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import numpy as np
from cmaes import MAPCMA
def rosenbrock(x):
dim = len(x)
if dim < 2:
raise ValueError("dimension must be greater one")
return sum(100 * (x[:-1] ** 2 - x[1:]) ** 2 + (x[:-1] - 1) ** 2)
if __name__ == "__main__":
dim = 20
optimizer = MAPCMA(mean=np.zeros(dim), sigma=0.5, momentum_r=dim)
print(" evals f(x)")
print("====== ==========")
evals = 0
while True:
solutions = []
for _ in range(optimizer.population_size):
x = optimizer.ask()
value = rosenbrock(x)
evals += 1
solutions.append((x, value))
if evals % 1000 == 0:
print(f"{evals:5d} {value:10.5f}")
optimizer.tell(solutions)
if optimizer.should_stop():
break
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