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import time
import random
import math
people = [('Seymour','BOS'),
('Franny','DAL'),
('Zooey','CAK'),
('Walt','MIA'),
('Buddy','ORD'),
('Les','OMA')]
# Laguardia
destination='LGA'
flights={}
#
for line in file('schedule.txt'):
origin,dest,depart,arrive,price=line.strip().split(',')
flights.setdefault((origin,dest),[])
# Add details to the list of possible flights
flights[(origin,dest)].append((depart,arrive,int(price)))
def getminutes(t):
x=time.strptime(t,'%H:%M')
return x[3]*60+x[4]
def printschedule(r):
for d in range(len(r)/2):
name=people[d][0]
origin=people[d][1]
out=flights[(origin,destination)][int(r[d])]
ret=flights[(destination,origin)][int(r[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 schedulecost(sol):
totalprice=0
latestarrival=0
earliestdep=24*60
for d in range(len(sol)/2):
# Get the inbound and outbound flights
origin=people[d][1]
outbound=flights[(origin,destination)][int(sol[d])]
returnf=flights[(destination,origin)][int(sol[d+1])]
# Total price is the price of all outbound and return flights
totalprice+=outbound[2]
totalprice+=returnf[2]
# Track the latest arrival and earliest departure
if latestarrival<getminutes(outbound[1]): latestarrival=getminutes(outbound[1])
if earliestdep>getminutes(returnf[0]): earliestdep=getminutes(returnf[0])
# Every person must wait at the airport until the latest person arrives.
# They also must arrive at the same time and wait for their flights.
totalwait=0
for d in range(len(sol)/2):
origin=people[d][1]
outbound=flights[(origin,destination)][int(sol[d])]
returnf=flights[(destination,origin)][int(sol[d+1])]
totalwait+=latestarrival-getminutes(outbound[1])
totalwait+=getminutes(returnf[0])-earliestdep
# Does this solution require an extra day of car rental? That'll be $50!
if latestarrival>earliestdep: totalprice+=50
return totalprice+totalwait
def randomoptimize(domain,costf):
best=999999999
bestr=None
for i in range(0,1000):
# Create a random solution
r=[float(random.randint(domain[i][0],domain[i][1]))
for i in range(len(domain))]
# Get the cost
cost=costf(r)
# Compare it to the best one so far
if cost<best:
best=cost
bestr=r
return r
def hillclimb(domain,costf):
# Create a random solution
sol=[random.randint(domain[i][0],domain[i][1])
for i in range(len(domain))]
# Main loop
while 1:
# Create list of neighboring solutions
neighbors=[]
for j in range(len(domain)):
# One away in each direction
if sol[j]>domain[j][0]:
neighbors.append(sol[0:j]+[sol[j]+1]+sol[j+1:])
if sol[j]<domain[j][1]:
neighbors.append(sol[0:j]+[sol[j]-1]+sol[j+1:])
# See what the best solution amongst the neighbors is
current=costf(sol)
best=current
for j in range(len(neighbors)):
cost=costf(neighbors[j])
if cost<best:
best=cost
sol=neighbors[j]
# If there's no improvement, then we've reached the top
if best==current:
break
return sol
def annealingoptimize(domain,costf,T=10000.0,cool=0.95,step=1):
# Initialize the values randomly
vec=[float(random.randint(domain[i][0],domain[i][1]))
for i in range(len(domain))]
while T>0.1:
# Choose one of the indices
i=random.randint(0,len(domain)-1)
# Choose a direction to change it
dir=random.randint(-step,step)
# Create a new list with one of the values changed
vecb=vec[:]
vecb[i]+=dir
if vecb[i]<domain[i][0]: vecb[i]=domain[i][0]
elif vecb[i]>domain[i][1]: vecb[i]=domain[i][1]
# Calculate the current cost and the new cost
ea=costf(vec)
eb=costf(vecb)
p=pow(math.e,(-eb-ea)/T)
# Is it better, or does it make the probability
# cutoff?
if (eb<ea or random.random()<p):
vec=vecb
# Decrease the temperature
T=T*cool
return vec
def geneticoptimize(domain,costf,popsize=50,step=1,
mutprod=0.2,elite=0.2,maxiter=100):
# Mutation Operation
def mutate(vec):
i=random.randint(0,len(domain)-1)
if random.random()<0.5 and vec[i]>domain[i][0]:
return vec[0:i]+[vec[i]-step]+vec[i+1:]
elif vec[i]<domain[i][1]:
return vec[0:i]+[vec[i]+step]+vec[i+1:]
# Crossover Operation
def crossover(r1,r2):
i=random.randint(1,len(domain)-2)
return r1[0:i]+r2[i:]
# Build the initial population
pop=[]
for i in range(popsize):
vec=[random.randint(domain[i][0],domain[i][1])
for i in range(len(domain))]
pop.append(vec)
# How many winners from each generation?
topelite=int(elite*popsize)
# Main loop
for i in range(maxiter):
scores=[(costf(v),v) for v in pop]
scores.sort()
ranked=[v for (s,v) in scores]
# Start with the pure winners
pop=ranked[0:topelite]
# Add mutated and bred forms of the winners
while len(pop)<popsize:
if random.random()<mutprob:
# Mutation
c=random.randint(0,topelite)
pop.append(mutate(ranked[c]))
else:
# Crossover
c1=random.randint(0,topelite)
c2=random.randint(0,topelite)
pop.append(crossover(ranked[c1],ranked[c2]))
# Print current best score
print scores[0][0]
return scores[0][1]
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