def arange_samplesize(): # シード値の固定 np.random.seed(1) # samplesize=10 size_10 = f.sigma_calc(size=10, n_trial=10000) size_10_df = pd.DataFrame({ "list_mean": size_10.create_source(), "size": np.tile("size 10", 10000) }) # samplesize=20 size_20 = f.sigma_calc(size=20, n_trial=10000) size_20_df = pd.DataFrame({ "list_mean": size_20.create_source(), "size": np.tile("size 20", 10000) }) # samplesize=30 size_30 = f.sigma_calc(size=30, n_trial=10000) size_30_df = pd.DataFrame({ "list_mean": size_30.create_source(), "size": np.tile("size 30", 10000) }) # 結合 sim_result = pd.concat([size_10_df, size_20_df, size_30_df]) # print(sim_result.head()) # グラフの表示 title = "arange_samplesize_graph" plt.title(title) graph = sns.violinplot(x="size", y="list_mean", data=sim_result, color="gray") canvas = f.image_graph(graph, title) canvas.view_option(me="", st="", va="")
def err_conf(): elist = np.arange(start=2, stop=102, step=2) box = np.zeros(len(elist)) for i in range(0, len(elist)): a = f.sigma_calc(size=10, n_trial=10) box[i] = sp.mean(a.create_source()) print(box)
def sigma_standard_error(): # prepare spm = np.arange(start=2, stop=102, step=2) std_box = np.zeros(len(spm)) # 単純な計算式 st_err_1 = 0.8 / np.sqrt(spm) # 標本平均の標準偏差 np.random.seed(1) for i in range(0, len(spm)): tmp = f.sigma_calc(size=spm[i], n_trial=100) std_box[i] = sp.std(tmp.create_source(), ddof=1) # 変数の確認用 def var_conf(v): print(v) # var_conf(std_box) # コメントアウト推奨 # 標本平均の標準偏差と標準誤差のグラフ title = "sigma_standard_error_graph" plt.title(title) graph = plt.plot(spm, std_box, color="black") graph = plt.plot(spm, st_err_1, linestyle="dotted", color="black") graph = plt.xlabel("smaple_size") graph = plt.ylabel("mean_std_value") canvas = f.image_graph(dt_graph=graph, dt_name=title) canvas.view_option(me="", st="", va="")
def sigma_mean_deviation(): # prepare base_samplesize = np.arange(start=2, stop=102, step=2) base_box = np.zeros(len(base_samplesize)) # シード値の固定 np.random.seed(1) for i in range(0, len(base_samplesize)): tmp_box = f.sigma_calc(size=base_samplesize[i], n_trial=100) base_box[i] = sp.std(tmp_box.create_source(), ddof=1) # グラフの描写 title = "sigma_mean_deviation_graph" plt.title(title) graph = plt.plot(base_samplesize, base_box, color="black") graph = plt.xlabel("base_samplesize") graph = plt.ylabel("base_box") canvas = f.image_graph(graph, title) canvas.view_option(me="", st="", va="")
def debug(): np.random.seed(1) t = f.sigma_calc(size=10, n_trial=10) tf = f.list_format(lv=1, data=t.create_source_var()) print(tf.format())
def var_check(): np.random.seed(1) var = f.sigma_calc(size=10, n_trial=10000) var_f = f.list_format(lv=1, data=var.create_source_var())
def exe_conf(): # np.random.seed(1) test = f.sigma_calc(size=10, n_trial=10000) print(test.create_source()) print(f'{sp.mean(test.create_source()):.3f}')