def get_exit_paths(instance):
start, finish = instance.level.botSpawnAreas[instance.game.enemyTeam.name]
enemy_base = Vector2(start.x, start.y)
instance.graph.add_node("enemy_base", position = (start.x, start.y), weight = 0.0)
instance.graph.node["enemy_base"]["exit_path"] = 0.0
instance.graph.node["enemy_base"]["camp_target"] = 0.0
instance.graph.node["enemy_base"]["camp_location"] = 0.0
for i, j in itertools.product(range(int(start.x), int(finish.x)), range(int(start.y), int(finish.y))):
instance.graph.add_edge("enemy_base", instance.terrain[j][i], weight = 1.0)
our_flag_node = regressions2.get_node_index(instance, instance.game.team.flag.position)
enemy_score_node = regressions2.get_node_index(instance, instance.game.enemyTeam.flagScoreLocation)
enemy_flag_node = regressions2.get_node_index(instance, instance.game.enemyTeam.flag.position)
our_score_node = regressions2.get_node_index(instance, instance.game.team.flagScoreLocation)
b_to_flag = nx.shortest_path(instance.graph, source="enemy_base", target = our_flag_node)
b_to_def = nx.shortest_path(instance.graph, source="enemy_base", target = enemy_flag_node)
b_to_def2 = nx.shortest_path(instance.graph, source="enemy_base", target = our_score_node)
#Calculate how the enemy is exiting from their base.
exit_paths = [(b_to_flag, 10), (b_to_def, 6), (b_to_def2, 2)]
for x in range(50):
position = instance.level.findRandomFreePositionInBox(instance.level.area)
base_seperation = position - enemy_base
base_seperation = base_seperation*15/base_seperation.length()
close_pos = enemy_base + base_seperation
x, y = regressions2.sanitize_position(instance, close_pos)
close_pos = Vector2(x, y)
node_index = regressions2.get_node_index(instance, close_pos)
path = nx.shortest_path(instance.graph, source="enemy_base", target = node_index)
exit_paths.append((path, 4))
return exit_paths
def register_waypoints(self, bot, waypoints):
edges = []
edges.append((bot.position, waypoints[0]))
for waypoint_index in range(len(waypoints) - 1):
edges.append(
(waypoints[waypoint_index], waypoints[waypoint_index + 1]))
total_nodes = set()
for edge in edges:
source_node = regressions2.get_node_index(self, edge[0])
target_node = regressions2.get_node_index(self, edge[1])
path = nx.shortest_path(self.graph,
source=source_node,
target=target_node)
for node in path:
total_nodes.add(node)
for node_index in total_nodes:
self.graph.node[node_index]["pheremone"] += 1
neighbors = self.graph.neighbors(node_index)
if neighbors is not None:
for neighbor_index in neighbors:
self.graph.node[neighbor_index]["pheremone"] += .5
neighbors2 = self.graph.neighbors(neighbor_index)
if neighbors2 is not None:
for neighbor_index2 in neighbors2:
self.graph.node[neighbor_index]["pheremone"] += .2
def calculate_nodes_in_range(instance, last_position, time_since, enemy_speed):
max_distance = time_since * enemy_speed
#used for inaccesible calculations.
real_max_distance = max_distance
if max_distance > instance.MAX_ENEMY_DISTANCE:
max_distance = instance.MAX_ENEMY_DISTANCE
if max_distance == 0.0:
return set([regressions2.get_node_index(instance, last_position)])
#Get bounds for the inital square of nodes to search.
left_bound = int(max(1, last_position.x - max_distance))
right_bound = int(min(88, last_position.x + max_distance))
top_bound = int(min(50, last_position.y + max_distance))
lower_bound = int(max(1, last_position.y - max_distance))
#Find nodes in initial square, and prune those that are out of range. (The square's corners.)
possible_nodes = set()
for x in range(left_bound, right_bound):
for y in range(lower_bound, top_bound):
distance_vector = Vector2(x, y) - last_position
if distance_vector.length() > max_distance:
continue
elif instance.level.blockHeights[int(x)][int(y)] > 0:
continue
node_index = regressions2.get_node_index(instance, Vector2(x, y))
possible_nodes.add(node_index)
if len(possible_nodes) == 0.0:
return set([regressions2.get_node_index(instance, last_position)])
return possible_nodes
def get_chokes(instance, choke_candidates):
#prevent writing over base space.
used_set = set()
start, finish = instance.level.botSpawnAreas[instance.game.enemyTeam.name]
for i, j in itertools.product(range(int(start.x), int(finish.x)), range(int(start.y), int(finish.y))):
node_index = regressions2.get_node_index(instance, Vector2(i,j))
used_set.add(node_index)
choke_dict = {}
master_chokes = set()
flag_node = regressions2.get_node_index(instance, instance.game.team.flag.position)
spawn_node = regressions2.get_node_index(instance, get_enemy_base(instance))
shortest_length = nx.shortest_path_length(instance.graph, source=spawn_node, target=flag_node, weight="choke_covered")
choke_count = 0
while shortest_length == 0.0:
if len(choke_candidates) == 0.0:
print "RAN OUT OF CANDIDATES!"
break
choke_count += 1
one_choke = set()
choke_center = choke_candidates.pop()
choke_vector = regressions2.get_node_vector(instance, choke_center)
#Ignore potential chokes too far from their spawn.
while (choke_vector.distance((get_enemy_base(instance))) > 5.0 or choke_center in used_set) and len(choke_candidates) > 0:
choke_vector = regressions2.get_node_vector(instance, choke_center)
choke_center = choke_candidates.pop()
if len(choke_candidates) == 0:
print "RAN OUT OF CANDIDATES!"
return choke_dict, master_chokes
if choke_vector.distance((get_enemy_base(instance))) > 5.0:
print "RAN OUT OF CANDIDATES, LAST CANDIDATE DIDN'T WORK!"
return choke_dict, master_chokes
one_choke.add(choke_center)
for x in range(4):
neighbors = set()
for node in one_choke:
neighbors2 = instance.graph.neighbors(node)
if neighbors2 is not None:
for neighbor2 in neighbors2:
if neighbor2 not in used_set:
neighbors.add(neighbor2)
one_choke = one_choke.union(neighbors)
used_set = used_set.union(one_choke)
for node in one_choke:
instance.graph.node[node]["choke_covered"] = 1.0
neighbors = instance.graph.neighbors(node)
for neighbor in neighbors:
instance.graph.edge[node][neighbor]["choke_covered"] = 1.0
choke_dict[choke_center] = { "nodes": one_choke, "redundancy": 0}
master_chokes = master_chokes.union(one_choke)
shortest_length = nx.shortest_path_length(instance.graph, source=spawn_node, target=flag_node, weight="choke_covered")
return choke_dict, master_chokes
def get_exit_paths(instance):
start, finish = instance.level.botSpawnAreas[instance.game.enemyTeam.name]
enemy_base = Vector2(start.x, start.y)
instance.graph.add_node("enemy_base",
position=(start.x, start.y),
weight=0.0)
instance.graph.node["enemy_base"]["exit_path"] = 0.0
instance.graph.node["enemy_base"]["camp_target"] = 0.0
instance.graph.node["enemy_base"]["camp_location"] = 0.0
for i, j in itertools.product(range(int(start.x), int(finish.x)),
range(int(start.y), int(finish.y))):
instance.graph.add_edge("enemy_base",
instance.terrain[j][i],
weight=1.0)
our_flag_node = regressions2.get_node_index(
instance, instance.game.team.flag.position)
enemy_score_node = regressions2.get_node_index(
instance, instance.game.enemyTeam.flagScoreLocation)
enemy_flag_node = regressions2.get_node_index(
instance, instance.game.enemyTeam.flag.position)
our_score_node = regressions2.get_node_index(
instance, instance.game.team.flagScoreLocation)
b_to_flag = nx.shortest_path(instance.graph,
source="enemy_base",
target=our_flag_node)
b_to_def = nx.shortest_path(instance.graph,
source="enemy_base",
target=enemy_flag_node)
b_to_def2 = nx.shortest_path(instance.graph,
source="enemy_base",
target=our_score_node)
#Calculate how the enemy is exiting from their base.
exit_paths = [(b_to_flag, 10), (b_to_def, 6), (b_to_def2, 2)]
for x in range(50):
position = instance.level.findRandomFreePositionInBox(
instance.level.area)
base_seperation = position - enemy_base
base_seperation = base_seperation * 15 / base_seperation.length()
close_pos = enemy_base + base_seperation
x, y = regressions2.sanitize_position(instance, close_pos)
close_pos = Vector2(x, y)
node_index = regressions2.get_node_index(instance, close_pos)
path = nx.shortest_path(instance.graph,
source="enemy_base",
target=node_index)
exit_paths.append((path, 4))
return exit_paths
def remove_spawn_nodes(instance, close_nodes):
start, finish = instance.level.botSpawnAreas[instance.game.enemyTeam.name]
for i, j in itertools.product(range(int(start.x), int(finish.x)), range(int(start.y), int(finish.y))):
node_index = regressions2.get_node_index(instance, Vector2(i,j))
if node_index in close_nodes:
close_nodes.remove(node_index)
return close_nodes
def weight_camp_locations_by_sight(instance, close_nodes):
#Calculate the weight of all squares close to the enemy base relying on how many of the exit squares can be shot.
enemy_base = get_enemy_base(instance)
for node_index in close_nodes:
node_position = regressions2.get_node_vector(instance, node_index)
cells = []
w = visibility.Wave((88, 50),
lambda x, y: instance.level.blockHeights[x][y] > 1,
lambda x, y: cells.append((x, y)))
w.compute(node_position)
for x, y in cells:
cell_position = Vector2(x, y)
cell_node_index = regressions2.get_node_index(
instance, cell_position)
if node_position.distance(
cell_position) < instance.level.firingDistance:
#Edges don't work with our functions, and are unlikely to be actual optimum. #TODO fully debug rather than hack.
if not (node_position.x < 1.0 or node_position.x > 87.0
or node_position.y < 1.0 or node_position.y > 47.0):
camp_value = instance.graph.node[cell_node_index][
"camp_target"] / (cell_position.distance(enemy_base) +
3)
instance.graph.node[node_index][
"camp_location"] += camp_value
def avoid_suicide_and_trades(self):
#New orders for all charging bots about to suicide.
if self.counter%self.COMMAND_RATE != 0 and self.counter%self.AVAIL_RATE !=0 and self.counter%self.HOLD_RATE != 0:
bots = []
for bot in self.game.team.members:
if bot.health > 0 and self.bots[bot.name]["command"] != None:
if type(self.bots[bot.name]["command"]) == commands.Charge or type(self.bots[bot.name]["command"]) == commands.Attack:
continue_command = regressions2.get_continue_command(self, bot, self.bots[bot.name]["command"])
if len(continue_command.target) == 1:
if self.graph.node[regressions2.get_node_index(self, continue_command.target[0])]["p_enemy_fire"] > .4:
bots.append(bot)
else:
for position in continue_command.target[0:int(len(continue_command.target)/2)]:
if self.graph.node[regressions2.get_node_index(self, position)]["p_enemy_fire"] > .4:
bots.append(bot)
self.command_routine(bots)
def calculate_nodes_in_range(instance, last_position, time_since, enemy_speed):
max_distance = time_since * enemy_speed
if max_distance > 55:
max_distance = 55
#Get bounds for the inital square of nodes to search.
left_bound = int(max(1, last_position.x - max_distance))
right_bound = int(min(88, last_position.x + max_distance))
top_bound = int(min(50, last_position.y + max_distance))
lower_bound = int(max(1, last_position.y - max_distance))
## print "enemy_speed: ", enemy_speed
## print "time_since: ", time_since
## print "left_bound: ", left_bound
## print "right_bound: " , right_bound
## print "top_bound: ", top_bound
## print "lower_bound: ", lower_bound
#Find nodes in initial square, and prune those that are out of range. (The square's corners.)
possible_nodes = set()
for x in range(left_bound, right_bound):
for y in range(lower_bound, top_bound):
distance_vector = Vector2(x, y) - last_position
if distance_vector.length() > max_distance:
continue
elif instance.level.blockHeights[int(x)][int(y)] > 0:
continue
#@terrain
node_index = regressions2.get_node_index(instance, Vector2(x, y))
possible_nodes.add(node_index)
return possible_nodes
def calculate_nodes_in_range(instance, last_position, time_since, enemy_speed):
max_distance = time_since * enemy_speed
if max_distance > 55:
max_distance = 55
#Get bounds for the inital square of nodes to search.
left_bound = int(max(1, last_position.x - max_distance))
right_bound = int(min(88, last_position.x + max_distance))
top_bound = int(min(50, last_position.y + max_distance))
lower_bound = int(max(1, last_position.y - max_distance))
## print "enemy_speed: ", enemy_speed
## print "time_since: ", time_since
## print "left_bound: ", left_bound
## print "right_bound: " , right_bound
## print "top_bound: ", top_bound
## print "lower_bound: ", lower_bound
#Find nodes in initial square, and prune those that are out of range. (The square's corners.)
possible_nodes = set()
for x in range(left_bound, right_bound):
for y in range(lower_bound, top_bound):
distance_vector = Vector2(x, y) - last_position
if distance_vector.length() > max_distance:
continue
elif instance.level.blockHeights[int(x)][int(y)] > 0:
continue
#@terrain
node_index = regressions2.get_node_index(instance, Vector2(x, y))
possible_nodes.add(node_index)
return possible_nodes
def score_flanking_position(self, position, bot):
#Score possible positions
node_index = regressions2.get_node_index(self, position)
they_can_shoot = self.graph.node[node_index]["p_enemy_fire"]
far_from_friendlies = 0
for friendly in self.game.team.members:
if friendly.health > 0.0:
command = self.bots[friendly.name]["command"]
if command != None and type(command) != commands.Defend:
far_from_friendlies += position.distance(
command.target[-1])
far_from_friendlies /= len(self.game.team.members)
rough_enemy_dist = 0.0
living = 0.0
for enemy_bot in self.game.enemyTeam.members:
if enemy_bot.health > 0.0:
living += 1
rough_enemy_dist += position.distance(enemy_bot.position)
rough_enemy_dist /= living + 1
goto_flank_brink = 0.0
try:
minimum_distance = min([
position.distance(enemy_bot.position)
for enemy_bot in self.game.enemyTeam.members
if enemy_bot.health > 0.0
])
except ValueError: #If the enemy are all dead, set their position is far away.
minimum_distance = 100
dist_from_flank_range = abs((self.level.firingDistance) -
minimum_distance)
if dist_from_flank_range > -1:
goto_flank_brink = 50 / (dist_from_flank_range**1.5 + 1)
average_friendly_dist = 0.0
friendlies = 0.0
for friendly in self.game.team.members:
if friendly.health > 0.0 and friendly != bot:
friendlies += 1
average_friendly_dist += position.distance(friendly.position)
average_friendly_dist /= (friendlies + 1)
if average_friendly_dist > rough_enemy_dist:
past_enemy = 50
else:
past_enemy = 0.0
a, b, c, d, e = far_from_friendlies * 20, goto_flank_brink * 50, they_can_shoot * -500, rough_enemy_dist * -20, past_enemy * -50
## print "far_from_friendlies: ", a
## print "goto_flank_brink: ", b
## print "they_can_shoot: ", c
## print "rough_enemy_dist: ", d
## print "distance: ", e
## print
score = a + b + c + d + e
return score
def remove_spawn_nodes(instance, close_nodes):
start, finish = instance.level.botSpawnAreas[instance.game.enemyTeam.name]
for i, j in itertools.product(range(int(start.x), int(finish.x)),
range(int(start.y), int(finish.y))):
node_index = regressions2.get_node_index(instance, Vector2(i, j))
if node_index in close_nodes:
close_nodes.remove(node_index)
return close_nodes
def extrapolate(instance):
points = []
bot_extraps = {}
for enemy_bot in instance.enemies.keys():
if enemy_bot.health > 0.0:
#Get enemy heading.
extrapolation = enemy_bot.position - instance.enemies[enemy_bot]
#If we have no new data on enemy location, assume they are heading in direction of flag.
#TODO account for pin bots, hunt bots, and defend bots.
#TODO account for probable pathing... pheremone?
if extrapolation.length() == 0.0:
extrapolation = instance.game.team.flag.position - enemy_bot.position
if extrapolation.length() == 0.0:
extrapolation = instance.game.enemyTeam.flagScoreLocation - enemy_bot.position
extrapolation.normalize()
#Calculate point enemy should have ran to. TODO make realistic with level speed.
#TODO use visualizer to simultaneously show with visualization via circle drawings.
enemy_speed = get_enemy_bot_speed(instance, enemy_bot)*.8 #.8 because not straight running lines.
#Resolve points going off map by having the bot calculated as at map edge.
if extrapolation.x > enemy_bot.position.x:
x_bound = 87
else:
x_bound = 2
if extrapolation.y > enemy_bot.position.y:
y_bound = 49
else:
y_bound = 2
if enemy_bot.seenlast != None:
extrapolated_change = extrapolation * enemy_speed * (enemy_bot.seenlast + 1.5)
else:
extrapolated_change = extrapolation * enemy_speed * 1.5
if extrapolated_change.x > abs(enemy_bot.position.x - x_bound):
extrapolated_change.x = x_bound
if extrapolated_change.y > abs(enemy_bot.position.y - y_bound):
extrapolated_change.y = y_bound
final_position = enemy_bot.position + extrapolated_change
x, y = regressions2.sanitize_position(instance, final_position)
final_position = Vector2(x, y)
node_index = regressions2.get_node_index(instance, final_position)
vector = regressions2.get_node_vector(instance, node_index)
points.append(vector)
bot_extraps[enemy_bot] = vector
if instance.DRAW_POINTS == "extrap":
instance.points = points
return bot_extraps
def score_flanking_position(self, position, bot):
#Score possible positions
node_index = regressions2.get_node_index(self, position)
they_can_shoot = self.graph.node[node_index]["p_enemy_fire"]
far_from_friendlies = 0
for friendly in self.game.team.members:
if friendly.health > 0.0:
command = self.bots[friendly.name]["command"]
if command != None and type(command) != commands.Defend:
far_from_friendlies += position.distance(command.target[-1])
far_from_friendlies /= len(self.game.team.members)
rough_enemy_dist = 0.0
living = 0.0
for enemy_bot in self.game.enemyTeam.members:
if enemy_bot.health > 0.0:
living += 1
rough_enemy_dist += position.distance(enemy_bot.position)
rough_enemy_dist /= living+1
goto_flank_brink = 0.0
try:
minimum_distance = min([position.distance(enemy_bot.position) for enemy_bot in self.game.enemyTeam.members if enemy_bot.health > 0.0])
except ValueError: #If the enemy are all dead, set their position is far away.
minimum_distance = 100
dist_from_flank_range = abs((self.level.firingDistance) - minimum_distance)
if dist_from_flank_range > -1:
goto_flank_brink = 50/(dist_from_flank_range**1.5+1)
average_friendly_dist = 0.0
friendlies = 0.0
for friendly in self.game.team.members:
if friendly.health > 0.0 and friendly != bot:
friendlies += 1
average_friendly_dist += position.distance(friendly.position)
average_friendly_dist /= (friendlies+1)
if average_friendly_dist > rough_enemy_dist:
past_enemy = 50
else:
past_enemy = 0.0
a, b, c, d, e = far_from_friendlies*20, goto_flank_brink*50, they_can_shoot*-500, rough_enemy_dist*-20, past_enemy*-50
## print "far_from_friendlies: ", a
## print "goto_flank_brink: ", b
## print "they_can_shoot: ", c
## print "rough_enemy_dist: ", d
## print "distance: ", e
## print
score = a + b + c + d + e
return score
def weight_camp_locations_by_choke_exposure(instance):
for node in instance.choke_dict.keys():
enemy_base_square = regressions2.get_node_vector(instance, node)
cells = []
w = visibility.Wave((88, 50), lambda x, y: instance.level.blockHeights[x][y] > 1, lambda x, y: cells.append((x,y)))
w.compute(enemy_base_square)
for x, y in cells:
cell_position = Vector2(x, y)
cell_node_index = regressions2.get_node_index(instance, cell_position)
if cell_position.distance(enemy_base_square) < instance.level.firingDistance + 3:
instance.graph.node[cell_node_index]["camp_location"] *= .8
def get_nodes_for_one_enemy(instance, enemy_bot):
if enemy_bot.seenlast == 0.0:
return set(regressions2.get_node_index(instance, enemy_bot.position))
else:
#Calculate all possible squares the enemy_bot could have reached in the elapsed time.
enemy_speed = get_enemy_bot_speed(instance, enemy_bot)
candidates = calculate_nodes_in_range(instance, enemy_bot.position, enemy_bot.seenlast, enemy_speed)
#Refresh the graph's knowledge of which squares your bots can see.
update_friendly_sight(instance)
#Remove candidate notes that we can already see
candidates = remove_sighted_squares(instance, candidates)
return candidates
def register_waypoints(self, bot, waypoints):
edges = []
edges.append((bot.position, waypoints[0]))
for waypoint_index in range(len(waypoints)-1):
edges.append((waypoints[waypoint_index], waypoints[waypoint_index+1]))
total_nodes = set()
for edge in edges:
source_node = regressions2.get_node_index(self, edge[0])
target_node = regressions2.get_node_index(self, edge[1])
path = nx.shortest_path(self.graph, source=source_node, target=target_node)
for node in path:
total_nodes.add(node)
for node_index in total_nodes:
self.graph.node[node_index]["pheremone"] += 1
neighbors = self.graph.neighbors(node_index)
if neighbors is not None:
for neighbor_index in neighbors:
self.graph.node[neighbor_index]["pheremone"] += .5
neighbors2 = self.graph.neighbors(neighbor_index)
if neighbors2 is not None:
for neighbor_index2 in neighbors2:
self.graph.node[neighbor_index]["pheremone"] += .2
def weight_camp_locations_by_base_exposure(instance):
#Adjust the weight based on what squares can be seen from the enemy base.
start, finish = instance.level.botSpawnAreas[instance.game.enemyTeam.name]
for i, j in itertools.product(range(int(start.x), int(finish.x)), range(int(start.y), int(finish.y))):
enemy_base_square = Vector2(i, j)
cells = []
w = visibility.Wave((88, 50), lambda x, y: instance.level.blockHeights[x][y] > 1, lambda x, y: cells.append((x,y)))
w.compute(enemy_base_square)
for x, y in cells:
cell_position = Vector2(x, y)
cell_node_index = regressions2.get_node_index(instance, cell_position)
if cell_position.distance(enemy_base_square) < instance.level.firingDistance + 3:
instance.graph.node[cell_node_index]["camp_location"] *= .8
instance.graph.node["enemy_base"]["camp_location"] = 0.0
def avoid_suicide_and_trades(self):
#New orders for all charging bots about to suicide.
if self.counter % self.COMMAND_RATE != 0 and self.counter % self.AVAIL_RATE != 0 and self.counter % self.HOLD_RATE != 0:
bots = []
for bot in self.game.team.members:
if bot.health > 0 and self.bots[bot.name]["command"] != None:
if type(
self.bots[bot.name]
["command"]) == commands.Charge or type(
self.bots[bot.name]["command"]) == commands.Attack:
continue_command = regressions2.get_continue_command(
self, bot, self.bots[bot.name]["command"])
if len(continue_command.target) == 1:
if self.graph.node[regressions2.get_node_index(
self, continue_command.target[0]
)]["p_enemy_fire"] > .4:
bots.append(bot)
else:
for position in continue_command.target[
0:int(len(continue_command.target) / 2)]:
if self.graph.node[regressions2.get_node_index(
self, position)]["p_enemy_fire"] > .4:
bots.append(bot)
self.command_routine(bots)
def weight_camp_locations_by_choke_exposure(instance):
for node in instance.choke_dict.keys():
enemy_base_square = regressions2.get_node_vector(instance, node)
cells = []
w = visibility.Wave((88, 50),
lambda x, y: instance.level.blockHeights[x][y] > 1,
lambda x, y: cells.append((x, y)))
w.compute(enemy_base_square)
for x, y in cells:
cell_position = Vector2(x, y)
cell_node_index = regressions2.get_node_index(
instance, cell_position)
if cell_position.distance(
enemy_base_square) < instance.level.firingDistance + 3:
instance.graph.node[cell_node_index]["camp_location"] *= .8
def put_exit_paths_in_graph(instance, exit_paths):
enemy_base = get_enemy_base(instance)
for path, weight in exit_paths:
edgesinpath=zip(path[0:],path[1:])
for vt, vf in edgesinpath[:-1]:
if "position" not in instance.graph.node[vf]:
continue
position = Vector2(*instance.graph.node[vf]["position"])
if "position" not in instance.graph.node[vt]:
continue
next_position = Vector2(*instance.graph.node[vt]["position"])
if position == next_position:
continue
x = position.x
y = position.y
instance.graph.node[regressions2.get_node_index(instance, Vector2(x,y))]["exit_path"] += 5.0*weight/(position.distance(enemy_base)**3+1)
instance.graph.node["enemy_base"]["exit_path"] = 0.0
def get_close_nodes(instance):
enemy_base = get_enemy_base(instance)
close_nodes = set()
enemy_base_normal_node = regressions2.get_node_index(instance, enemy_base)
close_nodes.add(enemy_base_normal_node)
#Calculate the weight of enemy exit squares.
for x in range(25):
addition_set = set()
for node_index1 in close_nodes:
neighbors = instance.graph.neighbors(node_index1)
for node_index2 in neighbors:
if node_index2 != None:
addition_set.add(node_index2)
close_nodes = close_nodes.union(addition_set)
return close_nodes
def get_close_nodes(instance):
enemy_base = get_enemy_base(instance)
close_nodes = set()
enemy_base_normal_node = regressions2.get_node_index(instance, enemy_base)
close_nodes.add(enemy_base_normal_node)
#Calculate the weight of enemy exit squares.
for x in range(25):
addition_set = set()
for node_index1 in close_nodes:
neighbors = instance.graph.neighbors(node_index1)
for node_index2 in neighbors:
if node_index2 != None:
addition_set.add(node_index2)
close_nodes = close_nodes.union(addition_set)
return close_nodes
def weight_camp_locations_by_sight(instance, close_nodes):
#Calculate the weight of all squares close to the enemy base relying on how many of the exit squares can be shot.
enemy_base = get_enemy_base(instance)
for node_index in close_nodes:
node_position = regressions2.get_node_vector(instance, node_index)
cells = []
w = visibility.Wave((88, 50), lambda x, y: instance.level.blockHeights[x][y] > 1, lambda x, y: cells.append((x,y)))
w.compute(node_position)
for x, y in cells:
cell_position = Vector2(x, y)
cell_node_index = regressions2.get_node_index(instance, cell_position)
if node_position.distance(cell_position) < instance.level.firingDistance:
#Edges don't work with our functions, and are unlikely to be actual optimum. #TODO fully debug rather than hack.
if not (node_position.x < 1.0 or node_position.x > 87.0 or node_position.y < 1.0 or node_position.y > 47.0):
camp_value = instance.graph.node[cell_node_index]["camp_target"]/(cell_position.distance(enemy_base)+3)
instance.graph.node[node_index]["camp_location"] += camp_value
def weight_camp_locations_by_base_exposure(instance):
#Adjust the weight based on what squares can be seen from the enemy base.
start, finish = instance.level.botSpawnAreas[instance.game.enemyTeam.name]
for i, j in itertools.product(range(int(start.x), int(finish.x)),
range(int(start.y), int(finish.y))):
enemy_base_square = Vector2(i, j)
cells = []
w = visibility.Wave((88, 50),
lambda x, y: instance.level.blockHeights[x][y] > 1,
lambda x, y: cells.append((x, y)))
w.compute(enemy_base_square)
for x, y in cells:
cell_position = Vector2(x, y)
cell_node_index = regressions2.get_node_index(
instance, cell_position)
if cell_position.distance(
enemy_base_square) < instance.level.firingDistance + 3:
instance.graph.node[cell_node_index]["camp_location"] *= .8
instance.graph.node["enemy_base"]["camp_location"] = 0.0
def put_exit_paths_in_graph(instance, exit_paths):
enemy_base = get_enemy_base(instance)
for path, weight in exit_paths:
edgesinpath = zip(path[0:], path[1:])
for vt, vf in edgesinpath[:-1]:
if "position" not in instance.graph.node[vf]:
continue
position = Vector2(*instance.graph.node[vf]["position"])
if "position" not in instance.graph.node[vt]:
continue
next_position = Vector2(*instance.graph.node[vt]["position"])
if position == next_position:
continue
x = position.x
y = position.y
instance.graph.node[regressions2.get_node_index(
instance, Vector2(x, y))]["exit_path"] += 5.0 * weight / (
position.distance(enemy_base)**3 + 1)
instance.graph.node["enemy_base"]["exit_path"] = 0.0
def update_enemy_graph(instance):
"""Updates each node with probability of enemy presence."""
regressions2.reset_graph(instance)
known_enemies = get_known_enemies(instance)
at_large_enemies = get_at_large_enemies(instance, known_enemies)
#Find last known whereabouts and condition of each enemy bots
at_large_enemies = account_for_spawns(instance, at_large_enemies)
#Account for at large enemies list complete with last seen positions and times of all unseen enemies.
bot_nodes_list = [] # Stores info on which bot accounted for which nodes.
for enemy_bot_info in at_large_enemies:
enemy_bot = enemy_bot_info[0]
last_position = enemy_bot_info[1]
time_of_position = enemy_bot_info[2]
#Skip if the information is very stale.
if instance.game.match.timePassed - time_of_position > 20.0:
print "SKIPPING DATA THAT IS OUTDATED ON BOT %s" % enemy_bot.name
continue
#Based on these variables, calculate nodes that the bot could occupy.
nodes = get_nodes_for_one_enemy(instance, enemy_bot, last_position, time_of_position)
set_probability_density(instance, nodes, last_position, enemy_bot)
bot_nodes_list.append((enemy_bot, nodes))
#Account for position and probability of all definitively known enemies.
known_enemy_nodes = set()
for enemy_bot in known_enemies:
node_index = regressions2.get_node_index(instance, enemy_bot.position)
known_enemy_nodes.add(node_index)
instance.graph.node[node_index]["friendly_sight"] = True
instance.graph.node[node_index]["p_enemy"] = 1.0
nodes = [node_index]
bot_nodes_list.append((enemy_bot, nodes))
#Set sight and cone of fire data based on all nodes enemy_bots could be present in.
#TODO - counts double for overlap squares...
for enemy_bot, nodes in bot_nodes_list:
set_fs_density(instance, nodes, enemy_bot)
def update_enemy_graph(instance):
"""Updates each node with probability of enemy presence."""
regressions2.reset_graph(instance)
known_enemies = get_known_enemies(instance)
at_large_enemies = get_at_large_enemies(instance, known_enemies)
#Find last known whereabouts and condition of each enemy bots
at_large_enemies = account_for_spawns(instance, at_large_enemies)
#Account for at large enemies list complete with last seen positions and times of all unseen enemies.
bot_nodes_list = [] # Stores info on which bot accounted for which nodes.
for enemy_bot_info in at_large_enemies:
enemy_bot = enemy_bot_info[0]
last_position = enemy_bot_info[1]
time_of_position = enemy_bot_info[2]
#Skip if the information is very stale.
if instance.game.match.timePassed - time_of_position > 20.0:
print "SKIPPING DATA THAT IS OUTDATED ON BOT %s" % enemy_bot.name
continue
#Based on these variables, calculate nodes that the bot could occupy.
nodes = get_nodes_for_one_enemy(instance, enemy_bot, last_position,
time_of_position)
set_probability_density(instance, nodes, last_position, enemy_bot)
bot_nodes_list.append((enemy_bot, nodes))
#Account for position and probability of all definitively known enemies.
known_enemy_nodes = set()
for enemy_bot in known_enemies:
node_index = regressions2.get_node_index(instance, enemy_bot.position)
known_enemy_nodes.add(node_index)
instance.graph.node[node_index]["friendly_sight"] = True
instance.graph.node[node_index]["p_enemy"] = 1.0
nodes = [node_index]
bot_nodes_list.append((enemy_bot, nodes))
#Set sight and cone of fire data based on all nodes enemy_bots could be present in.
#TODO - counts double for overlap squares...
for enemy_bot, nodes in bot_nodes_list:
set_fs_density(instance, nodes, enemy_bot)
def update_enemy_graph(instance):
regressions2.reset_graph(instance)
known_enemies = get_known_enemies(instance)
#Find last known whereabouts and condition of each enemy bots
at_large_enemies = get_at_large_enemies(instance, known_enemies)
#Get linear extrapolations of enemy movement
bot_extraps = extrapolate(instance)
store_enemy_positions(instance)
#Account for at large enemies list complete with last seen positions and times of all unseen enemies.
bot_nodes_list = [] # Stores info on which bot accounted for which nodes.
for enemy_bot in at_large_enemies:
#Skip if the information is very stale.
if enemy_bot.seenlast > 25.0:
continue
#Based on these variables, calculate nodes that the bot could occupy.
nodes = get_nodes_for_one_enemy(instance, enemy_bot)
set_probability_density(instance, nodes, enemy_bot.position, enemy_bot, bot_extraps)
bot_nodes_list.append((enemy_bot, nodes))
#Account for position and probability of all definitively known enemies.
known_enemy_nodes = set()
for enemy_bot in known_enemies:
#Enemy could be at start of game.
node_index = regressions2.get_node_index(instance, enemy_bot.position)
known_enemy_nodes.add(node_index)
instance.graph.node[node_index]["friendly_sight"] = True
instance.graph.node[node_index]["p_enemy"] = 1.0
nodes = [node_index]
bot_nodes_list.append((enemy_bot, nodes))
#Set sight and cone of fire data based on all nodes enemy_bots could be present in.
for enemy_bot, nodes in bot_nodes_list:
set_fs_density(instance, nodes, enemy_bot)
print "DONE UPDATING ENEMY GRAPH"
def get_chokes(instance, choke_candidates):
#prevent writing over base space.
used_set = set()
start, finish = instance.level.botSpawnAreas[instance.game.enemyTeam.name]
for i, j in itertools.product(range(int(start.x), int(finish.x)),
range(int(start.y), int(finish.y))):
node_index = regressions2.get_node_index(instance, Vector2(i, j))
used_set.add(node_index)
choke_dict = {}
master_chokes = set()
flag_node = regressions2.get_node_index(instance,
instance.game.team.flag.position)
spawn_node = regressions2.get_node_index(instance,
get_enemy_base(instance))
shortest_length = nx.shortest_path_length(instance.graph,
source=spawn_node,
target=flag_node,
weight="choke_covered")
choke_count = 0
while shortest_length == 0.0:
if len(choke_candidates) == 0.0:
print "RAN OUT OF CANDIDATES!"
break
choke_count += 1
one_choke = set()
choke_center = choke_candidates.pop()
choke_vector = regressions2.get_node_vector(instance, choke_center)
#Ignore potential chokes too far from their spawn.
while (choke_vector.distance((get_enemy_base(instance))) > 5.0
or choke_center in used_set) and len(choke_candidates) > 0:
choke_vector = regressions2.get_node_vector(instance, choke_center)
choke_center = choke_candidates.pop()
if len(choke_candidates) == 0:
print "RAN OUT OF CANDIDATES!"
return choke_dict, master_chokes
if choke_vector.distance((get_enemy_base(instance))) > 5.0:
print "RAN OUT OF CANDIDATES, LAST CANDIDATE DIDN'T WORK!"
return choke_dict, master_chokes
one_choke.add(choke_center)
for x in range(4):
neighbors = set()
for node in one_choke:
neighbors2 = instance.graph.neighbors(node)
if neighbors2 is not None:
for neighbor2 in neighbors2:
if neighbor2 not in used_set:
neighbors.add(neighbor2)
one_choke = one_choke.union(neighbors)
used_set = used_set.union(one_choke)
for node in one_choke:
instance.graph.node[node]["choke_covered"] = 1.0
neighbors = instance.graph.neighbors(node)
for neighbor in neighbors:
instance.graph.edge[node][neighbor]["choke_covered"] = 1.0
choke_dict[choke_center] = {"nodes": one_choke, "redundancy": 0}
master_chokes = master_chokes.union(one_choke)
shortest_length = nx.shortest_path_length(instance.graph,
source=spawn_node,
target=flag_node,
weight="choke_covered")
return choke_dict, master_chokes
