apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-09 12:02:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Reindented, did some profiling which looks promising

Browse source
This commit is contained in:
Alan Saul 2015-09-02 10:46:30 +03:00
parent 6ea3310c74
commit 81ef734908
2 changed files with 9 additions and 12 deletions
Download patch file Download diff file Expand all files Collapse all files

2
GPy/kern/_src/psi_comp/rbf_psi_comp.py
View file

@ -68,7 +68,6 @@ def __psi2computations(variance, lengthscale, Z, mu, S):
_psi2 = variance*variance*np.exp(_psi2_logdenom[:,None,None]+_psi2_exp1[None,:,:]+_psi2_exp2)
return _psi2
@profile
def psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior,
psi0=None, psi1=None, psi2=None, Lpsi0=None, Lpsi1=None, Lpsi2=None):
ARD = (len(lengthscale)!=1)
@ -122,7 +121,6 @@ def __psi1compDer(dL_dpsi1, variance, lengthscale, Z, mu, S, psi1=None, Lpsi1=No
return _dL_dvar, _dL_dl, _dL_dZ, _dL_dmu, _dL_dS
@profile
def __psi2compDer(dL_dpsi2, variance, lengthscale, Z, mu, S, psi2=None, Lpsi2=None):
"""
Z - MxQ

19
GPy/models/bayesian_gplvm_minibatch.py
View file

@ -122,15 +122,15 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
"""
super(BayesianGPLVMMiniBatch, self)._outer_values_update(full_values)
if self.has_uncertain_inputs():
meangrad_tmp, vargrad_tmp = self.kern.gradients_qX_expectations(
variational_posterior=self.X,
Z=self.Z, dL_dpsi0=full_values['dL_dpsi0'],
dL_dpsi1=full_values['dL_dpsi1'],
dL_dpsi2=full_values['dL_dpsi2'],
psi0=self.psi0, psi1=self.psi1, psi2=self.psi2,
Lpsi0=full_values['Lpsi0'], Lpsi1=full_values['Lpsi1'], Lpsi2=full_values['Lpsi2'])
full_values['meangrad'] += meangrad_tmp
full_values['vargrad'] += vargrad_tmp
meangrad_tmp, vargrad_tmp = self.kern.gradients_qX_expectations(
variational_posterior=self.X,
Z=self.Z, dL_dpsi0=full_values['dL_dpsi0'],
dL_dpsi1=full_values['dL_dpsi1'],
dL_dpsi2=full_values['dL_dpsi2'],
psi0=self.psi0, psi1=self.psi1, psi2=self.psi2,
Lpsi0=full_values['Lpsi0'], Lpsi1=full_values['Lpsi1'], Lpsi2=full_values['Lpsi2'])
full_values['meangrad'] += meangrad_tmp
full_values['vargrad'] += vargrad_tmp
else:
full_values['Xgrad'] = self.kern.gradients_X(full_values['dL_dKnm'], self.X, self.Z)
full_values['Xgrad'] += self.kern.gradients_X_diag(full_values['dL_dKdiag'], self.X)
@ -146,7 +146,6 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
full_values['meangrad'] = np.zeros((self.X.shape[0], self.X.shape[1]))
full_values['vargrad'] = np.zeros((self.X.shape[0], self.X.shape[1]))
#FIXME Hack
full_values['dL_dpsi0'] = ObsAr(np.zeros(self.X.shape[0]))
full_values['dL_dpsi1'] = ObsAr(np.zeros((self.X.shape[0], self.Z.shape[0])))
full_values['dL_dpsi2'] = ObsAr(np.zeros((self.Z.shape[0], self.Z.shape[0])))