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}')