class Combining_algorithms():
'''
The Combining_algorithms class brings the two types of algorithms that the
Optimum Prime program uses to solve this problem together: the algorithms
that devide the houses amoungst the batteries in the configuration and the
algorithms that lay the cables that connect these houses with the cables.
The initialisation is done using the number of district instance the user
wants to use.
'''
def __init__(self, district_numb):
# loads the batteries and houses into district variable of type configuration
self.the_district = Configuration(district_numb)
self.the_district.create_district()
# Greedy_house allocation & Astar cable drawing - assumption 1 cable 1 house
def greedy_house_manhatten_cable(self, cable_sharing):
'''
Uses a greedy algorithm with a huristic based on proximity of a house to
a battery to devide the houses amoungst the batteries. Than uses the manhatten
distance to lay cables between thouse houses and their batteries. Finally, prints
the total costs of the solution.
'''
self.greedy_house_devide = Greedy(self.the_district)
self.greedy_house_devide.proximity_sort()
self.astar_cable = Cable(self.the_district)
self.astar_cable.cable_list_batteries(self.the_district.all_batteries,
cable_sharing)
self.the_district.refresh_config()
return self.greedy_house_devide.district.cs50_check(cable_sharing)
def annealing_hill_climber(self, step_num, cable_sharing):
'''
This method first applies the simulating annealing algorithm to sort all the houses
in the batteries. Then it applies the hill climber algorithm to modify this solution
to an even better one. It takes step_num steps in the hill climber algorithm.
'''
self.annealing_house_devide1 = simulated_annealing(self.the_district)
self.annealing_house_devide1.creating_starting_possition()
self.annealing_house_devide1.running_simulated_annealing(cable_sharing)
self.astar_cable2 = Cable(self.the_district)
self.astar_cable2.cable_list_batteries(self.the_district.all_batteries,
False)
self.hill = Hill_climber(self.the_district)
self.hill.climb_the_hill(step_num)
self.the_district.refresh_config()
return self.annealing_house_devide1.district.cs50_check(cable_sharing)
def greedy_then_hill_climber(self, step_num, cable_sharing):
self.greedy_house_manhatten_cable(cable_sharing)
hill_climber = Hill_climber(self.the_district)
hill_climber.climb_the_hill(step_num)
return self.the_district.cs50_check(cable_sharing)
# simulated anealing house distribution & Astar cable drawing - assumption 1 cable 1 house
def simulated_annealing_house_manhatten_cable(self, cable_sharing):
'''
Combines a simulated annealing algorithm with a huristic based on proximity of a house to a
battery and the fullness of a battery compared to other batteries to devide the houses amoung
the batteries. Than uses the manhatten distance to lay cables between thouse houses and their
batteries. Finally, prints the total costs of the solution.
'''
self.sa_distribution = simulated_annealing(self.the_district)
self.sa_distribution.creating_starting_possition()
self.sa_distribution.running_simulated_annealing(cable_sharing)
self.astar_cable2 = Cable(self.the_district)
self.astar_cable2.cable_list_batteries(self.the_district.all_batteries,
cable_sharing)
self.the_district.refresh_config()
return self.sa_distribution.district.cs50_check(cable_sharing)
def random_greedy_astar_manhatten_cable(self, rounds, cable_sharing):
'''
Introduces some randomness too a greedy algorithm with a huristic based on proximity of a house
to a battery. Than uses the manhatten distance to lay cables between thouse houses and their
batteries. Finally, prints the total costs of the solution.
'''
self.random_greedy = Greedy(self.the_district)
self.random_greedy.random_greedy(rounds)
self.astar_cable2 = Cable(self.the_district)
self.astar_cable2.cable_list_batteries(self.the_district.all_batteries,
cable_sharing)
self.the_district.refresh_config()
return self.random_greedy.district.cs50_check(cable_sharing)
