613 lines
24 KiB
Python
613 lines
24 KiB
Python
# def partition_diversity(partition):
|
||
import numpy as np
|
||
import pandas as pd
|
||
from Code import Code
|
||
from variancediy import variancediy
|
||
from TOPSIS import TOPSIS
|
||
from adregionf import adregionf
|
||
from select import select
|
||
from crossnew import crossnew
|
||
from mutationnew import mutationnew
|
||
from hui_fun import liziqunfun
|
||
from hui_xfun import xfun
|
||
import os
|
||
import time
|
||
from three_dimensional_TOPSIS import three_dimensional_TOPSIS
|
||
|
||
|
||
bus_info = pd.DataFrame()
|
||
file_range = range(211, 236)
|
||
file_directory = "E:\\分方向的mfd的数据\\bus_info\\03_pcross"
|
||
for file_number in file_range:
|
||
file_name = f"20190903_{file_number:03d}.csv" # 根据文件名模式构造文件名
|
||
file_path = os.path.join(file_directory, file_name) # 构造完整的文件路径
|
||
temp_dataframe = pd.read_csv(file_path) # 使用 pandas 读取 CSV 文件
|
||
bus_info = pd.concat([bus_info, temp_dataframe], ignore_index=True) # 将 temp_dataframe 追加到 merged_dataframe 中
|
||
links = pd.read_csv('links.csv')
|
||
# 计算第16列相同值的第7列的平均值
|
||
average_values = bus_info.groupby(bus_info.columns[9])[bus_info.columns[5]].mean()
|
||
# 在links_osm的第六列找到average_values，将平均值放在该值所在行第五列的位置
|
||
for index, value in average_values.items():
|
||
links.loc[links[links.columns[5]] == index, links.columns[4]] = value
|
||
# links = links[links[links.columns[5]].isin(average_values.index)]
|
||
# 将结果保存到新的CSV文件
|
||
links.to_csv('links_processed.csv', index=False)
|
||
|
||
|
||
|
||
|
||
# final_partitions是最终分区的数量 Initial_partitions是初始分区的数量
|
||
final_partitions = 8
|
||
Initial_partitions = final_partitions * 5
|
||
|
||
# links = pd.read_csv('links.csv')
|
||
links = links.to_numpy()
|
||
|
||
df = pd.read_excel('stationidwan.xls')
|
||
stationid = df.to_numpy()
|
||
# for chuu in range(1, 17):
|
||
# for dic in range(1):
|
||
# tic()
|
||
# chu = 10
|
||
# zhong = 2
|
||
|
||
|
||
# 给道路起点和终点标注序列，eg从1到500，
|
||
# 因为一个路口可以是好几个道路的起点或终点，所以同一路口就会有同样的标记
|
||
node = np.concatenate((links[:, :2], links[:, 2:4]), axis=0) # np.concatenate 函数会将这两个子数组沿着轴 0 连接起来;
|
||
# axis 是指在数组操作时沿着哪个轴进行操作。当axis=0时，表示在第一个维度上进行拼接操作。这里就是纵轴
|
||
|
||
# 这里是给道路起点和终点标注序列，也就是路口表注序列，因为一个路口可以是好几个道路的起点或终点，所以同一路口就会有同样的标记
|
||
noi = 1
|
||
node = np.hstack((node, np.zeros((len(node), 1))))
|
||
print(node.shape[0])
|
||
for i in range(node.shape[0]): # node.shape[0] 是指 node 数组的第一维大小，即 node 数组的行数
|
||
print(i)
|
||
# node[:i, 0] 表示从 node 数组的第一行到第 i-1 行的所有行的第一列构成的数组
|
||
# np.where() 函数返回一个包含下标的元组，后面的[0]就代表返回第一个元素的下标
|
||
a = np.where(node[:i, 0] == node[i, 0])[0]
|
||
b = np.where(node[:i, 1] == node[i, 1])[0]
|
||
c = np.intersect1d(a, b) # intersect1d 返回两个数组的交集
|
||
if c.size > 0:
|
||
x = c.shape[0]
|
||
y = 1
|
||
else:
|
||
x, y = 0, 1
|
||
# 在 node 数组的最后添加一列全为0的列，并将添加后的新数组重新赋值给 node
|
||
if x > 0 and y > 0:
|
||
node[i, 2] = node[min(c), 2] # 如果c是矩阵，则min(A)是包含每一列的最小值的行向量
|
||
else:
|
||
node[i, 2] = noi
|
||
noi += 1
|
||
node = np.concatenate((node[:int(len(node) / 2), 2].reshape(-1, 1), node[int(len(node) / 2):, 2].reshape(-1, 1)),
|
||
axis=1)
|
||
|
||
np.save('node.npy', node)
|
||
|
||
|
||
|
||
|
||
# 这里的links多加了一行才能yanlinks，但这样yanlinks就不对了
|
||
links = np.hstack((links, np.zeros((len(links), 1))))
|
||
links = np.hstack((links, np.zeros((len(links), 1))))
|
||
links = np.hstack((links, np.zeros((len(links), 1))))
|
||
yanlinks = np.concatenate((node, links[:, [5, 6, 7, 4, 0, 1, 2, 3]], np.zeros((len(links), 4))), axis=1)
|
||
yanlinks[:, 4] = np.arange(1, len(yanlinks) + 1)
|
||
|
||
road = np.arange(1, node.shape[0] + 1)
|
||
adjacency = np.zeros((len(road), len(road)))
|
||
|
||
# 初始化分区
|
||
|
||
for i in range(len(road)):
|
||
temp1 = np.where(node[:, 0] == node[i, 0])[0] # 找出第一列每个数字在第一列出现的位置
|
||
temp2 = np.where(node[:, 1] == node[i, 0])[0] # 找出第一列每个数字在第二列出现的位置
|
||
temp3 = np.where(node[:, 0] == node[i, 1])[0] # 找出第二列每个数字在第一列出现的位置
|
||
temp4 = np.where(node[:, 1] == node[i, 1])[0] # 找出第二列每个数字在第二列出现的位置
|
||
temp = np.unique(np.intersect1d(np.arange(i + 1, node.shape[0]), np.concatenate((temp1, temp2, temp3, temp4))))
|
||
if len(temp) > 0:
|
||
adjacency[i, temp] = 1
|
||
adjacency[temp, i] = 1
|
||
row_sums = np.sum(adjacency, axis=1)
|
||
|
||
# 找到全零行的索引
|
||
zero_row_indices = np.where(row_sums == 0)[0]
|
||
from sklearn.cluster import KMeans
|
||
|
||
N = Initial_partitions # 设置聚类数目
|
||
|
||
# 利用 K-Means 算法对 yanlinks 矩阵的第 7 列和第 8 列（即经度和纬度）进行聚类，
|
||
# 将样本分成 N 类，idx是一个N x 2的矩阵，其中N是聚类数目。
|
||
# idx的每一行就是一个聚类中心，其中第一列是该中心的经度，第二列是该中心的纬度。
|
||
# 在计算每个点到聚类中心的距离时，就需要用到idx的值。
|
||
Cluster_Label, idx = KMeans(n_clusters=N).fit(yanlinks[:, [6, 7]]).labels_, KMeans(n_clusters=N).fit(
|
||
yanlinks[:, [6, 7]]).cluster_centers_
|
||
# df = pd.read_csv('idx.csv',header=None)
|
||
# idx = df.to_numpy()
|
||
# 计算每个点到聚类中心的距离
|
||
dis = 111000 * np.sqrt(
|
||
(yanlinks[:, 6] - idx[:, 0].reshape(N, 1)) ** 2 + (yanlinks[:, 7] - idx[:, 1].reshape(N, 1)) ** 2)
|
||
|
||
# 找到每个点最近的聚类中心，mm是最小值，nn是最小值在向量的索引
|
||
mm, nn = np.min(dis, axis=1, keepdims=True), np.argmin(dis, axis=1)
|
||
|
||
data = links[:, 4] # links第五行是路的长度
|
||
if data.size > 0:
|
||
m = data.shape[0]
|
||
n = 1
|
||
else:
|
||
m, n = 0, 1
|
||
|
||
pattern = np.zeros((m, n)) # zeros(m,n+1)返回由零组成的m×(n+1)数组
|
||
pattern[:, 0] = data # 前n列为data中的数据
|
||
pattern = np.hstack((pattern, np.zeros((len(pattern), 1))))
|
||
pattern[:, 1] = -1
|
||
center = np.zeros((N, n)) # 初始化聚类中心
|
||
pattern[:, :n] = data.reshape(-1, n)
|
||
center = np.hstack((center, np.zeros((len(center), 1))))
|
||
# 初始化聚类中心
|
||
for x in range(0, N):
|
||
|
||
center[x, 1] = nn[x]
|
||
center[x, 0] = data[int(center[x, 1])]
|
||
pattern[int(center[x, 1]), 1] = x
|
||
|
||
# 初始化距离和计数
|
||
distance = np.zeros(N)
|
||
num = np.zeros(N)
|
||
|
||
# 初始化新的聚类中心
|
||
new_center = np.zeros((N, n))
|
||
|
||
unassigned_links = 2
|
||
while unassigned_links > 1:
|
||
print(unassigned_links)
|
||
|
||
|
||
|
||
for x in range(0, N): # x表示当前聚类的编号
|
||
try:
|
||
|
||
selected_links = adjacency[pattern[:, 1] == x, :]
|
||
unassigned_roads = np.where(np.sum(selected_links, axis=0) > 0)[0]
|
||
selected_links = np.where(pattern[:, 1] > -1)[0]
|
||
unassigned_roads = np.setdiff1d(unassigned_roads, selected_links) # bound 是一个向量，表示与聚类 x 相关的未被分配到聚类中的道路的编号。
|
||
selected_links = np.where(pattern[:, 1] == x)[0] # 这里的yisou表示已经被分配到的道路编号
|
||
# yisou = np.array([1778], dtype=int)
|
||
# bound = np.array([1722, 1776, 1779, 1782])
|
||
bus = []
|
||
|
||
# for y=1:length(yisou)
|
||
for y in range(len(selected_links)):
|
||
visited_links = selected_links[y]
|
||
visited_links_indices = np.where(stationid[:, 5] == visited_links+1)[0]
|
||
for dengyuyisourow in visited_links_indices:
|
||
bus.append(stationid[dengyuyisourow, 6])
|
||
|
||
road_evaluation = np.zeros((len(unassigned_roads), 2))
|
||
for unassigned_road_index in range(len(unassigned_roads)):
|
||
|
||
if len(np.concatenate([bus, stationid[stationid[:, 5] == unassigned_roads[unassigned_road_index]+1, 6]])) > 1 and np.var((np.concatenate([bus,stationid[stationid[:,5]==unassigned_roads[unassigned_road_index]+1, 6]])),ddof=1) > 0:
|
||
selected_links_lengths_float = (pattern[selected_links , 0]).tolist()
|
||
unassigned_road_length_array = (pattern[unassigned_roads[unassigned_road_index] , 0])
|
||
unassigned_road_length_array = [unassigned_road_length_array]
|
||
abrr = selected_links_lengths_float+ unassigned_road_length_array
|
||
road_evaluation[unassigned_road_index, 0] = np.var(abrr, ddof=1) * \
|
||
np.var((np.concatenate((bus, stationid[stationid[:, 5] == unassigned_roads[unassigned_road_index]+1, 6]))), ddof=1)
|
||
|
||
else:
|
||
|
||
if np.var(bus, ddof=1) > 0: # if var(bus)>0%已分配道路的速度方差
|
||
selected_links_lengths_float = (pattern[selected_links , 0]).tolist() # 这里没问题！！！！
|
||
unassigned_road_length_array = (pattern[unassigned_roads[unassigned_road_index] , 0])
|
||
unassigned_road_length_array = [unassigned_road_length_array]
|
||
abrr = selected_links_lengths_float + unassigned_road_length_array
|
||
road_evaluation[unassigned_road_index, 0] = np.var(abrr, ddof=1) + np.var(bus, ddof=1)
|
||
else:
|
||
selected_links_lengths_float = (pattern[selected_links, 0]).tolist()
|
||
unassigned_road_length_array = (pattern[unassigned_roads[unassigned_road_index] , 0])
|
||
unassigned_road_length_array = [unassigned_road_length_array]
|
||
abrr = selected_links_lengths_float + unassigned_road_length_array
|
||
road_evaluation[unassigned_road_index, 0] = np.var(abrr,ddof=1)
|
||
aas = yanlinks[yanlinks[:, 4] == unassigned_roads[unassigned_road_index]+1, 6:8]
|
||
road_evaluation[unassigned_road_index, 1] = 111000 * np.sqrt(np.sum(
|
||
(yanlinks[yanlinks[:, 4] == unassigned_roads[unassigned_road_index]+1, 6:8] - idx[x , :]) ** 2))
|
||
if road_evaluation.shape[0] > 1:
|
||
m, n = TOPSIS(road_evaluation) # bestxuhao最优方案的序号，bestgoal最优得分
|
||
else:
|
||
n = 1
|
||
|
||
pattern[unassigned_roads[n - 1], 1] = x
|
||
except:
|
||
continue
|
||
unassigned_links = np.sum(pattern[:, 1] == -1)
|
||
# 因为我的pattern是从0到39的编号，所以要变成1到40
|
||
pattern[:, 1] = pattern[:, 1] + 1
|
||
|
||
np.save('pattern.npy', pattern)
|
||
# pattern = pd.read_excel('pattern.xls').to_numpy()
|
||
|
||
|
||
|
||
yanlinks[:, 3] = links[:, 9]
|
||
yanlinks[:, 10] = pattern[:, 1]
|
||
|
||
data_path = r''
|
||
df2 = pd.read_csv(data_path + 'links_processed.csv')
|
||
zero_rows = yanlinks[:, 10] == 0
|
||
# 获取已删除行的索引
|
||
deleted_rows_indices = np.where(zero_rows)[0]
|
||
|
||
# 从 links 中删除 deleted_rows_indices 中指定的行
|
||
df2 = df2.drop(deleted_rows_indices, errors='ignore')
|
||
|
||
df2.to_csv(data_path + 'links_test1.csv', index=False)
|
||
|
||
yanlinks = yanlinks[yanlinks[:, 10] != 0]
|
||
yanlinks = yanlinks[yanlinks[:, 10] != -1, :]
|
||
|
||
for i in range(len(stationid)):
|
||
try:
|
||
stationid[i, 7] = yanlinks[np.where(yanlinks[:, 4] == stationid[i, 5]), 10][0][0]
|
||
except:
|
||
stationid[i, 7] = -1
|
||
|
||
road = np.unique(np.concatenate((yanlinks[:, 1], yanlinks[:, 0]), axis=0))
|
||
adjacency = np.zeros((len(road), len(road)))
|
||
adregion = np.zeros((int(np.max(yanlinks[:, 4])), int(np.max(yanlinks[:, 4]))))
|
||
|
||
for i in range(len(yanlinks[:, 0])):
|
||
temp1 = np.where(node[:, 0] == node[i, 0])[0]
|
||
temp2 = np.where(node[:, 1] == node[i, 0])[0]
|
||
temp3 = np.where(node[:, 0] == node[i, 1])[0]
|
||
temp4 = np.where(node[:, 1] == node[i, 1])[0]
|
||
temp = np.unique(np.intersect1d(np.arange(i + 1, node.shape[0]), np.concatenate((temp1, temp2, temp3, temp4))))
|
||
if len(temp) > 0:
|
||
adregion[i, temp] = 1
|
||
adregion[temp, i] = 1
|
||
# adregion矩阵表示路段之间的邻接关系
|
||
np.save('adregion.npy', adregion)
|
||
# 给adregion矩阵乘上权重（道路的分组编号）
|
||
for i in range(len(yanlinks[:, 1])):
|
||
# print(adregion[:, int(yanlinks[i, 4])])
|
||
# print(int(yanlinks[i, 10]))
|
||
adregion[:, int(yanlinks[i, 4]) - 1] = adregion[:, int(yanlinks[i, 4]) - 1] * int(yanlinks[i, 10])
|
||
|
||
|
||
|
||
|
||
|
||
|
||
subregion_adj = np.zeros((Initial_partitions, Initial_partitions))
|
||
|
||
# 计算adregion中的每个元素出现的频率(判断是强相关还是弱相关）
|
||
|
||
for i in range(len(adregion[:, 1])):
|
||
|
||
a = adregion[i, :]
|
||
a = np.unique(a)
|
||
a = a[a != 0]
|
||
|
||
if a.size > 0:
|
||
x = 1
|
||
y = a.shape[0]
|
||
else:
|
||
x, y = 0, 1
|
||
if y > 1:
|
||
for j in range(len(a)):
|
||
for u in range(len(a)):
|
||
if j != u:
|
||
# subregion_adj表示子区域的邻接关系，其中数值的大小表示区域之间的相关程度
|
||
subregion_adj[int(a[j])-1, int(a[u])-1] += 1
|
||
subregion_adj[int(a[u])-1, int(a[j])-1] += 1
|
||
|
||
|
||
|
||
|
||
# 计算后存到directed_adjacency_matrix里
|
||
directed_adjacency_matrix = subregion_adj.copy()
|
||
# 对于子区域相关程度处于弱相关的邻接关系进行忽略
|
||
min_value = np.min(np.max(subregion_adj, axis=0)) - 2
|
||
subregion_adj[subregion_adj < min_value] = 0
|
||
subregion_adj[subregion_adj > 1] = 1
|
||
directed_adjacency_matrix[directed_adjacency_matrix > 1] = 1
|
||
|
||
|
||
np.save('adr.npy', subregion_adj)
|
||
np.save('dadr.npy', directed_adjacency_matrix)
|
||
np.save('yanlinks.npy', yanlinks)
|
||
df = pd.DataFrame(yanlinks)
|
||
df.to_csv('yanlinks_initial_partition.csv', index=False, header=False)
|
||
|
||
####################################################################################################################
|
||
|
||
start = time.time()
|
||
# 组合优化
|
||
print('step2 组合优化'.encode('utf-8').decode('utf-8'))
|
||
maxgen = 10 # 进化代数
|
||
sizepop =2 # 种群规模
|
||
|
||
|
||
|
||
pcross = 0.8 # 交叉概率
|
||
pmutation = 0.8 # 变异概率
|
||
lenchrom = [1] * Initial_partitions # 变量字串长度
|
||
unassigned_roads = np.tile([[1, final_partitions]], (Initial_partitions, 1))
|
||
|
||
|
||
individuals = {'fitness': np.zeros((sizepop, 1)), 'chrom': []}
|
||
avgfitness = [] # 种群平均适应度
|
||
bestfitness = [] # 种群最佳适应度
|
||
bestchrom = None # 种群最佳染色体
|
||
for i in range(sizepop):
|
||
individuals['chrom'].append([Code(lenchrom, unassigned_roads,final_partitions)]) # 给第一个个体随机产生染色体
|
||
print('初始化结束'.encode('utf-8').decode('utf-8'))
|
||
repeated_row1= np.array([[0]])
|
||
inff = np.repeat([repeated_row1], sizepop, axis=0)
|
||
inff_np = np.array(inff, dtype=np.int64)
|
||
|
||
chromosome_results = np.zeros((sizepop, 3))
|
||
|
||
for j in range(sizepop):
|
||
chromosome_results[j,:] = liziqunfun(individuals['chrom'][j],j)
|
||
inff_np = three_dimensional_TOPSIS(chromosome_results,None)
|
||
|
||
inff_np[inff_np == 0] = 1
|
||
# inff_np[j]
|
||
print('初始化的适应度算完了'.encode('utf-8').decode('utf-8'))
|
||
# isnan函数是判断输入是否为NaN（not a number）的函数，而inff是表示正无穷大的特殊值。
|
||
# 在这段代码中，如果inff是无限值，则将其值设为1000000
|
||
inff[np.isnan(inff_np)] = 1000000
|
||
|
||
|
||
for j in range(sizepop):
|
||
individuals['fitness'][j] = inff_np[j]
|
||
|
||
# 找最好的染色体
|
||
bestfitness = np.min(individuals['fitness'])
|
||
bestindex = np.argmin(individuals['fitness'])
|
||
|
||
bestchrom = individuals['chrom'][bestindex]
|
||
avgfitness = np.sum(individuals['fitness']) / sizepop
|
||
|
||
individuals['chrom'] = np.array(individuals['chrom'])
|
||
|
||
# 记录最好适应度和平均适应度
|
||
trace = {'avgfitness': [], 'bestfitness': []}
|
||
historychorm = individuals['chrom'].reshape(sizepop,-1)
|
||
historyfitness = individuals['fitness'].reshape(-1,1)
|
||
bighistorychorm = individuals['chrom']
|
||
bighistoryfitness = np.hstack([individuals['fitness'].reshape((-1, 1)), np.zeros((sizepop, 1))])
|
||
|
||
|
||
chromosome_results1 = np.zeros((sizepop, 3))
|
||
for i in range(1, maxgen + 1):
|
||
print("Generation:", i)
|
||
# Selection operation
|
||
individuals = select(individuals, sizepop)
|
||
avgfitness = np.sum(individuals['fitness']) / sizepop
|
||
# Crossover operation
|
||
individuals['chrom'] = crossnew(pcross, lenchrom, individuals['chrom'], sizepop, unassigned_roads,final_partitions)
|
||
|
||
# Mutation operation
|
||
individuals['chrom'] = mutationnew(pmutation, lenchrom, individuals['chrom'], sizepop, unassigned_roads,final_partitions)
|
||
|
||
|
||
inff = np.zeros(sizepop)
|
||
for j in range(sizepop):
|
||
if len(np.where(np.sum(np.abs(historychorm - individuals['chrom'][j, :]), axis=1) == 0)[0]) < 1:
|
||
chromosome_results1[j, :] = liziqunfun(individuals['chrom'][j, :],j)
|
||
|
||
|
||
else:
|
||
a = np.where(np.sum(np.abs(historychorm - individuals['chrom'][j, :]), axis=1) == 0)[0]
|
||
chromosome_results1[j, :] = historyfitness[a[0], 0]
|
||
inff = three_dimensional_TOPSIS(chromosome_results, chromosome_results1)
|
||
|
||
inff[inff == 0] = 1
|
||
inff[np.isnan(inff)] = 1000000
|
||
result = np.vstack((chromosome_results, chromosome_results1))
|
||
chromosome_results = np.amin(result, axis=0)
|
||
|
||
for j in range(sizepop):
|
||
individuals['fitness'][j] = inff[j]
|
||
|
||
# Find the best and worst fitness chromosomes and their positions in the population
|
||
newbestfitness, newbestindex = np.min(individuals['fitness']), np.argmin(individuals['fitness'])
|
||
worstfitness, worstindex = np.max(individuals['fitness']), np.argmax(individuals['fitness'])
|
||
|
||
# Replace the best chromosome from the previous generation
|
||
if bestfitness > newbestfitness:
|
||
bestfitness = newbestfitness
|
||
bestchrom = individuals['chrom'][newbestindex]
|
||
|
||
if isinstance(bestchrom, list):
|
||
bestchrom = bestchrom[0]
|
||
# Replace the worst chromosome with the best chromosome
|
||
individuals['chrom'][worstindex] = bestchrom
|
||
individuals['fitness'][worstindex] = bestfitness
|
||
|
||
avgfitness = np.sum(individuals['fitness']) / sizepop
|
||
|
||
# Add the chromosomes and their fitness values to the history if they are not already in it
|
||
for h in range(sizepop):
|
||
if len(np.where(np.sum(np.abs(historychorm - individuals['chrom'][h, :]), axis=1) == 0)[0]) < 1:
|
||
historychorm = np.vstack((historychorm, individuals['chrom'][h, :]))
|
||
historyfitness = np.vstack((historyfitness, individuals['fitness'].reshape(-1,1)))
|
||
|
||
|
||
trace['avgfitness'].append(avgfitness)
|
||
trace['bestfitness'].append(bestfitness)
|
||
################################################################################################
|
||
# if isinstance(bestchrom, list):
|
||
#
|
||
# bestchrom = bestchrom[0]
|
||
#
|
||
# # 根据前面算法得到的最佳染色体 bestchrom，更新 yanlinks 中的第 12 列，
|
||
# # 然后根据 yanlinks 中记录的节点信息，建立邻接矩阵 adregion。
|
||
# # index 38 is out of bounds for axis 0 with size 38!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||
#
|
||
# for a in range(len(yanlinks)):
|
||
#
|
||
# yanlinks[a, 11] = bestchrom[int(yanlinks[a, 10])-1]
|
||
#
|
||
#
|
||
# np.save('yanlinks.npy',yanlinks)
|
||
# df = pd.DataFrame(yanlinks)
|
||
# df.to_csv('yanlinks_merge.csv', index=False, header=False)
|
||
################################################################################################
|
||
end = time.time()
|
||
|
||
|
||
|
||
start1 = time.time()
|
||
print('Step 3 边界调整')
|
||
if isinstance(bestchrom, list):
|
||
|
||
bestchrom = bestchrom[0]
|
||
|
||
# 根据前面算法得到的最佳染色体 bestchrom，更新 yanlinks 中的第 12 列，
|
||
# 然后根据 yanlinks 中记录的节点信息，建立邻接矩阵 adregion。
|
||
# index 38 is out of bounds for axis 0 with size 38!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||
|
||
for i in range(len(yanlinks)):
|
||
|
||
yanlinks[i, 11] = bestchrom[int(yanlinks[i, 10])-1]
|
||
|
||
|
||
np.save('yanlinks.npy',yanlinks)
|
||
df = pd.DataFrame(yanlinks)
|
||
df.to_csv('yanlinks_merge.csv', index=False, header=False)
|
||
adregion = np.zeros((len(yanlinks), len(yanlinks)))
|
||
|
||
for i in range(len(yanlinks)):
|
||
temp1 = np.where(node[:, 0] == node[i, 0])[0]
|
||
temp2 = np.where(node[:, 1] == node[i, 0])[0]
|
||
temp3 = np.where(node[:, 0] == node[i, 1])[0]
|
||
temp4 = np.where(node[:, 1] == node[i, 1])[0]
|
||
temp = np.unique(np.intersect1d(np.arange(i + 1, len(node)), np.concatenate((temp1, temp2, temp3, temp4))))
|
||
# 这里temp是个一维数组，所以不能直接访问
|
||
# if 5026 in temp:
|
||
# temp = temp[temp != 5026]
|
||
if temp.size > 0:
|
||
adregion[i, temp-1] = 1
|
||
adregion[temp-1, i] = 1
|
||
|
||
|
||
# adregionf主要作用是更新 adregion 矩阵，用于记录节点之间的连接关系,通过这样的操作，
|
||
# 可以保证连接关系的权值与遗传算法的优化结果保持一致。
|
||
adregion = adregionf(yanlinks, node)
|
||
behindfun = 0
|
||
solution = xfun(yanlinks,node)
|
||
frontfun=solution
|
||
noi = 1
|
||
while behindfun < frontfun:
|
||
solution = xfun(yanlinks,node)
|
||
frontfun = solution
|
||
x = frontfun
|
||
print(noi)
|
||
noi+=1
|
||
for i in range(len(yanlinks)):
|
||
if i==2:
|
||
break
|
||
ar = yanlinks[i, 4]-1
|
||
af = yanlinks[i, 11]
|
||
|
||
ad = adregion[int(ar), :]
|
||
|
||
ad[int(ar)] = af
|
||
a = np.unique(ad)
|
||
if len(a) > 2:
|
||
unique_elements, counts = np.unique(ad, return_counts=True)
|
||
# pandas和numpy不一样
|
||
unia = pd.DataFrame({'Value': unique_elements,'Count': counts,})
|
||
unia = unia[unia.iloc[:, 0] != 0]
|
||
unia = unia.values
|
||
if unia[unia[:, 0] == af, 1] == 2:
|
||
a = a[a != 0]
|
||
ti = []
|
||
for j in range(len(a)):
|
||
if a[j] == af:
|
||
tiao = x
|
||
else:
|
||
yanlinks[i, 11] = a[j]
|
||
solution =xfun(yanlinks,node)
|
||
tiao = solution
|
||
ti.append(tiao)
|
||
x, y = np.min(ti), np.argmin(ti)
|
||
yanlinks[i, 11] = a[y]
|
||
adregion = adregionf(yanlinks, node)
|
||
behindfun = x
|
||
df = pd.DataFrame(yanlinks)
|
||
df1 = pd.DataFrame(bestchrom)
|
||
df.to_csv('yanlinks_boundary_adjustment.csv', index=False, header=False)
|
||
df1.to_csv("bestchrom.csv", index=False, header=False)
|
||
# df.to_csv(f'yanlinks_boundary_adjustment_{partition}.csv', index=False, header=False)
|
||
# df.to_csv(f"bestchrom_{partition}.csv", index=False, header=False)
|
||
end1 = time.time()
|
||
# 使用numpy的unique函数获取第12列（索引为11）中所有的唯一值以及它们的索引
|
||
df = pd.DataFrame(yanlinks)
|
||
df1 = pd.DataFrame(bestchrom)
|
||
df.to_csv('yanlinks_boundary_adjustment.csv', index=False, header=False)
|
||
df1.to_csv("bestchrom.csv", index=False, header=False)
|
||
# df.to_csv(f'yanlinks_boundary_adjustment_{partition}.csv', index=False, header=False)
|
||
# df.to_csv(f"bestchrom_{partition}.csv", index=False, header=False)
|
||
# 使用numpy的unique函数获取第12列（索引为11）中所有的唯一值以及它们的索引
|
||
unique_values, unique_indices = np.unique(yanlinks[:, 11], return_index=True)
|
||
|
||
adregion_ = np.load('adregion.npy')
|
||
for i in range(len(yanlinks[:, 1])):
|
||
adregion_[:, int(yanlinks[i, 4]) - 1] = adregion_[:, int(yanlinks[i, 4]) - 1] * int(yanlinks[i, 11])
|
||
region_adj = np.zeros((final_partitions, final_partitions))
|
||
# region_adj不对！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！/*
|
||
for i in range(len(adregion_[:, 1])):
|
||
|
||
a = adregion_[i, :]
|
||
a = np.unique(a)
|
||
a = a[a != 0]
|
||
|
||
|
||
if a.size > 0:
|
||
x = 1
|
||
y = a.shape[0]
|
||
else:
|
||
x, y = 0, 1
|
||
if y > 1:
|
||
for j in range(len(a)):
|
||
for u in range(len(a)):
|
||
if j != u:
|
||
# subregion_adj表示子区域的邻接关系，其中数值的大小表示区域之间的相关程度
|
||
region_adj[int(a[j])-1, int(a[u])-1] = 1
|
||
region_adj[int(a[u])-1, int(a[j])-1] = 1
|
||
Asb = 0
|
||
for i in unique_values:
|
||
wu = np.where( region_adj[int(i) - 1, :] == 1)
|
||
smrjj_divide_smrjj_ = 0
|
||
for element in wu:
|
||
selected_values_list = [yanlinks[yanlinks[:, 11] == e + 1][:, 5] for e in element]
|
||
selected_values = np.concatenate(selected_values_list)
|
||
average = np.mean(selected_values)
|
||
variance = np.var(selected_values)
|
||
selected_values1 = yanlinks[yanlinks[:, 11] == i][:, 5]
|
||
average1 = np.mean(selected_values1)
|
||
variance1 = np.var(selected_values1)
|
||
|
||
smrjj = 2*variance1
|
||
smrjj_ = variance+variance1+(average-average1)**2
|
||
smrjj_divide_smrjj_one = smrjj/smrjj_
|
||
smrjj_divide_smrjj_ +=smrjj_divide_smrjj_one
|
||
num_elements = len(wu)
|
||
Asb_one = smrjj_divide_smrjj_/num_elements
|
||
Asb += Asb_one
|
||
|
||
Tvb = 0
|
||
for i in unique_values:
|
||
selected_values = yanlinks[yanlinks[:, 11] == i][:, 5]
|
||
variance = np.var(selected_values)
|
||
Tvb+=variance
|
||
|
||
# for i in unique_values:
|
||
|
||
print(f'最好的适应度是{bestfitness}')
|
||
print('合并分区的时间:', end - start, 'seconds')
|
||
print('边界调整的时间:', end1 - start1, 'seconds') |