A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
In the above, the documentation for input X1 says:
An array of shape 1 x n_dimensions that contains a data single point...
Whereas the documentation for GPys posterior_covariance_between_points() that this method wraps around says:
:param X1: some input observations
:param X2: other input observations
You can try passing more than one data point to get_covariance_between_points() at a time and it runs just fine, producing the same output (up to numerical precision) as if the data points were passed one by one.
Does that mean the docs can be expanded to say that X1 is an array of shape n_points x n_dimensions?
https://github.com/EmuKit/emukit/blob/728212c287313874f9b0c62649e7e8769529b9c7/emukit/model_wrappers/gpy_model_wrappers.py#L108-L117
In the above, the documentation for input
X1
says:Whereas the documentation for
GPy
sposterior_covariance_between_points()
that this method wraps around says:You can try passing more than one data point to
get_covariance_between_points()
at a time and it runs just fine, producing the same output (up to numerical precision) as if the data points were passed one by one.Does that mean the docs can be expanded to say that
X1
is an array of shapen_points x n_dimensions
?