Лабораторная 2 / Вариант 10 / Lab2

import matplotlib.pyplot as plt
import numpy as np


class LFrog:
    def __init__(self, r, M, seed=1):
        self.r = r
        self.M = M
        self.modulus = 2 ** r
        self.A = seed

    def next(self):
        x = (self.A * self.M) % self.modulus
        self.A = x
        return x / self.modulus


class DiscreteRV:
    def __init__(self, xj, pj):
        self.z = LFrog(32, 3**12)
        self.Pj = [sum(pj[:i+1]) for i in range(len(pj))]
        self.xj = xj
        self.pj = pj

    def next(self):
        z = self.z.next()
        for p in range(len(self.Pj)):
            if z <= self.Pj[p]:
                return self.xj[p]


x = np.array([-58.2, -57.7, -47.3, -30.2, -13.1, 20.0, 76.1])
p = [0.105, 0.091, 0.220, 0.052, 0.271, 0.124, 0.137]
a = DiscreteRV(x, p)

A = []
N = 500
for i in range(N):
    A.append(a.next())

print("Первые 30 значений")
for i in range(30):
    print(A[i])
M = sum(A) / N
D = sum([x ** 2 for x in A]) / N - M ** 2
print(f"Эмпирические: M = {M:.5f}, D = {D:.5f}")
M_exp = sum([p[j] * x[j] for j in range(len(p))])
D_exp = sum([p[j] * x[j] ** 2 for j in range(len(p))]) - M_exp ** 2
print(f"Теоретические: M = {M_exp:.5f}, D = {D_exp:.5f}")


edges = (x[:-1] + x[1:]) / 2
bins = np.concatenate(([x[0] - (edges[0] - x[0])], edges, [x[-1] + (x[-1] - edges[-1])]))
plt.hist([A, x], bins=bins,
         weights=[np.ones_like(A) / len(A), np.ones_like(x) * p],
         label=['Эмпирическое', 'Теоретическое'])
plt.legend(loc='upper left')
plt.savefig("Leha/ms2.svg")
plt.show()