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Alan Saul 2015-04-10 10:40:18 +01:00
parent debc2f1a0e
commit ef2b11c799
1 changed files with 5 additions and 5 deletions
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10
GPy/core/gp.py
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@ -296,7 +296,7 @@ class GP(Model):
:type size: int.
:param full_cov: whether to return the full covariance matrix, or just the diagonal.
:type full_cov: bool.
:returns: Ysim: set of simulations
:returns: fsim: set of simulations
:rtype: np.ndarray (N x samples)
"""
m, v = self._raw_predict(X, full_cov=full_cov)
@ -304,11 +304,11 @@ class GP(Model):
m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
v = v.reshape(m.size,-1) if len(v.shape)==3 else v
if not full_cov:
Ysim = np.random.multivariate_normal(m.flatten(), np.diag(v.flatten()), size).T
fsim = np.random.multivariate_normal(m.flatten(), np.diag(v.flatten()), size).T
else:
Ysim = np.random.multivariate_normal(m.flatten(), v, size).T
fsim = np.random.multivariate_normal(m.flatten(), v, size).T
return Ysim
return fsim
def posterior_samples(self, X, size=10, full_cov=False, Y_metadata=None):
"""
@ -324,7 +324,7 @@ class GP(Model):
:type noise_model: integer.
:returns: Ysim: set of simulations, a Numpy array (N x samples).
"""
Ysim = self.posterior_samples_f(X, size, full_cov=full_cov)
fsim = self.posterior_samples_f(X, size, full_cov=full_cov)
Ysim = self.likelihood.samples(Ysim, Y_metadata)
return Ysim