apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-04-30 23:36:23 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

[logging] more on logging

Browse source
This commit is contained in:
mzwiessele 2014-07-02 10:13:30 -07:00
parent eb9fb180fb
commit a9443417d7
3 changed files with 19 additions and 8 deletions
Download patch file Download diff file Expand all files Collapse all files

10
GPy/core/sparse_gp.py
View file

@ -46,7 +46,7 @@ class SparseGP(GP):
self.num_inducing = Z.shape[0]
GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata)
self.logger.info("Adding Z as parameter")
self.add_parameter(self.Z, index=0)
def has_uncertain_inputs(self):
@ -66,10 +66,10 @@ class SparseGP(GP):
#gradients wrt Z
self.Z.gradient = self.kern.gradients_X(dL_dKmm, self.Z)
self.Z.gradient += self.kern.gradients_Z_expectations(
self.grad_dict['dL_dpsi0'],
self.grad_dict['dL_dpsi1'],
self.grad_dict['dL_dpsi2'],
Z=self.Z,
self.grad_dict['dL_dpsi0'],
self.grad_dict['dL_dpsi1'],
self.grad_dict['dL_dpsi2'],
Z=self.Z,
variational_posterior=self.X)
else:
#gradients wrt kernel

16
GPy/inference/latent_function_inference/var_dtc.py
View file

@ -9,7 +9,7 @@ import numpy as np
from ...util.misc import param_to_array
from . import LatentFunctionInference
log_2_pi = np.log(2*np.pi)
import logging
import logging, itertools
logger = logging.getLogger('vardtc')
class VarDTC(LatentFunctionInference):
@ -228,18 +228,28 @@ class VarDTCMissingData(LatentFunctionInference):
self._subarray_indices = []
csa = common_subarrays(inan, 1)
size = len(csa)
next_ten = 0
for i, (v,ind) in enumerate(csa.iteritems()):
if not np.all(v):
logger.info('preparing subarrays {:3.3%}'.format((i+1.)/size))
if ((i+1.)/size) >= next_ten:
logger.info('preparing subarrays {:3%}'.format((i+1.)/size))
next_ten += max(.1, 1./size)
v = ~np.array(v, dtype=bool)
ind = np.array(ind, dtype=int)
if ind.size == Y.shape[1]:
ind = slice(None)
self._subarray_indices.append([v,ind])
logger.info('preparing subarrays Y')
#logger.info('preparing subarrays {:3.3%}'.format((i+1.)/size))
#Ys = [Y[v, :][:, ind] for v, ind in self._subarray_indices]
logger.info('preparing traces Y')
next_ten = [0.]
count = itertools.count()
def trace(y, v, ind):
i = count.next()
if ((i+1.)/size) >= next_ten[0]:
logger.info('preparing traces {:3%}'.format((i+1.)/size))
next_ten[0] += .1
y = y[v,:][:,ind]
return np.einsum('ij,ij->', y,y)
traces = [trace(Y, v, ind) for v, ind in self._subarray_indices]

1
GPy/models/bayesian_gplvm.py
View file

@ -67,6 +67,7 @@ class BayesianGPLVM(SparseGP):
inference_method = VarDTC()
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, name, **kwargs)
self.logger.info("Adding X as parameter")
self.add_parameter(self.X, index=0)
def set_X_gradients(self, X, X_grad):