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added BGPLVM in parameterized

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Max Zwiessele 2013-11-06 15:15:13 +00:00
parent 3316d29341
commit 8c02e4af36
6 changed files with 103 additions and 72 deletions
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20
GPy/core/model.py
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@ -13,6 +13,9 @@ from domains import _POSITIVE, _REAL
from numpy.linalg.linalg import LinAlgError
from index_operations import ParameterIndexOperations
import itertools
from GPy.kern.parts.rbf import RBF
from GPy.kern.parts.rbf_inv import RBFInv
from GPy.kern.parts.linear import Linear
# import numdifftools as ndt
class Model(Parameterized):
@ -273,7 +276,7 @@ class Model(Parameterized):
these terms are present in the name the parameter is
constrained positive.
"""
positive_strings = ['variance', 'lengthscale', 'precision', 'kappa']
positive_strings = ['variance', 'lengthscale', 'precision', 'kappa', 'sensitivity']
# param_names = self._get_param_names()
for s in positive_strings:
paramlist = self.grep_param_names(".*"+s)
@ -308,7 +311,7 @@ class Model(Parameterized):
raise e
self._fail_count += 1
return np.inf
return -self.log_likelihood() - self.log_prior()
return -float(self.log_likelihood()) - self.log_prior()
def objective_function_gradients(self, x):
"""
@ -329,6 +332,7 @@ class Model(Parameterized):
if self._fail_count >= self._allowed_failures:
raise e
self._fail_count += 1
import ipdb;ipdb.set_trace()
obj_grads = np.clip(-self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients()), -1e100, 1e100)
return obj_grads
@ -342,7 +346,7 @@ class Model(Parameterized):
try:
self._set_params_transformed(x)
obj_f = -self.log_likelihood() - self.log_prior()
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())
except (LinAlgError, ZeroDivisionError, ValueError) as e:
@ -544,16 +548,16 @@ class Model(Parameterized):
if not hasattr(self, 'kern'):
raise ValueError, "this model has no kernel"
k = [p for p in self.kern._parameters_ if p.name in ['rbf', 'linear', 'rbf_inv']]
if (not len(k) == 1) or (not k[0].ARD):
k = [p for p in self.kern._parameters_ if hasattr(p, "ARD") and p.ARD]
if (not len(k) == 1):
raise ValueError, "cannot determine sensitivity for this kernel"
k = k[0]
if k.name == 'rbf':
if isinstance(k, RBF):
return 1. / k.lengthscale
elif k.name == 'rbf_inv':
elif isinstance(k, RBFInv):
return k.inv_lengthscale
elif k.name == 'linear':
elif isinstance(k, Linear):
return k.variances