def generate_state_space(self, search_file=None):
"""c
"""
super(DotAgent, self).generate_state_space()
self.dot_coordinates = set(self.dot_coordinates)
# Generate adjacency matrix of spaces with dots in them.
if self.check_loops:
self.look_for_loops()
base_space = self.base_state_space
dot_state_space = {}
agent = Agent(search_file, state_space=base_space)
dot_coordinates = list(self.dot_coordinates)
for x in range(len(dot_coordinates)):
coordx = dot_coordinates[x]
dot_state_space[coordx] = {}
for y in range(len(dot_coordinates)):
coordy = dot_coordinates[y]
# No need to check if the coordinates are equal. Basic
# pathfinding module will just check the while loop
# condition, and it will function as an if condition.
######################################################
# Get optimal path cost between points coordx and
# coordy
start = copy.copy(base_space[coordx])
goal = copy.copy(base_space[coordy])
agent.goal_state = goal
agent.start_state = start
agent.search("a*", new_state_space=False)
solution = agent.solution_node
dot_state_space[coordx][coordy] = solution
# Overwrite base state space with adjacency matrix.
# States should now be generated on the fly as needed based
# off of the adjacency matrix.
self.state_space = dot_state_space
def main():
"""c
"""
search_files = [
'designed_map_1.txt',
'designed_map_2.txt',
]
search_types = [
'gbfs',
'a*'
]
for search_file in search_files:
agent = Agent(search_file)
for search_type in search_types:
agent.search(search_type, do_not_print=False)
