# 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)
# 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)