def general_opt_reset(topology):
"""Returns a GeneralOptimizerPSO instance that has been run and reset to check
default value"""
pso = GeneralOptimizerPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5}, topology=topology)
pso.optimize(sphere_func, 10, verbose=0)
pso.reset()
return pso
def test_ftol_effect(options, topology):
"""Test if setting the ftol breaks the optimization process accordingly"""
pso = GeneralOptimizerPSO(10,
2,
options=options,
topology=topology,
ftol=1e-1)
pso.optimize(sphere, 2000)
assert np.array(pso.cost_history).shape != (2000, )
def optimizer_history(self, request, options):
opt = GeneralOptimizerPSO(
n_particles=10,
dimensions=2,
options=options,
topology=request.param,
)
opt.optimize(sphere, 1000)
return opt
def __optimize(self, cost_function: Callable) -> Tuple[float, np.array]:
opt = GeneralOptimizerPSO(n_particles=self.vae.latent_dim,
dimensions=self.vae.latent_dim,
options=self.options,
topology=self.topology,
bounds=self.bounds)
cost, z = opt.optimize(cost_function, iters=self.n_iter, fast=True)
return cost, z
def general_opt_history(topology):
"""Returns a GeneralOptimizerPSO instance run for 1000 iterations for checking
history"""
pso = GeneralOptimizerPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5}, topology=topology)
pso.optimize(sphere_func, 1000, verbose=0)
return pso
