def createModelBatteries(self):
modelBatteries = []
for i in range (0, len(self.batteries)):
modelBatteries.append(Model.Battery(i+1))
return modelBatteries
def randomize(env):
modelBatteries = []
# create the array of batteries with the id starting at 1
for i in range(0, len(env.batteries)):
maxCap = env.batteries[i].maxCapacity
battery = Model.Battery(i + 1)
modelBatteries.append(battery)
# assign every house to a random battery of which the capacity is still sufficient
listOfHouses = env.houses
random.shuffle(listOfHouses)
# assign random battery to every house
for house in listOfHouses:
batIndexes = []
for i in range(0, len(env.batteries)):
batIndexes.append(i)
assignToRandomBattery(batIndexes, env.batteries, house, modelBatteries)
# check if every house is connected
totalHouses = 0
for battery in modelBatteries:
totalHouses += len(battery.houses)
if totalHouses == len(env.houses):
# assign all the values into a model
model = Model(modelBatteries)
# calculate the costs of this option
model.calculateCosts(env.distanceTable)
return model
else:
return False
def genomeToModel(genome, env):
# create the array of batteries with the id starting at 1
modelBatteries = []
for i in range(0, len(env.batteries)):
battery = Model.Battery(i + 1)
modelBatteries.append(battery)
# create child model
newModel = Model(modelBatteries)
for battery in newModel.modelBatteries:
battery.setMaxCapacity(env)
# fill list of houses belonging to battery in new model according to genome
for gene in genome:
for house in env.houses:
if house.idHouse == (gene[0]):
corHouse = house
newModel.modelBatteries[gene[1] - 1].houses.append(corHouse)
newModel.calculateCosts(env.distanceTable)
return newModel