# simple example: minimize a quadratic around some solution point
import numpy as np
solution = np.array([0.5, 0.1, -0.3])
def f(w): return -np.sum((w - solution)**2)

npop = 50      # population size
sigma = 0.1    # noise standard deviation
alpha = 0.001  # learning rate
w = np.random.randn(3) # initial guess
for i in range(300):
  N = np.random.randn(npop, 3)
  R = np.zeros(npop)
  for j in range(npop):
    w_try = w + sigma*N[j]
    R[j] = f(w_try)
  A = (R - np.mean(R)) / np.std(R)
  w = w + alpha/(npop*sigma) * np.dot(N.T, A)