def visualizeSets_5(
cs=d.default_cs,
env=d.default_env,
agent=d.default_agent,
k=d.default_k_visualization,
ps=d.default_ps,
colors=d.default_colors_sets,
components=d.default_components,
loc=d.default_loc,
show=True,
conf=None,
test_chaos_theory=False,
):
if conf is None:
conf = Conf()
conf.test_chaos_theory = test_chaos_theory
trajs = []
for p in ps:
for c in cs:
trajs.append(getTraj(c, env, agent, p, conf=conf))
v = Visualisator()
v.show = show
name = tools.FileNaming.descrName(env, agent, c, conf)
filename = tools.FileNaming.imageTrajName(env.name, agent.name,
c, p, conf, k)
v.plotCompGP(trajs, colors=colors, name=name, components=components,
loc=loc, k=k, filename=filename)
def synthesize_4(
c=d.default_c,
env=d.default_env,
agent=d.default_agent,
k=d.default_k_prediction,
p=d.default_p,
save=d.default_save,
test_chaos_theory=False,
):
buf = getReplayBuffer(env=env, agent=agent)
buf = buf.normalize()
buf = buf.cut(c + k + 1)
test = buf.slice([(0, k + 1)])
train = buf.slice([(k + 1, c + k + 1)])
conf = Conf(n=len(buf.x[0]), m=len(buf.u[0]))
conf.test_chaos_theory = test_chaos_theory
cgp = CompGP(conf)
def convert(x):
return list(np.array(x.T)[0])
X = [convert(xx) for xx in train.x]
U = [convert(uu) for uu in train.u]
Y = [convert(yy) for yy in train.y]
cgp.fit(X, U, Y)
x_0 = convert(test.x[0])
U = [convert(uu) for uu in test.u]
traj = cgp.synthesizeSets(x_0, U, k, p)
traj.addBuf(test)
if save:
f = tools.FileNaming.trajName(
env.name, agent.name, c, p, conf)
traj.save(f)
return traj
