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pickling unified with __getstate__ and __setstate__

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Max Zwiessele 2013-06-25 18:01:18 +01:00
parent 1e06ca2d40
commit 05e8e75c58
5 changed files with 35 additions and 13 deletions
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18
GPy/core/gp.py
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@ -6,7 +6,7 @@ import numpy as np
import pylab as pb
from .. import kern
from ..util.linalg import pdinv, mdot, tdot, dpotrs, dtrtrs
#from ..util.plot import gpplot, Tango
# from ..util.plot import gpplot, Tango
from ..likelihoods import EP
from gp_base import GPBase
@ -46,12 +46,12 @@ class GP(GPBase):
# the gradient of the likelihood wrt the covariance matrix
if self.likelihood.YYT is None:
#alpha = np.dot(self.Ki, self.likelihood.Y)
alpha,_ = dpotrs(self.L, self.likelihood.Y,lower=1)
# alpha = np.dot(self.Ki, self.likelihood.Y)
alpha, _ = dpotrs(self.L, self.likelihood.Y, lower=1)
self.dL_dK = 0.5 * (tdot(alpha) - self.output_dim * self.Ki)
else:
#tmp = mdot(self.Ki, self.likelihood.YYT, self.Ki)
# tmp = mdot(self.Ki, self.likelihood.YYT, self.Ki)
tmp, _ = dpotrs(self.L, np.asfortranarray(self.likelihood.YYT), lower=1)
tmp, _ = dpotrs(self.L, np.asfortranarray(tmp.T), lower=1)
self.dL_dK = 0.5 * (tmp - self.output_dim * self.Ki)
@ -72,7 +72,7 @@ class GP(GPBase):
"""
self.likelihood.restart()
self.likelihood.fit_full(self.kern.K(self.X))
self._set_params(self._get_params()) # update the GP
self._set_params(self._get_params()) # update the GP
def _model_fit_term(self):
"""
@ -81,7 +81,7 @@ class GP(GPBase):
if self.likelihood.YYT is None:
tmp, _ = dtrtrs(self.L, np.asfortranarray(self.likelihood.Y), lower=1)
return -0.5 * np.sum(np.square(tmp))
#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)))
else:
return -0.5 * np.sum(np.multiply(self.Ki, self.likelihood.YYT))
@ -104,13 +104,13 @@ class GP(GPBase):
"""
return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
def _raw_predict(self, _Xnew, which_parts='all', full_cov=False,stop=False):
def _raw_predict(self, _Xnew, which_parts='all', full_cov=False, stop=False):
"""
Internal helper function for making predictions, does not account
for normalization or likelihood
"""
Kx = self.kern.K(_Xnew,self.X,which_parts=which_parts).T
#KiKx = np.dot(self.Ki, Kx)
Kx = self.kern.K(_Xnew, self.X, which_parts=which_parts).T
# KiKx = np.dot(self.Ki, Kx)
KiKx, _ = dpotrs(self.L, np.asfortranarray(Kx), lower=1)
mu = np.dot(KiKx.T, self.likelihood.Y)
if full_cov: