[CT421]: Assignment 1 code progress

2025-02-17 14:51:06 +00:00
parent 96e59ae0ff
commit 2d352ea302
2 changed files with 1081 additions and 7 deletions
--- a/year4/semester2/CT421/assignments/assignment1/code/output.csv
+++ b/year4/semester2/CT421/assignments/assignment1/code/output.csv
@ -0,0 +1,882 @@
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--- a/year4/semester2/CT421/assignments/assignment1/code/salesman.py
+++ b/year4/semester2/CT421/assignments/assignment1/code/salesman.py
@ -1,8 +1,9 @@
 #!/usr/bin/python3

 import argparse
-import re
+import random
 import math
+import time

 # function to read a TSP file in and construct a dictionary of its attributes
 def graph_from_file(input_file):
@ -10,8 +11,7 @@ def graph_from_file(input_file):

    print("Reading cities data from: " + input_file)

-    with open(input_file, "r") as file:
-        lines=file.readlines()
+    with open(input_file, "r") as file: lines=file.readlines()

    # assuming that the data will be available on specific line numbers and that fields must be included, which works for the provided datasets.
    # i would implement this in a better way if python didn't have such a terrible regex engine compared to perl (further evidence of why perl is the best and python sucks)
@ -34,6 +34,7 @@ def graph_from_file(input_file):
    return graph

 # function to generate an adjacency matrix from a graph
+# assumes each cities name is an integer starting at 1. city 1 is indexed at row,column 0, city 2 at row,column 1, ..., city n at row,column n-1
 def adjacency_matrix_from_graph(graph):
    matrix = []
    cities = sorted(graph["cities"], key=lambda city: city["name"]) # sorting list of cities in case not sorted, assuming each city's name is an int
@ -54,18 +55,209 @@ def dist(city1, city2):
    return math.sqrt( (city2["x"] - city1["x"] )**2 + ( city2["y"] - city1["y"] )**2 )


+# function to initialise a population of potential solutions
+# each potential solution is a list of city names, representing a permutation of the set of city names
+# the start city is random, and it is implied that the salesman must return from the last city in the list to the start city, i.e.  the start city appears only once in the list
+def initialise_population(size, graph):
+    city_names = [city["name"] for city in graph["cities"]]
+    tours = []
+
+    for i in range(size):
+        tours.append(random.sample(city_names, len(city_names)))
+
+    return tours
+
+
+# function to calculate the fitness (total length) of a given tour
+def fitness(tour, adjacency_matrix):
+    total_length = 0
+
+    for i in range(len(tour)):
+        # if city in question is the last city in the tour, get length to start city
+        if (i+1 == len(tour)):
+            total_length += adjacency_matrix[ tour[i] - 1 ][ tour[0] - 1 ]  # recall that the tour contains the name of the city using 1-based indexing, but the adjacency matrix uses 0-based indexing, so subtract 1 from the tour's name for a city
+        else:
+            total_length += adjacency_matrix[ tour[i] - 1 ][ tour[i+1] - 1 ]
+
+    return total_length
+
+
+# function to calculate the fitness for each element in a list
+def list_of_fitnesses(tours, adjacency_matrix):
+    return [fitness(tour, adjacency_matrix) for tour in tours]
+
+
+# function to return a dictionary containing the details of the current best solution
+def get_current_best(population, fitnesses, generation):
+    # not using min() and then indexof to find the best solution because that would be less efficient
+    index_of_current_best = min(enumerate(fitnesses), key=lambda index_fitness_tuple: index_fitness_tuple[1])[0]
+
+    # creating a dictionary to store the details of the current best solution
+    current_best = {
+        "tour": population[index_of_current_best],
+        "fitness": fitnesses[index_of_current_best],
+        "generation": generation
+    }
+    return current_best
+
+
+# function to randomly remove individuals for the population, with good solutions less likely to be removed and bad solutions more likely
+# the number to be removed is defined by how many offspring were produced in this generation, to keep population stable
+def deselect(population, fitnesses, num_to_remove):
+    # Compute survival probabilities in a single pass
+    total_inverse_fitness = sum(1.0 / fitness for fitness in fitnesses)
+    survival_probabilities = [(1.0 / fitness) / total_inverse_fitness for fitness in fitnesses]
+
+    # Select individuals to remove using weighted random sampling
+    indices_to_remove = set(random.choices( range(len(population)), weights=[1 - p for p in survival_probabilities], k=num_to_remove))
+
+    survivors = [solution for index, solution in enumerate(population) if index not in indices_to_remove]
+
+    return survivors
+
+# general crossover function
+def crossover(population, crossover_rate, number_to_replace):
+    offspring = []
+
+    # iterate until the desired number of offspring are produced (fraction of the current population)
+    while len(offspring) < number_to_replace:
+        # randomly select two parents
+        parent1, parent2 = random.sample(population, 2)
+
+        # probabilistically determine whether the pair will reproduce based off the crossover rate
+        if random.random() < crossover_rate:
+            # randomly alternate between the two crossover operators (50-50)
+            if random.random() < 0.5:
+                child = pmx_crossover(parent1, parent2)
+            else:
+                child = ox_crossover(parent1, parent2)
+
+            offspring.append(child)
+
+    return offspring
+
+# function to perform partially mapped crossover on two parents
+def pmx_crossover(parent1, parent2):
+    size = len(parent1)
+    child = [None] * size
+
+    # choose random crossover points
+    crossover_point1 = random.randint(0, size // 2)
+    crossover_point2 = random.randint(crossover_point1, size)
+
+    # copy the middle segment defined by the crossover points from parent1 to the child
+    for index in range(crossover_point1, crossover_point2):
+        child[index] = parent1[index]
+
+    # fill in the remaining positions using parent2 and the pmx mapping
+    for index in range(size):
+        if child[index] is None:
+            gene = parent2[index]
+            while gene in child:
+                gene = parent2[child.index(None)]
+            child[index] = gene
+
+    return child
+
+# function to perform order crossover on two parents
+def ox_crossover(parent1, parent2):
+    size = len(parent1)
+    child = [None] * size
+
+    # select a two random indexes from parent1 to create a segment for crossover
+    crossover_point1 = random.randint(0, size // 2)
+    crossover_point2 = random.randint(crossover_point1, size)
+
+    # copy the segment from parent1 to the offspring
+    for index in range(crossover_point1, crossover_point2):
+        child[index] = parent1[index]
+
+    # fill remaining positions with parent2, avoiding duplicates
+    current_position = crossover_point2
+    for index in range(size):
+        if parent2[index] not in child:
+            if current_position == size:
+                current_position = 0
+
+            child[current_position] = parent2[index]
+            current_position += 1
+
+    return child
+
+
+# general mutation function
+def mutate(offspring, mutation_rate):
+    offspring = []
+    return offspring
+
+
 def main():
    parser = argparse.ArgumentParser(description="Program to solve the travelling salesman problem for a given TSP file using a genetic algorithm.")
    parser.add_argument("-i", "--input-file", type=str, help="Path to input file in TSP format", required=True)
+    parser.add_argument("-s", "--size", type=int, help="Initial population size", required=False, default=100)
+    parser.add_argument("-g", "--num-generations", type=int, help="Number of generations", required=False, default=500)
+    parser.add_argument("-a", "--give-up-after", type=int, help="Number of generations to give up after if best solution has remained unchanged", required=False, default=100)
+    parser.add_argument("-d", "--deselection-proportion", type=float, help="The proportion of the population to be deselected on each generation", required=False, default=0.7)
+    parser.add_argument("-c", "--crossover-rate", type=float, help="Probability of a selected pair of solutions to sexually reproduce", required=False, default=0.8)
+    parser.add_argument("-m", "--mutation-rate", type=float, help="Probability of a selected offspring to undergo mutation", required=False, default=0.2)
    args=parser.parse_args()

-    input_file=args.input_file
+    print("Input file: " + str(args.input_file))
+    print("Initial population size: " + str(args.size))
+    print("Number of generations: " + str(args.num_generations))
+    print("Will give up after: " + str(args.give_up_after) + " generations")
+    print("Deselection proportion: " + str(args.deselection_proportion))
+    print("Crossover rate: " + str(args.crossover_rate))
+    print("Mutation rate: " + str(args.crossover_rate))

-    graph = graph_from_file(input_file)
+    graph = graph_from_file(args.input_file)
    adjacency_matrix = adjacency_matrix_from_graph(graph)

-    for row in adjacency_matrix:
-        print(row)
+    # get initial population & its details
+    population = initialise_population(args.size, graph)
+    fitnesses = list_of_fitnesses(population, adjacency_matrix)
+    current_best = get_current_best(population, fitnesses, 0)
+
+    # appending results to an array of strings rather than to a string as it's more efficient
+    results = ["timestamp,generation,population_size,avg_fitness,generation_best,current_best"]
+    results.append(str(time.time()) + "," + "0," + str(len(population)) + "," + str(sum(fitnesses)/len(fitnesses)) + "," + str(current_best["fitness"]) + "," + str(current_best["fitness"]))
+
+    number_to_replace = int(len(population) * args.deselection_proportion)
+
+    # this is where efficiency gets critical lol
+    for generation in range(args.num_generations):
+        print("Generation " + str(generation) + " of " + str(args.num_generations))
+        # deselect solutions from population probabilistically
+        population = deselect(population, fitnesses, number_to_replace)
+
+        # create a number of offspring with crossover to replace the number deselected
+        offspring = crossover(population, args.crossover_rate, number_to_replace)
+
+        # mutate offspring and add them to the original population to restore size
+        # population.append(mutate(offspring, args.mutation_rate))
+        population += offspring
+
+        # calculate fitnesses
+        fitnesses = list_of_fitnesses(population, adjacency_matrix)
+
+        # getting best solution from this generation and updating current_best if relevant
+        generation_best = get_current_best(population, fitnesses, generation)
+        if generation_best["fitness"] < current_best["fitness"]:
+            current_best = generation_best
+
+        results.append(str(time.time()) + "," + str(generation) + "," + str(len(population)) + "," + str(sum(fitnesses)/len(fitnesses)) + "," + str(generation_best["fitness"]) + "," + str(current_best["fitness"]))
+
+        if (generation - current_best["generation"]) >= args.give_up_after:
+            print("Best solution has not changed in " + str(args.give_up_after) + " generations. Giving up.")
+            break
+
+    print("Best solution found: " + str(current_best["tour"]))
+    print("Fitness of best solution: " + str(current_best["fitness"]))
+    print("Best solution found in generation: " + str(current_best["generation"]))
+
+    with open("output.csv", "w") as file:
+        for line in results:
+            file.write(line + "\n")


 if __name__ == "__main__":