--- a
+++ b/large_net/functions.py
@@ -0,0 +1,103 @@
+import numpy as np
+import random
+
+def quan_detector(dataset,label):
+    """
+    the percent of zeros(n) promoter
+    """
+    n = len(dataset[1])
+    N = len(dataset)
+    p = [0]*n
+    count = 0.
+    pos_count = 0.
+    neg_count = 0.
+    for i in range(N):
+        d = dataset[i]
+        if all(d == p):
+            count += 1.
+            if label[i][0] == 1:
+                pos_count += 1.
+            else:
+                neg_count += 1.
+    return count/N, pos_count,neg_count
+#quan_detector([[0,0,0],[0,1,0],[0,2,0],[1,0,1]])
+
+def most_repeared_promoter(dataset,label):
+    N = len(dataset)
+    n = len(dataset[1])
+    zeros = ','.join(map(str,['0']*n))
+    # print len(zeros)-n
+    dict_count = {}
+    for i in range(N):
+        str_prom = ','.join(map(str,dataset[i]))
+        if str_prom not in dict_count.keys():
+            dict_count[str_prom] = [1,0,0]
+            if label[i][0] == 1:
+                dict_count[str_prom][1] = 1
+            else:
+                dict_count[str_prom][2] = 1
+        else:
+            dict_count[str_prom][0] += 1
+            if label[i][0] == 1:
+                dict_count[str_prom][1] += 1
+            else:
+                dict_count[str_prom][2] += 1
+    if zeros in dict_count.keys():
+        dict_count.pop(zeros) # remove without
+    count = np.array(dict_count.values())[:,0]
+    max_count = max(count)
+    for k,v in dict_count.items():
+        if v[0] == max_count:
+            idx_temp = k
+    idx = idx_temp.split(',')
+    return idx, max_count, dict_count[idx_temp]
+
+###############################################################
+#########          Dataset generation            ##############
+###############################################################
+def indx(lab):
+    #     lab = np.argmax(lab,axis=1)
+    p = []  # positive samples index-- ALS
+    n = []  # negative samples index-- Non-ALS
+    for i in range(len(lab)):
+        if lab[i] == 0:
+            p.append(i)
+        else:
+            n.append(i)
+    return p, n
+
+
+def dataset(X, Y, test_ratio):
+    te_idx = []
+    tr_idx = []
+    lab = np.argmax(Y, axis=1)
+    pos_s, neg_s = indx(lab)
+
+    N = len(lab)
+    idx = range(N)
+
+    N_te = int(N * test_ratio) / 10 * 10  # number of test samples
+    N_tr = N - N_te  # number of training samples
+
+    pos_s_te = int(N_te * 0.5)
+    neg_s_te = int(N_te * 0.5)
+
+    random.shuffle(pos_s)
+    random.shuffle(neg_s)
+
+    pos_idx_te = pos_s[:pos_s_te]
+    neg_idx_te = neg_s[:neg_s_te]
+
+    te_idx = pos_idx_te + neg_idx_te
+    
+    tr_idx = list(set(idx) - set(te_idx))
+
+    random.shuffle(te_idx)
+    random.shuffle(tr_idx)
+
+    tr_X = X[tr_idx]
+    tr_Y = Y[tr_idx]
+#    print len(te_idx)
+    te_Y,te_X = Y[te_idx], X[te_idx]
+    
+    return tr_X, tr_Y, te_X, te_Y,tr_idx,te_idx