def main():
# Generate the population
pop = toolBox.toolbox.population(n=POPULATION_SIZE)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
pop, log = algorithms.eaSimple(pop,
toolBox.toolbox,
cxpb=CROSS_OVER_PROB,
mutpb=MUTATION_PROB,
ngen=NO_OF_GENERATION,
stats=stats,
halloffame=hof,
verbose=True)
## Evaluate the entire population
#fitnesses = list(map(toolBox.toolbox.evaluate, pop))
#for ind, fit in zip(pop, fitnesses):
# ind.fitness.values = fit
# Iterate trough a number of generations
# for g in range(NGEN):
# print("-- Generation %i --" % g)
# # Select individuals based on their fitness
# offspring = toolBox.toolbox.select(pop, len(pop))
# # Cloning those individuals into a new population
# offspring = list(map(toolBox.toolbox.clone, offspring))
# # Calling the crossover function
# crossover(offspring)
# mutation(offspring)
# invalidfitness(offspring)
# The Best Individual found
best_ind = tools.selBest(pop, 1)[0]
individual = sorted(best_ind, key=itemgetter(3))
individual = sorted(individual, key=itemgetter(0))
#print "InsertBusTrip and TimeTable......"
print("Best individual is %s, %s" % (individual, best_ind.fitness.values))
print("Length of best individual: " + str(len(best_ind)))
fitnessClass = Fitness()
timetable = fitnessClass.genTimetable(best_ind)
databaseClass = DB()
#databaseClass.insertBusTrip(timetable)
evaluate_timetable.eval(best_ind)
def main():
# Generate the population
pop = toolBox.toolbox.population(n=POPULATION_SIZE)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
pop, log = algorithms.eaSimple(pop, toolBox.toolbox, cxpb=CROSS_OVER_PROB,
mutpb=MUTATION_PROB, ngen=NO_OF_GENERATION, stats=stats,
halloffame=hof, verbose=True)
## Evaluate the entire population
#fitnesses = list(map(toolBox.toolbox.evaluate, pop))
#for ind, fit in zip(pop, fitnesses):
# ind.fitness.values = fit
# Iterate trough a number of generations
# for g in range(NGEN):
# print("-- Generation %i --" % g)
# # Select individuals based on their fitness
# offspring = toolBox.toolbox.select(pop, len(pop))
# # Cloning those individuals into a new population
# offspring = list(map(toolBox.toolbox.clone, offspring))
# # Calling the crossover function
# crossover(offspring)
# mutation(offspring)
# invalidfitness(offspring)
# The Best Individual found
best_ind = tools.selBest(pop, 1)[0]
individual = sorted(best_ind, key=itemgetter(3))
individual = sorted(individual, key=itemgetter(0))
#print "InsertBusTrip and TimeTable......"
print("Best individual is %s, %s" % (individual, best_ind.fitness.values))
print ("Length of best individual: " + str(len(best_ind)))
fitnessClass = Fitness()
timetable = fitnessClass.genTimetable(best_ind)
databaseClass = DB()
#databaseClass.insertBusTrip(timetable)
evaluate_timetable.eval(best_ind)
def generateBusTrip(self, trip):
fitness = Fitness()
db = DB()
line = 0
count = 0
chromosome = []
for tripInstance in trip:
for busInstance in tripInstance[3]:
if line != busInstance["line"] and count != 0:
chromosome.append([line, capacity, self.calculateFrequency(count), startTime])
count = 0
if count == 0:
capacity = busInstance["capacity"]
startTime = busInstance["startTime"]
line = busInstance["line"]
count += 1
chromosome.append([line, capacity, self.calculateFrequency(count), startTime])
individual = fitness.genTimetable(chromosome)
db.insertBusTrip2(individual)
def generateBusTrip(self, trip):
fitness = Fitness()
db = DB()
line = 0
count = 0
chromosome = []
for tripInstance in trip:
for busInstance in tripInstance[3]:
if line != busInstance["line"] and count != 0:
chromosome.append([
line, capacity,
self.calculateFrequency(count), startTime
])
count = 0
if count == 0:
capacity = busInstance["capacity"]
startTime = busInstance["startTime"]
line = busInstance["line"]
count += 1
chromosome.append(
[line, capacity,
self.calculateFrequency(count), startTime])
individual = fitness.genTimetable(chromosome)
db.insertBusTrip2(individual)
