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