def sample(self, ignored):
if self.cummulatives is None:
self.calculateCummulatives()
rand = random.random()
#print "DiscreteDistribution.sample.random" + str(rand)
#print "DiscreteDistribution.sample.cummulative" + str(self.cummulatives)
temp = ABAGAILArrays()
result = temp.search(self.cummulatives, rand)
# print "DiscreteDistribution.sample.search.result" + str(result)
return Instance(val = result)
print "Route:"
path = []
for x in range(0,N):
path.append(ga.getOptimal().getDiscrete(x))
print path
# for mimic we use a sort encoding
ef = TravelingSalesmanSortEvaluationFunction(points);
fill = [N] * N
ranges = list(array('i', fill))
odd = DiscreteUniformDistribution(ranges);
df = DiscreteDependencyTree(.1, ranges);
pop = GenericProbabilisticOptimizationProblem(ef, odd, df);
mimic = MIMIC(500, 100, pop)
fit = FixedIterationTrainer(mimic, 1000)
fit.train()
print "MIMIC Inverse of Distance: " + str(ef.value(mimic.getOptimal()))
print "Route:"
path = []
optimal = mimic.getOptimal()
fill = [0] * optimal.size()
ddata = list(array('d', fill))
for i in range(0,len(ddata)):
ddata[i] = optimal.getContinuous(i)
temp = ABAGAILArrays()
order = temp.indices(optimal.size())
temp.quicksort(ddata, order)
print order
def sample(self, ignored):
temp = ABAGAILArrays()
d = temp.dindices(self.n)
temp.permute(d)
return Instance(ds = d)
