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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. Array representing the parameters of this class.
There is only one copy of all parameters in memory, two during optimization. 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: if self.__dict__.get('_param_array_', None) is None:
self._param_array_ = np.empty(self.size, dtype=np.float64) self._param_array_ = np.empty(self.size, dtype=np.float64)

43
GPy/inference/latent_function_inference/var_dtc.py
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@ -273,21 +273,16 @@ class VarDTCMissingData(LatentFunctionInference):
Lm = jitchol(Kmm) Lm = jitchol(Kmm)
if uncertain_inputs: LmInv = dtrtri(Lm) if uncertain_inputs: LmInv = dtrtri(Lm)
VVT_factor_all = np.empty(Y.shape) #VVT_factor_all = np.empty(Y.shape)
full_VVT_factor = VVT_factor_all.shape[1] == Y.shape[1] #full_VVT_factor = VVT_factor_all.shape[1] == Y.shape[1]
if not full_VVT_factor: #if not full_VVT_factor:
psi1V = np.dot(Y.T*beta_all, psi1_all).T # psi1V = np.dot(Y.T*beta_all, psi1_all).T
for y, trYYT, [v, ind] in itertools.izip(Ys, traces, self._subarray_indices): for y, trYYT, [v, ind] in itertools.izip(Ys, traces, self._subarray_indices):
if het_noise: beta = beta_all[ind] if het_noise: beta = beta_all[ind]
else: beta = beta_all else: beta = beta_all
VVT_factor = (beta*y) 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] output_dim = y.shape[1]
psi0 = psi0_all[v] psi0 = psi0_all[v]
@ -347,12 +342,13 @@ class VarDTCMissingData(LatentFunctionInference):
psi0, psi1, beta, psi0, psi1, beta,
data_fit, num_data, output_dim, trYYT, Y) data_fit, num_data, output_dim, trYYT, Y)
if full_VVT_factor: woodbury_vector[:, ind] = Cpsi1Vf #if full_VVT_factor:
else: woodbury_vector[:, ind] = Cpsi1Vf
print 'foobar' #else:
tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0) # print 'foobar'
tmp, _ = dpotrs(LB, tmp, lower=1) # tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
woodbury_vector[:, ind] = dtrtrs(Lm, tmp, lower=1, trans=1)[0] # tmp, _ = dpotrs(LB, tmp, lower=1)
# woodbury_vector[:, ind] = dtrtrs(Lm, tmp, lower=1, trans=1)[0]
#import ipdb;ipdb.set_trace() #import ipdb;ipdb.set_trace()
Bi, _ = dpotri(LB, lower=1) Bi, _ = dpotri(LB, lower=1)
@ -376,23 +372,6 @@ class VarDTCMissingData(LatentFunctionInference):
'dL_dKnm':dL_dpsi1_all, 'dL_dKnm':dL_dpsi1_all,
'dL_dthetaL':dL_dthetaL} '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) 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 return post, log_marginal, grad_dict