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Multioutput models added
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Ricardo
parent
1c2a4c5c64
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4c7ebb6601
9 changed files with 251 additions and 62 deletions
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@ -6,6 +6,7 @@ import numpy as np
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from ..core import GP
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from .. import likelihoods
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from .. import kern
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from ..util import multioutput
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import pylab as pb
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@ -29,7 +30,7 @@ class GPMultioutput(GP):
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"""
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def __init__(self,X_list,Y_list,noise_list=[],kernel_list=None,normalize_X=False,normalize_Y=False,W=1): #TODO W
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def __init__(self,X_list,Y_list,kernel_list=None,normalize_X=False,normalize_Y=False,W=1,mixed_noise_list=[]): #TODO W
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assert len(X_list) == len(Y_list)
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index = []
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@ -40,53 +41,30 @@ class GPMultioutput(GP):
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i += 1
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index = np.vstack(index)
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if noise_list == []:
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likelihood_list = []
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self.likelihood_list = []
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if mixed_noise_list == []:
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for Y in Y_list:
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likelihood_list.append(likelihoods.Gaussian(Y,normalize = normalize_Y))
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self.likelihood_list.append(likelihoods.Gaussian(Y,normalize = normalize_Y))
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Y = np.vstack([l_.Y for l_ in self.likelihood_list])
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likelihood = likelihoods.Gaussian(Y,normalize=False)
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likelihood.index = index
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else:
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assert len(Y_list) == len(mixed_noise_list)
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for noise,Y in zip(mixed_noise_list,Y_list):
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self.likelihood_list.append(likelihoods.EP(Y,noise))
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likelihood = likelihoods.EP_Mixed_Noise(Y_list, mixed_noise_list)
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Y = np.vstack([l_.Y for l_ in likelihood_list])
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likelihood = likelihoods.Gaussian(Y,normalize=False)
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likelihood.index = index
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X = np.hstack([np.vstack(X_list),index])
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original_dim = X.shape[1] - 1
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if kernel_list is None:
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original_dim = X.shape[1]-1
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kernel_list = [kern.rbf(original_dim) + kern.white(original_dim)]
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kernel_list = [[kern.rbf(original_dim)],[kern.white(original_dim+1)]]
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mkernel = multioutput.build_cor_kernel(input_dim=original_dim, Nout=len(X_list), CK = kernel_list[0], NC = kernel_list[1], W=1)
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mkernel = kernel_list[0].prod(kern.coregionalise(len(X_list),W),tensor=True)
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for k in kernel_list[1:]:
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mkernel += k.prod(kern.coregionalise(len(X_list),W),tensor=True)
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self.multioutput = True
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GP.__init__(self, X, likelihood, mkernel, normalize_X=normalize_X)
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self.ensure_default_constraints()
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"""
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if likelihood is None:
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noise_model_list = []
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for Y in Y_list:
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noise_model_list.append(likelihoods.Gaussian(Y,normalize = normalize_Y))
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#noise_model_list = [likelihoods.gaussian(variance=1.) for Y in Y_list]
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#likelihood = likelihoods.EP_Mixed_Noise(Y_list, noise_model_list)
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elif Y_list is not None:
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if not all(np.vstack(Y_list).flatten() == likelihood.data.flatten()):
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raise Warning, 'likelihood.data and Y_list values are different.'
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X = np.hstack([np.vstack(X_list),likelihood.index])
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if kernel_list is None:
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original_dim = X.shape[1]-1
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kernel_list = [kern.rbf(original_dim) + kern.white(original_dim)]
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mkernel = kernel_list[0].prod(kern.coregionalise(len(X_list),W),tensor=True) #TODO W
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for k in kernel_list[1:]:
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mkernel += k.prod(kern.coregionalise(len(X_list),W),tensor=True) #TODO W
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#kern1 = kern.rbf(1) + kern.white(1)
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#kern2 = kern.coregionalise(2,1)
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#kern3 = kern1.prod(kern2,tensor=True)
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"""
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97
GPy/models/sparse_gp_multioutput.py
Normal file
97
GPy/models/sparse_gp_multioutput.py
Normal file
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@ -0,0 +1,97 @@
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# Copyright (c) 2013, Ricardo Andrade
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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from ..core import SparseGP
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from .. import likelihoods
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from .. import kern
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from ..util import multioutput
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import pylab as pb
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class SparseGPMultioutput(SparseGP):
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"""
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Multiple output Gaussian process
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This is a thin wrapper around the models.GP class, with a set of sensible defaults
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:param X_list: input observations
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:param Y_list: observed values
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:param L_list: a GPy likelihood, defaults to Binomial with probit link_function
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:param kernel_list: a GPy kernel, defaults to rbf
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:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
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:type normalize_X: False|True
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:param normalize_Y: whether to normalize the input data before computing (predictions will be in original scales)
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:type normalize_Y: False|True
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.. Note:: Multiple independent outputs are allowed using columns of Y
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"""
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def __init__(self,X_list,Y_list,kernel_list=None,normalize_X=False,normalize_Y=False,Z_list=None,num_inducing_list=10,X_variance=None,W=1,mixed_noise_list=[]): #TODO W
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assert len(X_list) == len(Y_list)
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index = []
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for x,y,j in zip(X_list,Y_list,range(len(X_list))):
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assert x.shape[0] == y.shape[0]
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index.append(np.repeat(j,y.size)[:,None])
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index = np.vstack(index)
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self.likelihood_list = []
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if mixed_noise_list == []:
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for Y in Y_list:
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self.likelihood_list.append(likelihoods.Gaussian(Y,normalize = normalize_Y))
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Y = np.vstack([l_.Y for l_ in self.likelihood_list])
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likelihood = likelihoods.Gaussian(Y,normalize=False)
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likelihood.index = index
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else:
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assert len(Y_list) == len(mixed_noise_list)
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for noise,Y in zip(mixed_noise_list,Y_list):
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self.likelihood_list.append(likelihoods.EP(Y,noise))
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likelihood = likelihoods.EP_Mixed_Noise(Y_list, mixed_noise_list)
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"""
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if noise_list == []:
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self.likelihood_list = []
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for Y in Y_list:
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self.likelihood_list.append(likelihoods.Gaussian(Y,normalize = normalize_Y))
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Y = np.vstack([l_.Y for l_ in self.likelihood_list])
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likelihood = likelihoods.Gaussian(Y,normalize=False)
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likelihood.index = index
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"""
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X = np.hstack([np.vstack(X_list),index])
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original_dim = X.shape[1] - 1
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if kernel_list is None:
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kernel_list = [[kern.rbf(original_dim)],[kern.white(original_dim+1)]]
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mkernel = multioutput.build_cor_kernel(input_dim=original_dim, Nout=len(X_list), CK = kernel_list[0], NC = kernel_list[1], W=1)
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z_index = []
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if Z_list is None:
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if isinstance(num_inducing_list,int):
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num_inducing_list = [num_inducing_list for Xj in X_list]
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Z_list = []
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for Xj,nj,j in zip(X_list,num_inducing_list,range(len(X_list))):
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i = np.random.permutation(Xj.shape[0])[:nj]
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z_index.append(np.repeat(j,nj)[:,None])
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Z_list.append(Xj[i].copy())
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else:
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assert len(Z_list) == len(X_list)
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for Zj,Xj,j in zip(Z_list,X_list,range(len(Z_list))):
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assert Zj.shape[1] == Xj.shape[1]
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z_index.append(np.repeat(j,Zj.shape[0])[:,None])
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Z = np.hstack([np.vstack(Z_list),np.vstack(z_index)])
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self.multioutput = True
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SparseGP.__init__(self, X, likelihood, mkernel, Z=Z, normalize_X=normalize_X, X_variance=X_variance)
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self.constrain_fixed('.*iip_\d+_1')
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self.ensure_default_constraints()
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