from autotune import TuningProblem
from autotune.space import *
# problem space
task_space = None
input_space = Space([Real(-15, 15, name=f'x_{i}') for i in range(10)])
output_space = Space([Real(-inf, inf, name='y')])
def myobj(point: dict):
def ackley(x, a=20, b=0.2, c=2 * pi):
x = np.asarray_chkfinite(x) # ValueError if any NaN or Inf
n = len(x)
s1 = sum(x**2)
s2 = sum(cos(c * x))
return -a * exp(-b * sqrt(s1 / n)) - exp(s2 / n) + a + exp(1)
x = np.array([point[f'x_{i}'] for i in range(len(point))])
objective = ackley(x)
return objective
Problem = TuningProblem(task_space=None,
input_space=input_space,
output_space=output_space,
objective=myobj,
constraints=None,
model=None)
from autotune import Space, TuningProblem
task_space = Space(matrix=["boyd1.mtx"])
input_space = Space(m=(10, 100), n=(10, 100))
problem = TuningProblem(task_space, input_space)
print('problem.to_json: ', problem.to_json(), end='\n')
problem_copy = TuningProblem(json_repr=problem.to_json())
print(problem_copy)