# x = np.array([8, 3, 6, 1, 3, 7, 2, 2, 2, 4, 6, 5, 8, 5, 5, 3, 1, 3, 2, 2, 7, 7,4, 8, 1, 7, 1, 1, 3, 7, 4, 7, 2, 6, 2, 8, 1, 3, 1, 6]) def xfun(yanlinks, node): from adregionf import adregionf import numpy as np from bus_line_match import pipei import pandas as pd import networkx as nx from objective_func import objective_func from convert_to_partition import convert_to_partition adr = adregionf(yanlinks, node) # 计算 yanlinks 第 12 列的频次 a = pd.value_counts(yanlinks[:, 11], sort=False).reset_index().values # 按照频次对 a 排序 a = a[a[:, 1].argsort()] # 初始化 fm 变量 fm = 0 for i in a[:, 0]: i_indices = np.where(yanlinks[:, 11] == i) a_sub_rows = yanlinks[i_indices, 4].flatten().astype(int)-1 a = adr[a_sub_rows, :][:, a_sub_rows] chu = 0 yisou = [chu] m = 0 while len(yisou) > m: m = len(yisou) b = np.where(np.sum(a[yisou, :], axis=0) > 0)[0] yisou = np.union1d(yisou, b) if len(yisou) != len(a): fm = 1 break if fm == 0: # Assuming the indices in yanlinks[:, 10] are integers data_path = r'' df2 = pd.read_csv(data_path + 'links_test1.csv') df2['L'] = yanlinks[:, 11] df2.to_csv(data_path + 'links_test1.csv', index=False) pipei() bus_line_partition = pd.read_csv('bus_line_partition.csv', header=None, encoding='gbk') bus_line_partition = bus_line_partition.drop(bus_line_partition.columns[0], axis=1) bus_line_partition = bus_line_partition .fillna(0) bus_line_partition = bus_line_partition.astype(int) bus_line_partition = bus_line_partition.values bus_line_sequence = pd.read_csv('bus_road_sequence.csv', header=None, encoding='gbk') bus_line_sequence = bus_line_sequence.drop(bus_line_sequence.columns[0], axis=1) bus_line_sequence = bus_line_sequence .fillna(0) bus_line_sequence = bus_line_sequence.astype(int) bus_line_sequence = bus_line_sequence.values road_velocity =yanlinks[:, [4, 5]] partition = convert_to_partition(yanlinks) G = nx.DiGraph() for row in yanlinks: G.add_edge(row[0], row[1], weight=1) # weight=row[3] G = G.to_undirected() dadr = np.load('dadr.npy') # 删除全为零的行 dadr = dadr[~np.all(dadr == 0, axis=1)] # 删除全为零的列 dadr = dadr[:, ~np.all(dadr == 0, axis=0)] adr = dadr # 这里的dadr矩阵没有重置过的所以是有问题的 # 从文件中加载 adregion adregion = np.load('adregion.npy') # 更新 adregion for i in range(len(yanlinks)): # 将节点 yanlinks[i,5] 对应的列乘以该行染色体编码 yanlinks[i,12] adregion[:, int(yanlinks[i, 4]) - 1] = adregion[:, int(yanlinks[i, 4]) - 1] * yanlinks[i, 11] # 初始化 adr adr = np.zeros((int(np.max(yanlinks[:, 11])), int(np.max(yanlinks[:, 11])))) # 更新 adr for i in range(adregion.shape[0]): a = adregion[i, :] a = np.unique(a) a = a[a != 0] if len(a) > 1: for j in range(len(a)): for u in range(len(a)): if j != u: adr[int(a[j]) - 1, int(a[u]) - 1] += 1 adr[int(a[u]) - 1, int(a[j]) - 1] += 1 # 将 adr 中大于 1 的元素设置为 1 adr[adr > 1] = 1 # 初始化 y = {} z = 0 for i in range(1, np.max(bus_line_partition) + 1): wu = np.where(adr[:, i - 1] == 1)[0] bus_line_partition_id = [] route_sequence = [] for index, row in enumerate(bus_line_partition): if i in row: bus_line_partition_id.append(index) route_sequence.append(bus_line_sequence[index, :]) route_sequence = np.array(route_sequence) # 初始化一个与 route_velocity 形状相同的新数组 bus_line_route_velocity = np.empty_like(route_sequence) # 遍历 route_velocity 中的每个值 for ii, row in enumerate(route_sequence): for j, value in enumerate(row): # 找到 road_velocity 第一列中与 value 相等的行索引 matching_row_index = np.where(road_velocity[:, 0] == value)[0] # 如果找到匹配行，将 road_velocity 第二列中对应行的值存储到新数组中 if matching_row_index.size != 0: bus_line_route_velocity[ii, j] = road_velocity[matching_row_index, 1] else: bus_line_route_velocity[ii, j] = 0 bus_line_partition_id = bus_line_partition[bus_line_partition_id] unique_matching_rows=[] for index, row in enumerate(bus_line_partition_id): unique_row = np.array([x for i, x in enumerate(row) if x not in row[:i]]) unique_matching_rows.append(unique_row) bus_region_direction = [np.array([x for x in array if x in wu]) for array in unique_matching_rows] max_length = max(len(item) for item in bus_region_direction) # 使用0填充长度不足的部分，并将数组转换为二维数组 bus_region_direction = np.array([np.pad(item, (0, max_length - len(item)), 'constant', constant_values=0) for item in bus_region_direction]) sorted_bus_region_direction = np.sort(bus_region_direction, axis=1) unique_sorted_bus_region_direction = np.unique(sorted_bus_region_direction, axis=0) same_direction_fitness= {} for aa, row in enumerate(unique_sorted_bus_region_direction): matching_row_indices = np.where(np.all(sorted_bus_region_direction == row, axis=1))[0] same_direction_bus_line_route_velocity = bus_line_route_velocity[matching_row_indices] same_direction_bus_line_partition_id = bus_line_partition_id[matching_row_indices] line_proportion =same_direction_bus_line_route_velocity.shape[0]/bus_line_route_velocity.shape[0] matching_indices = np.where(same_direction_bus_line_partition_id == i) matching_values = same_direction_bus_line_route_velocity[matching_indices] # 计算方差 variance = np.var(matching_values) #道路数量 route_quantity = np.count_nonzero(yanlinks[:, 11] == i) same_direction_fitness[aa] = line_proportion*variance links_number = np.count_nonzero(yanlinks[:, 11] == i) - yanlinks.shape[0] / np.max(bus_line_partition) z += abs(links_number) y[i] = sum(same_direction_fitness.values()) # a = objective_func(G, partition, yanlinks) y = sum(y.values()) # y = np.sqrt(a**2 + y**2 + z**2) else: y = 1000000000000000 return y # aa = liziqunfun(array) # print(aa)