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function predictive_quantiles added

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Ricardo 2014-03-18 16:02:36 +00:00
parent ef31b5f1c9
commit b44fee93c4
1 changed files with 19 additions and 23 deletions
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42
GPy/likelihoods/mixed_noise.py
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@ -11,7 +11,7 @@ import itertools
class MixedNoise(Likelihood):
def __init__(self, likelihoods_list, name='mixed_noise'):
#NOTE at the moment this likelihood only works for using a list of gaussians
super(Likelihood, self).__init__(name=name)
self.add_parameters(*likelihoods_list)
@ -38,35 +38,32 @@ class MixedNoise(Likelihood):
return np.array([dL_dKdiag[ind==i].sum() for i in range(len(self.likelihoods_list))])
def predictive_values(self, mu, var, full_cov=False, Y_metadata=None):
if all([isinstance(l, Gaussian) for l in self.likelihoods_list]):
ind = Y_metadata['output_index'].flatten()
_variance = np.array([self.likelihoods_list[j].variance for j in ind ])
if full_cov:
var += np.eye(var.shape[0])*_variance
else:
var += _variance
return mu, var
ind = Y_metadata['output_index'].flatten()
_variance = np.array([self.likelihoods_list[j].variance for j in ind ])
if full_cov:
var += np.eye(var.shape[0])*_variance
else:
raise NotImplementedError
var += _variance
return mu, var
def predictive_variance(self, mu, sigma, **other_shit):
if isinstance(noise_index,int):
_variance = self.variance[noise_index]
else:
_variance = np.array([ self.variance[j] for j in noise_index ])[:,None]
def predictive_variance(self, mu, sigma, Y_metadata):
_variance = self.gaussian_variance(Y_metadata)
return _variance + sigma**2
def predictive_quantiles(self, mu, var, quantiles, Y_metadata):
ind = Y_metadata['output_index'].flatten()
outputs = np.unique(ind)
Q = np.zeros( (mu.size,len(quantiles)) )
for j in outputs:
q = self.likelihoods_list[j].predictive_quantiles(mu[ind==j,:],
var[ind==j,:],quantiles,Y_metadata=None)
Q[ind==j,:] = np.hstack(q)
return [q[:,None] for q in Q.T]
def covariance_matrix(self, Y, Y_metadata):
#assert all([isinstance(l, Gaussian) for l in self.likelihoods_list])
#ind = Y_metadata['output_index'].flatten()
#variance = np.zeros(Y.shape[0])
#for lik, j in zip(self.likelihoods_list, range(len(self.likelihoods_list))):
# variance[ind==j] = lik.variance
#return np.diag(variance)
#TODO make more general, to allow non-gaussian likelihoods
return np.diag(self.gaussian_variance(Y_metadata).flatten())
def samples(self, gp, Y_metadata):
"""
Returns a set of samples of observations based on a given value of the latent variable.
@ -84,4 +81,3 @@ class MixedNoise(Likelihood):
_ysim = np.array([np.random.normal(lik.gp_link.transf(gpj), scale=np.sqrt(lik.variance), size=1) for gpj in gp_filtered.flatten()])
Ysim[flt,:] = _ysim.reshape(n1,N2)
return Ysim