def TSP(stops, Alg, steps, param, seed=None, coordfile='xycoords.txt'):
'''A wrapper function that attempts to optimize the traveling
salesperson problem using a specified algorithm. If coordfile
exists, a preexisting set of coordinates will be used. Otherwise,
a new set of "stops" coordinates will be generated for the person to
traverse, and will be written to the specified file.'''
# Create the distance matrix, which will be used to calculate
# the fitness of a given path
if os.path.isfile(coordfile):
coords = scipy.genfromtxt(coordfile)
distMat = DistanceMatrix(coords)
else:
distMat = GenerateMap(stops, fname=coordfile, seed=seed)
if Alg == 'HC':
# param is the number of solutions to try per step
bestSol, fitHistory = hc.HillClimber(steps, param, distMat, seed)
elif Alg == 'SA':
# param is a placeholder
bestSol, fitHistory = sa.SimulatedAnnealing(steps, param, distMat,
seed)
elif Alg == 'MC3':
# param is the number of chains
bestSol, fitHistory = mc3.MCMCMC(steps, param, distMat, seed)
elif Alg == 'GA':
# param is the population size
bestSol, fitHistory = ga.GeneticAlgorithm(steps, param, distMat, seed)
else:
raise ValueError('Algorithm must be "HC", "SA", "MC3", or "GA".')
outfname = coordfile + '-' + Alg + '-' + \
str(steps) + '-' + str(param) + '.txt'
scipy.savetxt(outfname, scipy.array(bestSol), fmt='%i')
return bestSol, fitHistory
