--- a
+++ b/python-scripts/runSingleVAE.py
@@ -0,0 +1,51 @@
+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 VAEclass import VAE
+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)
+
+    data = omics
+    # input_dim = data.shape[1]
+    encoding1_dim = 4096
+    encoding2_dim = 1024
+    middle_dim = 2
+    dims = [encoding1_dim, encoding2_dim, middle_dim]
+    vae = VAE(data, dims)
+    vae.autoencoder.summary()
+    vae.train()
+    encoded_factors = vae.predict(data)
+    if not os.path.exists("{}/VAE_FCTAE_EM.txt".format(resultpath)):
+        os.mknod("{}/VAE_FCTAE_EM.txt".format(resultpath))
+    np.savetxt("{}/VAE_FCTAE_EM.txt".format(resultpath), encoded_factors)
+
+
+
+
+
+
+
+
+