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Rewrite poster_samples_f to return NxDxsize

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Neil Lawrence 2018-01-01 01:18:08 +01:00 • committed by GitHub
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1 changed files with 14 additions and 23 deletions
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37
GPy/core/gp.py
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@ -538,7 +538,7 @@ class GP(Model):
mag[n] = np.sqrt(np.linalg.det(G[n, :, :]))
return mag
def posterior_samples_f(self,X, size=10, full_cov=True, **predict_kwargs):
def posterior_samples_f(self,X, size=10, **predict_kwargs):
"""
Samples the posterior GP at the points X.
@ -546,35 +546,28 @@ class GP(Model):
:type X: np.ndarray (Nnew x self.input_dim)
:param size: the number of a posteriori samples.
:type size: int.
:param full_cov: whether to return the full covariance matrix, or just the diagonal.
:type full_cov: bool.
:returns: fsim: set of simulations
:rtype: np.ndarray (D x N x samples) (if D==1 we flatten out the first dimension)
:returns: set of simulations
:rtype: np.ndarray (Nnew x D x samples)
"""
m, v = self._raw_predict(X, full_cov=full_cov, **predict_kwargs)
m, v = self._raw_predict(X, full_cov=True, **predict_kwargs)
if self.normalizer is not None:
m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
def sim_one_dim(m, v):
if not full_cov:
return np.random.multivariate_normal(m.flatten(), np.diag(v.flatten()), size).T
else:
return np.random.multivariate_normal(m.flatten(), v, size).T
return np.random.multivariate_normal(m, v, size).T
if self.output_dim == 1:
return sim_one_dim(m, v)
return sim_one_dim(m.flatten(), v)[:, np.newaxis, :]
else:
fsim = np.empty((self.output_dim, X.shape[1], size))
fsim = np.empty((X.shape[0], self.output_dim, size))
for d in range(self.output_dim):
if full_cov and v.ndim == 3:
fsim[d] = sim_one_dim(m[:, d], v[:, :, d])
elif (not full_cov) and v.shape[1]>1:
fsim[d] = sim_one_dim(m[:, d], v[:, d])
if v.ndim == 3:
fsim[:, d, :] = sim_one_dim(m[:, d], v[:, :, d])
else:
fsim[d] = sim_one_dim(m[:, d], v)
fsim[:, d, :] = sim_one_dim(m[:, d], v)
return fsim
def posterior_samples(self, X, size=10, full_cov=False, Y_metadata=None, likelihood=None, **predict_kwargs):
def posterior_samples(self, X, size=10, Y_metadata=None, likelihood=None, **predict_kwargs):
"""
Samples the posterior GP at the points X.
@ -582,19 +575,17 @@ class GP(Model):
:type X: np.ndarray (Nnew x self.input_dim.)
:param size: the number of a posteriori samples.
:type size: int.
:param full_cov: whether to return the full covariance matrix, or just the diagonal.
:type full_cov: bool.
:param noise_model: for mixed noise likelihood, the noise model to use in the samples.
:type noise_model: integer.
:returns: Ysim: set of simulations,
:rtype: np.ndarray (D x N x samples) (if D==1 we flatten out the first dimension)
"""
fsim = self.posterior_samples_f(X, size, full_cov=full_cov, **predict_kwargs)
fsim = self.posterior_samples_f(X, size, **predict_kwargs)
if likelihood is None:
likelihood = self.likelihood
if fsim.ndim == 3:
for d in range(fsim.shape[0]):
fsim[d] = likelihood.samples(fsim[d], Y_metadata=Y_metadata)
for d in range(fsim.shape[1]):
fsim[:, d] = likelihood.samples(fsim[:, d], Y_metadata=Y_metadata)
else:
fsim = likelihood.samples(fsim, Y_metadata=Y_metadata)
return fsim