import matplotlib.pyplot as plt
import numpy as np
from histogram import Histogram
import distributions as dst
graph_disc = 200
ns = [10, 50, 1000]
ds = dst.get_distributions()
for d in ds:
for n in ns:
data = [d.x() for i in range(n)]
hs = Histogram(data, d.discrete())
x = np.linspace(min(data), max(data), graph_disc)
yf = [d.f(k) for k in x]
yF = [d.F(k) for k in x]
plt.subplot(2, len(ns), ns.index(n) + 1)
plt.title(d.name + ", n = " + str(n))
plt.xlabel('normalNumbers')
plt.ylabel('Density')
plt.plot(x, yf, label='expected')
plt.step(hs.x, hs.y, label='received')
plt.legend()
plt.subplot(2, len(ns), ns.index(n) + len(ns) + 1)
plt.xlabel('x')
plt.ylabel('F(x)')
plt.plot(x, yF, label='expected')
plt.step(hs.x, hs.F(), label='received')
plt.legend()
plt.show()
import matplotlib.pyplot as plt
import numpy as np
from histogram import Histogram
import distributions as dst
graph_disc = 200
ns = [10, 100, 1000]
ds = dst.get_distributions()
for d in ds:
for n in ns:
data = [d.x() for i in range(n)]
hs = Histogram(data, d.discrete())
x = np.linspace(min(data), max(data), graph_disc)
yf = [d.f(k) for k in x]
yF = [d.F(k) for k in x]
plt.subplot(2, len(ns), ns.index(n) + 1)
plt.title(d.name + ", n = " + str(n))
plt.xlabel('x')
plt.ylabel('Функция плотности')
plt.plot(x, yf, label='ожидаемое')
plt.step(hs.x, hs.y, label='полученное')
plt.legend()
plt.subplot(2, len(ns), ns.index(n) + len(ns) + 1)
plt.xlabel('x')
plt.ylabel('Фукнция распределения')
plt.plot(x, yF, label='ожидаемое')
plt.step(hs.x, hs.F(), label='полученное')
plt.legend()
plt.show()
