import numpy as np
[docs]def show_search_results(history, N):
n = history.total_num_search
index = np.argmax(history.fx[0:n])
if N == 1:
print(
"%04d-th step: f(x) = %f (action=%d)"
% (n, history.fx[n - 1], history.chosen_actions[n - 1])
)
print(
" current best f(x) = %f (best action=%d) \n"
% (history.fx[index], history.chosen_actions[index])
)
else:
print(
"current best f(x) = %f (best action = %d) "
% (history.fx[index], history.chosen_actions[index])
)
print("list of simulation results")
st = history.total_num_search - N
en = history.total_num_search
for n in range(st, en):
print("f(x)=%f (action = %d)" % (history.fx[n], history.chosen_actions[n]))
print("\n")
[docs]def show_search_results_mo(history, N, disp_pareto_set=False):
n = history.total_num_search
pset, step = history.pareto.export_front()
def msg_pareto_set_updated(indent=False):
prefix = " " if indent else ""
if history.pareto.front_updated:
print(prefix + "Pareto set updated.")
if disp_pareto_set:
print(
prefix
+ "current Pareto set = %s (steps = %s) \n"
% (str(pset), str(step + 1))
)
else:
print(
prefix + "the number of Pareto frontiers = %s \n" % str(len(step))
)
if N == 1:
print(
"%04d-th step: f(x) = %s (action = %d)"
% (n, str(history.fx[n - 1]), history.chosen_actions[n - 1])
)
msg_pareto_set_updated(indent=True)
else:
msg_pareto_set_updated()
print("list of simulation results")
st = history.total_num_search - N
en = history.total_num_search
for n in range(st, en):
print(
"f(x) = %s (action = %d)"
% (str(history.fx[n]), history.chosen_actions[n])
)
print("\n")
[docs]def show_start_message_multi_search(N, score=None):
if score is None:
score = "random"
print(f"{N+1:04}-th multiple probe search ({score})")
[docs]def show_interactive_mode(simulator, history):
if simulator is None and history.total_num_search == 0:
print("interactive mode starts ... \n ")
[docs]def length_vector(t):
N = len(t) if hasattr(t, "__len__") else 1
return N
[docs]def is_learning(n, interval):
if interval == 0:
return n == 0
elif interval > 0:
return np.mod(n, interval) == 0
else:
return False