class, W=None, kappa=None, B=None, active_dims=None)[source]#

Covariance function for intrinsic/linear coregionalization models. Adapted from GPy

This covariance has the form:

\[\mathbf{B} = \mathbf{W}\mathbf{W}^\top + \text{diag}(\kappa)\]

and calls must use integers associated with the index of the matrix. This allows the api to remain consistent with other covariance objects:

\[k(x, x') = \mathbf{B}[x, x'^\top]\]
W: 2D array of shape (num_outputs, rank)

a low rank matrix that determines the correlations between the different outputs (rows)

kappa: 1D array of shape (num_outputs, )

a vector which allows the outputs to behave independently

B: 2D array of shape (num_outputs, rank)

the total matrix, exactly one of (W, kappa) and B must be provided


Exactly one dimension must be active for this kernel. Thus, if input_dim != 1, then active_dims must have a length of one.


Coregion.__init__(input_dim[, W, kappa, B, ...])


Coregion.full(X[, Xs])



The dimensionality of the input, as taken from the active_dims.