def predict(self,
Xnew,
point=None,
diag=False,
pred_noise=False,
model=None):
R"""
Return the mean vector and covariance matrix of the conditional
distribution as numpy arrays, given a `point`, such as the MAP
estimate or a sample from a `trace`.
Parameters
----------
Xnew: array-like
Function input values. If one-dimensional, must be a column
vector with shape `(n, 1)`.
point: pymc.model.Point
A specific point to condition on.
diag: bool
If `True`, return the diagonal instead of the full covariance
matrix. Default is `False`.
pred_noise: bool
Whether or not observation noise is included in the conditional.
Default is `False`.
"""
mu, cov = self._predict_at(Xnew, diag, pred_noise)
return replace_with_values([mu, cov], replacements=point, model=model)
def predict(
self, Xnew, point=None, diag=False, pred_noise=False, given=None, jitter=0.0, model=None
):
R"""
Return the mean vector and covariance matrix of the conditional
distribution as numpy arrays, given a `point`, such as the MAP
estimate or a sample from a `trace`.
Parameters
----------
Xnew: array-like
Function input values. If one-dimensional, must be a column
vector with shape `(n, 1)`.
point: pymc.model.Point
A specific point to condition on.
diag: bool
If `True`, return the diagonal instead of the full covariance
matrix. Default is `False`.
pred_noise: bool
Whether or not observation noise is included in the conditional.
Default is `False`.
given: dict
Same as `conditional` method.
jitter: scalar
A small correction added to the diagonal of positive semi-definite
covariance matrices to ensure numerical stability. For conditionals
the default value is 0.0.
"""
if given is None:
given = {}
mu, cov = self._predict_at(Xnew, diag, pred_noise, given, jitter)
return replace_with_values([mu, cov], replacements=point, model=model)