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added trace_sum for efficiency

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This commit is contained in:
James Hensman 2013-03-11 18:56:37 +00:00
parent 129bb3924e
commit cb082898d3
2 changed files with 10 additions and 3 deletions
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6
GPy/models/sparse_GP.py
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@ -3,7 +3,7 @@
import numpy as np
import pylab as pb
from ..util.linalg import mdot, jitchol, chol_inv, pdinv
from ..util.linalg import mdot, jitchol, chol_inv, pdinv, trace_dot
from ..util.plot import gpplot
from .. import kern
from GP import GP
@ -107,7 +107,7 @@ class sparse_GP(GP):
self.C = np.dot(tmp,tmp.T)
#self.C = mdot(self.Lmi.T, self.Bi, self.Lmi)
#self.E = mdot(self.C, self.psi1VVpsi1/sf2, self.C.T)
tmp = np.dot(self.C,self.psi1V/sf)
tmp = np.dot(self.C/sf,self.psi1V)
self.E = np.dot(tmp,tmp.T)
# Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertin inputs case
@ -156,7 +156,7 @@ class sparse_GP(GP):
beta = self.likelihood.precision
dbeta = 0.5 * self.N*self.D/beta - 0.5 * np.sum(np.square(self.likelihood.Y))
dbeta += - 0.5 * self.D * (self.psi0.sum() - np.trace(self.A)/beta*sf2)
dbeta += - 0.5 * self.D * np.sum(self.Bi*self.A)/beta
dbeta += - 0.5 * self.D * trace_dot(self.Bi,self.A)/beta
dbeta += np.sum((self.C - 0.5 * mdot(self.C,self.psi2_beta_scaled,self.C) ) * self.psi1VVpsi1 )/beta
self.partial_for_likelihood = -dbeta*self.likelihood.precision**2

7
GPy/util/linalg.py
View file

@ -14,6 +14,13 @@ import types
#import scipy.lib.lapack.flapack
import scipy as sp
def trace_dot(a,b):
"""
efficiently compute the trace of the matrix product of a and b
"""
assert a.shape==b.T.shape
return np.dot(a.flatten(),b.T.flatten())
def mdot(*args):
"""Multiply all the arguments using matrix product rules.
The output is equivalent to multiplying the arguments one by one