is_assignments/a1/code/main_t2.py

import pygad
import numpy as np
import random

# Create a maze class
global maze_ix

def fitness_func(path, solution_idx):
    maze = mazes[maze_ix]
    fitness = np.sum(path * maze.punish_matrix.reshape(-1))

    path = path.reshape(maze.punish_matrix.shape)

    if path[maze.start_pos] == 0:
        fitness -= 10000
    if path[maze.end_pos] == 0:
        fitness -= 10000 
    if path[maze.start_pos] == 1 and path[maze.end_pos] == 1:
        fitness += 300 
        # Check if there is a valid path
        complete_path = maze.walk_through_maze(path, critical_situation=False)
        complete_path_len = len(complete_path)

        # Set the first path found as the shotest one
        if maze.shortest_path == [] and complete_path_len > 0:
            maze.adjust_weights(complete_path)
            print('First path found')
            maze.shortest_path = complete_path
        #Check if the current path is shorter than the shortest one
        if complete_path_len != 0 and complete_path_len < len(maze.shortest_path):
            print('Found a better path')
            maze.shortest_path = complete_path
            maze.adjust_weights(complete_path)
        maze.ga_iteration += 1
    return fitness

def on_mutation(generations, ga_instance):
    maze = mazes[maze_ix]

    # Firtly find the instances where there are no walls
    no_wall_instances = np.where(maze.mutation_matrix.reshape(-1) == 1)[0]
    wall_instances = np.where(maze.mutation_matrix.reshape(-1) == 0)[0]
    
    # Loop through the population
    for i in range(len(generations)):
        #  select random number of the instances where there are walls 
        random_false_instances = np.random.choice(wall_instances, size=int(len(no_wall_instances)* random.uniform(0.01, 1.0)), replace=False)
        # Then randomly select random number of the instances where there are no walls
        random_true_instances = np.random.choice(no_wall_instances, size=int(len(no_wall_instances)* random.uniform(0.01, 1.0)), replace=False)
        # Then apply those values to generation 
        generations[i][random_true_instances] = 1 
        generations[i][random_false_instances] = 0

    return generations

class Maze:
    def __init__(self, maze, start_pos, end_pos, punish_matrix, mutation_matrix, shortest_path):
        self.maze = maze
        self.start_pos = start_pos
        self.end_pos = end_pos
        self.punish_matrix = punish_matrix
        self.mutation_matrix = mutation_matrix
        self.shortest_path = shortest_path
        self.ga_iteration = 0
        self.initial_population_size = 400 

    def run_genetic_algorithm(self):
        # Set global punish matrix
        punish_matrix = self.punish_matrix
        maze = self.maze
        ga_instance = pygad.GA(num_genes=punish_matrix.size,
                                   num_generations=3,
                                   sol_per_pop=self.initial_population_size,
                                   num_parents_mating=200,
                                   gene_type=np.uint8,
                                   fitness_func=fitness_func,
                                   parent_selection_type="random",
                                   keep_parents=2,
                                   allow_duplicate_genes=True, 
                                   parallel_processing=1,
                                   mutation_type=on_mutation,
                                  # initial_population=self.generate_initial_population(),
                                   gene_space=[0, 1])
        ga_instance.run()
    
        solution, solution_fitness, solution_idx = ga_instance.best_solution()


        print("The shortest path is", self.shortest_path, self.ga_iteration)
        self.print_shortest_path()

    def walk_through_maze(self, solution_matrix, critical_situation):
        queue = [[self.start_pos]]
    
        def add_to_queue(full_path, x, y):
            if (x,y) not in full_path:
                full_path = full_path.copy()
                full_path.append((x, y))
                queue.append(full_path)
    
        while queue != []:
            full_path = queue.pop() 
            x, y = full_path[-1]
            if(self.maze[x][y] == 'E'):
                return full_path 
            if x + 1 < len(self.maze) :
                if solution_matrix[x+1, y] == 1 and (critical_situation or (self.maze[x+1][y] == "." or self.maze[x+1][y] == "E")):
                    add_to_queue(full_path, x+1, y)
            if x - 1 >= 0:
                if solution_matrix[x-1, y] == 1 and (critical_situation or (self.maze[x-1][y] == "." or self.maze[x-1][y] == "E")):
                    add_to_queue(full_path, x-1, y)
            if y + 1 < len(self.maze) :
                if solution_matrix[x, y+1] == 1 and (critical_situation or(self.maze[x][y+1] == "." or self.maze[x][y+1] == "E")):
                    add_to_queue(full_path, x, y+1)
            if y - 1 >= 0:
                if solution_matrix[x, y-1] == 1 and (critical_situation or (self.maze[x][y-1] == "." or self.maze[x][y-1] == "E")):
                    add_to_queue(full_path, x, y-1)

        return [] 

    def adjust_weights(self, found_path):
        for (x, y) in found_path:
            self.punish_matrix[x,y] += 2000 

    def print_maze(self):
        for row in self.maze:
            print(' '.join(row))

    def print_shortest_path(self):
        for (x, y) in self.shortest_path:
            lst = list(self.maze[x])
            lst[y] = 'X'
            self.maze[x] = ''.join(lst)
        self.print_maze()

    def generate_initial_population(self):
        # Generate initial population
        # Firtly find the instances where there are no walls
        no_wall_instances = np.where(self.mutation_matrix.reshape(-1) == 1)[0]
        wall_instances = np.where(self.mutation_matrix.reshape(-1) == 0)[0]

        initial_population = np.random.choice([0, 1], size=(self.punish_matrix.size, self.initial_population_size))

        for population in initial_population:
            #  select random number of the instances where there are walls 
            random_false_instances = np.random.choice(wall_instances, size=int(len(no_wall_instances)* random.uniform(0.5, 1.0)), replace=False)
            # Then randomly select random number of the instances where there are no walls
            random_true_instances = np.random.choice(no_wall_instances, size=int(len(no_wall_instances)* random.uniform(0.5, 1.0)), replace=False)
            # Then apply those values to generation 
            population[random_true_instances] = 1 
            population[random_false_instances] = 0

        print(initial_population)

        return initial_population
 
def read_mazes():
    with open('./mazes.r', 'r') as f:
        mazes = []
        maze = []
        for line in f:
            if line == '\n':
                mazes.append(maze)
                maze = []
                continue
            maze.append(line.strip())
    return mazes

def prepare_maze(maze_ix, mazes):
    maze = mazes[maze_ix]
    punish_matrix = np.zeros((len(maze), len(maze)), dtype=np.int64)
    mutation_matrix = np.zeros((len(maze), len(maze)), dtype=np.uint8)
    
    start_index = 0, 0
    end_index = 0, 0
    treasures = []
    
    # Initialize punish matrix and find start and end index
    for i, x in enumerate(maze):
        for j, y in enumerate(x):
            if y == "#":
                punish_matrix[i, j] = -1000
                mutation_matrix[i, j] = 0
            if y == ".":
                punish_matrix[i, j] = +1000
                mutation_matrix[i, j] = 1
            if y == "S":
                start_index = i, j
                mutation_matrix[i, j] = 1
            if y == "E":
                end_index = i, j
                mutation_matrix[i, j] = 1
            if y == "T":
                mutation_matrix[i, j] = 2
                treasures.append((i, j))

    # Create maze class
    maze = Maze(maze, start_index, end_index, punish_matrix, mutation_matrix, [])

    return maze
    
def main():
    # Read mazes
    global maze_ix, mazes
    mazes = []
    text_mazes = read_mazes()
    for i in range(len(text_mazes)):
        print('MAZE: ', i)
        maze_ix = i
        maze = prepare_maze(i, text_mazes)
        mazes.append(maze)
        maze.run_genetic_algorithm()

if __name__ == "__main__":
    main()