apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-11 13:02:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

more massive and destructive changes everywhere

Browse source
This commit is contained in:
James Hensman 2013-12-05 15:09:31 -05:00
parent 881800126f
commit 5ec64d2279
18 changed files with 202 additions and 166 deletions
Download patch file Download diff file Expand all files Collapse all files

28
GPy/core/gp.py
View file

@ -3,6 +3,9 @@
import numpy as np
from gp_base import GPBase
from ..util.linalg import dtrtrs
from ..inference.latent_function_inference import exact_gaussian_inference, expectation_propagation
from .. import likelihoods
class GP(GPBase):
"""
@ -18,14 +21,25 @@ class GP(GPBase):
.. Note:: Multiple independent outputs are allowed using columns of Y
"""
def __init__(self, X, likelihood, kernel, normalize_X=False):
super(GP, self).__init__(X, likelihood, kernel, normalize_X=normalize_X)
self.Posterior = self.inference_method.inference(K, likelihood, self.Y)
def __init__(self, X, Y, kernel, likelihood, inference_method=None, name='gp'):
if inference_method is None:
if isinstance(likelihood, likelihoods.Gaussian):
inference_method = exact_gaussian_inference.ExactGaussianInference()
else:
inference_method = expectation_propagation
print "defaulting to ", inference_method, "for latent function inference"
super(GP, self).__init__(X, Y, kernel, likelihood, inference_method, name)
self.parameters_changed()
def parameters_changed(self):
super(GP, self).parameters_changed()
self.K = self.kern.K(self.X)
self.Posterior = self.inference_method.inference(K, likelihood, self.Y)
self.posterior = self.inference_method.inference(self.K, self.likelihood, self.Y)
def dL_dtheta_K(self):
return self.kern.dK_dtheta(self.posterior.dL_dK, self.X)
def log_likelihood(self):
return self.posterior.log_marginal
@ -41,15 +55,15 @@ class GP(GPBase):
"""
Kx = self.kern.K(_Xnew, self.X, which_parts=which_parts).T
LiKx, _ = dptrrs(self.posterior._woodbury_chol, np.asfortranarray(Kx), lower=1)
LiKx, _ = dtrtrs(self.posterior._woodbury_chol, np.asfortranarray(Kx), lower=1)
mu = np.dot(Kx.T, self.posterior._woodbury_vector)
if full_cov:
Kxx = self.kern.K(_Xnew, which_parts=which_parts)
var = Kxx - tdot(LiKx.T)
else:
Kxx = self.kern.Kdiag(_Xnew, which_parts=which_parts)
var = Kxx - np.sum(LiKx.T*LiKx, 0)
var = var.reshape(self.num_data, 1)
var = Kxx - np.sum(LiKx*LiKx, 0)
var = var.reshape(-1, 1)
return mu, var
def predict(self, Xnew, which_parts='all', full_cov=False, **likelihood_args):