Лабораторная 2 / Вариант 8 / Lab2

import matplotlib.pyplot as plt
import numpy as np


class MyRand:
    def __init__(self, r, M, seed=1):
        self.r = r
        self.M = M
        self.mod = 2 ** r
        self.A = seed % self.mod

    def next(self):
        self.A = (self.A * self.M) % self.mod

        return self.A / self.mod


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

    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([-96.9, -87.4, -55.7, -54.5, 59.7, 83.5, 99.3])
p = [0.114, 0.090, 0.270, 0.192, 0.086, 0.242, 0.006]
drv = DiscreteRandomValue(x, p)
DRV = []
N = 500
for i in range(N):
    DRV.append(drv.next())

print("Первые 30 значений:")
for i in range(30):
    print(DRV[i])
M = sum(DRV) / N
D = sum([x ** 2 for x in DRV]) / 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([DRV, x], bins=bins,
         weights=[np.ones_like(DRV) / len(DRV), np.ones_like(x) * p],
         label=['Эмпирическое', 'Теоретическое'])
plt.legend(loc='upper left')
plt.savefig("Vadim/ms2.svg")
plt.show()