--- a
+++ b/python-scripts/runSimulationsDAE2.py
@@ -0,0 +1,147 @@
+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 DAEclass import DAE
+import os
+
+if __name__ == '__main__':
+    datatypes=["equal","heterogeneous"]
+    typenums=[5,10,15]
+    noise_factor=0.5
+    for datatype in datatypes:
+        for typenum in typenums:
+            datapath='data/simulations/{}/{}'.format(datatype, typenum)
+            resultpath='result/simulations/{}/{}'.format(datatype, typenum)
+            groundtruth = np.loadtxt('{}/c.txt'.format(datapath))
+            groundtruth = list(np.int_(groundtruth))
+
+            omics1 = np.loadtxt('{}/o1.txt'.format(datapath))
+            omics1 = np.transpose(omics1)
+            omics1 = normalize(omics1, axis=0, norm='max')
+
+            omics2 = np.loadtxt('{}/o2.txt'.format(datapath))
+            omics2 = np.transpose(omics2)
+            omics2 = normalize(omics2, axis=0, norm='max')
+
+            omics3 = np.loadtxt('{}/o3.txt'.format(datapath))
+            omics3 = np.transpose(omics3)
+            omics3 = normalize(omics3, axis=0, norm='max')
+
+            omics = np.concatenate((omics1, omics2, omics3), axis=1)
+
+
+            # ae = AE(data, dims)
+            # ae.train()
+            # encoded_factors = ae.predict(data)
+
+            noise_factor = 0.1
+
+            encoding1_dim1 = 100
+            encoding2_dim1 = 50
+            if typenum==15:
+                middle_dim1 = 5
+            elif typenum==10:
+                middle_dim1 = 4
+            elif typenum==5:
+                middle_dim1 = 3
+            dims1 = [encoding1_dim1, encoding2_dim1, middle_dim1]
+            ae1 = DAE(omics1, dims1,noise_factor)
+            ae1.train()
+            ae1.autoencoder.summary()
+            encoded_factor1 = ae1.predict(omics1)
+
+            encoding1_dim2 = 80
+            encoding2_dim2 = 50
+            if typenum==15:
+                middle_dim2 = 5
+            elif typenum==10:
+                middle_dim2 = 3
+            elif typenum==5:
+                middle_dim2 = 1
+            dims2 = [encoding1_dim2, encoding2_dim2, middle_dim2]
+            ae2 = DAE(omics2, dims2,noise_factor)
+            ae2.train()
+            ae2.autoencoder.summary()
+            encoded_factor2 = ae2.predict(omics2)
+
+            encoding1_dim3 = 80
+            encoding2_dim3 = 50
+            if typenum==15:
+                middle_dim3 = 5
+            elif typenum==10:
+                middle_dim3 = 3
+            elif typenum==5:
+                middle_dim3 = 1
+            dims3 = [encoding1_dim3, encoding2_dim3, middle_dim3]
+            ae3 = DAE(omics3, dims3,noise_factor)
+            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("{}/DAE_FAETC_EM.txt".format(resultpath)):
+                os.mknod("{}/DAE_FAETC_EM.txt".format(resultpath))
+            np.savetxt("{}/DAE_FAETC_EM_{}.txt".format(resultpath,typenum), encoded_factors)
+
+            # if not os.path.exists("AE_FCTAE_Kmeans.txt"):
+            #     os.mknod("AE_FCTAE_Kmeans.txt")
+            # fo = open("AE_FCTAE_Kmeans.txt", "a")
+            # clf = KMeans(n_clusters=typenum)
+            # t0 = time.time()
+            # clf.fit(encoded_factors)  # 模型训练
+            # km_batch = time.time() - t0  # 使用kmeans训练数据消耗的时间
+
+            # print(datatype, typenum)
+            # print("K-Means算法模型训练消耗时间:%.4fs" % km_batch)
+
+            # # 效果评估
+            # score_funcs = [
+            #     metrics.adjusted_rand_score,  # ARI（调整兰德指数）
+            #     metrics.v_measure_score,  # 均一性与完整性的加权平均
+            #     metrics.adjusted_mutual_info_score,  # AMI（调整互信息）
+            #     metrics.mutual_info_score,  # 互信息
+            # ]
+
+            # centers = clf.cluster_centers_
+            # print("zlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzly")
+            # #print("centers:")
+            # #print(centers)
+            # print("zlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzly")
+            # labels = clf.labels_
+            # print("labels:")
+            # print(labels)
+            # labels = list(np.int_(labels))
+            # if not os.path.exists("{}/DAE_FAETC_CL.txt".format(resultpath)):
+            #     os.mknod("{}/DAE_FAETC_CL.txt".format(resultpath))
+            # np.savetxt("{}/DAE_FAETC_CL.txt".format(resultpath), labels,fmt='%d')
+            # print("zlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzly")
+            # # 2. 迭代对每个评估函数进行评估操作
+            # for score_func in score_funcs:
+            #     t0 = time.time()
+            #     km_scores = score_func(groundtruth, labels)
+            #     print("K-Means算法:%s评估函数计算结果值:%.5f；计算消耗时间:%0.3fs" % (score_func.__name__, km_scores, time.time() - t0))
+            # t0 = time.time()
+            # jaccard_score = jaccard_coefficient(groundtruth, labels)
+            # print("K-Means算法:%s评估函数计算结果值:%.5f；计算消耗时间:%0.3fs" % (
+            #     jaccard_coefficient.__name__, jaccard_score, time.time() - t0))
+            # silhouetteScore = silhouette_score(encoded_factors, labels, metric='euclidean')
+            # davies_bouldinScore = davies_bouldin_score(encoded_factors, labels)
+            # print("silhouetteScore:", silhouetteScore)
+            # print("davies_bouldinScore:", davies_bouldinScore)
+            # print("zlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzly")
+
+
+
+
+
+