197 lines
6.5 KiB
Python
197 lines
6.5 KiB
Python
import pygad
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import numpy as np
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import time
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# Create a maze class
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global maze_ix
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def fitness_func(path, solution_idx):
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maze = mazes[maze_ix]
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fitness = np.sum(path * maze.punish_matrix.reshape(-1))
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path = path.reshape(maze.punish_matrix.shape)
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if path[maze.start_pos] == 0:
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fitness -= 10000
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if path[maze.end_pos] == 0:
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fitness -= 10000
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if path[maze.start_pos] == 1 and path[maze.end_pos] == 1:
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fitness += 100
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if maze.ga_iteration >= 4000 and maze.shortest_path == []:
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critical = True
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else:
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critical = False
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# Check if there is a valid path
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complete_path = maze.walk_through_maze(path, critical_situation=critical)
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complete_path_len = len(complete_path)
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# Set the first path found as the shotest one
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if maze.shortest_path == [] and complete_path_len > 0:
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maze.adjust_weights(complete_path)
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print('First path found')
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maze.shortest_path = complete_path
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# Check if the current path is shorter than the shortest one
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elif complete_path_len != 0 and complete_path_len < len(maze.shortest_path):
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print('Found a better path')
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maze.shortest_path = complete_path
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maze.adjust_weights(complete_path)
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maze.ga_iteration += 1
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return fitness
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class Maze:
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def __init__(self, maze, start_pos, end_pos, punish_matrix, logfile, shortest_path):
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self.maze = maze
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self.start_pos = start_pos
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self.end_pos = end_pos
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self.punish_matrix = punish_matrix
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self.shortest_path = shortest_path
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self.ga_iteration = 0
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self.start_time = 0
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self.end_time = 0
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self.logfile = logfile
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def run_genetic_algorithm(self):
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# Set global punish matrix
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punish_matrix = self.punish_matrix
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maze = self.maze
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self.start_time = time.time()
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ga_instance = pygad.GA(num_genes=punish_matrix.size,
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num_generations=1000,
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sol_per_pop=20,
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num_parents_mating=15,
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gene_type=int,
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crossover_type="two_points",
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fitness_func=fitness_func,
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parent_selection_type="tournament",
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keep_parents=-1,
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allow_duplicate_genes=True,
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parallel_processing=4,
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gene_space=[0, 1])
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ga_instance.run()
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self.end_time = time.time()
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solution, solution_fitness, solution_idx = ga_instance.best_solution()
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self.log_experiment()
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#print("The shortest path is", self.shortest_path, self.ga_iteration)
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#self.print_shortest_path()
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def walk_through_maze(self, solution_matrix, critical_situation):
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queue = [[self.start_pos]]
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def add_to_queue(full_path, x, y):
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if (x,y) not in full_path:
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full_path = full_path.copy()
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full_path.append((x, y))
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queue.append(full_path)
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while queue != []:
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full_path = queue.pop()
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x, y = full_path[-1]
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if(self.maze[x][y] == 'E'):
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return full_path
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if x + 1 < len(self.maze) :
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if solution_matrix[x+1, y] == 1 and (critical_situation or (self.maze[x+1][y] == "." or self.maze[x+1][y] == "E")):
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add_to_queue(full_path, x+1, y)
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if x - 1 >= 0:
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if solution_matrix[x-1, y] == 1 and (critical_situation or (self.maze[x-1][y] == "." or self.maze[x-1][y] == "E")):
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add_to_queue(full_path, x-1, y)
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if y + 1 < len(self.maze) :
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if solution_matrix[x, y+1] == 1 and (critical_situation or(self.maze[x][y+1] == "." or self.maze[x][y+1] == "E")):
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add_to_queue(full_path, x, y+1)
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if y - 1 >= 0:
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if solution_matrix[x, y-1] == 1 and (critical_situation or (self.maze[x][y-1] == "." or self.maze[x][y-1] == "E")):
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add_to_queue(full_path, x, y-1)
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return []
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def adjust_weights(self, found_path):
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for (x, y) in found_path:
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self.punish_matrix[x,y] += 700
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def print_maze(self):
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for row in self.maze:
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print(' '.join(row))
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def print_shortest_path(self):
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for (x, y) in self.shortest_path:
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lst = list(self.maze[x])
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lst[y] = 'X'
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self.maze[x] = ''.join(lst)
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self.print_maze()
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def log_experiment(self):
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with open(self.logfile, 'a') as f:
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f.write(str(maze_ix) +',')
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f.write(str(self.end_time - self.start_time) +',')
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f.write(str(self.shortest_path))
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f.write('\n')
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f.flush()
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f.close()
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def read_mazes():
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with open('./mazes_classic.txt', 'r') as f:
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mazes = []
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maze = []
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for line in f:
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if line == '\n':
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mazes.append(maze)
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maze = []
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continue
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maze.append(line.strip())
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return mazes
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def prepare_maze(maze_ix, mazes, LOGFILE):
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maze = mazes[maze_ix]
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punish_matrix = np.zeros((len(maze), len(maze)), dtype=np.int64)
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start_index = 0, 0
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end_index = 0, 0
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treasures = []
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# Initialize punish matrix and find start and end index
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for i, x in enumerate(maze):
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for j, y in enumerate(x):
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if y == "#":
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punish_matrix[i, j] = -1000
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if y == ".":
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punish_matrix[i, j] = +700
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if y == "S":
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start_index = i, j
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if y == "E":
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end_index = i, j
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if y == "T":
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treasures.append((i, j))
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# Create maze class
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maze = Maze(maze, start_index, end_index, punish_matrix, LOGFILE, [])
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return maze
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def prepare_log(LOGFILE):
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with open(LOGFILE, 'a') as f:
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f.write('Maze,Time,Shortest path')
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f.write('\n')
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f.flush()
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f.close()
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def main():
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# Read mazes
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global maze_ix, mazes
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mazes = []
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text_mazes = read_mazes()
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LOGFILE = 'log_t1_classic.txt'
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prepare_log(LOGFILE)
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for i in range(len(text_mazes)):
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print('MAZE: ', i)
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maze_ix = i
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maze = prepare_maze(i, text_mazes, LOGFILE)
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mazes.append(maze)
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maze.run_genetic_algorithm()
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if __name__ == "__main__":
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main()
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