def optimize(self, start_point=None, **kwargs):
"""
Optimize the log(posterior) and returns a best-fit-point summary.
:param start_point: Parameter point from which to start the optimization, with the elements in the same order as in eos.Analysis.varied_parameters. If not specified, optimization starts at the current parameter point.
:param start_point: iterable, optional
"""
if start_point == None:
start_point = [float(p) for p in self.varied_parameters]
default_kwargs = {'method': 'SLSQP', 'options': {'ftol': 1.0e-13}}
if kwargs is None:
kwargs = default_kwargs
res = scipy.optimize.minimize(self.negative_log_pdf,
start_point,
args=None,
bounds=self.bounds,
**kwargs)
if not res.success:
eos.warn('Optimization did not succeed')
eos.warn(' optimizer' ' message reas: {}'.format(res.message))
else:
eos.info(
'Optimization goal achieved after {nfev} function evaluations'.
format(nfev=res.nfev))
for p, v in zip(self.varied_parameters, res.x):
p.set(v)
return eos.BestFitPoint(self, res.x)
def optimize(self, start_point=None, rng=np.random.mtrand, **kwargs):
"""
Optimize the log(posterior) and returns a best-fit-point summary.
:param start_point: Parameter point from which to start the optimization, with the elements in the same order as in eos.Analysis.varied_parameters.
If set to "random", optimization starts at the random point in the space of the priors.
If not specified, optimization starts at the current parameter point.
:type start_point: iterable, optional
:param rng: Optional random number generator
"""
if start_point == None:
start_point = [float(p) for p in self.varied_parameters]
elif start_point == "random":
start_point = [
p.inverse_cdf(rng.uniform())
for p in self._log_posterior.log_priors()
]
default_kwargs = {'method': 'SLSQP', 'options': {'ftol': 1.0e-13}}
if kwargs is None:
kwargs = default_kwargs
res = scipy.optimize.minimize(self.negative_log_pdf,
self._par_to_x(start_point),
args=None,
bounds=[(-1.0, 1.0)
for b in self.bounds],
**kwargs)
if not res.success:
eos.warn('Optimization did not succeed')
eos.warn(' optimizer' ' message reas: {}'.format(res.message))
else:
eos.info(
'Optimization goal achieved after {nfev} function evaluations'.
format(nfev=res.nfev))
bfp = self._x_to_par(res.x)
for p, v in zip(self.varied_parameters, bfp):
p.set(v)
return eos.BestFitPoint(self, bfp)