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build_cor_kernel is now called build_lcm

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Ricardo 2013-09-13 12:29:08 +01:00
parent 79aef1707f
commit cf2cf67ed2
1 changed files with 10 additions and 10 deletions
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20
GPy/util/multioutput.py
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@ -2,33 +2,33 @@ import numpy as np
import warnings import warnings
from .. import kern 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 :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 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 :param K: List of kernels that will be added up together with CK, but won't be multiplied by a coregionalise kernel
:W: :param W_columns: number tuples of the corregionalization parameters 'coregion_W'
:type W_columns: integer
""" """
for k in CK: for k in CK:
if k.input_dim <> input_dim: if k.input_dim <> input_dim:
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.") warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.")
for k in NC: for k in NC:
if k.input_dim <> input_dim + 1: if k.input_dim <> input_dim + 1:
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.") 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:]: 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: for k in NC:
kernel += k kernel += k