def train(train_dataa, train_dataa_size, cluster_size, iteration_size,
dimension_size, m, gama):
""" remove class column"""
train_data = train_dataa.T[:len(train_dataa.T) - 1].T
centers, memberShip_train = FCM.fcm(train_data, train_dataa_size,
cluster_size, iteration_size,
dimension_size, m)
""" to find out what G and W are see project definition"""
G = cal_G(train_data, centers, memberShip_train, gama, train_dataa_size)
W = cal_w(G, train_dataa, cluster_size, len(train_dataa))
return W, centers
arr1[:, 2] = arr[:, max1]
#print(arr1)
c1 = []
c1.append(c[min1])
c1.append(c[mid])
c1.append(c[max1])
return arr1, c1
dicid, dicnum, dicpingid = makeGroup()
arr = []
cdic = {}
for i in dicid:
if len(set(dicnum[i])) > 4:
#print(i)
arr1, c = FCM.fcm(dicnum[i])
#使数据按照大中小剂量排列
#print(arr1)
arr2, d = sortbydic(arr1, c)
for j in range(len(arr2)):
li = []
li.append(i)
li.append(dicid[i][j])
li.append(dicpingid[i][j])
li.append(dicnum[i][j])
li.append(list(arr2[j]))
arr.append(li)
cdic[i] = d
for i in cdic:
print(i, cdic[i])
csv_pd = pd.DataFrame(arr)