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)