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import numpy.random as l

import numpy as n

import matplotlib.pyplot as p

mean = 3

sigma = 1

count_of_elements = 8

# квантиль уровня P (X<x)=0.4750 для N(0,1)

# TODO это нужно только для 1 варианта, мб вам нужно тут что-то поменять

cvantil0025gaussian = 1.96

def test(m=mean, c=count_of_elements):

global h_one_true, h_two_true

# of course sum is 10000

h_one_true = 0

h_two_true = 0

for i in range(10000):

# TODO поменять тут на своё распределение

array_of_elements_from_distribution = l.normal(m, sigma, c)

average = sum(array_of_elements_from_distribution) / c

right = average + (cvantil0025gaussian * sigma / n.sqrt(c))

left = average - (cvantil0025gaussian * sigma / n.sqrt(c))

if left < mean < right:

h_one_true += 1

else:

h_two_true += 1

return h_one_true, h_two_true

test()

print("result is :\n"

"hypnosis than mean is 3 was true {0} times\n"

"hypnosis than mean isn't 3 was true {1} times\n"

"оценка вероятности ошибки первого рода {2} "

.format(h_one_true, h_two_true, h_two_true / 10000)

)

"""

result is :

hypnosis than mean is 3 was true 9454 times

hypnosis than mean isn't 3 was true 546 times

оценка вероятности ошибки первого рода 0.0546

"""

counter = 1

array_of_points = []

array_of_counters = []

for i in range(9):

test(counter)

array_of_points.append(h_two_true / 10000)

array_of_counters.append(counter)

counter += 0.5

p.plot(array_of_counters, array_of_points,

linewidth=3,

color='r')

counter = 1

array_of_points = []

array_of_counters = []

for i in range(9):

test(counter, 50)

array_of_points.append(h_two_true / 10000)

array_of_counters.append(counter)

counter += 0.5

p.plot(array_of_counters, array_of_points,

linewidth=3,

color='g')

p.xlabel("Мат ожидание генерируемой выборки(настоящее)")

p.ylabel("Вероятость отвергнуть основную гипотезу")

p.text(2.3, 0.9, "Выборка из 50 элементов", rotation=300, color='g')

p.text(1, 0.85, "Выборка из 8 элементов", rotation=300, color='r')

p.show()