main.py

#%%
# So?
#from jackknife import get_generator
#from jackknife import do_jackknife_estimation
# Oder so?
#import jackknife as jk
# Oder doch so?
import jackknife
import numpy as np

#%%
def f(x):
    return 1 / x

#%%
n = 100
mu = 2
sigma = 1
x_list = np.random.normal(mu, sigma, n)
x_generator = jackknife.get_sample_generator(x_list)
y_list = [f(x) for x in x_list]
y_generator = jackknife.get_sample_generator(y_list)

#%%
output = jackknife.do_jackknife_estimation(x_generator, f)
x_mean = sum(i for i in x_list) / n
print("{}{}".format("f(mean(x))             = ", f(x_mean)))
print("{}{}".format("mean(y')               = ", output[0]))
print("{}{}".format("variance(y')           = ", output[1]))
print("{}{}".format("standard deviation(y') = ", output[2]))

#%%
output = jackknife.do_jackknife_estimation(y_generator, lambda x: x)
print("{}{}".format("mean(y)               = ", output[0]))
print("{}{}".format("variance(y)           = ", output[1]))
print("{}{}".format("standard deviation(y) = ", output[2]))