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some work on ep, and some messing with wher ethe derivatives are computed (in the model, not the inference

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Ricardo 2014-02-26 11:38:46 +00:00
parent b6edc1a298
commit 0cf1e44c68
4 changed files with 40 additions and 5 deletions
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25
GPy/util/multioutput.py
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@ -2,6 +2,27 @@ import numpy as np
import warnings
from .. import kern
def build_XY(input_list,output_list=None,index=None):
num_outputs = len(input_list)
_s = [0] + [ _x.shape[0] for _x in input_list ]
_s = np.cumsum(_s)
slices = [slice(a,b) for a,b in zip(_s[:-1],_s[1:])]
if output_list is not None:
assert num_outputs == len(output_list)
Y = np.vstack(output_list)
else:
Y = None
if index is not None:
assert len(index) == num_outputs
I = np.vstack( [j*np.ones((_x.shape[0],1)) for _x,j in zip(input_list,index)] )
else:
I = np.vstack( [j*np.ones((_x.shape[0],1)) for _x,j in zip(input_list,range(num_outputs))] )
X = np.vstack(input_list)
X = np.hstack([X,I])
return X,Y,slices
def build_lcm(input_dim, num_outputs, CK = [], NC = [], W_columns=1,W=None,kappa=None):
#TODO build_icm or build_lcm
"""
@ -25,9 +46,9 @@ def build_lcm(input_dim, num_outputs, CK = [], NC = [], W_columns=1,W=None,kappa
k.input_dim = input_dim + 1
warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.")
kernel = CK[0].prod(kern.coregionalize(num_outputs,W_columns,W,kappa),tensor=True)
kernel = CK[0].prod(kern.Coregionalize(num_outputs,W_columns,W,kappa),tensor=True)
for k in CK[1:]:
k_coreg = kern.coregionalize(num_outputs,W_columns,W,kappa)
k_coreg = kern.Coregionalize(num_outputs,W_columns,W,kappa)
kernel += k.prod(k_coreg,tensor=True)
for k in NC:
kernel += k