def fitter_type4(self,i,j):
if i<0 or j>(len(self.observations)+1) or i>j:
raise Exception("fitting between {} and {} incompatible when number of acquisitions have been {}".format(i,j,len(self.observations)))
curr_trajectory = defs.trajectory(3,self.observations[i:j])
res4 = estimate_individual_type(curr_trajectory,3)
return res4,0.1
list_of_observations.append(curr_obs)
if idx == 55:
print("Adding changepoint")
set_of_possible_new_types = set_of_types.copy()
set_of_possible_new_types.remove(pre_type)
post_type = random.sample(set_of_possible_new_types, 1)
a.agents[tracking_agent_id].tp = post_type[0]
print("set {} iter {} exper {} tp {}".format(lm, idx, j, i))
if a.terminal:
print("Simulation ended")
curr_time.append(idx)
curr_traj = defs.trajectory(a.agents[tracking_agent_id].tp,
list_of_observations)
if idx > 56:
cp_curr_traj = defs.cp_trajectory(pre_type, post_type, 25,
list_of_observations)
cp_traj_list.append(cp_curr_traj)
traj_list.append(curr_traj)
times.append(curr_time)
times = np.array(times)
# print(np.mean(times,axis=1))
tot_times.append(times)
# print(np.mean(tot_times,axis=0))
import pickle
import src.agents.adhoc_agent as sadhoc