def custom_cmp(self, no_trgts, no_obs, obs_range):
(no_targets, no_observers, no_obstacles, targets, observers, obstacles,
observer_strategy) = self.initialize_custom(no_trgts, no_obs,
obs_range)
t1 = deepcopy(targets)
t2 = deepcopy(targets)
t3 = deepcopy(targets)
t4 = deepcopy(targets)
o1 = deepcopy(observers)
o2 = deepcopy(observers)
o3 = deepcopy(observers)
o4 = deepcopy(observers)
ob1 = deepcopy(obstacles)
ob2 = deepcopy(obstacles)
ob3 = deepcopy(obstacles)
ob4 = deepcopy(obstacles)
tc = self.run(no_targets, no_observers, no_obstacles, targets,
observers, obstacles, observer_strategy)
strategy1 = strat1()
c1 = strategy1.run(no_targets, no_observers, no_obstacles, t1, o1, ob1)
strategy2 = strat2()
c2 = strategy2.run(no_targets, no_observers, no_obstacles, t1, o1, ob1)
strategy3 = strat3()
c3 = strategy3.run(no_targets, no_observers, no_obstacles, t1, o1, ob1)
strategy4 = strat4()
c4 = strategy4.run(no_targets, no_observers, no_obstacles, t1, o1, ob1)
return [tc, c1, c2, c3, c4]
def run(self, no_targets, no_observers, no_obstacles, targets, observers,
obstacles, observer_strategy):
step = 0
total_observed = 0
s1 = strat1()
s2 = strat2()
s3 = strat3()
s4 = strat4()
while (step <= self.total_steps):
if (step % self.update_steps == 0):
for i in range(len(observers)):
if observer_strategy[i] == 1:
s1.run_obs(no_targets, no_obstacles, targets,
obstacles, observers[i])
if observer_strategy[i] == 2:
s2.run_obs(no_targets, no_obstacles, targets,
obstacles, observers[i])
if observer_strategy[i] == 3:
s3.run_obs(no_targets, no_obstacles, targets,
obstacles, observers[i])
if observer_strategy[i] == 4:
s4.run_obs(no_targets, no_obstacles, targets,
obstacles, observers[i])
[target_observer_dict, target_obstacle_dict
] = self.update_for_targets(observers, obstacles, targets)
for i in target_observer_dict:
temp_arr_x = []
temp_arr_y = []
for j in target_observer_dict[i]:
temp_arr_x.append(observers[j].x)
temp_arr_y.append(observers[j].y)
if (len(temp_arr_x) and len(temp_arr_y)):
mean_x = mean(temp_arr_x)
mean_y = mean(temp_arr_y)
targets[i].update_target(self.x_limit, self.y_limit,
mean_x, mean_y)
for i in observers:
i.update(self.x_limit, self.y_limit)
for i in targets:
i.update(self.x_limit, self.y_limit)
[observer_target_dict, observer_obstacle_dict
] = self.update_for_observers(observers, obstacles, targets)
for i in observer_target_dict:
total_observed += len(observer_target_dict[i])
step += 1
print("Heterogeneous strategy: ", total_observed)
return total_observed
def run(self, no_targets, no_observers, no_obstacles, targets, observers,
obstacles, observer_strategy):
step = 0
total_observed = 0
s1 = strat1()
s2 = strat2()
s3 = strat3()
s4 = strat4()
while (step <= self.total_steps):
if (step % self.update_steps == 0):
for i in range(len(observers)):
# RUN FOR THE ONE WHICH WOULD YIELD BEST RESULTS NOW
d_obst = {}
targets_copy = deepcopy(targets)
obstacles_copy = deepcopy(obstacles)
o_copy1 = deepcopy(observers[i])
d_obst[s1.run_obs(no_targets, no_obstacles, targets_copy,
obstacles_copy, o_copy1)] = 1
targets_copy = deepcopy(targets)
obstacles_copy = deepcopy(obstacles)
o_copy2 = deepcopy(observers[i])
d_obst[s2.run_obs(no_targets, no_obstacles, targets_copy,
obstacles_copy, o_copy2)] = 2
targets_copy = deepcopy(targets)
obstacles_copy = deepcopy(obstacles)
o_copy3 = deepcopy(observers[i])
d_obst[s3.run_obs(no_targets, no_obstacles, targets_copy,
obstacles_copy, o_copy3)] = 3
targets_copy = deepcopy(targets)
obstacles_copy = deepcopy(obstacles)
o_copy4 = deepcopy(observers[i])
d_obst[s4.run_obs(no_targets, no_obstacles, targets_copy,
obstacles_copy, o_copy4)] = 4
m = max(d_obst)
m = d_obst[m]
xyz = [0, o_copy1, o_copy2, o_copy3, o_copy4]
observers[i] = deepcopy(xyz[m])
[target_observer_dict, target_obstacle_dict
] = self.update_for_targets(observers, obstacles, targets)
for i in target_observer_dict:
temp_arr_x = []
temp_arr_y = []
for j in target_observer_dict[i]:
temp_arr_x.append(observers[j].x)
temp_arr_y.append(observers[j].y)
if (len(temp_arr_x) and len(temp_arr_y)):
mean_x = mean(temp_arr_x)
mean_y = mean(temp_arr_y)
targets[i].update_target(self.x_limit, self.y_limit,
mean_x, mean_y)
for i in observers:
i.update(self.x_limit, self.y_limit)
for i in targets:
i.update(self.x_limit, self.y_limit)
[observer_target_dict, observer_obstacle_dict
] = self.update_for_observers(observers, obstacles, targets)
for i in observer_target_dict:
total_observed += len(observer_target_dict[i])
step += 1
print("Heterogeneous strategy: ", total_observed)
return total_observed