def run(self):
print("The handle of the message has been started!")
while True:
if self.msg_list.empty():
time.sleep(2)
continue
else:
msg = self.msg_list.get()
new_msg = self.process(msg)
if new_msg.statu == 0:
print(new_msg.content)
self.data = new_msg.content
self.dist = self.init_distance(self.data)
msg = message(2, self.server_port)
send_to(msg)
elif new_msg.statu == 1:
send_to(message(2, new_msg.content), port=self.next_port)
elif new_msg.statu == 444:
#print("test content:",new_msg.content)
pass
elif new_msg.statu == 669:
print("new_msg.content: ", new_msg.content)
elif new_msg.statu == 666:
self.next_port = new_msg.content
msg = message(444, 'your message has been processed.')
send_to(msg, port=self.next_port)
dist_msg = message(801, self.dist)
send_to(dist_msg, port=self.next_port)
for i in range(self.nodes):
solutions = self.init_solutions()
#print('solutions:',solutions)
msg = message(1, solutions).msg_encode()
self.msg_list.put(msg)
elif new_msg.statu == 700:
print('a final result rcvd.')
self.count += 1
self.result.append(new_msg.content)
if self.count == self.nodes:
sorted_solution = sorted(
solutions, key=lambda x: cr.cost(x, self.dist))[0]
print("final result is:", sorted_solution)
print("least cost is :",
cr.cost(sorted_solution, self.dist))
else:
print("something wrong! error %d" % new_msg.statu,
new_msg.content)
def next_generation(solutions, matrix=m):
length = len(solutions)
tmp = []
for i in range(length):
for j in range(length):
if i != j:
tmp.append(cross_and_mutation(solutions[i], solutions[j]))
tmp = np.array(tmp)
#print("tmp.shape",tmp.shape)
#print(solutions.shape)
solutions = np.vstack((solutions, tmp))
sorted_solutions = sorted(solutions,
key=lambda x: tc.cost(x, matrix))[:length]
return np.array(sorted_solutions)
def get_res(s1,s2):
if tc.cost(s1,m)<tc.cost(s2,m):
return s1
else:
return s2
return s1
else:
return s2
print("start work.")
while True:
generations=to.wait_np_file(work_path,'generations.npy',delete=True)
print("generation information getted. Now setting the gen_rdd.")
gen_rdd=sc.parallelize(generations)
print("rdd setted,now maping.")
res=gen_rdd.map(it.iteration)
print("mapping over,now reducing.")
res2=res.reduce(get_res)
print("The best solution is:",res2)
c=tc.cost(res2,m)
print("The lowest cost is :",c)
to.set_np_file(work_path,'final_solution.npy',[res2,c])
# msg=to.wait_np_file_start(work_path,'solution',delete=True)
# #print(msg)
# solution,c_orig,file_name=msg
# if file_name not in dic:
# dic[file_name]=[solution,c_orig,0]
# to.set_np_file(work_path,'cal_'+file_name[9:],msg)
# else:
# dic[file_name][:2]=[solution,c_orig]
# dic[file_name][2]+=1
# if dic[file_name][2]==10:
import create as tc
import os_file as to
import numpy as np
import time
work_path='./work/'
m=to.wait_np_file(work_path,'distance_matrix.npy')
while True:
s_orig,c_orig,name=to.wait_np_file_start(work_path,'cal',delete=True)
s=tc.mutation_1(s_orig)
c=tc.cost(s,m)
if c<c_orig:
s_orig=s
c_orig=c
s=tc.mutation_2(s_orig)
c=tc.cost(s,m)
if c<c_orig:
s_orig=s
c_orig=c
res=np.array([s_orig,c_orig,name])
to.set_np_file(work_path,name,res)
