From 7ac0689156df48a798126bb6be67049a649a4a67 Mon Sep 17 00:00:00 2001 From: Ricardo Date: Mon, 17 Mar 2014 10:27:10 +0000 Subject: [PATCH] Changes in kernel parameters definition --- GPy/util/multioutput.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/GPy/util/multioutput.py b/GPy/util/multioutput.py index 79022a5f..d9e8b704 100644 --- a/GPy/util/multioutput.py +++ b/GPy/util/multioutput.py @@ -35,7 +35,8 @@ def build_likelihood(Y_list,noise_index,likelihoods_list=None): likelihoods_list = [GPy.likelihoods.Gaussian(name="Gaussian_noise_%s" %j) for y,j in zip(Y_list,range(Ny))] else: assert len(likelihoods_list) == Ny - likelihood = GPy.likelihoods.mixed_noise.MixedNoise(likelihoods_list=likelihoods_list, noise_index=noise_index) + #likelihood = GPy.likelihoods.mixed_noise.MixedNoise(likelihoods_list=likelihoods_list, noise_index=noise_index) + likelihood = GPy.likelihoods.mixed_noise.MixedNoise(likelihoods_list=likelihoods_list) return likelihood @@ -43,7 +44,7 @@ def ICM(input_dim, num_outputs, kernel, W_rank=1,W=None,kappa=None,name='X'): """ Builds a kernel for an Intrinsic Coregionalization Model - :input_dim: Input dimensionality + :input_dim: Input dimensionality (does not include dimension of indices) :num_outputs: Number of outputs :param kernel: kernel that will be multiplied by the coregionalize kernel (matrix B). :type kernel: a GPy kernel @@ -54,7 +55,8 @@ def ICM(input_dim, num_outputs, kernel, W_rank=1,W=None,kappa=None,name='X'): kernel.input_dim = input_dim warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.") - K = kernel.prod(GPy.kern.Coregionalize([input_dim], num_outputs,W_rank,W,kappa,name='B'),name=name) + K = kernel.prod(GPy.kern.Coregionalize(1, num_outputs, active_dims=[input_dim], rank=W_rank,W=W,kappa=kappa,name='B'),name=name) + #K = kernel * GPy.kern.Coregionalize(1, num_outputs, active_dims=[input_dim], rank=W_rank,W=W,kappa=kappa,name='B') #K = kernel ** GPy.kern.Coregionalize(input_dim, num_outputs,W_rank,W,kappa, name= 'B') K['.*variance'] = 1. K['.*variance'].fix() @@ -65,7 +67,7 @@ def LCM(input_dim, num_outputs, kernels_list, W_rank=1,name='X'): """ Builds a kernel for an Linear Coregionalization Model - :input_dim: Input dimensionality + :input_dim: Input dimensionality (does not include dimension of indices) :num_outputs: Number of outputs :param kernel: kernel that will be multiplied by the coregionalize kernel (matrix B). :type kernel: a GPy kernel