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stability improvements in sparse_GP

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James Hensman 2013-04-10 11:04:49 +01:00
parent 45403105f9
commit eb1d8f211f
3 changed files with 16 additions and 9 deletions
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2
GPy/examples/dimensionality_reduction.py
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@ -68,8 +68,6 @@ def BGPLVM_oil(optimize=True,N=100,Q=10,M=15):
m = GPy.models.Bayesian_GPLVM(data['X'][:N], Q, kernel = kernel,M=M) m = GPy.models.Bayesian_GPLVM(data['X'][:N], Q, kernel = kernel,M=M)
m.data_labels = data['Y'][:N].argmax(axis=1) m.data_labels = data['Y'][:N].argmax(axis=1)
#initial conditions
# optimize # optimize
if optimize: if optimize:
m.constrain_fixed('noise',0.05) m.constrain_fixed('noise',0.05)

7
GPy/inference/SCG.py
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@ -23,6 +23,7 @@
import numpy as np import numpy as np
import sys
def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xtol=1e-6, ftol=1e-6): def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xtol=1e-6, ftol=1e-6):
""" """
@ -103,11 +104,13 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
iteration += 1 iteration += 1
if display: if display:
print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r',
sys.stdout.flush()
if success: if success:
# Test for termination # Test for termination
if np.max(np.abs(alpha*d)) < xtol or np.max(np.abs(fnew-fold)) < ftol: if (np.max(np.abs(alpha*d)) < xtol) or (np.abs(fnew-fold) < ftol):
status='converged'
return x, flog, function_eval, status return x, flog, function_eval, status
else: else:

16
GPy/models/sparse_GP.py
View file

@ -7,6 +7,7 @@ from ..util.linalg import mdot, jitchol, chol_inv, pdinv, trace_dot
from ..util.plot import gpplot from ..util.plot import gpplot
from .. import kern from .. import kern
from GP import GP from GP import GP
from scipy import linalg
#Still TODO: #Still TODO:
# make use of slices properly (kernel can now do this) # make use of slices properly (kernel can now do this)
@ -96,21 +97,26 @@ class sparse_GP(GP):
self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm) self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm)
self.V = (self.likelihood.precision/self.scale_factor)*self.likelihood.Y self.V = (self.likelihood.precision/self.scale_factor)*self.likelihood.Y
self.A = mdot(self.Lmi, self.psi2_beta_scaled, self.Lmi.T)
#Compute A = L^-1 psi2 beta L^-T
tmp = linalg.lapack.flapack.dtrtrs(self.Lm,self.psi2_beta_scaled.T,lower=1)[0]
self.A = linalg.lapack.flapack.dtrtrs(self.Lm,np.asarray(tmp.T,order='F'),lower=1)[0]
self.B = np.eye(self.M)/sf2 + self.A self.B = np.eye(self.M)/sf2 + self.A
self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B) self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B)
self.psi1V = np.dot(self.psi1, self.V) self.psi1V = np.dot(self.psi1, self.V)
tmp = np.dot(self.Lmi.T, self.LBi.T) #tmp = np.dot(self.Lmi.T, self.LBi.T)
self.C = np.dot(tmp,tmp.T) tmp = linalg.lapack.clapack.dtrtrs(self.Lm.T,np.asarray(self.LBi.T,order='C'),lower=0)[0]
self.C = np.dot(tmp,tmp.T) #TODO: tmp is triangular. replace with dtrmm (blas) when available
self.Cpsi1V = np.dot(self.C,self.psi1V) self.Cpsi1V = np.dot(self.C,self.psi1V)
self.Cpsi1VVpsi1 = np.dot(self.Cpsi1V,self.psi1V.T) self.Cpsi1VVpsi1 = np.dot(self.Cpsi1V,self.psi1V.T)
self.E = np.dot(self.Cpsi1VVpsi1,self.C)/sf2 #self.E = np.dot(self.Cpsi1VVpsi1,self.C)/sf2
self.E = np.dot(self.Cpsi1V/sf,self.Cpsi1V.T/sf)
# Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertin inputs case # Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertin inputs case
self.dL_dpsi0 = - 0.5 * self.D * (self.likelihood.precision * np.ones([self.N,1])).flatten() self.dL_dpsi0 = - 0.5 * self.D * (self.likelihood.precision * np.ones([self.N,1])).flatten()
#self.dL_dpsi1 = mdot(self.V, self.psi1V.T,self.C).T
self.dL_dpsi1 = np.dot(self.Cpsi1V,self.V.T) self.dL_dpsi1 = np.dot(self.Cpsi1V,self.V.T)
if self.likelihood.is_heteroscedastic: if self.likelihood.is_heteroscedastic:
if self.has_uncertain_inputs: if self.has_uncertain_inputs: