apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-12 13:32:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

adjusted parameters to report their changes

Browse source
This commit is contained in:
Max Zwiessele 2013-11-03 13:58:15 +00:00
parent 067206e83e
commit 6feb5dd2f1
10 changed files with 84 additions and 57 deletions
Download patch file Download diff file Expand all files Collapse all files

23
GPy/core/sparse_gp.py
View file

@ -7,6 +7,7 @@ from ..util.linalg import mdot, jitchol, tdot, symmetrify, backsub_both_sides, c
from scipy import linalg
from ..likelihoods import Gaussian, EP,EP_Mixed_Noise
from gp_base import GPBase
from GPy.core.parameter import Param
class SparseGP(GPBase):
"""
@ -30,7 +31,7 @@ class SparseGP(GPBase):
"""
def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X, name="sparse GP")
self.Z = Z
self.num_inducing = Z.shape[0]
@ -50,6 +51,14 @@ class SparseGP(GPBase):
if self.has_uncertain_inputs:
self.X_variance /= np.square(self._Xscale)
self.Z = Param('inducing input', self.Z)
self.add_parameter(self.Z, gradient=self.dL_dZ, index=0)
self.add_parameter(self.kern, gradient=self.dL_dtheta)
self._compute_kernel_matrices()
self.Z.add_observer(self, lambda Z: self._compute_kernel_matrices())
#self.Z._notify_observers()
self._const_jitter = None
def getstate(self):
@ -197,13 +206,17 @@ class SparseGP(GPBase):
D = 0.5 * self.data_fit
return A + B + C + D + self.likelihood.Z
def _set_params(self, p):
self.Z = p[:self.num_inducing * self.input_dim].reshape(self.num_inducing, self.input_dim)
self.kern._set_params(p[self.Z.size:self.Z.size + self.kern.num_params])
self.likelihood._set_params(p[self.Z.size + self.kern.num_params:])
#def _set_params(self, p):
def parameters_changed(self):
#self.Z = p[:self.num_inducing * self.input_dim].reshape(self.num_inducing, self.input_dim)
#self.kern._set_params(p[self.Z.size:self.Z.size + self.kern.num_params])
#self.likelihood._set_params(p[self.Z.size + self.kern.num_params:])
#self._compute_kernel_matrices()
self._compute_kernel_matrices()
import ipdb;ipdb.set_trace()
self._computations()
self.Cpsi1V = None
super(SparseGP, self).parameters_changed()
def _get_params(self):
return np.hstack([self.Z.flatten(), self.kern._get_params_transformed(), self.likelihood._get_params()])