In [1]:
get_ipython().ast_node_interactivity = 'all' import os import matplotlib.pyplot as plt import numpy as np import random np.set_printoptions(suppress=True)
In [2]:
targetDev = 1 def fitness(): vals = np.array([random.gauss(0, targetDev) for _ in range(2 ** 20)]) hist = np.histogram(vals, bins=100)[1] plt.plot(hist) fitness()
Out:
In [3]:
bruh = lambda m, s, n: np.sqrt(n) * sum([m + np.random.uniform(-s, s) for _ in range(n)]) / n _ = plt.hist([bruh(0, 1, 25) for _ in range(2**14)], bins=50)
Out:
In [4]:
_ = plt.hist([np.random.normal(0, 1) for _ in range(2**14)], bins=50)
Out:
