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zoning_functions_v4_2.py
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zoning_functions_v4_2.py
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import pandas as pd
import numpy as np
import copy
import random as rd
from collections import defaultdict
import matplotlib.pyplot as plt
import time
###################################
def check_connection(area1,area2,region,neighbors):
unvisited = copy.deepcopy(region)
visited = []
queue = []
reach = 0
visited.append(area1)
unvisited.pop(unvisited.index(area1))
queue.append(area1)
while queue != []:
# print("visited: ", visited)
# print("unvisited: ", unvisited)
# print("queue: ", queue)
nei = intersection(neighbors[queue[0]],unvisited)
# print("node:", queue[0])
# print("Downstream: ", nei)
queue.pop(0)
for aaa in nei:
visited.append(aaa)
unvisited.pop(unvisited.index(aaa))
queue.append(aaa)
if aaa == area2:
# print(area1, "is able to reach", area2, "!!!")
reach = 1
if reach == 1:
break
return reach
###################################
def min_g_A(N, R_k, d, A):
g_min = np.inf
for i in N:
g = 0
for j in R_k:
g += d[A.index(i),A.index(j)]
if g < g_min:
g_min = g
min_g_i = i
# print(min_g_i)
return min_g_i
###################################
def AssignEnclaves(partition,d,A,neighbors):
parti = copy.deepcopy(partition)
A_a = []
for rrr in partition:
for aaa in rrr:
A_a.append(aaa)
e = []
for aaa in A:
if aaa not in A_a:
e.append(aaa)
e_ori = copy.deepcopy(e)
while e != []:
A_i = select(e,A_a,neighbors)
i = A.index(A_i)
ita = region_share_border_area(parti, A_i, neighbors)
R_k = min_g_R(parti, ita, A_i, d, A)
R_k_new = copy.deepcopy(R_k)
R_k_new.append(A_i)
parti.pop(parti.index(R_k))
parti.append(R_k_new)
A_a.append(A_i)
e.pop(e.index(A_i))
P_feasible = copy.deepcopy(parti)
P_feasible = list(np.sort(P_feasible))
return P_feasible, e_ori
###################################
def select(e,A_a,neighbors):
neib = set()
for aaa in A_a:
neib.update(set(neighbors[aaa]))
to_choose_from = list(neib.intersection(set(e)))
A_i = to_choose_from[rd.randrange(0, len(to_choose_from), 1)]
return A_i
###################################
def intersection(lst1, lst2):
lst3 = [value for value in lst1 if value in lst2]
return lst3
###################################
def min_g_R(parti, ita, A_i, d,A):
g_min = np.inf
for rrr in ita:
region = parti[rrr]
g = 0
for j in region:
if A.index(A_i) <= j:
g += d[A.index(A_i),A.index(j)]
else:
g += d[A.index(j),A.index(A_i)]
if g < g_min:
g_min = g
min_g_i = parti[rrr]
return min_g_i
###################################
def region_share_border_area(parti, area, neighbors):
region_i = []
nei = neighbors[area]
for i in range(len(parti)):
region = parti[i]
for aaa in region:
if aaa in nei:
region_i.append(i)
region_i = np.unique(region_i)
return region_i
###################################
def p_np_gen(P,A):
new_p = []
tot = len(A)
for li in P:
sub_p = [1 if i+1 in li else 0 for i in range(tot)]
new_p.append(sub_p)
p_np = np.array([np.array(l) for l in new_p])
return p_np
###################################
def neighbor_region(p_np, neighbors, area, region): #find the neignbor regions of an area
neighhbors_region = []
neighhbors_area = neighbors[area]
for i in neighhbors_area:
if i in region:
continue
else:
rrr = np.where(p_np[:,i-1] == 1)[0] ### generate p_np!!!
neighhbors_region.append(rrr)
neighhbors_region = list(np.unique(np.array(neighhbors_region)))
return neighhbors_region
###################################
def region_area_adj_matrix(p_np, P, neighbors,A): #find the neignbor regions of a region
region_area_adj = np.zeros((p_np.shape))
for region in P:
for area in region:
neighhbors_region_ = neighbor_region(p_np, neighbors, area, region)
for ii in range(len(neighhbors_region_)):
region_area_adj[neighhbors_region_[ii],area-1] = 1
return region_area_adj
###################################
def H(P,A,d):
H = 0
for region in P:
h = 0
for area_index_1 in range(len(region)):
for area_index_2 in range(area_index_1+1,len(region)):
if A.index(region[area_index_1])<A.index(region[area_index_2]):
h += d[A.index(region[area_index_1]), A.index(region[area_index_2])]
else:
h += d[A.index(region[area_index_2]), A.index(region[area_index_1])]
H += h
return H
###################################
def H_np(p_reg_ind,d):
H = sum(sum(np.multiply(p_reg_ind,d)))
return H
###################################
def p_reg_ind_gen(p,A):
p_reg_ind = np.zeros((len(A),len(A)))
for region in p:
for area_ind_1 in range(len(region)):
for area_ind_2 in range(area_ind_1+1,len(region)):
if region[area_ind_1]<region[area_ind_2]:
p_reg_ind[region[area_ind_1]-1][region[area_ind_2]-1] = 1
else:
p_reg_ind[region[area_ind_2]-1][region[area_ind_1]-1] = 1
return p_reg_ind
###################################
def H_per_region(p,A,d):
H_region = []
for region in p:
p_reg_ind_r = np.zeros((len(A),len(A)))
for area_ind_1 in range(len(region)):
for area_ind_2 in range(area_ind_1+1,len(region)):
if region[area_ind_1]<region[area_ind_2]:
p_reg_ind_r[region[area_ind_1]-1][region[area_ind_2]-1] = 1
else:
p_reg_ind_r[region[area_ind_2]-1][region[area_ind_1]-1] = 1
H_r = sum(sum(np.multiply(p_reg_ind_r,d)))
H_region.append(H_r)
return H_region
################################################################################################################################
################################################################################################################################
################################################################################################################################
################################################################################################################################
################################################################################################################################
################################################################################################################################
###################################
def MOE_perc_region_VRT_multi(region,A,data,data_vrt,count_data,z):
m = dict()
MOE = dict()
MOE_perc = dict()
for iii in data:
data_mean_ = data[iii]
data_vrt_ = data_vrt[iii]
count_data_ = count_data[iii]
vr_data_sum = np.zeros((1,len(data_vrt_[0])))
m_ = 0
for i in region:
ind_i = A.index(i)
vr_data_sum = vr_data_sum + data_vrt_[ind_i]
m_ += data_mean_[ind_i]
if count_data_ == 0:
m_ = m_/len(region)
MOE_ = z*np.std(vr_data_sum)
MOE_perc_ = MOE_/m_
m[iii] = m_
MOE[iii] = MOE_
MOE_perc[iii] = MOE_perc_
return m, MOE, MOE_perc
###################################
def GrowRegions_vr_multi(A,neighbors,data,d,data_vrt,data_moe,threshold,tighter_param,z,count_data, PRINT):
tighter_threshold = threshold * tighter_param
if PRINT == 1:
print("Tighter threshold:",threshold,"*",tighter_param,"=",tighter_threshold)
# Grow regions from initial seeds
# such that the value of attribute l in each region is above threshold.
for_enclave_partitions = []
e = []
A_u = copy.deepcopy(A)
A_a = []
while A_u != []:
k = rd.randrange(0, len(A_u), 1)
A_k = A_u[k]
A_u.pop(k)
A_a.append(A_k)
ind_k = A.index(A_k)
m = dict() ########
MOE = dict() ########
MOE_perc = dict() ########
for iii in data:
m[iii] = data[iii][ind_k]
MOE[iii] = data_moe[iii][ind_k]
MOE_perc[iii] = MOE[iii]/m[iii]
threshold_hold = 1
for iii in MOE_perc:
if MOE_perc[iii] > tighter_threshold:
threshold_hold = 0
if threshold_hold == 1:
R_k = [A_k]
for_enclave_partitions.append(R_k)
if threshold_hold == 0:
R_k = [A_k]
if PRINT == 1:
print("Seed area: ", R_k)
N = copy.deepcopy(neighbors[A_k])
for aaa in A_a:
if aaa in N:
N.pop(N.index(aaa))
feasible = 1
while threshold_hold == 0:
if N!= []:
i = min_g_A(N, R_k, d, A)
if PRINT == 1:
print("Zone added: ",i)
R_k.append(i)
N.pop(N.index(i))
for aaa in neighbors[i]:
N.append(aaa)
for aaa in A_a:
if aaa in N:
N.pop(N.index(aaa))
N = list(np.unique(np.array(N)))
m, MOE, MOE_perc = MOE_perc_region_VRT_multi(R_k,A,data,data_vrt,count_data,z)
if PRINT == 1:
for iii in MOE_perc:
print("Data"+str(iii)+"MOE% = ",MOE_perc[iii]*100,"%")
if i not in A_u:
print('i not in A_u!!!')
print("i:",i)
print('A_u:',A_u)
A_u.pop(A_u.index(i))
A_a.append(i)
threshold_hold = 1
for iii in MOE_perc:
if MOE_perc[iii] > tighter_threshold:
threshold_hold = 0
if PRINT == 1:
print("Data"+str(iii)+"MOE%: NOT GOOD!")
break
if N == [] and threshold_hold == 0:
for aaa in R_k:
e.append(aaa)
feasible = 0
if PRINT == 1:
print("Infeasible!!!")
for aaa in R_k:
A_a.pop(A_a.index(aaa))
if PRINT == 1:
print("Unassigned:", A_u)
break
if feasible == 1:
R_k = list(np.sort(R_k))
for_enclave_partitions.append(R_k)
if PRINT == 1:
print("for_enclave_partitions:",for_enclave_partitions)
print("----------")
return for_enclave_partitions, e, A_a
###################################
def GrowRegions_enclaveassign_example_vr_multi(maxitr, A,neighbors,data,d,data_vrt,data_moe,threshold,tighter_param, count_data,z,PRINT):
feasi_partitions = []
for_enclave_partitions = []
maxP = 0
#construction phase
for i in range(1,maxitr):
if PRINT == 1:
print("--------------")
print("iteration ",i)
parti, e, A_a = GrowRegions_vr_multi(A,neighbors,data,d,data_vrt,data_moe,threshold,tighter_param,z, count_data, PRINT)
if PRINT == 1:
print("GrowRegion: ")
print("Partition: ",parti)
print("Enclave areas: ",e)
print("Assigned areas: ", A_a)
p = len(parti) #number of resgions in "parti"
if p > maxP:
for_enclave_partitions = [parti]
maxP = p
elif p == maxP:
for_enclave_partitions.append(parti)
elif p < maxP:
pass
for_enclave_partitions_uni = []
for i in for_enclave_partitions:
if i not in for_enclave_partitions_uni:
for_enclave_partitions_uni.append(i)
if PRINT == 1:
print("-------------------------------")
print("FINAL for Enclave Partitions: ")
for i in range(len(for_enclave_partitions_uni)):
print(for_enclave_partitions_uni[i])
for parti in for_enclave_partitions_uni:
P_feasi, e = AssignEnclaves(parti,d,A,neighbors) ######## func
feasi_partitions.append(P_feasi)
if PRINT == 1:
print(P_feasi)
print("Enclaves:", e)
feasi_partitions_uni = []
for i in feasi_partitions:
if i not in feasi_partitions_uni:
feasi_partitions_uni.append(i)
if PRINT == 1:
print("-------------------------------")
print("P_feasi after enclave assign: ")
for i in range(len(feasi_partitions)):
for ind_reg in range(len(feasi_partitions[i])):
feasi_partitions[i][ind_reg] = list(np.sort(np.array(feasi_partitions[i][ind_reg])))
if PRINT == 1:
print(feasi_partitions[i])
if PRINT == 1:
print("------------")
return feasi_partitions, for_enclave_partitions_uni, e
###################################
def check_feasibility_vr_multi(P,A,data_vrt,data,z,count_data,threshold):
for region in P:
m, MOE, MOE_perc = MOE_perc_region_VRT_multi(region,A,data,data_vrt,count_data,z)
threshold_hold = 1
for iii in MOE_perc:
if MOE_perc[iii] > threshold:
threshold_hold = 0
return threshold_hold
###################################
def neighbor_set_1swap_vr_multi(p,A,data_vrt,data,d,z,threshold,count_data,neighbors,PRINT):
neighbor_sol = []
neighbor_H = []
### generate the region_area_adj_matrix
p_np = p_np_gen(p,A)
r_a_adj = region_area_adj_matrix(p_np, p, neighbors,A)
p_reg_ind_ori = p_reg_ind_gen(p,A)
H_p = H_np(p_reg_ind_ori, d)
# print(p_reg_ind_ori)
H_region = H_per_region(p,A,d)
counttt = 0
switch_list = []
pair_weight_list = []
for reg_ind_1 in range(len(p)):
for reg_ind_2 in range(reg_ind_1+1,len(p)):
switch_list.append((reg_ind_1,reg_ind_2))
pair_weight = H_region[reg_ind_1] + H_region[reg_ind_2]
pair_weight_list.append(pair_weight)
new = [(i,j) for i,j in zip(switch_list, pair_weight_list)]
sorted_ = sorted(new, key = lambda x:x[1])
switch_list_sorted = [i[0] for i in sorted_]
pair_weight_list_sorted = [i[1] for i in sorted_]
for itr in range(len(switch_list_sorted)):
pair_H_accu_list = []
pair_H_accu = 0
for iii in range(len(switch_list_sorted)):
pair_H_accu += pair_weight_list_sorted[iii]
pair_H_accu_list.append(pair_H_accu)
k = rd.randrange(0, pair_H_accu_list[-1]+1, 1)
pair = switch_list_sorted[np.where((np.array(pair_H_accu_list) >= k) == True)[0][0]]
pop_ind = switch_list_sorted.index(pair)
switch_list_sorted.pop(pop_ind)
pair_weight_list_sorted.pop(pop_ind)
reg_ind_1 = pair[0]
reg_ind_2 = pair[1]
aaa = p_np[reg_ind_1,:]
bbb = r_a_adj[reg_ind_2,:]
ind_a = np.where(aaa == 1)[0]
ind_b = np.where(bbb == 1)[0]
border_areas_1_2 = intersection(ind_a,ind_b)
for aaa in border_areas_1_2:
region_1_copy = copy.deepcopy(p[reg_ind_1])
region_2_copy = copy.deepcopy(p[reg_ind_2])
region_1_copy.pop(region_1_copy.index(aaa+1))
region_2_copy.append(aaa+1)
### 连续性check
reach_1 = 1
for iii in range(1,len(region_1_copy)):
if check_connection(region_1_copy[0],region_1_copy[iii],region_1_copy,neighbors) == 1:
pass
else:
reach_1 = 0
if PRINT == 1:
print('region not connected: ',region_1_copy)
break
reach_2 = 1
for iii in range(1,len(region_2_copy)):
if check_connection(region_2_copy[0],region_2_copy[iii],region_2_copy,neighbors) == 1:
pass
else:
reach_2 = 0
if PRINT == 1:
print('region not connected: ',region_2_copy)
break
if reach_1 == 1 and reach_2 == 1:
new_nei = []
for reg in p:
if reg == p[reg_ind_1] or reg == p[reg_ind_2]:
pass
else:
new_nei.append(reg)
new_nei.append(region_1_copy)
new_nei.append(region_2_copy)
# print(new_nei)
### feasibility check
feasi = check_feasibility_vr_multi(new_nei,A,data_vrt,data,z,count_data,threshold)
if feasi == 1:
neighbor_sol.append(new_nei)
## p_reg_ind_ori --> new_nei_reg_ind
new_nei_reg_ind = copy.deepcopy(p_reg_ind_ori)
for area in p[reg_ind_1]:
if area<(aaa+1):
new_nei_reg_ind[area-1][aaa] = 0
else:
new_nei_reg_ind[aaa][area-1] = 0
for area in p[reg_ind_2]:
if area<(aaa+1):
new_nei_reg_ind[area-1][aaa] = 1
else:
new_nei_reg_ind[aaa][area-1] = 1
neighbor_H.append(H_np(new_nei_reg_ind, d))
if H_np(new_nei_reg_ind, d) < H_p:
# print("stopped",H_np(new_nei_reg_ind, d),H_p)
return [new_nei],[H_np(new_nei_reg_ind, d)]
# print(new_nei_reg_ind)
# print(H_np(new_nei_reg_ind, d))
if PRINT == 1:
print('Feasible!! Appended.')
aaa = p_np[reg_ind_2,:]
bbb = r_a_adj[reg_ind_1,:]
ind_a = np.where(aaa == 1)[0]
ind_b = np.where(bbb == 1)[0]
border_areas_2_1 = intersection(ind_a,ind_b)
for aaa in border_areas_2_1:
region_2_copy = copy.deepcopy(p[reg_ind_2])
region_1_copy = copy.deepcopy(p[reg_ind_1])
region_2_copy.pop(region_2_copy.index(aaa+1))
region_1_copy.append(aaa+1)
### 连续性check
reach_1 = 1
for iii in range(1,len(region_1_copy)):
if check_connection(region_1_copy[0],region_1_copy[iii],region_1_copy,neighbors) == 1:
pass
else:
reach_1 = 0
if PRINT == 1:
print('region not connected: ',region_1_copy)
break
reach_2 = 1
for iii in range(1,len(region_2_copy)):
if check_connection(region_2_copy[0],region_2_copy[iii],region_2_copy,neighbors) == 1:
pass
else:
reach_2 = 0
if PRINT == 1:
print('region not connected: ',region_2_copy)
break
if reach_1 == 1 and reach_2 == 1:
new_nei = []
for reg in p:
if reg == p[reg_ind_1] or reg == p[reg_ind_2]:
pass
else:
new_nei.append(reg)
new_nei.append(region_1_copy)
new_nei.append(region_2_copy)
# print(new_nei)
### feasibility check
feasi = check_feasibility_vr_multi(new_nei,A,data_vrt,data,z,count_data,threshold)
if feasi == 1:
neighbor_sol.append(new_nei)
## p_reg_ind_ori --> new_nei_reg_ind
new_nei_reg_ind = copy.deepcopy(p_reg_ind_ori)
for area in p[reg_ind_2]:
if area<(aaa+1):
new_nei_reg_ind[area-1][aaa] = 0
else:
new_nei_reg_ind[aaa][area-1] = 0
for area in p[reg_ind_1]:
if area<(aaa+1):
new_nei_reg_ind[area-1][aaa] = 1
else:
new_nei_reg_ind[aaa][area-1] = 1
neighbor_H.append(H_np(new_nei_reg_ind, d))
if H_np(new_nei_reg_ind, d) < H_p:
# print("stopped",H_np(new_nei_reg_ind, d),H_p)
return [new_nei],[H_np(new_nei_reg_ind, d)]
# print(new_nei_reg_ind)
# print(H_np(new_nei_reg_ind, d))
if PRINT == 1:
print('Feasible!! Appended.')
### rank them based on H (small -> large)
neighbor_new = [(i,j) for i,j in zip(neighbor_sol, neighbor_H)]
neighbor_sorted = sorted(neighbor_new, key = lambda x:x[1])
neighbor_sol_sorted = [i[0] for i in neighbor_sorted]
neighbor_H_sorted = [i[1] for i in neighbor_sorted]
for row_sol_ind in range(len(neighbor_sol_sorted)):
row_sol = neighbor_sol_sorted[row_sol_ind]
row_avg = [np.mean(i) for i in row_sol]
row_sol_new = [(i,j) for i,j in zip(row_sol, row_avg)]
row_sorted = sorted(row_sol_new, key=lambda x:x[1])
row_sol_sorted = [i[0] for i in row_sorted]
neighbor_sol_sorted[row_sol_ind] = row_sol_sorted
if PRINT == 1:
print("Sorted neighbors and H:")
for i in range(len(neighbor_sol_sorted)):
# print(neighbor_sol_sorted[i])
print(neighbor_H_sorted[i])
# print("H computing of the neighbors finished!")
return neighbor_sol_sorted, neighbor_H_sorted
###################################
def Tabu_search_vr_multi(Tabu_len, max_iter, feasi_partitions, A, data, d, data_vrt, neighbors, SWAP, z, threshold, count_data, PRINT):
p_best_overall = feasi_partitions[0]
plot_iter = []
plot_time = []
plot_time_cumu = []
plot_H = []
try:
for ppp in range(len(feasi_partitions)):
p = feasi_partitions[ppp]
print(" ")
print("Start from the Partition ",ppp," from GrowRegion")
print("$$$$$$$$$$$$$$$$$")
best_p_record = []
best_p_x_axis = []
Tabu_list = []
Tabu_iter = []
p_current = p
p_best = p
p_best_reg_ind = p_reg_ind_gen(p_best,A)
H_p_best = H_np(p_best_reg_ind,d)
if PRINT == 1:
print(p_best, A, d)
print("H_best: ",H_p_best)
Tabu_list.append(p_current)
Tabu_iter.append(1)
for iiii in range(max_iter):
start_iiii = time.time()
if SWAP == 1:
neighbor_sol_sorted, neighbor_H_sorted = neighbor_set_1swap_vr_multi(p_current,A,data_vrt,data,d,z,threshold,count_data,neighbors,PRINT)
else:
neighbor_sol_sorted, neighbor_H_sorted = neighbor_set_2swap_vr(p_current,A,data_vrt,data,z,threshold,count_data,neighbors,PRINT)
for nei_ind in range(len(neighbor_sol_sorted)):
nei_current = neighbor_sol_sorted[nei_ind] ## pick the best one from the neighborhood
if PRINT == 1:
print("H of neighbor: ", neighbor_H_sorted[nei_ind], "H of BEST: ", H_p_best, p_best)
### move to the top 1
if nei_current not in Tabu_list:
if neighbor_H_sorted[nei_ind] < H_p_best:
# move to it
if PRINT == 1:
print("Move to the neighbor", nei_ind, ": ", nei_current, "(Better sol, not in Tabu)")
p_current = nei_current
p_best = nei_current
p_best_reg_ind = p_reg_ind_gen(p_best,A)
H_p_best = H_np(p_best_reg_ind,d)
# add it to Tabulist & set Tabuiter = tabu_len
Tabu_list.append(nei_current)
Tabu_iter.append(1)
### check Tabu list iterations
Tabu_iter = np.array(Tabu_iter)
pop_index = list(np.where(Tabu_iter == Tabu_len)[0])
Tabu_list = np.delete(Tabu_list, pop_index, axis=0)
Tabu_iter = np.delete(Tabu_iter, pop_index, axis=0)
Tabu_list = list(Tabu_list)
for i in range(len(Tabu_list)):
Tabu_list[i] = list(Tabu_list[i])
Tabu_iter = list(Tabu_iter)
for iii in range(len(Tabu_iter)):
Tabu_iter[iii] = Tabu_iter[iii]+1
break
else:
# move to it
if PRINT == 1:
print("Move to the neighbor", nei_ind, ": ", nei_current, "(Worsen allowed, not in Tabu)")
p_current = nei_current
# add it to Tabulist
Tabu_list.append(nei_current)
Tabu_iter.append(1)
### check Tabu list iterations
Tabu_iter = np.array(Tabu_iter)
pop_index = list(np.where(Tabu_iter == Tabu_len)[0])
Tabu_list = np.delete(Tabu_list, pop_index, axis=0)
Tabu_iter = np.delete(Tabu_iter, pop_index, axis=0)
Tabu_list = list(Tabu_list)
for i in range(len(Tabu_list)):
Tabu_list[i] = list(Tabu_list[i])
Tabu_iter = list(Tabu_iter)
for iii in range(len(Tabu_iter)):
Tabu_iter[iii] = Tabu_iter[iii]+1
break
else: # if neighbor in Tabu list
if neighbor_H_sorted[nei_ind] < H_p_best:
# move to it
if PRINT == 1:
print("Move to the neighbor", nei_ind, ": ", nei_current, "(Better sol, in Tabu)")
p_current = nei_current
p_best = nei_current
p_best_reg_ind = p_reg_ind_gen(p_best,A)
H_p_best = H_np(p_best_reg_ind,d)
# remove from Tabulist
pop_index = Tabu_list.index(nei_current)
Tabu_list = np.delete(Tabu_list, pop_index, axis=0)
Tabu_iter = np.delete(Tabu_iter, pop_index, axis=0)
Tabu_list = list(Tabu_list)
for i in range(len(Tabu_list)):
Tabu_list[i] = list(Tabu_list[i])
Tabu_iter = list(Tabu_iter)
# add it to Tabulist & set Tabuiter = tabu_len
Tabu_list.append(nei_current)
Tabu_iter.append(1)
### check Tabu list iterations
Tabu_iter = np.array(Tabu_iter)
pop_index = list(np.where(Tabu_iter == Tabu_len)[0])
Tabu_list = np.delete(Tabu_list, pop_index, axis=0)
Tabu_iter = np.delete(Tabu_iter, pop_index, axis=0)
Tabu_list = list(Tabu_list)
for i in range(len(Tabu_list)):
Tabu_list[i] = list(Tabu_list[i])
Tabu_iter = list(Tabu_iter)
for iii in range(len(Tabu_iter)):
Tabu_iter[iii] = Tabu_iter[iii]+1
Tabu_iter = list(Tabu_iter)
break
else:
# NOT move to it
if PRINT == 1:
print("NOT MOVING to the neighbor", nei_ind, ": ", nei_current, "(Worsen, in Tabu)")
### check Tabu list iterations
Tabu_iter = np.array(Tabu_iter)
pop_index = list(np.where(Tabu_iter == Tabu_len)[0])
Tabu_list = np.delete(Tabu_list, pop_index, axis=0)
Tabu_iter = np.delete(Tabu_iter, pop_index, axis=0)
Tabu_list = list(Tabu_list)
for i in range(len(Tabu_list)):
Tabu_list[i] = list(Tabu_list[i])
Tabu_iter = list(Tabu_iter)
for iii in range(len(Tabu_iter)):
Tabu_iter[iii] = Tabu_iter[iii]+1
best_p_record.append(H_p_best)
best_p_x_axis.append(iiii)
end_iiii = time.time()
time_iiii = end_iiii - start_iiii
plot_iter.append(iiii)
plot_H.append(H_p_best)
plot_time.append(time_iiii)
plot_time_cumu.append(sum(plot_time))
if iiii%100 == 0 and iiii!=0:
print("Iteration",iiii)
print("Current best: ", H_p_best)
plt.plot(plot_iter, plot_time)
plt.title("Computation Time of Each Iteration")
plt.xlabel("Iteration")
plt.ylabel("Seconds")
plt.savefig("time_fig/" + str(iiii) + "iter_time.pdf")
plt.show()
plt.plot(plot_iter, plot_H)
plt.title("Heterogeneity of Each Iteration")
plt.xlabel("Iteration")
plt.savefig("time_fig/" + str(iiii) + "iter_H.pdf")
plt.show()
plt.plot(plot_time_cumu, plot_H)
plt.title("Heterogeneity Change with Computation Time")
plt.xlabel("Cumulative Computation Time (Seconds)")
plt.savefig("time_fig/" + str(iiii) + "time_H.pdf")
plt.show()
p_best_overall_reg_ind = p_reg_ind_gen(p_best_overall,A)
H_p_best_overall = H_np(p_best_overall_reg_ind,d)
if H_p_best < H_p_best_overall:
p_best_overall = p_best
return p_best_overall
except:
print("Keyboard interrupted")
print("Current best overall: ", H(p_best_overall, A, d))
print("Current best of the starting point: ", H_p_best)
if H_p_best < H(p_best_overall, A, d):
p_best_overall = p_best
plt.plot(plot_iter, plot_time)
plt.title("Computation Time of Each Iteration")
plt.xlabel("Iteration")
plt.ylabel("Seconds")
plt.savefig("time_fig/" + str(iiii) + "iter_time.pdf")
plt.show()
plt.plot(plot_iter, plot_H)
plt.title("Heterogeneity of Each Iteration")
plt.xlabel("Iteration")
plt.savefig("time_fig/" + str(iiii) + "iter_H.pdf")
plt.show()
plt.plot(plot_time_cumu, plot_H)
plt.title("Heterogeneity Change with Computation Time")
plt.xlabel("Cumulative Computation Time (Seconds)")
plt.savefig("time_fig/" + str(iiii) + "time_H.pdf")
plt.show()
return p_best_overall