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Allow calculation of full predictive covariance matrices with multiple outputs and normalization

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Mark Pullin 2018-02-05 11:21:02 +00:00
parent a6c154753d
commit ccfcfa1a85
4 changed files with 91 additions and 42 deletions
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46
GPy/core/gp.py
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@ -282,10 +282,12 @@ class GP(Model):
mu += self.mean_function.f(Xnew)
return mu, var
def predict(self, Xnew, full_cov=False, Y_metadata=None, kern=None, likelihood=None, include_likelihood=True):
def predict(self, Xnew, full_cov=False, Y_metadata=None, kern=None,
likelihood=None, include_likelihood=True):
"""
Predict the function(s) at the new point(s) Xnew. This includes the likelihood
variance added to the predicted underlying function (usually referred to as f).
Predict the function(s) at the new point(s) Xnew. This includes the
likelihood variance added to the predicted underlying function
(usually referred to as f).
In order to predict without adding in the likelihood give
`include_likelihood=False`, or refer to self.predict_noiseless().
@ -295,33 +297,49 @@ class GP(Model):
:param full_cov: whether to return the full covariance matrix, or just
the diagonal
:type full_cov: bool
:param Y_metadata: metadata about the predicting point to pass to the likelihood
:param Y_metadata: metadata about the predicting point to pass to the
likelihood
:param kern: The kernel to use for prediction (defaults to the model
kern). this is useful for examining e.g. subprocesses.
:param bool include_likelihood: Whether or not to add likelihood noise to the predicted underlying latent function f.
:param include_likelihood: Whether or not to add likelihood noise to
the predicted underlying latent function f.
:type include_likelihood: bool
:returns: (mean, var):
mean: posterior mean, a Numpy array, Nnew x self.input_dim
var: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
var: posterior variance, a Numpy array, Nnew x 1 if full_cov=False,
Nnew x Nnew otherwise
If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim. If self.input_dim == 1, the return shape is Nnew x Nnew.
This is to allow for different normalizations of the output dimensions.
If full_cov and self.input_dim > 1, the return shape of var is
Nnew x Nnew x self.input_dim. If self.input_dim == 1, the return
shape is Nnew x Nnew. This is to allow for different normalizations
of the output dimensions.
Note: If you want the predictive quantiles (e.g. 95% confidence interval) use :py:func:"~GPy.core.gp.GP.predict_quantiles".
Note: If you want the predictive quantiles (e.g. 95% confidence
interval) use :py:func:"~GPy.core.gp.GP.predict_quantiles".
"""
#predict the latent function values
mu, var = self._raw_predict(Xnew, full_cov=full_cov, kern=kern)
# Predict the latent function values
mean, var = self._raw_predict(Xnew, full_cov=full_cov, kern=kern)
if include_likelihood:
# now push through likelihood
if likelihood is None:
likelihood = self.likelihood
mu, var = likelihood.predictive_values(mu, var, full_cov, Y_metadata=Y_metadata)
mean, var = likelihood.predictive_values(mean, var, full_cov,
Y_metadata=Y_metadata)
if self.normalizer is not None:
mu, var = self.normalizer.inverse_mean(mu), self.normalizer.inverse_variance(var)
mean = self.normalizer.inverse_mean(mean)
return mu, var
# We need to create 3d array for the full covariance matrix with
# multiple outputs.
if full_cov & (mean.shape[1] > 1):
var = self.normalizer.inverse_covariance(var)
else:
var = self.normalizer.inverse_variance(var)
return mean, var
def predict_noiseless(self, Xnew, full_cov=False, Y_metadata=None, kern=None):
"""