Make a python class rather..
Gasper Spagnolo
parent
cdf16d00cc
commit
3193a8853a
234
a1/code/main.py
234
a1/code/main.py
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@ -1,113 +1,93 @@
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import pygad
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import numpy as np
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global punish_matrix
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global start_pos
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global end_pos
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global shortest_path
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def walk_through_maze(solution_matrix):
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global punish_matrix, start_pos, end_pos, maze
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solution_matrix = solution_matrix.reshape((len(maze), len(maze)))
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queue = [[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.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(maze[x][y] == 'E'):
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return len(full_path)
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if x + 1 < len(maze) :
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if solution_matrix[x+1, y] == 1:
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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:
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add_to_queue(full_path, x-1, y)
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if y + 1 < len(maze) :
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if solution_matrix[x, y+1] == 1:
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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:
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add_to_queue(full_path, x, y-1)
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return 42069
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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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global punish_matrix, start_pos, end_pos, shortest_path
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punish = np.sum(path * punish_matrix.reshape(-1))
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print('Matrix multiplication', punish)
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# chekc if path is valied from start to end
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path = path.reshape(len(punish_matrix), len(punish_matrix))
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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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print('After matrix multiplication', fitness)
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path = path.reshape(maze.punish_matrix.shape)
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if path[start_pos] == 0:
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punish -= 1000
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if path[end_pos] == 0:
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punish -= 1000
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if path[start_pos] == 1 and path[end_pos] == 1:
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walk = walk_through_maze(path)
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if walk < shortest_path:
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punish += 10000
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shortest_path = walk
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return punish
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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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# Check if there is a valid path
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complete_path = maze.walk_through_maze(path)
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print('My complete path is', complete_path)
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complete_path_len = len(complete_path)
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if complete_path_len != 0 and complete_path_len < len(maze.shortest_path):
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maze.shortest_path = complete_path
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maze.adjust_weights(complete_path)
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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, 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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def run_genetic_algorithm(m, pm, sp, ep):
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# Set global punish matrix
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global punish_matrix, start_pos, end_pos, maze, shortest_path
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punish_matrix = pm
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start_pos = sp
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end_pos = ep
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maze = m
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shortest_path = len(maze) *2
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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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ga_instance = pygad.GA(num_genes=punish_matrix.size,
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num_generations=1000,
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sol_per_pop=2,
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num_parents_mating=2,
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gene_type=int,
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fitness_func=fitness_func,
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keep_parents=2,
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allow_duplicate_genes=True,
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gene_space=[0, 1])
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ga_instance.run()
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ga_instance.plot_fitness()
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solution, solution_fitness, solution_idx = ga_instance.best_solution()
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print("Parameters of the best solution : {solution}".format(
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solution=solution))
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print(solution.reshape(len(maze), len(maze)))
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print("Fitness value of the best solution = {solution_fitness}".format(
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solution_fitness=solution_fitness))
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fitness_function = fitness_func
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num_generations = 500
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num_parents_mating = 4
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sol_per_pop = 20
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num_genes = punish_matrix.size
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init_range_low = 0
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init_range_high = 1
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parent_selection_type = "sss"
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keep_parents = 1
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crossover_type = "single_point"
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mutation_type = "random"
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ga_instance = pygad.GA(num_generations=num_generations,
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num_parents_mating=num_parents_mating,
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fitness_func=fitness_function,
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sol_per_pop=sol_per_pop,
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num_genes=num_genes,
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init_range_low=init_range_low,
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init_range_high=init_range_high,
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parent_selection_type=parent_selection_type,
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keep_parents=keep_parents,
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crossover_type=crossover_type,
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mutation_type=mutation_type,
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mutation_num_genes=2,
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gene_space=[0, 1])
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ga_instance.run()
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solution, solution_fitness, solution_idx = ga_instance.best_solution()
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print("Parameters of the best solution : {solution}".format(
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solution=solution))
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print(solution.reshape(len(maze), len(maze)))
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print("Fitness value of the best solution = {solution_fitness}".format(
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solution_fitness=solution_fitness))
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def walk_through_maze(self, solution_matrix):
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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.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 # Adjust weights for the path :tipspepe:
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if x + 1 < len(self.maze) :
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if solution_matrix[x+1, y] == 1:# and maze[x+1][y] == ".":
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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 maze[x-1][y] == ".":
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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 maze[x][y+1] == ".":
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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 maze[x][y-1] == ".":
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add_to_queue(full_path, x, y-1)
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return [] # You return an empty array bro!
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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] += 100
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def read_mazes():
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with open('./mazes.r', 'r') as f:
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mazes = []
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@ -122,48 +102,42 @@ def read_mazes():
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def prepare_maze(maze_ix, mazes):
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maze = mazes[maze_ix]
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punish_matrix_t = np.zeros((len(maze), len(maze)), dtype=np.int64)
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punish_matrix = np.zeros((len(maze), len(maze)), dtype=np.int64)
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start_index_t = 0, 0
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end_index_t = 0, 0
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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_t[i, j] = -300
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punish_matrix[i, j] = -300
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if y == ".":
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punish_matrix_t[i, j] = +200
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punish_matrix[i, j] = +200
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if y == "S":
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start_index_t = i, j
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start_index = i, j
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if y == "E":
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end_index_t = i, j
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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, [])
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return maze
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return maze, punish_matrix_t, start_index_t, end_index_t
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def print_maze(maze):
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for row in maze:
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print(row)
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def main():
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# Read mazes
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mazes = 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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for i in range(1):
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maze, punish_matrix_t, start_index_t, end_index_t = prepare_maze(1, mazes)
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run_genetic_algorithm(maze, punish_matrix_t, start_index_t, end_index_t)
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print_maze(maze)
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maze_ix = i
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maze = prepare_maze(1, text_mazes)
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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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"""
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mutascija -> nesme mutirati u zid
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-> more mutirat v start ce tam ni poti
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-> more mutirat v end ce tam ni poti
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-> ce je na zidu enka mutiri v 0
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tresure kdr bo
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crossover ->
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malo z weights
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TRESURE -> spremeni fitrnes mutacijo in crossover
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"""
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main()
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