def cumulativegraph(data, v1, high, k=5, weightf=gaussian):
from pylab import arange, array, plot, show
t1 = arange(0., high, .1)
cprob = array([probguess(data, vec1, 0, v, k, weightf) for v in t1])
plot(t1, cprob)
show()
def probabilitygraph(data, vec1, high, k=5, weightf=gaussian, ss=5.):
t1 = arange(0., high, .1)
probs = [probguess(data, vec1, v, v+.1, k, weightf) for v in t1]
smoothed = []
for i in range(len(probs)):
sv = 0.
for j in range(len(probs)):
dist = abs(i-j)*.1
weight = gaussian(dist, sigma=ss)
sv += weight * probs[j]
smoothed.append(sv)
smoothed = array(smoothed)
plot(t1, smoothed)
show()
if __name__ == '__main__':
import optimization as op
data = wineset2()
costf = createcostfunction(weightedknn, data)
print op.genetic_optimize(weightdomain, costf, popsize=5, maxiter=20)
def task_flight():
"""
优化算法应用于航班选择。
"""
# 名字和出发地
people = [('Seymour', 'BOS'), ('Franny', 'DAL'), ('Zooey', 'CAK'),
('Walt', 'MIA'), ('Buddy', 'ORD'), ('Les', 'OMA')]
# 目的地
destination = 'LGA'
# 航班
flights = {}
for line in file('schedule.txt'):
# 起点，终点，起飞时间，到达时间，价格
origin, dest, depart, arrive, price = line.strip().split(',')
flights.setdefault((origin, dest), [])
flights[(origin, dest)].append((depart, arrive, int(price)))
def get_minutes(t):
x = time.strptime(t, '%H:%M')
# [year, month, day, hour, min, sec, ...]
return x[3] * 60 + x[4]
def print_schedule(r):
for d in range(len(r) / 2):
name = people[d][0]
origin = people[d][1]
out = flights[(origin, destination)][int(r[2 * d])]
ret = flights[(destination, origin)][int(r[2 * d + 1])]
print '%10s%10s %5s-%5s $%3s %5s-%5s $%3s' % (
name, origin, out[0], out[1], out[2], ret[0], ret[1], ret[2])
def schedule_cost(sol):
total_price = 0
latest_arrival = 0
earliest_dep = 24 * 60
for d in range(len(sol) / 2):
origin = people[d][1]
outbound = flights[(origin, destination)][int(sol[2 * d])]
return_flight = flights[(destination, origin)][int(sol[2 * d + 1])]
# 计算总金额
total_price += outbound[2]
total_price += return_flight[2]
# 记录最晚到达时间和最早离开时间
if latest_arrival < get_minutes(outbound[1]):
latest_arrival = get_minutes(outbound[1])
if earliest_dep > get_minutes(return_flight[0]):
earliest_dep = get_minutes(return_flight[0])
# 从某人到达 至 所有人到齐前 均为等待时间
# 从第一个人离开 至 某人离开前 均为等待时间
total_wait = 0
for d in range(len(sol) / 2):
origin = people[d][1]
outbound = flights[(origin, destination)][int(sol[2 * d])]
return_flight = flights[(destination, origin)][int(sol[2 * d + 1])]
total_wait += latest_arrival - get_minutes(outbound[1])
total_wait += get_minutes(return_flight[0]) - earliest_dep
# 若最早离开时间比最迟到达时间小（早），则说明过了一天，那么汽车租费需要增加
if latest_arrival > earliest_dep:
total_price += 50
return total_price + total_wait
domain = [(0, 9) for _ in range(len(people) * 2)]
s, c = random_optimize(domain, schedule_cost)
print '随机搜索优化算法 总花费: %d，行程如下' % c
print_schedule(s)
s, c = hill_climb(domain, schedule_cost)
print '\n爬山法优化算法 总花费: %d，行程如下' % c
print_schedule(s)
s, c = annealing_optimize(domain, schedule_cost)
print '\n模拟退火优化算法 总花费: %d，行程如下' % c
print_schedule(s)
s, c = genetic_optimize(domain, schedule_cost)
print '\n遗传算法优化 总花费: %d，行程如下' % c
print_schedule(s)
def task_draw_social_network():
people = [
'Charlie', 'Augustus', 'Veruca', 'Violet', 'Mike', 'Joe', 'Willy',
'Miranda'
]
links = [('Augustus', 'Willy'), ('Mike', 'Joe'), ('Miranda', 'Mike'),
('Violet', 'Augustus'), ('Miranda', 'Willy'), ('Charlie', 'Mike'),
('Veruca', 'Joe'), ('Miranda', 'Augustus'), ('Willy', 'Augustus'),
('Joe', 'Charlie'), ('Veruca', 'Augustus'), ('Miranda', 'Joe')]
def cross_cost(v):
loc = dict([(people[i], (v[i * 2], v[i * 2 + 1]))
for i in range(0, len(people))])
total = 0
# 交叉判罚
for i in range(len(links)):
for j in range(i + 1, len(links)):
(x1, y1), (x2, y2) = loc[links[i][0]], loc[links[i][1]]
(x3, y3), (x4, y4) = loc[links[j][0]], loc[links[j][1]]
# 下面判断是否两条线 交叉
den = (y4 - y3) * (x2 - x1) - (x4 - x3) * (y2 - y1)
if den == 0:
continue
ua = float((x4 - x3) * (y1 - y3) - (y4 - y3) * (x1 - x3)) / den
ub = float((x2 - x1) * (y1 - y3) - (y2 - y1) * (x1 - x3)) / den
if 0 < ua < 1 and 0 < ub < 1:
total += 1
# 距离判罚
for i in range(len(people)):
for j in range(i + 1, len(people)):
(x1, y1), (x2, y2) = loc[people[i]], loc[people[j]]
distance = math.sqrt(
math.pow(x1 - x2, 2) + math.pow(y1 - y2, 2))
if distance < 50:
total += (1.0 - (distance / 50.0))
return total
def draw_network(sol, img_name):
img = Image.new('RGB', (400, 400), (255, 255, 255))
draw = ImageDraw.Draw(img)
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.save(img_name, 'JPEG')
domain = [(10, 390)] * len(people) * 2
pos1, cost1 = random_optimize(domain, cross_cost)
pos2, cost2 = genetic_optimize(domain, cross_cost)
pos3, cost3 = hill_climb(domain, cross_cost)
pos4, cost4 = annealing_optimize(domain, cross_cost)
print cost4
draw_network(pos4, 'social_network/annealing.jpg')
print cost2
draw_network(pos2, 'social_network/genetic.jpg')
print cost3
draw_network(pos3, 'social_network/hill.jpg')
print cost1
draw_network(pos1, 'social_network/random.jpg')
del slots[x]
cost = 0
for dorm, (p1, p2) in occupy.items():
cost += 3 - ratings[p1][p2]
cost += 3 - ratings[p2][p1]
if debug:
print cost, dorms[dorm], prefs[p1][0], prefs[p2][0], ratings[p1][
p2], ratings[p2][p1]
return cost
if __name__ == '__main__':
import optimization as opt
'''
s = opt.random_optimize(domain, dormcost)
print s
print dormcost(s)
print_solution(s)
s = opt.genetic_optimize(domain, dormcost)
print s
print dormcost(s)
print_solution(s)
'''
s = opt.genetic_optimize(domain, dormcost2)
print s
print dormcost2(s, 1)
print_solution(s)
from pylab import arange, array, plot, show
t1 = arange(0., high, .1)
cprob = array([probguess(data, vec1, 0, v, k, weightf) for v in t1])
plot(t1, cprob)
show()
def probabilitygraph(data, vec1, high, k=5, weightf=gaussian, ss=5.):
t1 = arange(0., high, .1)
probs = [probguess(data, vec1, v, v + .1, k, weightf) for v in t1]
smoothed = []
for i in range(len(probs)):
sv = 0.
for j in range(len(probs)):
dist = abs(i - j) * .1
weight = gaussian(dist, sigma=ss)
sv += weight * probs[j]
smoothed.append(sv)
smoothed = array(smoothed)
plot(t1, smoothed)
show()
if __name__ == '__main__':
import optimization as op
data = wineset2()
costf = createcostfunction(weightedknn, data)
print op.genetic_optimize(weightdomain, costf, popsize=5, maxiter=20)
cost = 0
for dorm, (p1, p2) in occupy.items():
cost += 3-ratings[p1][p2]
cost += 3-ratings[p2][p1]
if debug:
print cost, dorms[dorm], prefs[p1][0], prefs[p2][0], ratings[p1][p2], ratings[p2][p1]
return cost
if __name__ == '__main__':
import optimization as opt
'''
s = opt.random_optimize(domain, dormcost)
print s
print dormcost(s)
print_solution(s)
s = opt.genetic_optimize(domain, dormcost)
print s
print dormcost(s)
print_solution(s)
'''
s = opt.genetic_optimize(domain, dormcost2)
print s
print dormcost2(s, 1)
print_solution(s)
