def run_experiment(experiment_name,
network,
alpha,
edit_type,
n,
mcls,
temps,
cooling_schedules,
reps=10):
d_ns = []
d_costs = []
d_temps = []
d_alphas = []
d_ls = []
d_cs = []
d_init_temps = []
print("Run " + experiment_name + " for " + network.name + " with " +
str(reps) + " reps.")
for start_temp in temps:
for cooling_schedule in cooling_schedules:
for mcl in mcls:
for _ in range(reps):
ns, costs, temps, _ = SA(network).run(
start_temp,
alpha,
edit_type,
n,
mcl,
cooling_schedule=cooling_schedule)
d_ns += ns
d_costs += costs
d_temps += temps
d_alphas += [alpha] * len(ns)
d_ls += [mcl] * len(ns)
d_cs += [cooling_schedule] * len(ns)
d_init_temps += [start_temp] * len(ns)
print("L: " + str(mcl) + "\tT0: " + str(start_temp) +
"\tcooling_schedule: " + cooling_schedule +
"\tinitial cost: " + str(costs[0]) +
"\tfinal cost: " + str(costs[-1]))
dt = {
"n": d_ns,
"cost": d_costs,
"temp": d_temps,
"alpha": d_alphas,
"l": d_ls,
"cooling_schedule": d_cs,
"init_temp": d_init_temps
}
df = pd.DataFrame().from_dict(dt)
save_pickle(df, "data/" + network.name + "_" + experiment_name)
def eggCarton(M, N, K):
board = [[False] * M] * N # build and empty N*M carton
for k in range(K):
board[0][k] = True # start at fill K eggs in first row
state = Node(board, K) # build first state
Tmax, dT = 99, 1 # Tmax max temperature; dT: reduce temperature size
Ftarget = math.min(M, N)
while (True):
solution = SA(state, Tmax, Ftarget, dT)
Ftarget = math.max(Ftarget, solution)
def type_to_class(type_name):
"""
Converts automaton type name to automata object
:param type_name: automaton type name
:return: automaton object
"""
return {
"INFA": SA(),
"LFA": LFA(),
"INT": ST(),
"GBA": BA(),
}[type_name]
# import tensorflow as tf import numpy as np from sa import SA from common import * model = SA(shape=[48, 48, 1], no_classes=7) model.build_model() data = np.zeros((7, 100, 48, 48, 1)) val = np.ones((20, 48, 48, 1)) labels_val = one_hot(np.ones((20)), 7) model.train(data, val, labels_val, period_save_model=5)
# -*- coding: utf-8 -*-
"""
experiment on different anneal rate(gamma). For more detail, please reference to Readme.md
"""
import numpy as np
from sa import SA
data = np.loadtxt('location.txt', delimiter=',', usecols=[1, 2])
seed = None
sa = SA(data,
T=200,
anneal_rate=0.95,
final_T=0.001,
inner_iters=120,
random_init=True)
gammas = [0.1, 0.3, 0.5, 0.8, 0.9, 0.95, 0.98, 0.99]
n = 5
with open('./experiment/2.3_gamma.txt', 'w') as f:
for gamma in gammas:
t = 0
dis = 0
for i in range(n):
sa.reset()
sa.gamma = gamma
log = sa.train(mode='reverse')
t += log['time']
dis += log['dis']
s = 'gamma={}\ttime={:.3f}\tdistance={}'.format(gamma, t / n, dis / n)
print(s, file=f, flush=True)
print(s)
result = {"code": 200, "score": str(score)}
except Exception as e:
print(e)
result = {"code": 500, "message": "后端发生异常，请检查"}
return jsonify({"result": result})
@app.route('/')
def hhhh():
print('23232')
return render_template('index.html')
class ARGS():
def __init__(self) -> None:
self.no_cuda = False
self.local_rank = -1
self.max_seq_length = 256
self.model_type = "bert"
# self.model_name_or_path="/home/mhxia/whou/workspace/pretrained_models/roberta-large #roberta-large/",
self.output_dir = "./output/checkpoint-best/"
self.seed = 1
if __name__ == '__main__':
args = ARGS()
# print(args)
sa = SA(args)
server = pywsgi.WSGIServer(('0.0.0.0', 10001), app)
server.serve_forever()
default='reverse',
help=
'the method of get new solution. It must be one of [reverse,cross](default:reverse)'
)
parser.add_argument('--anneal_mode',
type=int,
default=0,
help='anneal mode. It must be one of [0,1,2](default:0)')
ars = parser.parse_args()
np.random.seed(ars.seed) # set random seed for reproducing experiment results
data = np.loadtxt(ars.file_path, delimiter=',', usecols=[1, 2]) # load data
# init algorithm
sa = SA(data,
T=ars.init_T,
anneal_rate=ars.anneal_rate,
final_T=ars.final_T,
inner_iters=ars.inner_iters,
random_init=ars.random_init)
# run algorithm
log = sa.train(mode=ars.mode, anneal_mode=ars.anneal_mode)
# log information
t = log['time']
dis = log['dis']
path = log['path']
dis_lst = deepcopy(sa.dis_lst)
print('time:{:.3f}\tbest distance:{}'.format(t, dis))
print('Now you can use test.exe to verify solution.')
# save best result. So you can excute test.exe to verify the solution
np.savetxt('city.txt', path + 1, fmt='%d', newline=',')
# plot distance over time
fig = plt.figure()
def multiroute2(point_file, line_file, cluster_num):
f = open(point_file) # read point file
content = f.read()
lines = content.splitlines()
point_remain = [] # ID
start_id = -1
max_num = 0
point_id = 0
position = {}
weight = {}
for line in lines:
line = line.split(" ")
point_remain.append(int(line[0]))
position[point_id] = [float(line[1]), float(line[2])]
if int(line[3]) != 0: # get the start point ID
start_id = point_id
max_num = int(line[3])
if int(line[4]) != 0:
weight[point_id] = int(line[4])
point_id += 1
f.close()
f = open(line_file) # read line file
content = f.read()
lines = content.splitlines()
line_cost = [] # cost of each line
# get_the_route = {} # display the route
for line in lines:
line = line.split(" ")
line_cost.append(float(line[2]))
# key1 = (int(line[0]), int(line[1])) # small-big
# key2 = (int(line[1]), int(line[0])) # big-small
# temp = line[3].split("->")
# value1 = []
# value2 = []
# for i in range(len(temp)):
# value1.append(int(temp[i]))
# value2.append(int(temp[len(temp) - 1 - i]))
#
# get_the_route[key1] = value1
# get_the_route[key2] = value2
f.close()
matrix = [[float(0.0) for i in range(len(point_remain))]
for j in range(len(point_remain))] # initialize map
num = 0
for i in range(len(matrix)):
for j in range(i + 1, len(matrix)):
matrix[i][j] = line_cost[num]
matrix[j][i] = line_cost[num]
num += 1
multiclustering = MULTICLUSTERING(position, weight, start_id, matrix)
ID = multiclustering.partition(max_num, cluster_num) # partition 2
print(ID)
for i in range(len(ID)):
ID[i].append(start_id)
sa = [0 for i in range(len(ID))]
for i in range(len(ID)):
sa[i] = SA(ID[i], matrix)
get_value = [[] for i in range(len(ID))]
for i in range(len(ID)):
for j in range(2):
get_value[i].append(
sa[i].min_path_2(start_id)) # save the iteration result
dis = [float("inf") for i in range(len(ID))]
path = [[] for i in range(len(ID))]
for i in range(len(ID)):
for j in range(len(get_value[i])):
if dis[i] > get_value[i][j][0]:
dis[i] = get_value[i][j][0]
path[i] = []
for k in range(len(ID[i])):
path[i].append(point_remain[get_value[i][j][1]
[k]]) # get the shortest
print(path)
result = [[] for i in range(len(ID))]
for i in range(len(ID)):
result[i] = path[i]
for i in range(len(ID)):
result[i].append(point_remain[start_id])
result_route = ["" for i in range(len(ID))]
for i in range(len(ID)):
for j in range(len(result[i])): # display the route
result_route[i] += str(result[i][j])
if j != len(result[i]) - 1:
result_route[i] += "->" # save like id->id
print(result_route)
print(dis)
return result
def multimission(point_file, line_file):
f = open(point_file) # read point file
content = f.read()
lines = content.splitlines()
point_remain = [] # ID
start_id = -1
max_num = 0
point_id = 0
position = {}
weight = {}
for line in lines:
line = line.split(" ")
point_remain.append(int(line[0]))
position[point_id] = [float(line[1]), float(line[2])]
if int(line[3]) != 0: # get the start point ID
start_id = point_id
max_num = int(line[3])
if int(line[4]) != 0:
weight[point_id] = int(line[4])
point_id += 1
f.close()
f = open(line_file) # read line file
content = f.read()
lines = content.splitlines()
line_cost = [] # cost of each line
get_the_route = {} # display the route
for line in lines:
line = line.split(" ")
line_cost.append(float(line[2]))
key1 = (int(line[0]), int(line[1])) # small-big
key2 = (int(line[1]), int(line[0])) # big-small
temp = line[3].split("->")
value1 = []
value2 = []
for i in range(len(temp)):
value1.append(int(temp[i]))
value2.append(int(temp[len(temp) - 1 - i]))
get_the_route[key1] = value1
get_the_route[key2] = value2
f.close()
matrix = [[float(0.0) for i in range(len(point_remain))]
for j in range(len(point_remain))] # initialize map
num = 0
for i in range(len(matrix)):
for j in range(i + 1, len(matrix)):
matrix[i][j] = line_cost[num]
matrix[j][i] = line_cost[num]
num += 1
clustering = CLUSTERING(position, weight, start_id, matrix)
ID1, ID2 = clustering.partition(max_num) # partition 2
ID1.append(start_id)
ID2.append(start_id)
sa1 = SA(ID1, matrix)
get_value1 = []
for i in range(20): # 20 iteration
get_value1.append(sa1.min_path_2(start_id))
dis1 = float("inf")
path1 = list(range(len(ID1)))
for i in range(len(get_value1)):
if dis1 > get_value1[i][0]:
dis1 = get_value1[i][0] # get the smallest
for j in range(len(ID1)):
path1[j] = point_remain[get_value1[i][1][
j]] # path contains the nodes in the new graph
result1 = [] # result contains the nodes in the raw graph
for i in range(len(path1) - 1):
key = (path1[i], path1[i + 1])
for j in range(len(get_the_route[key]) - 1):
result1.append(get_the_route[key][j]) # a-b
for i in range(len(get_the_route[(path1[len(path1) - 1],
path1[0])])): # b-a
result1.append(get_the_route[(path1[len(path1) - 1], path1[0])][i])
result_route1 = ""
for i in range(len(result1)): # display the route
result_route1 += str(result1[i])
if i != len(result1) - 1:
result_route1 += "->"
print(result_route1)
print(dis1)
sa2 = SA(ID2, matrix)
get_value2 = []
for i in range(20): # 20 iteration
get_value2.append(sa2.min_path_2(start_id))
dis2 = float("inf")
path2 = list(range(len(ID2)))
for i in range(len(get_value2)):
if dis2 > get_value2[i][0]:
dis2 = get_value2[i][0] # get the smallest
for j in range(len(ID2)):
path2[j] = point_remain[get_value2[i][1][
j]] # path contains the nodes in the new graph
result2 = [] # result contains the nodes in the raw graph
for i in range(len(path2) - 1):
key = (path2[i], path2[i + 1])
for j in range(len(get_the_route[key]) - 1):
result2.append(get_the_route[key][j]) # a-b
for i in range(len(get_the_route[(path2[len(path2) - 1],
path2[0])])): # b-a
result2.append(get_the_route[(path2[len(path2) - 1], path2[0])][i])
result_route2 = ""
for i in range(len(result2)): # display the route
result_route2 += str(result2[i])
if i != len(result2) - 1:
result_route2 += "->"
print(result_route2)
print(dis2)
return result_route1, result_route2
def sa(point_file, line_file):
f = open(point_file) # read point file
content = f.read()
lines = content.splitlines()
point_remain = [] # ID
start_id = -1
point_id = 0
for line in lines:
line = line.split(" ")
point_remain.append(int(line[0]))
if int(line[3]) != 0: # get the start point ID
start_id = point_id
point_id += 1
f.close()
f = open(line_file) # read line file
content = f.read()
lines = content.splitlines()
line_cost = [] # cost of each line
get_the_route = {} # display the route
for line in lines:
line = line.split(" ")
line_cost.append(float(line[2]))
key1 = (int(line[0]), int(line[1])) # small-big
key2 = (int(line[1]), int(line[0])) # big-small
temp = line[3].split("->")
value1 = []
value2 = []
for i in range(len(temp)):
value1.append(int(temp[i]))
value2.append(int(temp[len(temp) - 1 - i]))
get_the_route[key1] = value1
get_the_route[key2] = value2
f.close()
matrix = [[float(0.0) for i in range(len(point_remain))]
for j in range(len(point_remain))] # initialize map
num = 0
for i in range(len(matrix)):
for j in range(i + 1, len(matrix)):
matrix[i][j] = line_cost[num]
matrix[j][i] = line_cost[num]
num += 1
sa = SA(point_remain, matrix)
get_value = []
for i in range(20): # 20 iteration
get_value.append(sa.min_path(start_id))
dis = float("inf")
path = list(range(len(point_remain)))
for i in range(len(get_value)):
if dis > get_value[i][0]:
dis = get_value[i][0] # get the smallest
for j in range(len(point_remain)):
path[j] = point_remain[get_value[i][1][
j]] # path contains the nodes in the new graph
result = [] # result contains the nodes in the raw graph
for i in range(len(path) - 1):
key = (path[i], path[i + 1])
for j in range(len(get_the_route[key]) - 1):
result.append(get_the_route[key][j]) # a-b
for i in range(len(get_the_route[(path[len(path) - 1], path[0])])): # b-a
result.append(get_the_route[(path[len(path) - 1], path[0])][i])
result_route = ""
for i in range(len(result)): # display the route
result_route += str(result[i])
if i != len(result) - 1:
result_route += "->"
print(result_route)
print(dis)
return result_route
# from settings import Settings from ga_settings import GA_settings from ga import GA from ts_settings import TS_settings from ts import TS from sa_settings import SA_settings from sa import SA ga_settings = GA_settings() ga = GA(ga_settings) ga.main() ts_settings = TS_settings() ts = TS(ts_settings) ts.main() sa_settings = SA_settings() sa = SA(sa_settings) sa.main()
# -*- coding: utf-8 -*-
"""
experiment on different anneal mode. For more detail, please reference to Readme.md
"""
import numpy as np
from sa import SA
from copy import deepcopy
data = np.loadtxt('location.txt', delimiter=',', usecols=[1, 2])
seed = None
sa = SA(data,
T=200,
anneal_rate=0.95,
final_T=0.001,
inner_iters=120,
random_init=True)
modes = [0, 1, 2]
n = 5
dis_lst = None
with open('./experiment/2.2_annealMode.txt', 'w') as f:
for mode in modes:
t = 0
dis = 0
for i in range(n):
sa.reset()
log = sa.train(mode='reverse', anneal_mode=mode)
t += log['time']
dis += log['dis']
dis_lst = deepcopy(sa.dis_lst)
sa.plot_dis(title=f'Mode {mode}')
s = 'anneal mode={}\ttime={:.3f}\tdistance={}'.format(
