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made the basic GP class use dtrtrs where possible

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James Hensman 2013-04-22 11:59:32 +01:00
parent 7fbcae2503
commit 56ecd4782a
1 changed files with 15 additions and 5 deletions
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20
GPy/models/GP.py
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@ -9,6 +9,7 @@ from ..core import model
from ..util.linalg import pdinv,mdot
from ..util.plot import gpplot,x_frame1D,x_frame2D, Tango
from ..likelihoods import EP
from scipy import linalg
class GP(model):
"""
@ -78,10 +79,13 @@ class GP(model):
#the gradient of the likelihood wrt the covariance matrix
if self.likelihood.YYT is None:
alpha = np.dot(self.Ki,self.likelihood.Y)
#alpha = np.dot(self.Ki,self.likelihood.Y)
alpha,info = linalg.lapack.flapack.dpotrs(self.L,np.asfortranarray(self.likelihood.Y),lower=1)
self.dL_dK = 0.5*(np.dot(alpha,alpha.T)-self.D*self.Ki)
else:
tmp = mdot(self.Ki, self.likelihood.YYT, self.Ki)
#tmp = mdot(self.Ki, self.likelihood.YYT, self.Ki)
tmp,info = linalg.lapack.flapack.dpotrs(self.L,np.asfortranarray(self.likelihood.YYT),lower=1)
tmp,info = linalg.lapack.flapack.dpotrs(self.L,np.asfortranarray(tmp.T),lower=1)
self.dL_dK = 0.5*(tmp - self.D*self.Ki)
def _get_params(self):
@ -105,10 +109,13 @@ class GP(model):
Computes the model fit using YYT if it's available
"""
if self.likelihood.YYT is None:
return -0.5*np.sum(np.square(np.dot(self.Li,self.likelihood.Y)))
#return -0.5*np.sum(np.square(np.dot(self.Li,self.likelihood.Y)))
tmp,info = linalg.lapack.flapack.dtrtrs(self.L,np.asfortranarray(self.likelihood.Y),lower=1)
return -0.5*np.sum(np.square(tmp))
else:
return -0.5*np.sum(np.multiply(self.Ki, self.likelihood.YYT))
def log_likelihood(self):
"""
The log marginal likelihood of the GP.
@ -136,8 +143,11 @@ class GP(model):
for normalization or likelihood
"""
Kx = self.kern.K(self.X,_Xnew, slices1=self.Xslices,slices2=slices)
mu = np.dot(np.dot(Kx.T,self.Ki),self.likelihood.Y)
KiKx = np.dot(self.Ki,Kx)
#mu = np.dot(np.dot(Kx.T,self.Ki),self.likelihood.Y)
tmp,info = linalg.lapack.flapack.dpotrs(self.L,np.asfortranarray(self.likelihood.Y),lower=1)
mu = np.dot(Kx.T,tmp)
#KiKx = np.dot(self.Ki,Kx)
KiKx,info = linalg.lapack.flapack.dpotrs(self.L,np.asfortranarray(Kx),lower=1)
if full_cov:
Kxx = self.kern.K(_Xnew, slices1=slices,slices2=slices)
var = Kxx - np.dot(KiKx.T,Kx)