--- a
+++ b/DAE-DCAP.py
@@ -0,0 +1,120 @@
+
+
+import tensorflow as tf
+import numpy as np  
+import matplotlib.pyplot as plt  
+import pandas as pd
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score
+
+with open(r"simulation.csv", 'r') as f:
+    data = pd.read_csv(f)
+
+#print(data.shape)
+tcga_input=np.transpose(data)
+print(tcga_input.shape[1])
+length1 = tcga_input.shape[1]
+learning_rate = 0.0001
+training_epochs = 100
+batch_size = 125
+display_step = 2
+examples_to_show = 10
+n_input = tcga_input.shape[1]
+  
+
+X = tf.placeholder("float", [None, n_input])  
+  
+
+n_hidden_1 = 200
+n_hidden_2 = 50
+n_hidden_3 = 2
+
+weights = {  
+    'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),  
+    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
+    'encoder_h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3])),
+    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_2])),
+    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),
+    'decoder_h3': tf.Variable(tf.random_normal([n_hidden_1, n_input])),
+
+
+}  
+biases = {  
+    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),  
+    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),
+    'encoder_b3': tf.Variable(tf.random_normal([n_hidden_3])),
+    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_2])),
+    'decoder_b2': tf.Variable(tf.random_normal([n_hidden_1])),
+    'decoder_b3': tf.Variable(tf.random_normal([n_input])),
+}  
+  
+
+def encoder(x):  
+    layer_1 = tf.nn.tanh(tf.add(tf.matmul(x, weights['encoder_h1']),
+                                   biases['encoder_b1']))  
+    layer_2 = tf.nn.tanh(tf.add(tf.matmul(layer_1, weights['encoder_h2']),
+                                   biases['encoder_b2']))
+    layer_3 = tf.nn.tanh(tf.add(tf.matmul(layer_2, weights['encoder_h3']),
+                                   biases['encoder_b3']))
+    return layer_3
+  
+  
+
+def decoder(x):  
+    layer_1 = tf.nn.tanh(tf.add(tf.matmul(x, weights['decoder_h1']),
+                                   biases['decoder_b1']))  
+    layer_2 = tf.nn.tanh(tf.add(tf.matmul(layer_1, weights['decoder_h2']),
+                                   biases['decoder_b2']))
+    layer_3 = tf.nn.tanh(tf.add(tf.matmul(layer_2, weights['decoder_h3']),
+                                   biases['decoder_b3']))
+    return layer_3
+  
+
+encoder_op = encoder(X)  
+decoder_op = decoder(encoder_op)  
+  
+
+y_pred = decoder_op  
+y_true = X  
+  
+
+cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))
+optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)  
+  
+with tf.Session() as sess:
+    if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:  
+        init = tf.initialize_all_variables()  
+    else:  
+        init = tf.global_variables_initializer()  
+    sess.run(init)  
+
+    total_batch = int(len(tcga_input)/batch_size)
+    for epoch in range(training_epochs):  
+        for i in range(total_batch):
+            batch_xs = tcga_input[((i) * batch_size):((i + 1) * batch_size)] + 0.3 * np.random.rand(length1)   #added nosie
+            # Run optimization op (backprop) and cost op (to get loss value)
+            _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})
+        if epoch % display_step == 0:  
+            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c))
+        if epoch == training_epochs - 1:
+                fea_output = sess.run([encoder_op], feed_dict={X: tcga_input})
+                # print(fea_output)
+                print(np.array(fea_output).shape)
+                np.savetxt(r'fea.csv', np.array(fea_output[0]), delimiter=',')
+                dd = np.array(fea_output[0])
+    print("Optimization Finished!")
+    print(dd.shape)
+    clf = KMeans(n_clusters=2)
+    clf.fit(dd)
+    centers = clf.cluster_centers_
+    labels = clf.labels_
+    silhouetteScore = silhouette_score(dd, labels, metric='euclidean')
+    print(centers)
+    print(silhouetteScore)
+    # encode_decode = sess.run(
+    #     y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})
+    # f, a = plt.subplots(2, 10, figsize=(10, 2))  #return fig，axes
+    # for i in range(examples_to_show):  
+    #     a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))  
+    #     a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))  
+    # plt.show() 
\ No newline at end of file