def pred3(traj):
traj = traj[0]
theta = np.array(traj).T[2]
return min(0.2 - np.abs(theta))
smooth_details_r3 = []
ns_details_r3 = []
random_details_r3 = []
for r in rand_nums:
np.random.seed(r)
node0 = pred_node(f=lambda traj: pred1(traj))
node1 = pred_node(f=lambda traj: pred2(traj))
node2 = pred_node(f=lambda traj: pred3(traj))
node3 = max_node(children=[node0, node1, node2])
TM = test_module(bounds=bounds,
sut=lambda x0: sut(200, x0),
f_tree=node3,
init_sample=60,
optimize_restarts=3,
exp_weight=2,
normalizer=True)
TM.initialize()
TM.run_BO(140)
np.random.seed(r)
node0_ns = pred_node(f=lambda traj: pred1(traj))
node1_ns = pred_node(f=lambda traj: pred2(traj))
node2_ns = pred_node(f=lambda traj: pred3(traj))
node3_ns = max_node(children=[node0_ns, node1_ns, node2_ns])
return -iters / 350.
def pred3(traj):
traj = traj[0]
v_s = np.array(traj).T[1]
return min(0.025 - np.abs(v_s))
for r in rand_nums:
np.random.seed(r)
node0 = pred_node(f=pred1)
node1 = pred_node(f=pred2)
node2 = pred_node(f=pred3)
node3 = min_node(children=[node0, node2])
node4 = max_node(children=[node3, node1])
TM = test_module(bounds=bounds,
sut=lambda x0: sut(x0, max_steps=350),
f_tree=node4,
init_sample=60,
optimize_restarts=5,
exp_weight=2,
normalizer=True)
TM.initialize()
TM.run_BO(140)
smooth_vals = TM.f_acqu.find_GP_func()
smooth_details_r3.append([
np.sum(smooth_vals < -0.25),
np.sum(smooth_vals < -0.30), TM.smooth_min_x, TM.smooth_min_val,
TM.smooth_min_loc
])
import numpy as np
from matplotlib import pyplot as plt
bounds = [(0, 10)]
opt = delta_opt(bounds=bounds, epsilon=0.0001)
random_ints = [
1414065923, 2448114469, 1906628456, 2644070884, 24268670, 2664125290,
1182137443, 100813220, 1822558109, 2027862653
]
i = 0
for r in random_ints:
np.random.seed(r)
node1 = pred_node(f=lambda x: np.sin(x) + 0.65)
node2 = pred_node(f=lambda x: np.cos(x) + 0.65)
node0 = max_node(children=[node1, node2])
TM = test_module(bounds=bounds,
sut=lambda x: x,
f_tree=node0,
with_ns=True,
with_random=True,
init_sample=5,
optimizer=opt,
optimize_restarts=3,
exp_weight=2)
TM.initialize()
TM.run_BO(50)
plt.figure(i + 1)
plt.plot(TM.smooth_X[5:])
def pred2(traj, gamma=0.25):
traj = traj[0]
cos_thetas = np.array(traj).T[0]
theta_dots = np.array(traj).T[2]
stab_vals = 0
for ct, td in zip(cos_thetas, theta_dots):
stab_vals = (np.pi - np.abs(np.arccos(ct)))**2 + np.abs(td)**2 + stab_vals*gamma
return -stab_vals
# This set assumes random sampling and checking
for r in rand_nums:
np.random.seed(r)
node0 = pred_node(f = lambda traj:pred1(traj))
node1 = pred_node(f = lambda traj:pred2(traj))
node2 = max_node(children=[node0, node1])
TM = test_module(bounds=bounds, sut=lambda x0: sut(500,x0, ead=True),
f_tree = node2,init_sample = 60,
optimize_restarts=5, exp_weight=10, normalizer=True)
TM.initialize()
TM.run_BO(30)
TM.k =5
TM.run_BO(40)
TM.k = 2
TM.run_BO(70)
smooth_vals = np.array(TM.f_acqu.find_GP_func())
smooth_details_r3.append([np.sum(smooth_vals < -1.00),
np.sum(smooth_vals < -10.0),
TM.smooth_min_x,TM.smooth_min_val,
TM.smooth_min_loc])
traj = traj[0]
cos_thetas = np.array(traj).T[0]
theta_dots = np.array(traj).T[2]
stab_vals = 0
for ct, td in zip(cos_thetas, theta_dots):
stab_vals = (np.pi - np.abs(
np.arccos(ct)))**2 + np.abs(td)**2 + stab_vals * gamma
return -stab_vals
# This set assumes random sampling and checking
for r in rand_nums:
np.random.seed(r)
node0 = pred_node(f=lambda traj: pred1(traj))
node1 = pred_node(f=lambda traj: pred2(traj))
node2 = max_node(children=[node0, node1])
TM = test_module(bounds=bounds,
sut=lambda x0: sut(500, x0, ead=True),
f_tree=node2,
init_sample=60,
optimize_restarts=5,
exp_weight=2,
normalizer=True)
TM.initialize()
TM.run_BO(140)
smooth_vals = np.array(TM.f_acqu.find_GP_func())
smooth_details_r3.append([
np.sum(smooth_vals < -1.00),
np.sum(smooth_vals < -10.0), TM.smooth_min_x, TM.smooth_min_val,
TM.smooth_min_loc
])