--- a
+++ b/python-scripts/runSingleAE.py
@@ -0,0 +1,111 @@
+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 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)
+
+    # omics1 = np.loadtxt('{}/ata.txt'.format(datapath))
+    # omics1 = np.transpose(omics1)
+    # omics1 = normalize(omics1, axis=0, norm='max')
+    #
+    # omics2 = np.loadtxt('{}/rna.txt'.format(datapath))
+    # omics2 = np.transpose(omics2)
+    # omics2 = normalize(omics2, axis=0, norm='max')
+    #
+    # omics = np.concatenate((omics1, omics2), axis=1)
+
+    print(omics1.shape)
+    print(omics2.shape)
+
+    # data = omics
+    # input_dim = data.shape[1]
+    # encoding1_dim = 4096
+    # encoding2_dim = 1024
+    # middle_dim = 2
+    # dims = [encoding1_dim, encoding2_dim, middle_dim]
+    # ae = AE(data, dims)
+    # ae.autoencoder.summary()
+    # ae.train()
+    # encoded_factors = ae.predict(data)
+    # if not os.path.exists("{}/AE_FCTAE_EM.txt".format(resultpath)):
+    #     os.mknod("{}/AE_FCTAE_EM.txt".format(resultpath))
+    # np.savetxt("{}/AE_FCTAE_EM.txt".format(resultpath), 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("centers:")
+    # # print(centers)
+    # print("zlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzly")
+    # labels = clf.labels_
+    # print("labels:")
+    # print(labels)
+    # labels = list(np.int_(labels))
+    # if not os.path.exists("{}/AE_FCTAE_CL.txt".format(resultpath)):
+    #     os.mknod("{}/AE_FCTAE_CL.txt".format(resultpath))
+    # np.savetxt("{}/AE_FCTAE_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")
+
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