From Werner KRAUTH
import math
import random
import matplotlib.pyplot as plt
def u(x):
return x ** 2 / 2.0 + x ** 4 / 4.0
x = 0.0
delta = 0.1
sigma = random.choice([-1, 1])
data = []
n_samples = 10 ** 6
for i in range(n_samples):
new_x = x + sigma * random.uniform(0.0, delta)
delta_u = u(new_x) - u(x)
if random.random() < math.exp(-delta_u): x = new_x
else: sigma *= -1
data.append(x)
plt.title('Lifted Metropolis algorithm, anharmonic oscillator' )
plt.xlabel('$x$')
plt.ylabel('$\pi(x)$')
plt.hist(data, bins=100, density=True,label='data')
XValues = []
YValues = []
for i in range(-1000,1000):
x = i / 400.0
XValues.append(x)
YValues.append(math.exp(- u(x)) / 1.93525)
plt.plot(XValues, YValues, label='theory')
plt.legend(loc='upper right')
plt.show()