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[parameterized] merge in jacobian for priors?

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mzwiessele 2015-06-29 10:18:07 +02:00
parent ff558ff57c
commit 208a025751
2 changed files with 10 additions and 11 deletions
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2
GPy/core/parameterization/index_operations.py
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@ -109,7 +109,7 @@ class ParameterIndexOperations(object):
try:
return self._properties.itervalues()
except AttributeError:
#Changed this from itervalues to values for Py3 compatibility. It didn't break the test suite.
#Changed this from itervalues to values for Py3 compatibility. It didn't break the test suite.
return self._properties.values()
def indices(self):

19
GPy/core/parameterization/parameter_core.py
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@ -433,35 +433,34 @@ class Indexable(Nameable, Updateable):
if self.priors.size == 0:
return 0.
x = self.param_array
#evaluate the prior log densities
#evaluate the prior log densities
log_p = reduce(lambda a, b: a + b, (p.lnpdf(x[ind]).sum() for p, ind in self.priors.items()), 0)
#account for the transformation by evaluating the log Jacobian (where things are transformed)
log_j = 0.
priored_indexes = np.hstack([i for p, i in self.priors.items()])
#account for the transformation by evaluating the log Jacobian (where things are transformed)
log_j = 0.
priored_indexes = np.hstack([i for p, i in self.priors.items()])
for c,j in self.constraints.items():
if c is 'fixed':continue
for jj in j:
if jj in priored_indexes:
log_j += c.log_jacobian(x[jj])
return log_p + log_j
return log_p + log_j
def _log_prior_gradients(self):
"""evaluate the gradients of the priors"""
if self.priors.size == 0:
return 0.
x = self.param_array
ret = np.zeros(x.size)
#compute derivate of prior density
#compute derivate of prior density
[np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]
#add in jacobian derivatives if transformed
priored_indexes = np.hstack([i for p, i in self.priors.items()])
#add in jacobian derivatives if transformed
priored_indexes = np.hstack([i for p, i in self.priors.items()])
for c,j in self.constraints.items():
for jj in j:
if jj in priored_indexes:
ret[jj] += c.log_jacobian_grad(x[jj])
return ret
return ret
#===========================================================================
# Tie parameters together