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Merge branch 'params' of github.com:SheffieldML/GPy into params

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Max Zwiessele 2014-03-18 16:30:49 +00:00
commit a3287c38ea
4 changed files with 8 additions and 9 deletions
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4
GPy/core/gp.py
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@ -42,9 +42,7 @@ class GP(Model):
assert Y.shape[0] == self.num_data assert Y.shape[0] == self.num_data
_, self.output_dim = self.Y.shape _, self.output_dim = self.Y.shape
if Y_metadata is None: #TODO: check the type of this is okay?
self.Y_metadata = {}
else:
self.Y_metadata = Y_metadata self.Y_metadata = Y_metadata
assert isinstance(kernel, kern.Kern) assert isinstance(kernel, kern.Kern)

5
GPy/inference/latent_function_inference/exact_gaussian_inference.py
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@ -3,6 +3,7 @@
from posterior import Posterior from posterior import Posterior
from ...util.linalg import pdinv, dpotrs, tdot from ...util.linalg import pdinv, dpotrs, tdot
from ...util import diag
import numpy as np import numpy as np
log_2_pi = np.log(2*np.pi) log_2_pi = np.log(2*np.pi)
@ -41,7 +42,9 @@ class ExactGaussianInference(object):
K = kern.K(X) K = kern.K(X)
Wi, LW, LWi, W_logdet = pdinv(K + likelihood.covariance_matrix(Y, Y_metadata)) Ky = K.copy()
diag.add(Ky, likelihood.gaussian_variance(Y, Y_metadata))
Wi, LW, LWi, W_logdet = pdinv(Ky)
alpha, _ = dpotrs(LW, YYT_factor, lower=1) alpha, _ = dpotrs(LW, YYT_factor, lower=1)

2
GPy/inference/latent_function_inference/var_dtc.py
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@ -65,7 +65,7 @@ class VarDTC(object):
_, output_dim = Y.shape _, output_dim = Y.shape
#see whether we've got a different noise variance for each datum #see whether we've got a different noise variance for each datum
beta = 1./np.fmax(likelihood.gaussian_variance(Y_metadata), 1e-6) beta = 1./np.fmax(likelihood.gaussian_variance(Y, Y_metadata), 1e-6)
# VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency! # VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!
#self.YYTfactor = self.get_YYTfactor(Y) #self.YYTfactor = self.get_YYTfactor(Y)
#VVT_factor = self.get_VVTfactor(self.YYTfactor, beta) #VVT_factor = self.get_VVTfactor(self.YYTfactor, beta)

4
GPy/likelihoods/gaussian.py
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@ -51,14 +51,12 @@ class Gaussian(Likelihood):
self.log_concave = True self.log_concave = True
def betaY(self,Y,Y_metadata=None): def betaY(self,Y,Y_metadata=None):
#TODO: ~Ricardo this does not live here
return Y/self.gaussian_variance(Y_metadata) return Y/self.gaussian_variance(Y_metadata)
def gaussian_variance(self, Y_metadata=None): def gaussian_variance(self, Y_metadata=None):
return self.variance return self.variance
def covariance_matrix(self, Y, Y_metadata=None):
return np.eye(Y.shape[0]) * self.variance
def update_gradients(self, grad): def update_gradients(self, grad):
self.variance.gradient = grad self.variance.gradient = grad