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.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")

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.chosed_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-1]), history.chosed_actions[n-1]))
        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