpath in zip(data_dir_list,result_dir_list):
+        get_EM(datapath, resultpath)
+    # datapath='data/cancer/gbm'
+    # resultpath='result/cancer/gbm'
+    get_EM(datapath, resultpath)
+
+
+
+
+
+
+
+
+