import time
import random
import math
import optimization
def randomoptimize(domain, costf):
best = 999999999
bestr = None
for i in range(1000):
# 無作為解の生成
r = [random.randint(domain[i][0], domain[i][1])
for i in range(len(domain))]
# コストの取得
cost = costf(r)
# 最良解と比較
if cost < best:
best = cost
bestr = r
return bestr # 書籍にはrと書かれていた
if __name__ == '__main__':
domain = [(0,9)]*(len(optimization.people)*2) # 書籍には(0.8)と書かれていた
s = randomoptimize(domain, optimization.schedulecost)
optimization.printschedule(s)
cost = optimization.schedulecost(s)
print('cost is %4s' % (cost))
def test_print_schedule():
s = [1, 4, 3, 2, 7, 3, 6, 3, 2, 4, 5, 3]
optimization.printschedule(s)
def createschedule(people, dest, dep, ret):
sid = getkayaksession()
flights = {}
for p in people:
name, origin = p
# outbound flight
searchid = flightsearch(sid, origin, dest, dep)
flights[(origin, dest)] = flightsearchresults(sid, searchid)
# return flight
searchid = flightsearch(sid, dest, origin, ret)
flights[(origin, dest)] = flightsearchresults(sid, searchid)
return flights
if __name__ == '__main__':
import optimization
f = createschedule([('Nico', 'SFO'), ('Kerstin', 'SFO')],
'LGA',
dep='12/30/2008',
ret='1/2/2009')
domain = [(0, 30)] * len(f)
s = optimization.geneticoptimize(domain, optimization.costfunc(f, 'LGA'))
optimization.printschedule(s, 'LGA')
def printschedule(name, s):
print(name)
print('----------------------------------')
optimization.printschedule(s)
def test_find_optimized_schedule(optimization_method):
domain = [(0, 8)] * (len(optimization.people) * 2)
r = optimization_method(domain, optimization.schedulecost)
optimization.printschedule(r)
print optimization.schedulecost(r)
[p.firstChild.data.split(' ')[1] for p in arrivals],
[parseprice(p.firstChild.data) for p in prices])
def createschedule(people, dest, dep, ret):
sid = getkayaksession()
flights = {}
for p in people:
name, origin = p
# outbound flight
searchid = flightsearch(sid, origin, dest, dep)
flights[(origin, dest)] = flightsearchresults(sid, searchid)
# return flight
searchid = flightsearch(sid, dest, origin, ret)
flights[(origin, dest)] = flightsearchresults(sid, searchid)
return flights
if __name__ == '__main__':
import optimization
f = createschedule([('Nico', 'SFO'), ('Kerstin', 'SFO')], 'LGA',
dep='12/30/2008', ret='1/2/2009')
domain = [(0, 30)] * len(f)
s = optimization.geneticoptimize(domain, optimization.costfunc(f, 'LGA'))
optimization.printschedule(s, 'LGA')
import optimization
import dorm
import socialnetwork
# Group Travel Problem
print('<----Group Travel---->')
s = [1, 4, 3, 2, 7, 3, 6, 3, 2, 4, 5, 3]
optimization.printschedule(s)
cost = optimization.schedulecost(s)
print('Present cost:%f' % cost)
print('\n<----Random Searching---->')
domain = [(0, 9)] * (len(optimization.people) * 2)
print(s)
s = optimization.randomoptimize(domain, optimization.schedulecost)
cost = optimization.schedulecost(s)
print('Present cost:%f' % cost)
optimization.printschedule(s)
print('\n<----Hill Climbing---->')
s = optimization.hillclimb(domain, optimization.schedulecost)
print(s)
cost = optimization.schedulecost(s)
print('Present cost:%f' % cost)
optimization.printschedule(s)
print('\n<----Simulated Annealing---->')
s = optimization.annealingoptimize(domain, optimization.schedulecost)
print(s)
cost = optimization.schedulecost(s)
import optimization as op import time s = [1,4,3,2,7,3,6,3,2,4,5,3] domain = [(0,9)]*len(s) print 'fix:', start = time.clock() print op.schedulecost(s) op.printschedule(s) print 'running time',time.clock()-start print print 'random optimization:', start = time.clock() sol = op.randomoptimize(domain) print op.schedulecost(sol) op.printschedule(sol) print 'running time',time.clock()-start print print 'hill climb:', start = time.clock() sol = op.hillclimb(domain) print op.schedulecost(sol) op.printschedule(sol) print 'running time',time.clock()-start print print 'annealing optimization:', start = time.clock()
# uWorldBank,uRiver,uEarth =201,202,203
# mynet.generatehiddennode([wWorld,wBank],[uWorldBank,uRiver,uEarth])
# for c in mynet.con.execute('select * from wordhidden'):print c
#
# for c in mynet.con.execute('select * from hiddenurl'):print c
#
# mynet.trainquery([wWorld,wBank],[uWorldBank,uRiver,uEarth],uWorldBank)
# mynet.getresult([wWorld,wBank],[uWorldBank,uRiver,uEarth])
#
# s=[1,4,3,2,7,3,6,3,2,4,5,3]
# from improve_schedulecost import improve_schedulecost
# money=improve_schedulecost(s)
# print 'totalprice='+str(money)
from annealing_algorithm import annealing_algorithm
from optimization import schedulecost
from optimization import people
from optimization import printschedule
domain=[(0,9)]*(len(people)*2)
# s,costf=annealing_algorithm(domain,schedulecost)
# printschedule(s)
# print costf
from genetic_algorithm import genetic_algorithm
s=genetic_algorithm(domain,schedulecost)
printschedule(s)
# from optimization import geneticoptimize
# s=geneticoptimize(domain,schedulecost)
# printschedule(s)
# uWorldBank,uRiver,uEarth =201,202,203
# mynet.generatehiddennode([wWorld,wBank],[uWorldBank,uRiver,uEarth])
# for c in mynet.con.execute('select * from wordhidden'):print c
#
# for c in mynet.con.execute('select * from hiddenurl'):print c
#
# mynet.trainquery([wWorld,wBank],[uWorldBank,uRiver,uEarth],uWorldBank)
# mynet.getresult([wWorld,wBank],[uWorldBank,uRiver,uEarth])
#
# s=[1,4,3,2,7,3,6,3,2,4,5,3]
# from improve_schedulecost import improve_schedulecost
# money=improve_schedulecost(s)
# print 'totalprice='+str(money)
from annealing_algorithm import annealing_algorithm
from optimization import schedulecost
from optimization import people
from optimization import printschedule
domain = [(0, 9)] * (len(people) * 2)
# s,costf=annealing_algorithm(domain,schedulecost)
# printschedule(s)
# print costf
from genetic_algorithm import genetic_algorithm
s = genetic_algorithm(domain, schedulecost)
printschedule(s)
# from optimization import geneticoptimize
# s=geneticoptimize(domain,schedulecost)
# printschedule(s)
from pprint import pprint import optimization domain = [(0, 9)] * (len(optimization.people) * 2) # random search s = optimization.randomoptimize(domain, optimization.schedulecost) print(optimization.schedulecost(s)) pprint(optimization.printschedule(s)) # hillclimb s = optimization.hillclimb(domain, optimization.schedulecost) print(optimization.schedulecost(s)) optimization.printschedule(s) # simulated annealing s = optimization.annealingoptimize(domain, optimization.schedulecost) print(optimization.schedulecost(s)) pprint(optimization.printschedule(s)) # genetic algorithms s = optimization.geneticoptimize(domain, optimization.schedulecost) # print(optimization.schedulecost(s)) pprint(optimization.printschedule(s))
def test_printschedule(s):
sys.stderr.write("testing printschedule...\n")
optimization.printschedule(s)
def test_genetic(domain):
sys.stderr.write("testing genetic optimization...\n")
s = optimization.geneticoptimize(domain, optimization.schedulecost)
optimization.printschedule(s)
def test_annealing(domain):
sys.stderr.write("testing annealing optimization...\n")
s = optimization.annealingoptimize(domain, optimization.schedulecost)
print optimization.schedulecost(s)
optimization.printschedule(s)
def test_hillclimb(domain):
sys.stderr.write("testing hillclimb optimization...\n")
s = optimization.hillclimb(domain, optimization.schedulecost)
print optimization.schedulecost(s)
optimization.printschedule(s)
def test_randomoptimize(domain):
sys.stderr.write("testing random optimization...\n")
s = optimization.randomoptimize(domain, optimization.schedulecost)
print optimization.schedulecost(s)
optimization.printschedule(s)