def generate_data(self, num_iterations, remaining, num_alternatives, plot_maps=False, plot_limit=10):
maps = []
goals = []
plot_count = 0
# Generate the cost_map
cost_map = CostMap(self.width, self.height)
cost_map.create_random_map(self.obstacle_width, self.obstacle_height, self.num_obstacles)
# Generate the possible goals (alternatives)
self.generate_goals(cost_map, num_alternatives)
for i in range(num_iterations):
if i != 0:
if not self.one_map:
# Update cost_map and possible goals (alternatives)
cost_map = CostMap(self.width, self.height)
cost_map.create_random_map(self.obstacle_width, self.obstacle_height, self.num_obstacles)
self.generate_goals(cost_map, num_alternatives)
elif not self.static_alternatives:
# Update just the possible goals (alternatives)
self.generate_goals(cost_map, num_alternatives)
paths = self.generate_paths(cost_map)
for path in paths:
planner_map, planner_goal = self.write_alternatives_on_map(path[-1], cost_map, num_alternatives)
planner_partial_path = self.cut_path(path, remaining)
planner_map = self.write_path_on_map(planner_partial_path, planner_map)
if plot_maps and plot_count < plot_limit:
self.plot_map(planner_map, planner_goal)
plot_count += 1
goals.append(planner_goal)
maps.append(planner_map)
maps, goals = shuffle(maps, goals, random_state=0)
return maps, goals
if save_fig:
plt.savefig('%s.%s' % (filename, fig_format), format=fig_format)
if show_fig:
plt.show()
# Environment's parameters
WIDTH = 160
HEIGHT = 120
OBSTACLE_WIDTH = 20
OBSTACLE_HEIGHT = 15
NUM_OBSTACLES = 20
cost_map = CostMap(WIDTH, HEIGHT)
# Initializing the random seed so we have reproducible results
# Please, do not change the seed
random.seed(15)
# Create a random map
cost_map.create_random_map(OBSTACLE_WIDTH, OBSTACLE_HEIGHT, NUM_OBSTACLES)
# Create the path planner using the cost map
path_planner = PathPlanner(cost_map)
# These vectors will hold the computation time and path cost for each iteration,
# so we may compute mean and standard deviation statistics in the Monte Carlo analysis.
times = np.zeros((num_iterations, 1))
costs = np.zeros((num_iterations, 1))
for i in range(num_iterations):
problem_valid = False
while not problem_valid:
# Trying to generate a new problem