physbo.search.utility のソースコード

import numpy as np


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.chosed_actions[n - 1])
        )
        print(
            "   current best f(x) = %f (best action=%d) \n"
            % (history.fx[index], history.chosed_actions[index])
        )
    else:
        print(
            "current best f(x) = %f (best action = %d) "
            % (history.fx[index], history.chosed_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.chosed_actions[n]))
        print("\n")


def show_start_message_multi_search(N, score=None):
    if score == "EI":
        print("%04d-th multiple probe search (EI) \n" % (N + 1))
    elif score == "PI":
        print("%04d-th multiple probe search (PI) \n" % (N + 1))
    elif score == "TS":
        print("%04d-th multiple probe search (TS) \n" % (N + 1))
    else:
        print("%04d-th multiple probe search (random) \n" % (N + 1))


def show_interactive_mode(simulator, history):
    if simulator is None and history.total_num_search == 0:
        print("interactive mode stars ... \n ")


def length_vector(t):
    N = len(t) if hasattr(t, "__len__") else 1
    return N


def is_learning(n, interval):
    if interval == 0:
        return True if n == 0 else False
    elif interval > 0:
        return True if np.mod(n, interval) == 0 else False
    else:
        return False