def test_population(new_maze_population):
print "--------------------"
print "Population Solvability Check"
for curr_maze_count in range(new_maze_population.qsize()):
curr_maze = new_maze_population.queue[curr_maze_count]
curr_maze_result_dict = dfs_traversal(copy.deepcopy(curr_maze[1]), dim)
if not curr_maze_result_dict["is_solvable"]:
print "ERROR!!!"
maze_runner.visualize_maze(copy.deepcopy(curr_maze[1]))
print curr_maze_result_dict["max_fringe_length"]
print "--------------------"
def test_a_star(dim, p):
test_maze = maze_runner.get_maze(dim, p)
# a-star with manhattan heuristic
maze = copy.deepcopy(test_maze)
manhattan_result_dict = a_star_traversal(maze, "manhattan")
if manhattan_result_dict["is_solvable"]:
path = maze_runner.get_path(dim - 1, dim - 1, maze)
print "Path", path
print "Length of path: ", len(path)
maze_runner.trace_path(maze, path)
else:
maze_runner.visualize_maze(maze)
# a-star with euclidian heuristic
maze = copy.deepcopy(test_maze)
euclidian_result_dict = a_star_traversal(maze, "euclidian")
if euclidian_result_dict["is_solvable"]:
path = maze_runner.get_path(dim - 1, dim - 1, maze)
print "Path", path
print "Length of path: ", len(path)
maze_runner.trace_path(maze, path)
else:
maze_runner.visualize_maze(maze)
maze_runner.visualize_maze(maze)
# a-star with euclidian heuristic
maze = copy.deepcopy(test_maze)
euclidian_result_dict = a_star_traversal(maze, "euclidian")
if euclidian_result_dict["is_solvable"]:
path = maze_runner.get_path(dim - 1, dim - 1, maze)
print "Path", path
print "Length of path: ", len(path)
maze_runner.trace_path(maze, path)
else:
maze_runner.visualize_maze(maze)
#Fire code
'''
dim = 50
p = 0.306
q = 0.1
maze = maze_runner.get_maze(dim, p)
maze_runner.visualize_maze(maze)
manhattan_result, manhattan_steps, manhattan_max_fringe_length, manhattan_avg_fringe_length, manhattan_closed_set, x_cord, y_cord = a_star_traversal_with_fire(maze, "manhattan", q)
maze_runner.visualize_maze(maze)
if manhattan_result == 1:
print "Solution found"
path = maze_runner.get_path(dim-1, dim-1, maze)
maze_runner.trace_path(maze, path)
elif manhattan_result == 0:
print "Solution not found"
maze_runner.visualize_maze(maze)
else:
columns=column_list,
index=[generation_count])
generation_stats_df = generation_stats_df.append(df_entry)
print "fitness of the fittest maze: ", fitness_fittest
print "average fitness of the new population: ", fitness_average
"""
Approach1: We take the weakest 10% of the popluation to the new population
# print "fitness of fittest: ", fitness_fittest
# print "average fitness of fittest population: ", fitness_average
# while maze_population.qsize()>worst_mazes_count:
# maze_population.get()
# for count in range(worst_mazes_count):
# (fitness, maze) = maze_population.get()
# if count == worst_mazes_count-1
# fitness_worst = fitness
# new_maze_population.put((fitness, copy.deepcopy(maze)))
"""
maze_population = new_maze_population
print generation_stats_df
generation_stats_df.to_csv("generation_tmp.csv")
hardest_maze = maze_population.get()
print "fitness of the hardest maze: ", hardest_maze[0]
maze_runner.visualize_maze(hardest_maze[1])
hardest_maze_result_dict = a_star_traversal(copy.deepcopy(hardest_maze[1]),
"manhattan")
maze_runner.visualize_explored_cells(hardest_maze[1],
hardest_maze_result_dict["closed_set"])
pdb.set_trace()
if fringe_len>max_fringe_length: max_fringe_length = fringe_len avg_fringe_length = avg_fringe_length + (fringe_len - avg_fringe_length)/exploration_steps closed_set.add((x,y)) # Spreads fire to the neighboring cell as mentioned in the question. Since we might have # more cells on fire, the boundary_cells also get updated maze, boundary_cells = spread_fire(maze, dim, q, boundary_cells) fringe = update_fringe_using_boundary_cells(fringe, maze, boundary_cells) # No Solution return 0, exploration_steps, max_fringe_length, avg_fringe_length, closed_set, -1, -1 # Main code dim = 30 p = 0.22 q = 0.2 maze = mr.get_maze(dim, p) mr.visualize_maze(maze) manhattan_result, manhattan_steps, manhattan_max_fringe_length, manhattan_avg_fringe_length, manhattan_closed_set, x_cord, y_cord = fire_traversal(maze, q) mr.visualize_maze(maze) if manhattan_result == 1: print "Solution found" path = mr.get_path(dim-1, dim-1, maze) mr.trace_path(maze, path) elif manhattan_result == 0: print "Solution not found" mr.visualize_maze(maze) else: print "Burnt at " + str(x_cord) + ", " + str(y_cord) maze[x_cord][y_cord].value = 1.5 path = mr.get_path(x_cord, y_cord, maze) mr.trace_path(maze, path)