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Attempted to introduce gradient methods, won't work yet I doubt

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Alan Saul 2013-04-19 12:23:00 +01:00
parent 7b44a4cb53
commit 1420aa532c
5 changed files with 177 additions and 37 deletions
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16
GPy/models/GP.py
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@ -8,7 +8,7 @@ from .. import kern
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 ..likelihoods import EP, Laplace
class GP(model):
"""
@ -128,7 +128,19 @@ class GP(model):
For the likelihood parameters, pass in alpha = K^-1 y
"""
return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK,X=self.X,slices1=self.Xslices,slices2=self.Xslices), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
if isinstance(self.likelihood, Laplace):
dL_dthetaK_explicit = self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X, slices1=self.Xslices, slices2=self.Xslices)
#Need to pass in a matrix of ones to get access to raw dK_dthetaK values without being chained
fake_dL_dKs = np.ones(self.dL_dK.shape)
dK_dthetaK = self.kern.dK_dtheta(dL_dK=fake_dL_dKs, X=self.X, slices1=self.Xslices, slices2=self.Xslices)
dL_dthetaK_implicit = self.likelihood._Kgradients(self.dL_dK, dK_dthetaK)
dL_dthetaK = dL_dthetaK_explicit + dL_dthetaK_implicit
dL_dthetaL = self.likelihood._gradients(partial=np.diag(self.dL_dK))
else:
dL_dthetaK = self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X, slices1=self.Xslices, slices2=self.Xslices)
dL_dthetaL = self.likelihood._gradients(partial=np.diag(self.dL_dK))
return np.hstack((dL_dthetaK, dL_dthetaL))
def _raw_predict(self,_Xnew,slices=None, full_cov=False):
"""