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[vardtc missing] performance fixes

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This commit is contained in:
mzwiessele 2014-06-27 08:03:45 -07:00
parent 66390cbf8a
commit 83e2df838d
2 changed files with 18 additions and 36 deletions
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3
GPy/core/parameterization/parameter_core.py
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@ -874,6 +874,9 @@ class Parameterizable(OptimizationHandlable):
"""
Array representing the parameters of this class.
There is only one copy of all parameters in memory, two during optimization.
!WARNING!: setting the parameter array MUST always be done in memory:
m.param_array[:] = m_copy.param_array
"""
if self.__dict__.get('_param_array_', None) is None:
self._param_array_ = np.empty(self.size, dtype=np.float64)

51
GPy/inference/latent_function_inference/var_dtc.py
View file

@ -36,11 +36,11 @@ class VarDTC(LatentFunctionInference):
return param_to_array(np.sum(np.square(Y)))
def __getstate__(self):
# has to be overridden, as Cacher objects cannot be pickled.
# has to be overridden, as Cacher objects cannot be pickled.
return self.limit
def __setstate__(self, state):
# has to be overridden, as Cacher objects cannot be pickled.
# has to be overridden, as Cacher objects cannot be pickled.
self.limit = state
from ...util.caching import Cacher
self.get_trYYT = Cacher(self._get_trYYT, self.limit)
@ -203,11 +203,11 @@ class VarDTCMissingData(LatentFunctionInference):
self._Y.limit = limit
def __getstate__(self):
# has to be overridden, as Cacher objects cannot be pickled.
# has to be overridden, as Cacher objects cannot be pickled.
return self._Y.limit, self._inan
def __setstate__(self, state):
# has to be overridden, as Cacher objects cannot be pickled.
# has to be overridden, as Cacher objects cannot be pickled.
from ...util.caching import Cacher
self.limit = state[0]
self._inan = state[1]
@ -273,21 +273,16 @@ class VarDTCMissingData(LatentFunctionInference):
Lm = jitchol(Kmm)
if uncertain_inputs: LmInv = dtrtri(Lm)
VVT_factor_all = np.empty(Y.shape)
full_VVT_factor = VVT_factor_all.shape[1] == Y.shape[1]
if not full_VVT_factor:
psi1V = np.dot(Y.T*beta_all, psi1_all).T
#VVT_factor_all = np.empty(Y.shape)
#full_VVT_factor = VVT_factor_all.shape[1] == Y.shape[1]
#if not full_VVT_factor:
# psi1V = np.dot(Y.T*beta_all, psi1_all).T
for y, trYYT, [v, ind] in itertools.izip(Ys, traces, self._subarray_indices):
if het_noise: beta = beta_all[ind]
else: beta = beta_all
VVT_factor = (beta*y)
try:
VVT_factor_all[v, ind].flat = VVT_factor.flat
except ValueError:
mult = np.ravel_multi_index((v.nonzero()[0][:,None],ind[None,:]), VVT_factor_all.shape)
VVT_factor_all.flat[mult] = VVT_factor
output_dim = y.shape[1]
psi0 = psi0_all[v]
@ -347,12 +342,13 @@ class VarDTCMissingData(LatentFunctionInference):
psi0, psi1, beta,
data_fit, num_data, output_dim, trYYT, Y)
if full_VVT_factor: woodbury_vector[:, ind] = Cpsi1Vf
else:
print 'foobar'
tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
tmp, _ = dpotrs(LB, tmp, lower=1)
woodbury_vector[:, ind] = dtrtrs(Lm, tmp, lower=1, trans=1)[0]
#if full_VVT_factor:
woodbury_vector[:, ind] = Cpsi1Vf
#else:
# print 'foobar'
# tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
# tmp, _ = dpotrs(LB, tmp, lower=1)
# woodbury_vector[:, ind] = dtrtrs(Lm, tmp, lower=1, trans=1)[0]
#import ipdb;ipdb.set_trace()
Bi, _ = dpotri(LB, lower=1)
@ -376,23 +372,6 @@ class VarDTCMissingData(LatentFunctionInference):
'dL_dKnm':dL_dpsi1_all,
'dL_dthetaL':dL_dthetaL}
#get sufficient things for posterior prediction
#TODO: do we really want to do this in the loop?
#if not full_VVT_factor:
# print 'foobar'
# psi1V = np.dot(Y.T*beta_all, psi1_all).T
# tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
# tmp, _ = dpotrs(LB_all, tmp, lower=1)
# woodbury_vector, _ = dtrtrs(Lm, tmp, lower=1, trans=1)
#import ipdb;ipdb.set_trace()
#Bi, _ = dpotri(LB_all, lower=1)
#symmetrify(Bi)
#Bi = -dpotri(LB_all, lower=1)[0]
#from ...util import diag
#diag.add(Bi, 1)
#woodbury_inv = backsub_both_sides(Lm, Bi)
post = Posterior(woodbury_inv=woodbury_inv_all, woodbury_vector=woodbury_vector, K=Kmm, mean=None, cov=None, K_chol=Lm)
return post, log_marginal, grad_dict