stochastic_hill_climbing

Complexity

Total Complexity

1

Size/Duplication

Total Lines	29
Duplicated Lines	0 %

Importance

Changes

0

1 Function

Rating	Name	Duplication	Size	Complexity
A	sphere_function()	0	5	1

import numpy as np

from hyperactive import Hyperactive
from hyperactive.optimizers import StochasticHillClimbingOptimizer


def sphere_function(para):
    x = para["x"]
    y = para["y"]

    return -(x * x + y * y)


search_space = {
    "x": list(np.arange(-10, 10, 0.1)),
    "y": list(np.arange(-10, 10, 0.1)),
}

opt = StochasticHillClimbingOptimizer(
    epsilon=0.01,
    n_neighbours=5,
    distribution="laplace",
    p_accept=0.05,
)

hyper = Hyperactive()
hyper.add_search(sphere_function, search_space, n_iter=1500, optimizer=opt)
hyper.run()