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Moved to (sparse_)gp_multioutput_regression

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Ricardo 2013-09-13 13:06:01 +01:00
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GPy/models/gp_multioutput.py
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# Copyright (c) 2013, Ricardo Andrade
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from ..core import GP
from .. import likelihoods
from .. import kern
from ..util import multioutput
import pylab as pb
class GPMultioutput(GP):
"""
Multiple output Gaussian process
This is a thin wrapper around the models.GP class, with a set of sensible defaults
:param X_list: input observations
:param Y_list: observed values
:param L_list: a GPy likelihood, defaults to Binomial with probit link_function
:param kernel_list: a GPy kernel, defaults to rbf
:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_X: False|True
:param normalize_Y: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_Y: False|True
.. Note:: Multiple independent outputs are allowed using columns of Y
"""
def __init__(self,X_list,Y_list,kernel_list=None,normalize_X=False,normalize_Y=False,W=1,mixed_noise_list=[]): #TODO W
#TODO: split into 2 models gp_mixed_noise and ep_mixed_noise
assert len(X_list) == len(Y_list)
index = []
i = 0
for x,y in zip(X_list,Y_list):
assert x.shape[0] == y.shape[0]
index.append(np.repeat(i,y.size)[:,None])
i += 1
index = np.vstack(index)
"""
if mixed_noise_list == []:
for Y in Y_list:
self.likelihood_list.append(likelihoods.Gaussian(Y,normalize = normalize_Y))
Y = np.vstack([l_.Y for l_ in self.likelihood_list])
likelihood = likelihoods.Gaussian(Y,normalize=False)
likelihood.index = index
"""
if mixed_noise_list == []:
likelihood = likelihoods.Gaussian_Mixed_Noise(Y_list,normalize=normalize_Y)
#TODO: allow passing the variance parameter into the model
else:
self.likelihood_list = [] #TODO this is not needed
assert len(Y_list) == len(mixed_noise_list)
for noise,Y in zip(mixed_noise_list,Y_list):
self.likelihood_list.append(likelihoods.EP(Y,noise))
#TODO: allow normalization
likelihood = likelihoods.EP_Mixed_Noise(Y_list, mixed_noise_list)
X = np.hstack([np.vstack(X_list),index])
original_dim = X.shape[1] - 1
if kernel_list is None:
kernel_list = [[kern.rbf(original_dim)],[kern.white(original_dim+1)]]
mkernel = multioutput.build_cor_kernel(input_dim=original_dim, Nout=len(X_list), CK = kernel_list[0], NC = kernel_list[1], W=W)
self.multioutput = True
self.num_outputs = len(Y_list)
GP.__init__(self, X, likelihood, mkernel, normalize_X=normalize_X)
self.ensure_default_constraints()