def _init_optimizer(self, outdir, algorithm, xtol, ftol, learn_rho,
single):
super()._init_optimizer(outdir, algorithm, xtol, ftol, single)
if learn_rho:
rho_bounds = lambda: (self._theta / 100, 100 * self._theta)
self._optimizer.register_plugin(
parameter_optimizer.ParameterOptimizer("rho", rho_bounds))
def _init_optimizer(self, outdir, algorithm, xtol, ftol, learn_rho=False):
super()._init_optimizer(outdir, algorithm, xtol, ftol)
if learn_rho:
rho_bounds = 2. * self._N0 * np.array([1e-10, 1e-5])
self._optimizer.register_plugin(
parameter_optimizer.ParameterOptimizer("rho",
tuple(rho_bounds)))
def _init_optimizer(self, outdir, algorithm, xtol, ftol, learn_rho,
single):
super()._init_optimizer(outdir, algorithm, xtol, ftol, single)
# alpha_bounds = (.0, .2)
# self._optimizer.register_plugin(
# parameter_optimizer.ParameterOptimizer("alpha", alpha_bounds))
if learn_rho:
rho_bounds = self._theta / 10, 10 * self._theta
self._optimizer.register_plugin(
parameter_optimizer.ParameterOptimizer("rho",
tuple(rho_bounds)))
def _init_optimizer(self, outdir, algorithm, xtol, ftol):
super()._init_optimizer(outdir, algorithm, xtol, ftol)
self._optimizer.register_plugin(
parameter_optimizer.ParameterOptimizer("split",
(0., self._max_split),
"model"))
