def select_opt_method(self):
print self.flag
if self.flag==1:
return optimization.hillclimb(self.domain,self.fetcher.cost_func)
elif self.flag==2:
return optimization.annealingoptimize(self.domain,self.fetcher.cost_func)
elif self.flag==3:
return optimization.geneticoptimize(self.domain,self.fetcher.cost_func)
else:
return optimization.randomoptimize(self.domain,self.fetcher.cost_func)
def Last_process(self):
self.Create_available_course_list()
if self.v.get() == 0:
solution_listt = optimization.hillclimb(self.domainn,
self.Cost_function)
self.write_listbox(solution_listt)
elif self.v.get() == 1:
solution_listt = optimization.annealingoptimize(
self.domainn, self.Cost_function)
self.write_listbox(solution_listt)
elif self.v.get() == 2:
solution_listt = optimization.geneticoptimize(
self.domainn, self.Cost_function)
self.write_listbox(solution_listt)
elif self.v.get() == 3:
solution_listt = optimization.randomoptimize(
self.domainn, self.Cost_function)
self.write_listbox(solution_listt)
def testGenScale():
data = wineset2()
print crossvalidate(knnestimate, data)
print crossvalidate(weightedknn, data)
weighdomain = [(0, 20)] * 4
costfunction = createcostfunction(knnestimate, data)
#rating,age,aisle,bottlesize
lenghuo_S = optimization.annealingoptimize(weighdomain,
costfunction,
step=2)
print lenghuo_S
lenghuo_data = rescale(data, lenghuo_S)
print crossvalidate(knn3, lenghuo_data)
climbHill_S = optimization.hillclimb(weighdomain, costfunction)
print climbHill_S
climbHill_data = rescale(data, climbHill_S)
print crossvalidate(knn3, climbHill_data)
def optimize(self):
self.consumers = [[con.strip()] for con in self.consumers.split(',')]
#append an empty list to each consumer to be filled by resources
for consumer in self.consumers:
consumer.append([]);
#What a solution looks like:
# [0,1,2,0,2,0,1,0,0,2,1,2,0,0,1,0,2,1,1,0,2,1,0...]
#where each list slot represents a particular resource
#from the resources list, and the value of a slot
#represents the consumer that resource is assigned to
#What the domain looks like
# [(0,2),(0,2),(0,2),(0,2),(0,2),(0,2),(0,2)...]
domain = [(0,len(self.consumers)-1)] * len(self.resources)
#Add up the total strength for each attribute possessed by a resource
total_attr_strengths = []
for a in range(0, len(self.resources[0]) - 1):
total_attr_strengths.append(sum([r[a+1] for r in self.resources]))
#Ideal attribute strength at each consumer.
#This is simple right now becuase we're just dividing total
#strengths by # of consumers.
self.optimal_attr_strengths = []
for a in range(0, len(total_attr_strengths)):
self.optimal_attr_strengths.append(total_attr_strengths[a]/len(self.consumers))
if self.algo == 'annealing':
cost, sol = optimization.annealingoptimize(domain, self.provisioning_cost)
elif self.algo == 'hillclimb':
cost, sol = optimization.hillclimb(domain, self.provisioning_cost)
elif self.algo == 'random':
cost, sol = optimization.randomoptimize(domain, self.provisioning_cost)
elif self.algo == 'genetic':
cost, sol = optimization.geneticoptimize(domain, self.provisioning_cost)
else:
raise ValueError('Invalid algorithm')
return cost, sol
draw = ImageDraw.Draw(img)
# 建立标识位置信息的字典
pos = dict([(people[i], (sol[i * 2], sol[i * 2 + 1]))
for i in range(0, len(people))])
for (a, b) in links:
draw.line((pos[a], pos[b]), fill=(255, 0, 0))
for n, p in pos.items():
draw.text(p, n, (0, 0, 0))
img.show()
domain = [(10, 370)] * (len(people) * 2) #设定题解范围
if __name__ == "__main__": #只有在执行当前模块时才会运行此函数
print(domain)
s = optimization.randomoptimize(domain, crosscount) # 随机搜索法，寻找最优题解
print(s)
drawnetwork(s) # 绘制关系网
s = optimization.hillclimb(domain, crosscount) # 爬山法，寻找最优题解
print(s)
drawnetwork(s) # 绘制关系网
s = optimization.annealingoptimize(domain, crosscount) # 模拟退火算法，寻找最优题解
print(s)
drawnetwork(s) # 绘制关系网
s = optimization.geneticoptimize(domain, crosscount) # 遗传算法，寻找最优题解
print(s)
drawnetwork(s) # 绘制关系网
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)
print('Present cost:%f' % cost)
optimization.printschedule(s)
print('\n<----Genetic Algorithms---->')
s = optimization.geneticoptimize(domain, optimization.schedulecost)
print(s)
# Loop over each student
for i in range(len(vec)):
x = int(vec[i])
dorm = dorms[slots[x]]
pref = prefs[i][1]
# First choice costs 0, second choice costs 1
if pref[0] == dorm:
cost += 0
elif pref[1] == dorm:
cost += 1
else:
cost += 3
# Not on the list costs 3
# Remove selected slot
del slots[x]
#print(cost)
return cost
print(domain)
s=optimization.randomoptimize(domain,dormcost)
printsolution(s)
s=optimization.hillclimb(domain,dormcost)
printsolution(s)
s=optimization.annealingoptimize(domain,dormcost)
printsolution(s)
s=optimization.geneticoptimize(domain,dormcost,elite=0.2)
printsolution(s)
# op.printschedule(s)
# print(op.schedulecost(s))
domain = [(0, 9)] * (len(op.people) * 2)
# s = op.randomoptimize(domain, op.schedulecost)
# s = op.hillclimb(domain, op.schedulecost)
# s = op.annealingoptimize(domain, op.schedulecost)
# s = op.geneticoptimize(domain, op.schedulecost)
# print(domain,s)
# print(op.schedulecost(s))
# op.printschedule(s)
result = {'rand': [], 'hill': [], 'anne': [], 'gene': []}
for i in range(10):
s1 = op.randomoptimize(domain, op.schedulecost)
result['rand'].append(op.schedulecost(s1))
s2 = op.hillclimb(domain, op.schedulecost)
result['hill'].append(op.schedulecost(s2))
s3 = op.annealingoptimize(domain, op.schedulecost)
result['anne'].append(op.schedulecost(s3))
s4 = op.geneticoptimize(domain, op.schedulecost)
result['gene'].append(op.schedulecost(s4))
print(result)
print('rand', sum(result['rand']) / 10, min(result['rand']))
print('hill', sum(result['hill']) / 10, min(result['hill']))
print('anne', sum(result['anne']) / 10, min(result['anne']))
print('gene', sum(result['gene']) / 10, min(result['gene']))
# 遍历每一名学生
for i in range(len(vec)):
x = int(vec[i])
dorm = dorms[slots[x]]
pref = prefs[i][1]
# 首选成本值为0，次选成本值为1
if pref[0] == dorm: cost += 0
elif pref[1] == dorm: cost += 1
else: cost += 3
# 不在选择之列则成本值为3
# 删除选中的槽
del slots[x]
return cost
if __name__ == "__main__": #只有在执行当前模块时才会运行此函数
print(domain)
s = optimization.randomoptimize(domain, dormcost) # 随机搜索法，寻找最优题解
print(s)
printsolution(s) # 打印最优解代表的含义
s = optimization.hillclimb(domain, dormcost) # 爬山法，寻找最优题解
print(s)
printsolution(s) # 打印最优解代表的含义
s = optimization.annealingoptimize(domain, dormcost) # 模拟退火算法，寻找最优题解
print(s)
printsolution(s) # 打印最优解代表的含义
s = optimization.geneticoptimize(domain, dormcost) # 遗传算法，寻找最优题解
print(s)
printsolution(s) # 打印最优解代表的含义
#coding:gbk ''' Created on 2015Äê8ÔÂ30ÈÕ @author: fxy ''' import socialnetwork import optimization sol = optimization.hillclimb(socialnetwork.domain, socialnetwork.crosscount) print socialnetwork.crosscount(sol) print sol socialnetwork.drawNetWork(sol) print "lll"
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() sol = op.annealingoptimize(domain) print op.schedulecost(sol) op.printschedule(sol) print 'running time',time.clock()-start print print 'genetic optimization:', start = time.clock()
def Command_of_schedule_showing(self):
if len(
self.Course_code_shower.get(0, END)
) == 0: #If there is no course code in listbox,program will show an error.
self.Error_Message_Function(
"""Please First use the\nFetching Button""")
return
selections = map(int, self.Course_code_shower.curselection(
)) #Returning the list of indexes selected in the listbox
all_course_types = self.Course_code_shower.get(0, END)
selected_course_types = [all_course_types[i] for i in selections]
if len(
selected_course_types
) == 0: #If demanded course codes is not selected,program will show an error.
self.Error_Message_Function("""Please Select\nSome Course Code""")
return
#Returning the list of selected course codes.
self.List_of_all_avaiable_courses = list()
for course_types in selected_course_types: #Creating a list of courses from the course code dictionary with adding lists togetgher.
self.List_of_all_avaiable_courses.extend(
self.Dictionary_of_course_codes[course_types])
if len(
self.Course_number_taker.get()
) == 0: #If demanded course number is not provided,program will show an error.
self.Error_Message_Function("""Please give\nCourse number""")
return
try:
number_of_courses = int(self.Course_number_taker.get(
)) #Getting the demanded number of course from entry
except: #If it is not a number,program will comply.
self.Error_Message_Function(
"""Please give\nCourse number\nproperlyas integer""")
return
if number_of_courses > len(
self.List_of_all_avaiable_courses
): #If demeanded course number exceed avaiable course number,there will be an error.
self.Error_Message_Function(
"""These course codes\nhave insufficient course""")
return
self.domain = [(0, len(self.List_of_all_avaiable_courses) - i - 1)
for i in range(number_of_courses)]
#Domain
type_of_optimization = Optimization_Dictionary[self.Radio_Values.get()]
#Type of optimization come from the dictionary.
if type_of_optimization == "Hill Climbing":
solution = optimization.hillclimb(self.domain,
self.My_Cost_Function)
elif type_of_optimization == "Simulated Annealing":
solution = optimization.annealingoptimize(self.domain,
self.My_Cost_Function)
elif type_of_optimization == "Genetic Optimization":
if number_of_courses < 3: #For genetic optimization,we need at least three course demand
self.Error_Message_Function(
"""Genetic Optimization is\n valid for \nmore than 3 courses"""
)
return
solution = optimization.geneticoptimize(self.domain,
self.My_Cost_Function)
elif type_of_optimization == "Random Optimization":
solution = optimization.randomoptimize(self.domain,
self.My_Cost_Function)
Last_Showing = self.My_Representation_function(
solution
) #This returns a dictionary where keys are days and values will be list of tuple(time,course).
self.Last_Showing_Part.delete(0.0, END) #Deleting all the elements
for days in Days_of_Week: #List of days
self.Last_Showing_Part.insert(END, days + ":\n")
if days in Last_Showing: #If day inside of dictionary, inserting all tuples in the list(value of day).
for times, course in Last_Showing[days]:
self.Last_Showing_Part.insert(
END,
times + '(%s %s),' % (course.code, course.course_name))
else: #If day is not inside of the dictionary inserting no classes.
self.Last_Showing_Part.insert(END, 'NO CLASSES')
self.Last_Showing_Part.insert(END, '\n')
# draw people for n,p in pos.items(): draw.text(p, n, (0,0,0)) img.show() domain = [(10,370)] * (len(people)*2) print '--- random ---' s = optimization.randomoptimize(domain, crosscount) print crosscount(s) print s print '--- hill climb ---' s = optimization.hillclimb(domain, crosscount) print crosscount(s) print s print '--- simulated annealing ---' s = optimization.annealingoptimize(domain, crosscount) print crosscount(s) print s print '--- genetic optimize ---' gen = optimization.genetic() s = gen.geneticoptimize(domain, crosscount) print crosscount(s) print s drawnetwork(s)
dorm = dorms[slots[student]]
pref = prefs[i][1]
if dorm == pref[0]: continue
elif dorm == pref[1]: cost += 1
else: cost += 3
del slots[student]
return cost
if __name__ == '__main__':
sol = randomOptimize(domain, dormCost)
print 'RandomOptimize'
print 'Cost: ', dormCost(sol)
printSolution(sol)
print '------------------------------'
sol = hillclimb(domain, dormCost)
print 'HillClimb'
print 'Cost: ', dormCost(sol)
printSolution(sol)
print '------------------------------'
sol = randomStartHillClimb(domain, dormCost)
print 'RandomStartHillClimb'
print 'Cost: ', dormCost(sol)
printSolution(sol)
print '------------------------------'
sol = simulatedAnnealing(domain, dormCost)
print 'SimulatedAnnealing'
print 'Cost: ', dormCost(sol)
printSolution(sol)
print '------------------------------'
sol = geneticOptimize(domain, dormCost)
print dorm.dormcost(s) dorm.printsolution(s) print 'running time', time.clock() - start print print 'random optimization:', start = time.clock() s = op.randomoptimize(dorm.domain, costf=dorm.dormcost) print dorm.dormcost(s) dorm.printsolution(s) print 'running time', time.clock() - start print print 'hill climb:', start = time.clock() s = op.hillclimb(dorm.domain, costf=dorm.dormcost) print dorm.dormcost(s) dorm.printsolution(s) print 'running time', time.clock() - start print print 'annealing optimization:', start = time.clock() s = op.annealingoptimize(dorm.domain, costf=dorm.dormcost) print dorm.dormcost(s) dorm.printsolution(s) print 'running time', time.clock() - start print print 'genetic optimization:', start = time.clock()
#coding:gbk ''' Created on 2015Äê8ÔÂ30ÈÕ @author: fxy ''' import socialnetwork import optimization sol=optimization.hillclimb(socialnetwork.domain, socialnetwork.crosscount) print socialnetwork.crosscount(sol) print sol socialnetwork.drawNetWork(sol) print "lll"
import optimization s = [1,4,3,2,7,3,6,3,2,4,5,3] reload(optimization) domain = [(0,9)] * (len(optimization.people) * 2) s = optimization.randomoptimize(domain, optimization.schedulecost) optimization.schedulecost(s) optimization.printschedule(s) s = optimization.hillclimb(domain, optimization.schedulecost) optimization.schedulecost(s) optimization.printschedule(s) s = optimization.annealingoptimize(domain, optimization.schedulecost, T=10000, cool=0.95, step=3) optimization.schedulecost(s) optimization.printschedule(s) s = optimization.geneticoptimize(domain, optimization.schedulecost) optimization.printschedule(s) ####################### import dorm dorm.printsolution([0,0,0,0,0,0,0,0,0,0]) s = optimization.randomoptimize(dorm.domain, dorm.dormcost) dorm.dormcost(s) s = optimization.geneticoptimize(dorm.domain, dorm.dormcost) dorm.printsolution(s) ####################### import socialnetwork
import optimization reload(optimization) domain=[(0,9)]*(len(optimization.people)*2) s=optimization.hillclimb(domain,optimization.schedulecost) print optimization.schedulecost(s)
import optimization as op import socialnetwork as so import time print 'random optimization:', start = time.clock() sol = op.randomoptimize(so.domain,so.crosscount) print so.crosscount(sol) print 'running time',time.clock()-start print so.drawnetwork(sol) print 'hill climb:', start = time.clock() sol = op.hillclimb(so.domain,so.crosscount) print so.crosscount(sol) print 'running time',time.clock()-start print so.drawnetwork(sol) print 'annealing optimization:', start = time.clock() sol = op.annealingoptimize(so.domain,so.crosscount) print so.crosscount(sol) print 'running time',time.clock()-start print so.drawnetwork(sol) print 'genetic optimization:', start = time.clock()
# draw people
for n, p in pos.items():
draw.text(p, n, (0, 0, 0))
img.show()
domain = [(10, 370)] * (len(people) * 2)
print '--- random ---'
s = optimization.randomoptimize(domain, crosscount)
print crosscount(s)
print s
print '--- hill climb ---'
s = optimization.hillclimb(domain, crosscount)
print crosscount(s)
print s
print '--- simulated annealing ---'
s = optimization.annealingoptimize(domain, crosscount)
print crosscount(s)
print s
print '--- genetic optimize ---'
gen = optimization.genetic()
s = gen.geneticoptimize(domain, crosscount)
print crosscount(s)
print s
drawnetwork(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)