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import numpy as np
from cmaes import CatCMA
def sphere_com(x, c):
dim_co = len(x)
dim_ca = len(c)
if dim_co < 2:
raise ValueError("dimension must be greater one")
sphere = sum(x * x)
com = dim_ca - sum(c[:, 0])
return sphere + com
def rosenbrock_clo(x, c):
dim_co = len(x)
dim_ca = len(c)
if dim_co < 2:
raise ValueError("dimension must be greater one")
rosenbrock = sum(100 * (x[:-1] ** 2 - x[1:]) ** 2 + (x[:-1] - 1) ** 2)
clo = dim_ca - (c[:, 0].argmin() + c[:, 0].prod() * dim_ca)
return rosenbrock + clo
def mc_proximity(x, c, cat_num):
dim_co = len(x)
dim_ca = len(c)
if dim_co < 2:
raise ValueError("dimension must be greater one")
if dim_co != dim_ca:
raise ValueError(
"number of dimensions of continuous and categorical variables "
"must be equal in mc_proximity"
)
c_index = np.argmax(c, axis=1) / cat_num
return sum((x - c_index) ** 2) + sum(c_index)
if __name__ == "__main__":
cont_dim = 5
cat_dim = 5
cat_num = np.array([3, 4, 5, 5, 5])
# cat_num = 3 * np.ones(cat_dim, dtype=np.int64)
optimizer = CatCMA(mean=3.0 * np.ones(cont_dim), sigma=1.0, cat_num=cat_num)
for generation in range(200):
solutions = []
for _ in range(optimizer.population_size):
x, c = optimizer.ask()
value = mc_proximity(x, c, cat_num)
if generation % 10 == 0:
print(f"#{generation} {value}")
solutions.append(((x, c), value))
optimizer.tell(solutions)
if optimizer.should_stop():
break
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