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TSPAlgorithm.py
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TSPAlgorithm.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Feb 27 14:05:10 2016
@author: s0568630
"""
import numpy as np
import sys
import itertools as it
import math
import time
import unittest
#==============================================================================
class TSPAlgorithm(object):
"""Parent class for Travelling Sales Person Algorithms.
Instantiate and instance with a n*n numpy array corresponding to distance
matrix of network to be solved.
This class can only solve by brute force, so keep distance matrix small.
"""
def __init__(self, data):
"""constructor to instantiate an instance"""
if isinstance(data,np.ndarray):
self._distMatrix = data
self._length = len(self._distMatrix[0])
self._iteration = math.factorial((self._length-1))
self._optimalD = sys.maxint #best possible solution solved wiht brute force via full solve
self._bestD = sys.maxint #best from some other algorithm, via solve, in TSPAlgorithm class this is still brute force
self._worstD = 0 #worst possible solution solved with brute force via fullsolve
self._optimalTour = [] #optimal tour
self._bestTour = [] #best tour found by solve
self._worstTour = [] #worst possible tour
self._optSolved = False #checker for if full solve has been run
self._solved = False #checker for it solve has been run
self._timer = 0
self._allSolutions = [] #List of all possible solutions for self._distmatrix
self._allrange = 0
self._efficacyList = []
self._efficacyMean = 0
self._solutionsrange = 0
else:
raise Exception("data must by type 'np.ndarray'. import numpy as np")
#------------------------------------------------------------------------------
def getData(self):
"""return self._distMatrix"""
return self._distMatrix
#------------------------------------------------------------------------------
def getAllSolutions(self):
"""return list of all possible solution as list of tuples,
(dist, route) types: (int,list). If solutionList hasn't been run
raise exception."""
if len(self._allSolutions) > 1:
return self._allSolutions
else:
raise Exception('self._allSolutions = []. Run self.solutionList()')
#------------------------------------------------------------------------------
def getOptimalD(self):
"""Return optimal distance as int. If not fullsolved raise exception"""
if self._optSolved:
return self._optimalD
else:
raise Exception("self._optSolved = False")
#------------------------------------------------------------------------------
def getOptimalTour(self):
"""Return optimal tour as list. If not fullsolved raise exception"""
if self._optSolved:
return self._optimalTour
else:
raise Exception("self._optSolved = False")
#------------------------------------------------------------------------------
def getworstD(self):
"""Return Worst distance as int. If not fullsolved raise exception"""
if self._optSolved:
if self._worstD > 0:
return self._worstD
else:
raise Exception("must run fullSolved to return self._worstD")
else:
raise Exception("self._optSolved = False")
#------------------------------------------------------------------------------
def getWorstTour(self):
"""Return worst tour as list. If not fullsolved raise exception"""
if self._optSolved:
if self._worstD > 0:
return self._worstTour
else:
raise Exception("must run fullSolved to return self._worstTour")
else:
raise Exception("self._solved = False")
#------------------------------------------------------------------------------
def getBestD(self):
"""Return best distance as int. If not solved raise exception"""
if self._Solved:
return self._optimalD
else:
raise Exception("self._Solved = False")
#------------------------------------------------------------------------------
def getBestTour(self):
"""Return best tour as list. If not solved raise exception"""
if self._solved:
return self._bestTour
else:
raise Exception("self._solved = False")
#------------------------------------------------------------------------------
def getIter(self):
"""method to return iterations required to reach bestD"""
return self._iteration
#------------------------------------------------------------------------------
def getTime(self):
"""method to return time required to solve"""
if self._timer>0:
return self._timer
else:
raise Exception("must run solve to calulate time")
#------------------------------------------------------------------------------
def getOptSolved(self):
"""method to check state of algorithm re being optimally solved
returns bool"""
return self._optSolved
#------------------------------------------------------------------------------
def getSolved(self):
"""method to check state of algorithm re being solved
returns bool"""
return self._solved
#------------------------------------------------------------------------------
def getLength(self):
"""return length of self._distMatrix as int"""
return self._length
#------------------------------------------------------------------------------
def checkOptimal(self,d,route,v=0):
"""Method to check if new d is shorter than optimald and update steps
accordingly. pass v=1 for verbose"""
if d < self._optimalD:
self._optimalD = d
self._optimalTour = route
if v == 1:
print 'current optimal dist is: ',self._optimalD
print 'current optimal Tour is: ',self._optimalTour
#------------------------------------------------------------------------------
def checkBest(self,d,route,i,v=0):
"""Method to check if new d is shorter than bestD and update steps
accordingly. pass v=1 for verbose"""
if d < self._bestD:
self._bestD = d
self._iteration = i
if v == 1:
print 'current best dist is: ',self._bestD
print 'self._bestTour is: ',self._bestTour
return True
else:
return False
#------------------------------------------------------------------------------
def checkWorst(self,d,route,v=0):
"""Method to check if new d is longer than worstd and update steps
accordingly. pass v=1 for verbose"""
if d > self._worstD:
self._worstD = d
self._worstTour = route
if v == 1:
print 'current worst dist is: ',self._worstD
#------------------------------------------------------------------------------
def resultFormatter(self):
"""Method to print formatted results"""
self.resultHeader()
if self._solved:
print '\nBest Route from solve is: ',self._bestTour
print '\nShortest distance from solve is: ',self._bestD
print '\nFound best route after ',self._iteration,' iterations'
print '\nTime taken was',self._timer,' seconds'
if self._optSolved:
print '\nOptimal route is: ',self._optimalTour
print '\nOptimal distance from fullsolve is: ',self._optimalD
print '\nWorst distance from fullsolve is: ',self._worstD
if not(self._solved or self._optSolved):
print '\nNo results'
print '---------------------------------------------------'
#------------------------------------------------------------------------------
def fullReset(self):
"""reset states to as they were for instantiation"""
self.solveReset()
self._optimalD = sys.maxint
self._worstD = 0
self._optimalTour = []
self._worstTour = []
self._optSolved = False
self._allSolutions = [] #List of all possible solutions for self._distmatrix
self._allrange = 0
self._timer = 0
self._bestD = sys.maxint
self._bestTour = []
self._efficacyList = []
self._efficacyMean = 0
self._solutionsrange = 0
#------------------------------------------------------------------------------
def solveReset(self):
"""reset states to as thtey were before solve"""
self._bestD = sys.maxint
self._bestTour = []
self._timer = 0
self._solved = False
#------------------------------------------------------------------------------
def fullSolve(self,v=0):
"""solve using brute force algorithm"""
n=self._length
print '\nstart fullsolve'
for l in it.permutations(range(1,n)):
fullRoute=[0]+list(l)+[0]
iters = 0
dist=0
for j in range(n):
c1=fullRoute[j]
c2=fullRoute[j+1]
d=self._distMatrix[c1,c2]
dist=dist+d
self.checkOptimal(dist,fullRoute,v)
self.checkWorst(dist,fullRoute,v)
self._allrange = self._worstD-self._optimalD
if v == 1:
iters += 1
if iters%1000000 == 0:
print 'number of iterations: ',iters
self._optSolved = True #update self._optSolved
print 'end fullsolve'
#------------------------------------------------------------------------------
def solve(self,v=0,**kwargs):
"""Method to solve just checking for best_D should be (slightly)
faster than fullsolve"""
n=self._length
i=0
t = time.clock()
for l in it.permutations(range(1,n)):
fullRoute=[0]+list(l)+[0]
i += 1
dist=0
for j in range(n):
c1=fullRoute[j]
c2=fullRoute[j+1]
d=self._distMatrix[c1,c2]
dist=dist+d
if dist < self._bestD:
self._bestD = dist
self._bestTour= fullRoute
self._solved = True #update self._optSolved
self._timer = time.clock() - t
#------------------------------------------------------------------------------
def solutionList(self,sols=[],v=0):
"""Method equivalent to fullSolve, but which makes a list of
every possible solution for self._distMatrix.
List will be (self._length-1)! long
Can pass already computed set as list"""
t = time.clock()
if len(sols)>0:
self._allSolutions = sols
else:
n=self._length
print '\nstart solution list'
print 'calculating ',math.factorial(n-1),' solutions...'
routes = []
dists = []
iters=0
for l in it.permutations(range(1,n)):
fullRoute=[0]+list(l)+[0]
routes.append(fullRoute)
dist=0
for j in range(n):
c1=fullRoute[j]
c2=fullRoute[j+1]
d=self._distMatrix[c1,c2]
dist=dist+d
dists.append(dist)
if v == 1:
iters += 1
if iters%1000000 == 0:
print 'number of iterations: ',iters
self._allSolutions = sorted(zip(dists,routes), key = lambda pair : pair[0])
#print self._allSolutions
print '...finished generating solution list \n'
if self._optSolved == False:
self._optimalD = self._allSolutions[0][0]
self._worstD = self._allSolutions[-1][0]
self._optimalTour = self._allSolutions[0][1]
self._worstTour = self._allSolutions[-1][1]
self._optSolved = True
self._timer = self._timer = time.clock() - t
#------------------------------------------------------------------------------
def efficacy(self,runs=100,iterations=1000,**kwargs):
"""Method takes number of runs and iterations, then evaluates efficacy
for self.solve(). self._optSolved must be True"""
i = 0
dists=[]
effs = []
iters = []
if not self._optSolved:
self.fullSolve()
while i < runs:
self.solveReset()
self.solve(iterations = iterations,**kwargs)
#print "self._bestD is: ",self._bestD
#print "self._optimalD is: ",self._optimalD
efficacy = ((float(self._bestD) - float(self._optimalD))/float(self._optimalD))
dists.append(self._bestD)
effs.append(efficacy)
iters.append(self._iteration)
i += 1
self._efficacyList = [dists,effs,iters]
self._efficacyMean = float(sum(effs)/float(runs))
return self._efficacyList, self._efficacyMean
#------------------------------------------------------------------------------
def getEfficacy(self):
"""method returns list of lists [bestDs,efficacys,iterss] and their mean"""
return self._efficacyList,self._efficacyMean
#==============================================================================
class TSPAlgorithm_TestCase(unittest.TestCase):
def setUp(self):
print "Setting up test...\n"
self.row1 = [0,20,15,10,20]
self.row2 = [200,0,50,30,45]
self.row3 = [40,20,0,200,200]
self.row4 = [200,200,200,0,70]
self.row5 = [200,200,200,200,0]
self.distanceMatrix = np.array([self.row1,self.row2,self.row3,
self.row4,self.row5])
self.locations = {1: 'a', 2: 'b', 3: 'c', 4: 'd'}
self.myTSPAlgorithm = TSPAlgorithm(self.distanceMatrix)
#------------------------------------------------------------------------------
def tearDown(self):
self.row1 = None
self.row2 = None
self.row3 = None
self.row4 = None
self.row5 = None
self.distanceMatrix = None
self.myTSPAlgorithm.fullReset()
print "\n...test complete.\n\n"
print " ###############################\n\n"
#------------------------------------------------------------------------------
def test_getEfficacy(self):
print "Testing getEfficacy"
self.myTSPAlgorithm.efficacy(runs=5)
efflist,effmean = self.myTSPAlgorithm.getEfficacy()
self.assertEqual(len(efflist),3)
self.assertEqual(len(efflist[0]),5)
self.assertEqual(len(efflist[1]),5)
self.assertEqual(len(efflist[2]),5)
self.assertEqual(effmean,0)
print "getEfficacy Testing Complete"
#------------------------------------------------------------------------------
def test_efficacy(self):
print "Testing efficacy"
self.myTSPAlgorithm.efficacy(runs=5)
self.assertEqual(len(self.myTSPAlgorithm._efficacyList),3)
self.assertEqual(len(self.myTSPAlgorithm._efficacyList[0]),5)
self.assertEqual(len(self.myTSPAlgorithm._efficacyList[1]),5)
self.assertEqual(len(self.myTSPAlgorithm._efficacyList[2]),5)
self.assertEqual(self.myTSPAlgorithm._efficacyMean,0)
print "efficacy Testing Complete"
#------------------------------------------------------------------------------
def test_solve(self):
print "Testing Solve"
self.myTSPAlgorithm.solve()
self.assertListEqual([0, 2, 1, 3, 4, 0],self.myTSPAlgorithm._bestTour)
self.assertListEqual([],self.myTSPAlgorithm._worstTour)
self.assertEqual(335,self.myTSPAlgorithm._bestD)
self.assertLessEqual(self.myTSPAlgorithm._iteration,24)
print "solve Testing Complete"
#------------------------------------------------------------------------------
def test_fullSolve(self):
print "TestingfullSolve"
self.myTSPAlgorithm.fullSolve()
self.assertListEqual([0, 2, 1, 3, 4, 0],self.myTSPAlgorithm._optimalTour)
self.assertEqual(335,self.myTSPAlgorithm._optimalD)
self.assertListEqual([0, 4, 2, 3, 1, 0],self.myTSPAlgorithm._worstTour)
self.assertEqual(820,self.myTSPAlgorithm._worstD)
print "fullSolve Testing Complete"
#------------------------------------------------------------------------------
def test_solutionList(self):
print "Testing solutionList"
self.myTSPAlgorithm.solutionList()
self.assertIsInstance(self.myTSPAlgorithm._allSolutions[0],tuple)
self.assertEqual(24,len(self.myTSPAlgorithm._allSolutions))
self.assertEqual(335,self.myTSPAlgorithm._allSolutions[0][0])
self.assertEqual(820,self.myTSPAlgorithm._allSolutions[23][0])
self.myTSPAlgorithm.fullSolve()
self.assertListEqual([0, 2, 1, 3, 4, 0],self.myTSPAlgorithm._optimalTour)
self.assertEqual(335,self.myTSPAlgorithm._optimalD)
self.assertListEqual([0, 4, 2, 3, 1, 0],self.myTSPAlgorithm._worstTour)
self.assertEqual(820,self.myTSPAlgorithm._worstD)
print "solutionList Testing Complete"
#------------------------------------------------------------------------------
def test_getSolved(self):
print "Testing getoptSolved"
self.assertFalse(self.myTSPAlgorithm.getOptSolved())
self.myTSPAlgorithm._optSolved = True
self.assertTrue(self.myTSPAlgorithm.getOptSolved())
"getoptSolved Testing Complete"
#------------------------------------------------------------------------------
def test_gets(self):
"""class variables assigned directly to preserve unit testing"""
print "Testing gets"
self.myTSPAlgorithm._optimalTour = [0, 2, 1, 3, 4, 0]
self.myTSPAlgorithm._optimalD = 335
self.myTSPAlgorithm._worstTour = [0, 4, 2, 3, 1, 0]
self.myTSPAlgorithm._worstD = 820
self.myTSPAlgorithm._optSolved = True
self.myTSPAlgorithm._allSolutions = [(335, [0, 2, 1, 3, 4, 0]), (360, [0, 1, 3, 4, 2, 0]), (370, [0, 3, 4, 1, 2, 0]), (470, [0, 4, 2, 1, 3, 0]), (475, [0, 3, 2, 1, 4, 0]), (480, [0, 2, 1, 4, 3, 0]), (490, [0, 4, 1, 3, 2, 0]), (495, [0, 3, 1, 4, 2, 0]), (500, [0, 3, 4, 2, 1, 0]), (505, [0, 1, 4, 3, 2, 0]), (510, [0, 4, 3, 1, 2, 0]), (540, [0, 1, 2, 3, 4, 0]), (640, [0, 4, 3, 2, 1, 0]), (645, [0, 2, 4, 1, 3, 0]), (650, [0, 1, 3, 2, 4, 0]), (660, [0, 2, 3, 1, 4, 0]), (660, [0, 3, 1, 2, 4, 0]), (665, [0, 1, 4, 2, 3, 0]), (670, [0, 1, 2, 4, 3, 0]), (670, [0, 4, 1, 2, 3, 0]), (685, [0, 2, 3, 4, 1, 0]), (810, [0, 3, 2, 4, 1, 0]), (815, [0, 2, 4, 3, 1, 0]), (820, [0, 4, 2, 3, 1, 0])]
self.myTSPAlgorithm._timer = 42
self.assertListEqual(self.myTSPAlgorithm.getAllSolutions(),[(335, [0, 2, 1, 3, 4, 0]), (360, [0, 1, 3, 4, 2, 0]), (370, [0, 3, 4, 1, 2, 0]), (470, [0, 4, 2, 1, 3, 0]), (475, [0, 3, 2, 1, 4, 0]), (480, [0, 2, 1, 4, 3, 0]), (490, [0, 4, 1, 3, 2, 0]), (495, [0, 3, 1, 4, 2, 0]), (500, [0, 3, 4, 2, 1, 0]), (505, [0, 1, 4, 3, 2, 0]), (510, [0, 4, 3, 1, 2, 0]), (540, [0, 1, 2, 3, 4, 0]), (640, [0, 4, 3, 2, 1, 0]), (645, [0, 2, 4, 1, 3, 0]), (650, [0, 1, 3, 2, 4, 0]), (660, [0, 2, 3, 1, 4, 0]), (660, [0, 3, 1, 2, 4, 0]), (665, [0, 1, 4, 2, 3, 0]), (670, [0, 1, 2, 4, 3, 0]), (670, [0, 4, 1, 2, 3, 0]), (685, [0, 2, 3, 4, 1, 0]), (810, [0, 3, 2, 4, 1, 0]), (815, [0, 2, 4, 3, 1, 0]), (820, [0, 4, 2, 3, 1, 0])])
self.assertListEqual(self.myTSPAlgorithm.getOptimalTour(),[0, 2, 1, 3, 4, 0])
self.assertEqual(self.myTSPAlgorithm.getOptimalD(),335)
self.assertEqual(self.myTSPAlgorithm.getWorstTour(),[0, 4, 2, 3, 1, 0])
self.assertEqual(self.myTSPAlgorithm.getworstD(),820)
self.assertEqual(self.myTSPAlgorithm.getLength(),5)
self.assertEqual(self.myTSPAlgorithm.getIter(),24)
self.assertEqual(self.myTSPAlgorithm.getTime(),42)
print "gets Testing Complete"
#==============================================================================
if __name__ == "__main__":
unittest.main()