--- a
+++ b/python-scripts/runSingleMMDVAE2.py
@@ -0,0 +1,63 @@
+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 MMDVAEclass import VAE
+#from AEclass import AE
+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)
+
+    encoding1_dim1 = 2048
+    encoding2_dim1 = 512
+    middle_dim1 = 1
+    dims1 = [encoding1_dim1, encoding2_dim1, middle_dim1]
+    ae1 = VAE(omics1, dims1)
+    ae1.train()
+    ae1.autoencoder.summary()
+    encoded_factor1 = ae1.predict(omics1)
+
+    encoding1_dim2 = 2048
+    encoding2_dim2 = 512
+    middle_dim2 = 1
+    dims2 = [encoding1_dim2, encoding2_dim2, middle_dim2]
+    ae2 = VAE(omics2, dims2)
+    ae2.train()
+    ae2.autoencoder.summary()
+    encoded_factor2 = ae2.predict(omics2)
+
+
+    encoded_factors = np.concatenate((encoded_factor1, encoded_factor2), axis=1)
+
+    if not os.path.exists("{}/MMDVAE_FAETC_EM.txt".format(resultpath)):
+        os.mknod("{}/MMDVAE_FAETC_EM.txt".format(resultpath))
+    np.savetxt("{}/MMDVAE_FAETC_EM.txt".format(resultpath), encoded_factors)
+
+
+
+
+
+
+
+
+