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added gplbm and sparse gp to new parameterized structure

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Max Zwiessele 2013-11-06 11:40:54 +00:00
parent c8eac84d55
commit 3316d29341
13 changed files with 106 additions and 96 deletions
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38
GPy/likelihoods/gaussian.py
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@ -17,7 +17,7 @@ class Gaussian(likelihood):
def __init__(self, data, variance=1., normalize=False):
super(Gaussian, self).__init__('gaussian')
self.is_heteroscedastic = False
self.num_params = 1
#self.num_params = 1
self.Z = 0. # a correction factor which accounts for the approximation made
N, self.output_dim = data.shape
@ -34,10 +34,10 @@ class Gaussian(likelihood):
self.set_data(data)
self.variance = Param('variance', variance)
self._variance = variance + 1
self.variance.add_observer(self, self.update_variance)
self.add_parameter(self.variance)
self.parameters_changed()
#self.parameters_changed()
# self._set_params(np.asarray(variance))
@ -63,17 +63,21 @@ class Gaussian(likelihood):
#
# def _set_params(self, x):
# self.variance = x[0]
def parameters_changed(self):
if np.any(self._variance != self.variance):
if np.all(self.variance == 0.):#special case of zero noise
self.precision = np.inf
self.V = None
else:
self.precision = 1. / self.variance
self.V = (self.precision) * self.Y
self.VVT_factor = self.precision * self.YYT_factor
self.covariance_matrix = np.eye(self.N) * self.variance
self._variance = self.variance.copy()
def update_variance(self, v):
if np.all(self.variance == 0.): #special case of zero noise
self.precision = np.inf
self.V = None
else:
self.precision = (1. / self.variance).squeeze()
self.V = (self.precision) * self.Y
self.VVT_factor = self.precision * self.YYT_factor
self.covariance_matrix = np.eye(self.N) * self.variance
#self._variance = self.variance.copy()
# def parameters_changed(self):
# if np.any(self._variance != self.variance):
# self.update_variance()
def predictive_values(self, mu, var, full_cov):
"""
@ -87,11 +91,11 @@ class Gaussian(likelihood):
# This will mess up computations of diag(true_var), below.
# note that the upper, lower quantiles should be the same shape as mean
# Augment the output variance with the likelihood variance and rescale.
true_var = (var + np.eye(var.shape[0]) * self._variance) * self._scale ** 2
true_var = (var + np.eye(var.shape[0]) * self.variance) * self._scale ** 2
_5pc = mean - 2.*np.sqrt(np.diag(true_var))
_95pc = mean + 2.*np.sqrt(np.diag(true_var))
else:
true_var = (var + self._variance) * self._scale ** 2
true_var = (var + self.variance) * self._scale ** 2
_5pc = mean - 2.*np.sqrt(true_var)
_95pc = mean + 2.*np.sqrt(true_var)
return mean, true_var, _5pc, _95pc