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from __future__ import annotations
import sys
import numpy as np
from kurobako import problem
from kurobako.problem import Problem
from typing import Optional
class SphereEvaluator(problem.Evaluator):
def __init__(self, params: list[Optional[float]]):
self.n = len(params)
self.x = np.array(params, dtype=float)
self._current_step = 0
def evaluate(self, next_step: int) -> list[float]:
self._current_step = 1
value = np.mean(self.x**2)
return [value]
def current_step(self) -> int:
return self._current_step
class SphereProblem(problem.Problem):
def create_evaluator(
self, params: list[Optional[float]]
) -> Optional[problem.Evaluator]:
return SphereEvaluator(params)
class SphereProblemFactory(problem.ProblemFactory):
def __init__(self, dim):
self.dim = dim
def create_problem(self, seed: int) -> Problem:
return SphereProblem()
def specification(self) -> problem.ProblemSpec:
params = [
problem.Var(f"x{i + 1}", problem.ContinuousRange(-5.12, 5.12))
for i in range(self.dim)
]
return problem.ProblemSpec(
name=f"Sphere (dim={self.dim})",
params=params,
values=[problem.Var("Sphere")],
)
if __name__ == "__main__":
dim = int(sys.argv[1]) if len(sys.argv) == 2 else 2
runner = problem.ProblemRunner(SphereProblemFactory(dim))
runner.run()
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