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Dont call parameters_changed ever yourself anymore and parameters are now inplace once in memory

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Max Zwiessele 2014-03-04 17:32:46 +00:00
parent 56d749ded8
commit 0df263956f
21 changed files with 601 additions and 284 deletions
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19
GPy/core/model.py
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@ -15,6 +15,7 @@ import itertools
class Model(Parameterized):
_fail_count = 0 # Count of failed optimization steps (see objective)
_allowed_failures = 10 # number of allowed failures
def __init__(self, name):
super(Model, self).__init__(name) # Parameterized.__init__(self)
self.optimization_runs = []
@ -25,14 +26,8 @@ class Model(Parameterized):
raise NotImplementedError, "this needs to be implemented to use the model class"
def _log_likelihood_gradients(self):
g = np.zeros(self.size)
try:
[p._collect_gradient(g[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_) if not p.is_fixed]
except ValueError:
raise ValueError, 'Gradient for {} not defined, please specify gradients for parameters to optimize'.format(p.name)
return g
raise NotImplementedError, "this needs to be implemented to use the model class"
return self.gradient
def _getstate(self):
"""
Get the current state of the class.
@ -208,8 +203,8 @@ class Model(Parameterized):
try:
self._set_params_transformed(x)
obj_f = -float(self.log_likelihood()) - self.log_prior()
self._fail_count = 0
obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
self._fail_count = 0
except (LinAlgError, ZeroDivisionError, ValueError) as e:
if self._fail_count >= self._allowed_failures:
raise e
@ -275,9 +270,8 @@ class Model(Parameterized):
The gradient is considered correct if the ratio of the analytical
and numerical gradients is within <tolerance> of unity.
"""
x = self._get_params_transformed()
x = self._get_params_transformed().copy()
if not verbose:
# make sure only to test the selected parameters
if target_param is None:
@ -368,6 +362,7 @@ class Model(Parameterized):
ng = '%.6f' % float(numerical_gradient)
grad_string = "{0:<{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name, r, d, g, ng, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4])
print grad_string
self._set_params_transformed(x)
return ret