From cf2cf67ed216120e72cd4a7817226a46cd3be66d Mon Sep 17 00:00:00 2001 From: Ricardo Date: Fri, 13 Sep 2013 12:29:08 +0100 Subject: [PATCH] build_cor_kernel is now called build_lcm --- GPy/util/multioutput.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/GPy/util/multioutput.py b/GPy/util/multioutput.py index 44b70b6f..2b06ba95 100644 --- a/GPy/util/multioutput.py +++ b/GPy/util/multioutput.py @@ -2,33 +2,33 @@ import numpy as np import warnings from .. import kern -def build_cor_kernel(input_dim, Nout, CK = [], NC = [], W=1): +def build_lcm(input_dim, num_outputs, CK = [], NC = [], W_columns=1,W=None,kappa=None): + #TODO build_icm or build_lcm """ - Builds an appropiate coregionalized kernel + Builds a kernel for a linear coregionalization model :input_dim: Input dimensionality - :Nout: Number of outputs + :num_outputs: Number of outputs :param CK: List of coregionalized kernels (i.e., this will be multiplied by a coregionalise kernel). - :param K: List of kernels that won't be multiplied by a coregionalise kernel - :W: + :param K: List of kernels that will be added up together with CK, but won't be multiplied by a coregionalise kernel + :param W_columns: number tuples of the corregionalization parameters 'coregion_W' + :type W_columns: integer """ for k in CK: if k.input_dim <> input_dim: k.input_dim = input_dim - #raise Warning("kernel's input dimension overwritten to fit input_dim parameter.") warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.") for k in NC: if k.input_dim <> input_dim + 1: k.input_dim = input_dim + 1 - #raise Warning("kernel's input dimension overwritten to fit input_dim parameter.") warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.") - kernel = CK[0].prod(kern.coregionalise(Nout,W),tensor=True) + kernel = CK[0].prod(kern.coregionalise(num_outputs,W_columns,W,kappa),tensor=True) for k in CK[1:]: - kernel += k.prod(kern.coregionalise(Nout,W),tensor=True) - + k_coreg = kern.coregionalise(num_outputs,W_columns,W,kappa) + kernel += k.prod(k_coreg,tensor=True) for k in NC: kernel += k