Esempi in Python per get_best_solution_index

Esempio n. 1

# 局所解とバイアスに分ける
solutions, bias = functions.divide_solutions_bias(solutions_data)

# 評価値の結果のリスト
evaluations_result = []

for num_experiment in range(1, 101):
    print(num_experiment)
    # 対象のデータの読み込み
    data = functions.read_csv(read_filename)
    del data[0]
    data = functions.transform_to_float(data)
    # before_index = functions.get_result(data, functions.get_evaluations_list(data, solutions, bias), num_experiment, functions.get_best_solution_index(bias), solutions)[2]
    # 次の世代の作成
    for num in range(num_execute):
        # print('-------')
        # print(functions.get_evaluation_value(data[before_index], solutions, bias))
        data = next_generation_MGG_improve(data, solutions, bias, num_parents, num_children, num_elite_preservation, num_dimentions)
        
        # print(functions.get_result(data, functions.get_evaluations_list(data, solutions, bias), num_experiment, functions.get_best_solution_index(bias), solutions))
        
        # before_index = functions.get_result(data, functions.get_evaluations_list(data, solutions, bias), num_experiment, functions.get_best_solution_index(bias), solutions)[2]
    # 新しい世代をcsvに書き込む
    functions.write_csv(write_filename + '_%i' % num_experiment, data)

    evaluations = functions.get_evaluations_list(data, solutions, bias)
    evaluation_vector = functions.get_result(data, evaluations, num_experiment, functions.get_best_solution_index(bias), solutions)
    evaluations_result.append(evaluation_vector)
final_result = functions.get_final_result(evaluations_result)

functions.write_result(result_file, evaluations_result, final_result)

Esempio n. 2

# 局所解ファイルの読み込み
solutions_data = functions.read_csv(solutions_file)
del solutions_data[0]
solutions_data = functions.transform_to_float(solutions_data)

# 局所解とバイアスに分ける
solutions, bias = functions.divide_solutions_bias(solutions_data)
solutions = np.array(solutions)
bias = np.array(bias)

evaluations_result = []

for num_experiment in range(1, 101):
    print(num_experiment)
    random = make_random_matrix(5000, 100)

    # 評価値の結果のリスト
    evaluations = functions.get_evaluations_list(random, solutions, bias)
    rankings = functions.get_ranking_list(evaluations)
    matrix100, evaluations100, rankings100 = take_top_100(
        random, evaluations, rankings, solutions, bias)

    evaluation_vector = functions.get_result(
        matrix100, evaluations100, num_experiment,
        functions.get_best_solution_index(bias), solutions)
    evaluations_result.append(evaluation_vector)

final_result = functions.get_final_result(evaluations_result)
functions.write_result(result_file, evaluations_result, final_result)