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env.py
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env.py
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#!/usr/bin/python3
# Author: Hanyang MSL Lab, Industrial Engineering
#
# Copyright (c) 2019 Evans Sowah Okpoti
#
# MIT License
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import torch
import torch.nn as nn
import numpy as np
import torch.optim as optim
import matplotlib.pyplot as plt
import tsp
#city = numItems
#coor = itemDim = l x w x h
#road = seq
#point = (l,w,h)
def get_point(batch,city,coor):
#output:(batch,city,coor),tensor
return torch.FloatTensor(np.random.normal(size=(batch,city,coor)))
def get_length(point,road):
'''point:(batch,city,coor),tensor
road:(batch,city),numpy
output:(batch,city),tensor'''
try:
length=torch.zeros(torch.IntTensor(road).size())
except TypeError:
length=torch.zeros(torch.LongTensor(road).size())
batch=length.size()[0]
city=length.size()[1]
for i in range(batch):
for j in range(city):
if j!=city-1:
length[i,j]=float(torch.sum(torch.pow(point[i,road[i,j],:]-point[i,road[i,j+1],:],2)))
else:
length[i,j]=float(torch.sum(torch.pow(point[i,road[i,j],:]-point[i,road[i,0],:],2)))
return length
def get_length_sum(point,road):
'''point:(batch,city,coor),tensor
road:(batch,city),numpy
output:(batch),tensor'''
try:
dim=road.ndim
except AttributeError:
road=road.numpy()
dim=road.ndim
if dim==1:
point=torch.FloatTensor(point)
city=point.size()[0]
length=0
for j in range(city):
if j!=city-1:
length+=float(torch.sqrt(torch.sum(torch.pow(point[road[j],:]-point[road[j+1],:],2))))
else:
length+=float(torch.sqrt(torch.sum(torch.pow(point[road[j],:]-point[road[0],:],2))))
print(length)
return length
try:
length=torch.zeros(torch.IntTensor(road).size())
except TypeError:
length=torch.zeros(torch.LongTensor(road).size())
batch=length.size()[0]
city=length.size()[1]
for i in range(batch):
for j in range(city):
if j!=city-1:
length[i,j]=float(torch.sqrt(torch.sum(torch.pow(point[i,road[i,j],:]-point[i,road[i,j+1],:],2))))
else:
length[i,j]=float(torch.sqrt(torch.sum(torch.pow(point[i,road[i,j],:]-point[i,road[i,0],:],2))))
return torch.sum(length,dim=1)
# =============================================================================
# def draw(points,roads):
# '''point:(batch,city,coor)
# road:(batch,city)'''
# if roads.ndim==1:
# city=len(roads)
# fig=plt.figure()
# point=points.numpy()
# ax=plt.subplot(1,1,1)
# road=roads
# for i in range(city-1):
# ax.plot(point[[road[i],road[i+1]],0],point[[road[i],road[i+1]],1],color='b')
# ax.plot(point[[road[city-1],road[0]],0],point[[road[city-1],road[0]],1],color='b')
# fig.show()
# return 'good'
# batch=min(roads.shape[0],2)
# #print(batch)
# city=len(roads[0])
# fig=plt.figure()
# for j in range(batch):
# ax=plt.subplot(1,batch,j+1)
# point=points[j].numpy()
# road=roads[j]
# for i in range(city-1):
# ax.plot(point[[road[i],road[i+1]],0],point[[road[i],road[i+1]],1],color='b')
# ax.plot(point[[road[city-1],road[0]],0],point[[road[city-1],road[0]],1],color='b')
# fig.show()
# =============================================================================
def opt_road(points):
points=points.numpy()
if points.ndim==2:
solution=tsp.tsp(points)
roads=np.array(solution[1])
return roads
batch=points.shape[0]
roads=[]
for i in range(batch):
solution=tsp.tsp(points[i])
roads.append(solution[1])
return np.array(roads)