import scipy.stats as sts
D_theoretical = integrate.quad(lambda x: 1.5 * (x ** 4), -1, 1)[0] - \
(integrate.quad(lambda x: 1.5 * (x ** 3), -1, 1))[0]
print("D (theoretical) = ", D_theoretical)
gamma = [0.9, 0.95, 0.99]
N = [20, 30, 50, 70, 100, 150]
all_intervals = list()
for ni in N:
intervals = []
intervals2 = []
T = []
Y = get_Y(ni)
print("Вариационный ряд: ")
print(Y)
My = sum(Y) / ni
print('My = ', My)
Dy = 0
for y in Y:
Dy += (y - My)**2
Dy = Dy / (ni - 1)
print('Dy = ', Dy)
t_rv = sts.t(ni - 1)
arr = t_rv.rvs(1000000)
for i in gamma:
import matplotlib.pyplot as plt
import numpy as np
import math
from creature_Y import get_Y, empiric_func
n = 30
Y = get_Y(n)
print("Вариационный ряд: ")
print(Y)
fig, ax = plt.subplots(1, 1, figsize=(16, 9))
y = empiric_func(Y)
plt.step(Y, y, color='r', label='Эмпирическая функция распределения')
plt.plot([-1.5, Y[0]], [0, 0], color='r')
plt.plot([Y[-1], 1.5], [1, 1], color='r')
plt.plot([Y[0], Y[0]], [0, y[1]], color='r')
y_theoretical = list()
for yi in Y:
y_theoretical.append((yi**3 + 1) / 2)
x_theoretical = np.linspace(-1, 1, 1000)
plt.plot(x_theoretical, (x_theoretical**3 + 1) / 2,
color='blue',
label='Теоретическая функция распределения')
plt.legend()
d_plus = []
d_minus = []
for i in range(0, n):
d_plus.append((i / n) - ((Y[i])**3 + 1) / 2)
d_minus.append((((Y[i])**3 + 1) / 2) - ((i - 1) / n))