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Merge branch 'params' of github.com:SheffieldML/GPy into params

Browse source 
Conflicts:
	GPy/likelihoods/mixed_noise.py
This commit is contained in:
Ricardo 2014-03-19 09:39:35 +00:00
commit fcb6742b60
21 changed files with 197 additions and 228 deletions
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2
GPy/examples/__init__.py
View file

@ -6,4 +6,4 @@ import regression
import dimensionality_reduction
import tutorials
import stochastic
import non_gaussian
import non_gaussian

11
GPy/examples/coreg_example.py
View file

@ -23,13 +23,10 @@ K = Bias.prod(Coreg,name='X')
#K.coregion.W = 0
#print K.coregion.W
#print Bias.K(_X,_X)
#print K.K(X,X)
#pb.matshow(K.K(X,X))
Mlist = [GPy.kern.Matern32(1,lengthscale=20.,name="Mat")]
kern = GPy.util.multioutput.LCM(input_dim=1,num_outputs=2,kernels_list=Mlist,name='H')
kern.B.W = 0
@ -37,16 +34,22 @@ kern.B.kappa = 1.
#kern.B.W.fix()
#kern.B.kappa.fix()
#m = GPy.models.GPCoregionalizedRegression(X_list=[X1,X2], Y_list=[Y1,Y2], kernel=kern)
m = GPy.models.SparseGPCoregionalizedRegression(X_list=[X1], Y_list=[Y1], kernel=kern)
Z1 = np.array([1.5,2.5])[:,None]
m = GPy.models.SparseGPCoregionalizedRegression(X_list=[X1], Y_list=[Y1], Z_list = [Z1], kernel=kern)
#m.optimize()
m.checkgrad(verbose=1)
"""
fig = pb.figure()
ax0 = fig.add_subplot(211)
ax1 = fig.add_subplot(212)
slices = GPy.util.multioutput.get_slices([Y1,Y2])
m.plot(fixed_inputs=[(1,0)],which_data_rows=slices[0],ax=ax0)
#m.plot(fixed_inputs=[(1,1)],which_data_rows=slices[1],ax=ax1)
"""

10
GPy/examples/non_gaussian.py
View file

@ -158,7 +158,7 @@ def boston_example(optimize=True, plot=True):
#Gaussian GP
print "Gauss GP"
mgp = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelgp.copy())
mgp.constrain_fixed('white', 1e-5)
mgp.constrain_fixed('.*white', 1e-5)
mgp['rbf_len'] = rbf_len
mgp['noise'] = noise
print mgp
@ -176,7 +176,7 @@ def boston_example(optimize=True, plot=True):
g_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), g_distribution)
mg = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu.copy(), likelihood=g_likelihood)
mg.constrain_positive('noise_variance')
mg.constrain_fixed('white', 1e-5)
mg.constrain_fixed('.*white', 1e-5)
mg['rbf_len'] = rbf_len
mg['noise'] = noise
print mg
@ -194,10 +194,10 @@ def boston_example(optimize=True, plot=True):
t_distribution = GPy.likelihoods.noise_model_constructors.student_t(deg_free=df, sigma2=noise)
stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution)
mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu.copy(), likelihood=stu_t_likelihood)
mstu_t.constrain_fixed('white', 1e-5)
mstu_t.constrain_bounded('t_noise', 0.0001, 1000)
mstu_t.constrain_fixed('.*white', 1e-5)
mstu_t.constrain_bounded('.*t_noise', 0.0001, 1000)
mstu_t['rbf_len'] = rbf_len
mstu_t['t_noise'] = noise
mstu_t['.*t_noise'] = noise
print mstu_t
if optimize:
mstu_t.optimize(optimizer=optimizer, messages=messages)

2
GPy/inference/latent_function_inference/exact_gaussian_inference.py
View file

@ -43,7 +43,7 @@ class ExactGaussianInference(object):
K = kern.K(X)
Ky = K.copy()
diag.add(Ky, likelihood.gaussian_variance(Y, Y_metadata))
diag.add(Ky, likelihood.gaussian_variance(Y_metadata))
Wi, LW, LWi, W_logdet = pdinv(Ky)
alpha, _ = dpotrs(LW, YYT_factor, lower=1)

23
GPy/inference/latent_function_inference/laplace.py
View file

@ -51,12 +51,11 @@ class Laplace(object):
Ki_f_init = self._previous_Ki_fhat
f_hat, Ki_fhat = self.rasm_mode(K, Y, likelihood, Ki_f_init, Y_metadata=Y_metadata)
self.f_hat = f_hat
self.Ki_fhat = Ki_fhat
self.K = K.copy()
#Compute hessian and other variables at mode
log_marginal, woodbury_vector, woodbury_inv, dL_dK, dL_dthetaL = self.mode_computations(f_hat, Ki_fhat, K, Y, likelihood, kern, Y_metadata)
log_marginal, woodbury_inv, dL_dK, dL_dthetaL = self.mode_computations(f_hat, Ki_fhat, K, Y, likelihood, kern, Y_metadata)
self._previous_Ki_fhat = Ki_fhat.copy()
return Posterior(woodbury_vector=Ki_fhat, woodbury_inv=woodbury_inv, K=K), log_marginal, {'dL_dK':dL_dK, 'dL_dthetaL':dL_dthetaL}
@ -86,13 +85,13 @@ class Laplace(object):
#define the objective function (to be maximised)
def obj(Ki_f, f):
return -0.5*np.dot(Ki_f.flatten(), f.flatten()) + likelihood.logpdf(f, Y, extra_data=Y_metadata)
return -0.5*np.dot(Ki_f.flatten(), f.flatten()) + likelihood.logpdf(f, Y, Y_metadata=Y_metadata)
difference = np.inf
iteration = 0
while difference > self._mode_finding_tolerance and iteration < self._mode_finding_max_iter:
W = -likelihood.d2logpdf_df2(f, Y, extra_data=Y_metadata)
grad = likelihood.dlogpdf_df(f, Y, extra_data=Y_metadata)
W = -likelihood.d2logpdf_df2(f, Y, Y_metadata=Y_metadata)
grad = likelihood.dlogpdf_df(f, Y, Y_metadata=Y_metadata)
W_f = W*f
@ -136,13 +135,12 @@ class Laplace(object):
At the mode, compute the hessian and effective covariance matrix.
returns: logZ : approximation to the marginal likelihood
woodbury_vector : variable required for calculating the approximation to the covariance matrix
woodbury_inv : variable required for calculating the approximation to the covariance matrix
dL_dthetaL : array of derivatives (1 x num_kernel_params)
dL_dthetaL : array of derivatives (1 x num_likelihood_params)
"""
#At this point get the hessian matrix (or vector as W is diagonal)
W = -likelihood.d2logpdf_df2(f_hat, Y, extra_data=Y_metadata)
W = -likelihood.d2logpdf_df2(f_hat, Y, Y_metadata=Y_metadata)
K_Wi_i, L, LiW12 = self._compute_B_statistics(K, W, likelihood.log_concave)
@ -151,11 +149,10 @@ class Laplace(object):
Ki_W_i = K - C.T.dot(C) #Could this be wrong?
#compute the log marginal
log_marginal = -0.5*np.dot(Ki_f.flatten(), f_hat.flatten()) + likelihood.logpdf(f_hat, Y, extra_data=Y_metadata) - np.sum(np.log(np.diag(L)))
log_marginal = -0.5*np.dot(Ki_f.flatten(), f_hat.flatten()) + likelihood.logpdf(f_hat, Y, Y_metadata=Y_metadata) - np.sum(np.log(np.diag(L)))
#Compute vival matrices for derivatives
dW_df = -likelihood.d3logpdf_df3(f_hat, Y, extra_data=Y_metadata) # -d3lik_d3fhat
woodbury_vector = likelihood.dlogpdf_df(f_hat, Y, extra_data=Y_metadata)
dW_df = -likelihood.d3logpdf_df3(f_hat, Y, Y_metadata=Y_metadata) # -d3lik_d3fhat
dL_dfhat = -0.5*(np.diag(Ki_W_i)[:, None]*dW_df) #why isn't this -0.5? s2 in R&W p126 line 9.
#BiK, _ = dpotrs(L, K, lower=1)
#dL_dfhat = 0.5*np.diag(BiK)[:, None]*dW_df
@ -169,7 +166,7 @@ class Laplace(object):
explicit_part = 0.5*(np.dot(Ki_f, Ki_f.T) - K_Wi_i)
#Implicit
implicit_part = np.dot(woodbury_vector, dL_dfhat.T).dot(I_KW_i)
implicit_part = np.dot(Ki_f, dL_dfhat.T).dot(I_KW_i)
dL_dK = explicit_part + implicit_part
else:
@ -179,7 +176,7 @@ class Laplace(object):
#compute dL_dthetaL#
####################
if likelihood.size > 0 and not likelihood.is_fixed:
dlik_dthetaL, dlik_grad_dthetaL, dlik_hess_dthetaL = likelihood._laplace_gradients(f_hat, Y, extra_data=Y_metadata)
dlik_dthetaL, dlik_grad_dthetaL, dlik_hess_dthetaL = likelihood._laplace_gradients(f_hat, Y, Y_metadata=Y_metadata)
num_params = likelihood.size
# make space for one derivative for each likelihood parameter
@ -200,7 +197,7 @@ class Laplace(object):
else:
dL_dthetaL = np.zeros(likelihood.size)
return log_marginal, woodbury_vector, K_Wi_i, dL_dK, dL_dthetaL
return log_marginal, K_Wi_i, dL_dK, dL_dthetaL
def _compute_B_statistics(self, K, W, log_concave):
"""

5
GPy/inference/latent_function_inference/var_dtc.py
View file

@ -65,7 +65,7 @@ class VarDTC(object):
_, output_dim = Y.shape
#see whether we've got a different noise variance for each datum
beta = 1./np.fmax(likelihood.gaussian_variance(Y, Y_metadata), 1e-6)
beta = 1./np.fmax(likelihood.gaussian_variance(Y_metadata), 1e-6)
# VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!
#self.YYTfactor = self.get_YYTfactor(Y)
#VVT_factor = self.get_VVTfactor(self.YYTfactor, beta)
@ -176,7 +176,6 @@ class VarDTC(object):
#construct a posterior object
post = Posterior(woodbury_inv=woodbury_inv, woodbury_vector=woodbury_vector, K=Kmm, mean=None, cov=None, K_chol=Lm)
return post, log_marginal, grad_dict
class VarDTCMissingData(object):
@ -365,7 +364,7 @@ class VarDTCMissingData(object):
return post, log_marginal, grad_dict
def _compute_dL_dpsi(num_inducing, num_data, output_dim, beta, Lm, VVT_factor, Cpsi1Vf, DBi_plus_BiPBi, psi1, het_noise, uncertain_inputs):
dL_dpsi0 = -0.5 * output_dim * (beta * np.ones([num_data, 1])).flatten()
dL_dpsi0 = -0.5 * output_dim * (beta[:,None] * np.ones([num_data, 1])).flatten()
dL_dpsi1 = np.dot(VVT_factor, Cpsi1Vf.T)
dL_dpsi2_beta = 0.5 * backsub_both_sides(Lm, output_dim * np.eye(num_inducing) - DBi_plus_BiPBi)
if het_noise:

20
GPy/likelihoods/bernoulli.py
View file

@ -95,7 +95,7 @@ class Bernoulli(Likelihood):
else:
return np.nan
def pdf_link(self, link_f, y, extra_data=None):
def pdf_link(self, link_f, y, Y_metadata=None):
"""
Likelihood function given link(f)
@ -106,7 +106,7 @@ class Bernoulli(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data not used in bernoulli
:param Y_metadata: Y_metadata not used in bernoulli
:returns: likelihood evaluated for this point
:rtype: float
@ -118,7 +118,7 @@ class Bernoulli(Likelihood):
objective = np.where(y, link_f, 1.-link_f)
return np.exp(np.sum(np.log(objective)))
def logpdf_link(self, link_f, y, extra_data=None):
def logpdf_link(self, link_f, y, Y_metadata=None):
"""
Log Likelihood function given link(f)
@ -129,7 +129,7 @@ class Bernoulli(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data not used in bernoulli
:param Y_metadata: Y_metadata not used in bernoulli
:returns: log likelihood evaluated at points link(f)
:rtype: float
"""
@ -140,7 +140,7 @@ class Bernoulli(Likelihood):
np.seterr(**state)
return np.sum(objective)
def dlogpdf_dlink(self, link_f, y, extra_data=None):
def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
"""
Gradient of the pdf at y, given link(f) w.r.t link(f)
@ -151,7 +151,7 @@ class Bernoulli(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data not used in bernoulli
:param Y_metadata: Y_metadata not used in bernoulli
:returns: gradient of log likelihood evaluated at points link(f)
:rtype: Nx1 array
"""
@ -162,7 +162,7 @@ class Bernoulli(Likelihood):
np.seterr(**state)
return grad
def d2logpdf_dlink2(self, link_f, y, extra_data=None):
def d2logpdf_dlink2(self, link_f, y, Y_metadata=None):
"""
Hessian at y, given link_f, w.r.t link_f the hessian will be 0 unless i == j
i.e. second derivative logpdf at y given link(f_i) link(f_j) w.r.t link(f_i) and link(f_j)
@ -175,7 +175,7 @@ class Bernoulli(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data not used in bernoulli
:param Y_metadata: Y_metadata not used in bernoulli
:returns: Diagonal of log hessian matrix (second derivative of log likelihood evaluated at points link(f))
:rtype: Nx1 array
@ -190,7 +190,7 @@ class Bernoulli(Likelihood):
np.seterr(**state)
return d2logpdf_dlink2
def d3logpdf_dlink3(self, link_f, y, extra_data=None):
def d3logpdf_dlink3(self, link_f, y, Y_metadata=None):
"""
Third order derivative log-likelihood function at y given link(f) w.r.t link(f)
@ -201,7 +201,7 @@ class Bernoulli(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data not used in bernoulli
:param Y_metadata: Y_metadata not used in bernoulli
:returns: third derivative of log likelihood evaluated at points link(f)
:rtype: Nx1 array
"""

43
GPy/likelihoods/exponential.py
View file

@ -18,13 +18,12 @@ class Exponential(Likelihood):
L(x) = \exp(\lambda) * \lambda**Y_i / Y_i!
$$
"""
def __init__(self,gp_link=None,analytical_mean=False,analytical_variance=False):
super(Exponential, self).__init__(gp_link,analytical_mean,analytical_variance)
def __init__(self,gp_link=None):
if gp_link is None:
gp_link = link_functions.Log()
super(Exponential, self).__init__(gp_link, 'ExpLikelihood')
def _preprocess_values(self,Y):
return Y
def pdf_link(self, link_f, y, extra_data=None):
def pdf_link(self, link_f, y, Y_metadata=None):
"""
Likelihood function given link(f)
@ -35,16 +34,15 @@ class Exponential(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in exponential distribution
:param Y_metadata: Y_metadata which is not used in exponential distribution
:returns: likelihood evaluated for this point
:rtype: float
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
log_objective = link_f*np.exp(-y*link_f)
return np.exp(np.sum(np.log(log_objective)))
#return np.exp(np.sum(-y/link_f - np.log(link_f) ))
def logpdf_link(self, link_f, y, extra_data=None):
def logpdf_link(self, link_f, y, Y_metadata=None):
"""
Log Likelihood Function given link(f)
@ -55,17 +53,16 @@ class Exponential(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in exponential distribution
:param Y_metadata: Y_metadata which is not used in exponential distribution
:returns: likelihood evaluated for this point
:rtype: float
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
log_objective = np.log(link_f) - y*link_f
#logpdf_link = np.sum(-np.log(link_f) - y/link_f)
return np.sum(log_objective)
def dlogpdf_dlink(self, link_f, y, extra_data=None):
def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
"""
Gradient of the log likelihood function at y, given link(f) w.r.t link(f)
@ -76,7 +73,7 @@ class Exponential(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in exponential distribution
:param Y_metadata: Y_metadata which is not used in exponential distribution
:returns: gradient of likelihood evaluated at points
:rtype: Nx1 array
@ -86,7 +83,7 @@ class Exponential(Likelihood):
#grad = y/(link_f**2) - 1./link_f
return grad
def d2logpdf_dlink2(self, link_f, y, extra_data=None):
def d2logpdf_dlink2(self, link_f, y, Y_metadata=None):
"""
Hessian at y, given link(f), w.r.t link(f)
i.e. second derivative logpdf at y given link(f_i) and link(f_j) w.r.t link(f_i) and link(f_j)
@ -99,7 +96,7 @@ class Exponential(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in exponential distribution
:param Y_metadata: Y_metadata which is not used in exponential distribution
:returns: Diagonal of hessian matrix (second derivative of likelihood evaluated at points f)
:rtype: Nx1 array
@ -112,7 +109,7 @@ class Exponential(Likelihood):
#hess = -2*y/(link_f**3) + 1/(link_f**2)
return hess
def d3logpdf_dlink3(self, link_f, y, extra_data=None):
def d3logpdf_dlink3(self, link_f, y, Y_metadata=None):
"""
Third order derivative log-likelihood function at y given link(f) w.r.t link(f)
@ -123,7 +120,7 @@ class Exponential(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in exponential distribution
:param Y_metadata: Y_metadata which is not used in exponential distribution
:returns: third derivative of likelihood evaluated at points f
:rtype: Nx1 array
"""
@ -132,18 +129,6 @@ class Exponential(Likelihood):
#d3lik_dlink3 = 6*y/(link_f**4) - 2./(link_f**3)
return d3lik_dlink3
def _mean(self,gp):
"""
Mass (or density) function
"""
return self.gp_link.transf(gp)
def _variance(self,gp):
"""
Mass (or density) function
"""
return self.gp_link.transf(gp)**2
def samples(self, gp):
"""
Returns a set of samples of observations based on a given value of the latent variable.

49
GPy/likelihoods/gamma.py
View file

@ -1,11 +1,12 @@
# Copyright (c) 2012, 2013 Ricardo Andrade
# Copyright (c) 2012 - 2014, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from scipy import stats,special
import scipy as sp
from GPy.util.univariate_Gaussian import std_norm_pdf,std_norm_cdf
from ..util.univariate_Gaussian import std_norm_pdf,std_norm_cdf
from ..core.parameterization import Param
import link_functions
from likelihood import Likelihood
@ -18,14 +19,16 @@ class Gamma(Likelihood):
\\alpha_{i} = \\beta y_{i}
"""
def __init__(self,gp_link=None,analytical_mean=False,analytical_variance=False,beta=1.):
self.beta = beta
super(Gamma, self).__init__(gp_link,analytical_mean,analytical_variance)
def __init__(self,gp_link=None,beta=1.):
if gp_link is None:
gp_link = link_functions.Log()
super(Gamma, self).__init__(gp_link, 'Gamma')
def _preprocess_values(self,Y):
return Y
self.beta = Param('beta', beta)
self.add_parameter(self.beta)
self.beta.fix()#TODO: gradients!
def pdf_link(self, link_f, y, extra_data=None):
def pdf_link(self, link_f, y, Y_metadata=None):
"""
Likelihood function given link(f)
@ -37,7 +40,7 @@ class Gamma(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in poisson distribution
:param Y_metadata: Y_metadata which is not used in poisson distribution
:returns: likelihood evaluated for this point
:rtype: float
"""
@ -47,7 +50,7 @@ class Gamma(Likelihood):
objective = (y**(alpha - 1.) * np.exp(-self.beta*y) * self.beta**alpha)/ special.gamma(alpha)
return np.exp(np.sum(np.log(objective)))
def logpdf_link(self, link_f, y, extra_data=None):
def logpdf_link(self, link_f, y, Y_metadata=None):
"""
Log Likelihood Function given link(f)
@ -59,7 +62,7 @@ class Gamma(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in poisson distribution
:param Y_metadata: Y_metadata which is not used in poisson distribution
:returns: likelihood evaluated for this point
:rtype: float
@ -71,7 +74,7 @@ class Gamma(Likelihood):
log_objective = alpha*np.log(self.beta) - np.log(special.gamma(alpha)) + (alpha - 1)*np.log(y) - self.beta*y
return np.sum(log_objective)
def dlogpdf_dlink(self, link_f, y, extra_data=None):
def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
"""
Gradient of the log likelihood function at y, given link(f) w.r.t link(f)
@ -83,7 +86,7 @@ class Gamma(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in gamma distribution
:param Y_metadata: Y_metadata which is not used in gamma distribution
:returns: gradient of likelihood evaluated at points
:rtype: Nx1 array
@ -94,7 +97,7 @@ class Gamma(Likelihood):
#return -self.gp_link.dtransf_df(gp)*self.beta*np.log(obs) + special.psi(self.gp_link.transf(gp)*self.beta) * self.gp_link.dtransf_df(gp)*self.beta
return grad
def d2logpdf_dlink2(self, link_f, y, extra_data=None):
def d2logpdf_dlink2(self, link_f, y, Y_metadata=None):
"""
Hessian at y, given link(f), w.r.t link(f)
i.e. second derivative logpdf at y given link(f_i) and link(f_j) w.r.t link(f_i) and link(f_j)
@ -108,7 +111,7 @@ class Gamma(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in gamma distribution
:param Y_metadata: Y_metadata which is not used in gamma distribution
:returns: Diagonal of hessian matrix (second derivative of likelihood evaluated at points f)
:rtype: Nx1 array
@ -122,7 +125,7 @@ class Gamma(Likelihood):
#return -self.gp_link.d2transf_df2(gp)*self.beta*np.log(obs) + special.polygamma(1,self.gp_link.transf(gp)*self.beta)*(self.gp_link.dtransf_df(gp)*self.beta)**2 + special.psi(self.gp_link.transf(gp)*self.beta)*self.gp_link.d2transf_df2(gp)*self.beta
return hess
def d3logpdf_dlink3(self, link_f, y, extra_data=None):
def d3logpdf_dlink3(self, link_f, y, Y_metadata=None):
"""
Third order derivative log-likelihood function at y given link(f) w.r.t link(f)
@ -134,22 +137,10 @@ class Gamma(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in gamma distribution
:param Y_metadata: Y_metadata which is not used in gamma distribution
:returns: third derivative of likelihood evaluated at points f
:rtype: Nx1 array
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
d3lik_dlink3 = -special.polygamma(2, self.beta*link_f)*(self.beta**3)
return d3lik_dlink3
def _mean(self,gp):
"""
Mass (or density) function
"""
return self.gp_link.transf(gp)
def _variance(self,gp):
"""
Mass (or density) function
"""
return self.gp_link.transf(gp)/self.beta

51
GPy/likelihoods/gaussian.py
View file

@ -35,12 +35,7 @@ class Gaussian(Likelihood):
if gp_link is None:
gp_link = link_functions.Identity()
if isinstance(gp_link, link_functions.Identity):
analytical_variance = True
analytical_mean = True
else:
analytical_variance = False
analytical_mean = False
assert isinstance(gp_link, link_functions.Identity), "the likelihood only implemented for the identity link"
super(Gaussian, self).__init__(gp_link, name=name)
@ -100,7 +95,7 @@ class Gaussian(Likelihood):
def predictive_quantiles(self, mu, var, quantiles, Y_metadata=None):
return [stats.norm.ppf(q/100.)*np.sqrt(var) + mu for q in quantiles]
def pdf_link(self, link_f, y, extra_data=None):
def pdf_link(self, link_f, y, Y_metadata=None):
"""
Likelihood function given link(f)
@ -111,14 +106,14 @@ class Gaussian(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data not used in gaussian
:param Y_metadata: Y_metadata not used in gaussian
:returns: likelihood evaluated for this point
:rtype: float
"""
#Assumes no covariance, exp, sum, log for numerical stability
return np.exp(np.sum(np.log(stats.norm.pdf(y, link_f, np.sqrt(self.variance)))))
def logpdf_link(self, link_f, y, extra_data=None):
def logpdf_link(self, link_f, y, Y_metadata=None):
"""
Log likelihood function given link(f)
@ -129,7 +124,7 @@ class Gaussian(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data not used in gaussian
:param Y_metadata: Y_metadata not used in gaussian
:returns: log likelihood evaluated for this point
:rtype: float
"""
@ -139,7 +134,7 @@ class Gaussian(Likelihood):
return -0.5*(np.sum((y-link_f)**2/self.variance) + ln_det_cov + N*np.log(2.*np.pi))
def dlogpdf_dlink(self, link_f, y, extra_data=None):
def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
"""
Gradient of the pdf at y, given link(f) w.r.t link(f)
@ -150,7 +145,7 @@ class Gaussian(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data not used in gaussian
:param Y_metadata: Y_metadata not used in gaussian
:returns: gradient of log likelihood evaluated at points link(f)
:rtype: Nx1 array
"""
@ -159,7 +154,7 @@ class Gaussian(Likelihood):
grad = s2_i*y - s2_i*link_f
return grad
def d2logpdf_dlink2(self, link_f, y, extra_data=None):
def d2logpdf_dlink2(self, link_f, y, Y_metadata=None):
"""
Hessian at y, given link_f, w.r.t link_f.
i.e. second derivative logpdf at y given link(f_i) link(f_j) w.r.t link(f_i) and link(f_j)
@ -173,7 +168,7 @@ class Gaussian(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data not used in gaussian
:param Y_metadata: Y_metadata not used in gaussian
:returns: Diagonal of log hessian matrix (second derivative of log likelihood evaluated at points link(f))
:rtype: Nx1 array
@ -186,7 +181,7 @@ class Gaussian(Likelihood):
hess = -(1.0/self.variance)*np.ones((N, 1))
return hess
def d3logpdf_dlink3(self, link_f, y, extra_data=None):
def d3logpdf_dlink3(self, link_f, y, Y_metadata=None):
"""
Third order derivative log-likelihood function at y given link(f) w.r.t link(f)
@ -197,7 +192,7 @@ class Gaussian(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data not used in gaussian
:param Y_metadata: Y_metadata not used in gaussian
:returns: third derivative of log likelihood evaluated at points link(f)
:rtype: Nx1 array
"""
@ -206,7 +201,7 @@ class Gaussian(Likelihood):
d3logpdf_dlink3 = np.zeros((N,1))
return d3logpdf_dlink3
def dlogpdf_link_dvar(self, link_f, y, extra_data=None):
def dlogpdf_link_dvar(self, link_f, y, Y_metadata=None):
"""
Gradient of the log-likelihood function at y given link(f), w.r.t variance parameter (noise_variance)
@ -217,7 +212,7 @@ class Gaussian(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data not used in gaussian
:param Y_metadata: Y_metadata not used in gaussian
:returns: derivative of log likelihood evaluated at points link(f) w.r.t variance parameter
:rtype: float
"""
@ -228,7 +223,7 @@ class Gaussian(Likelihood):
dlik_dsigma = -0.5*N/self.variance + 0.5*s_4*np.sum(np.square(e))
return np.sum(dlik_dsigma) # Sure about this sum?
def dlogpdf_dlink_dvar(self, link_f, y, extra_data=None):
def dlogpdf_dlink_dvar(self, link_f, y, Y_metadata=None):
"""
Derivative of the dlogpdf_dlink w.r.t variance parameter (noise_variance)
@ -239,7 +234,7 @@ class Gaussian(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data not used in gaussian
:param Y_metadata: Y_metadata not used in gaussian
:returns: derivative of log likelihood evaluated at points link(f) w.r.t variance parameter
:rtype: Nx1 array
"""
@ -248,7 +243,7 @@ class Gaussian(Likelihood):
dlik_grad_dsigma = -s_4*y + s_4*link_f
return dlik_grad_dsigma
def d2logpdf_dlink2_dvar(self, link_f, y, extra_data=None):
def d2logpdf_dlink2_dvar(self, link_f, y, Y_metadata=None):
"""
Gradient of the hessian (d2logpdf_dlink2) w.r.t variance parameter (noise_variance)
@ -259,7 +254,7 @@ class Gaussian(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data not used in gaussian
:param Y_metadata: Y_metadata not used in gaussian
:returns: derivative of log hessian evaluated at points link(f_i) and link(f_j) w.r.t variance parameter
:rtype: Nx1 array
"""
@ -269,16 +264,16 @@ class Gaussian(Likelihood):
d2logpdf_dlink2_dvar = np.ones((N,1))*s_4
return d2logpdf_dlink2_dvar
def dlogpdf_link_dtheta(self, f, y, extra_data=None):
dlogpdf_dvar = self.dlogpdf_link_dvar(f, y, extra_data=extra_data)
def dlogpdf_link_dtheta(self, f, y, Y_metadata=None):
dlogpdf_dvar = self.dlogpdf_link_dvar(f, y, Y_metadata=Y_metadata)
return np.asarray([[dlogpdf_dvar]])
def dlogpdf_dlink_dtheta(self, f, y, extra_data=None):
dlogpdf_dlink_dvar = self.dlogpdf_dlink_dvar(f, y, extra_data=extra_data)
def dlogpdf_dlink_dtheta(self, f, y, Y_metadata=None):
dlogpdf_dlink_dvar = self.dlogpdf_dlink_dvar(f, y, Y_metadata=Y_metadata)
return dlogpdf_dlink_dvar
def d2logpdf_dlink2_dtheta(self, f, y, extra_data=None):
d2logpdf_dlink2_dvar = self.d2logpdf_dlink2_dvar(f, y, extra_data=extra_data)
def d2logpdf_dlink2_dtheta(self, f, y, Y_metadata=None):
d2logpdf_dlink2_dvar = self.d2logpdf_dlink2_dvar(f, y, Y_metadata=Y_metadata)
return d2logpdf_dlink2_dvar
def _mean(self, gp):

80
GPy/likelihoods/likelihood.py
View file

@ -153,6 +153,10 @@ class Likelihood(Parameterized):
return mean
def _conditional_mean(self, f):
"""Quadrature calculation of the conditional mean: E(Y_star|f)"""
raise NotImplementedError, "implement this function to make predictions"
def predictive_variance(self, mu,variance, predictive_mean=None, Y_metadata=None):
"""
Numerical approximation to the predictive variance: V(Y_star)
@ -204,31 +208,31 @@ class Likelihood(Parameterized):
# V(Y_star) = E[ V(Y_star|f_star) ] + E(Y_star**2|f_star) - E[Y_star|f_star]**2
return exp_var + var_exp
def pdf_link(self, link_f, y, extra_data=None):
def pdf_link(self, link_f, y, Y_metadata=None):
raise NotImplementedError
def logpdf_link(self, link_f, y, extra_data=None):
def logpdf_link(self, link_f, y, Y_metadata=None):
raise NotImplementedError
def dlogpdf_dlink(self, link_f, y, extra_data=None):
def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
raise NotImplementedError
def d2logpdf_dlink2(self, link_f, y, extra_data=None):
def d2logpdf_dlink2(self, link_f, y, Y_metadata=None):
raise NotImplementedError
def d3logpdf_dlink3(self, link_f, y, extra_data=None):
def d3logpdf_dlink3(self, link_f, y, Y_metadata=None):
raise NotImplementedError
def dlogpdf_link_dtheta(self, link_f, y, extra_data=None):
def dlogpdf_link_dtheta(self, link_f, y, Y_metadata=None):
raise NotImplementedError
def dlogpdf_dlink_dtheta(self, link_f, y, extra_data=None):
def dlogpdf_dlink_dtheta(self, link_f, y, Y_metadata=None):
raise NotImplementedError
def d2logpdf_dlink2_dtheta(self, link_f, y, extra_data=None):
def d2logpdf_dlink2_dtheta(self, link_f, y, Y_metadata=None):
raise NotImplementedError
def pdf(self, f, y, extra_data=None):
def pdf(self, f, y, Y_metadata=None):
"""
Evaluates the link function link(f) then computes the likelihood (pdf) using it
@ -239,14 +243,14 @@ class Likelihood(Parameterized):
:type f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in student t distribution - not used
:param Y_metadata: Y_metadata which is not used in student t distribution - not used
:returns: likelihood evaluated for this point
:rtype: float
"""
link_f = self.gp_link.transf(f)
return self.pdf_link(link_f, y, extra_data=extra_data)
return self.pdf_link(link_f, y, Y_metadata=Y_metadata)
def logpdf(self, f, y, extra_data=None):
def logpdf(self, f, y, Y_metadata=None):
"""
Evaluates the link function link(f) then computes the log likelihood (log pdf) using it
@ -257,14 +261,14 @@ class Likelihood(Parameterized):
:type f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in student t distribution - not used
:param Y_metadata: Y_metadata which is not used in student t distribution - not used
:returns: log likelihood evaluated for this point
:rtype: float
"""
link_f = self.gp_link.transf(f)
return self.logpdf_link(link_f, y, extra_data=extra_data)
return self.logpdf_link(link_f, y, Y_metadata=Y_metadata)
def dlogpdf_df(self, f, y, extra_data=None):
def dlogpdf_df(self, f, y, Y_metadata=None):
"""
Evaluates the link function link(f) then computes the derivative of log likelihood using it
Uses the Faa di Bruno's formula for the chain rule
@ -276,16 +280,16 @@ class Likelihood(Parameterized):
:type f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in student t distribution - not used
:param Y_metadata: Y_metadata which is not used in student t distribution - not used
:returns: derivative of log likelihood evaluated for this point
:rtype: 1xN array
"""
link_f = self.gp_link.transf(f)
dlogpdf_dlink = self.dlogpdf_dlink(link_f, y, extra_data=extra_data)
dlogpdf_dlink = self.dlogpdf_dlink(link_f, y, Y_metadata=Y_metadata)
dlink_df = self.gp_link.dtransf_df(f)
return chain_1(dlogpdf_dlink, dlink_df)
def d2logpdf_df2(self, f, y, extra_data=None):
def d2logpdf_df2(self, f, y, Y_metadata=None):
"""
Evaluates the link function link(f) then computes the second derivative of log likelihood using it
Uses the Faa di Bruno's formula for the chain rule
@ -297,18 +301,18 @@ class Likelihood(Parameterized):
:type f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in student t distribution - not used
:param Y_metadata: Y_metadata which is not used in student t distribution - not used
:returns: second derivative of log likelihood evaluated for this point (diagonal only)
:rtype: 1xN array
"""
link_f = self.gp_link.transf(f)
d2logpdf_dlink2 = self.d2logpdf_dlink2(link_f, y, extra_data=extra_data)
d2logpdf_dlink2 = self.d2logpdf_dlink2(link_f, y, Y_metadata=Y_metadata)
dlink_df = self.gp_link.dtransf_df(f)
dlogpdf_dlink = self.dlogpdf_dlink(link_f, y, extra_data=extra_data)
dlogpdf_dlink = self.dlogpdf_dlink(link_f, y, Y_metadata=Y_metadata)
d2link_df2 = self.gp_link.d2transf_df2(f)
return chain_2(d2logpdf_dlink2, dlink_df, dlogpdf_dlink, d2link_df2)
def d3logpdf_df3(self, f, y, extra_data=None):
def d3logpdf_df3(self, f, y, Y_metadata=None):
"""
Evaluates the link function link(f) then computes the third derivative of log likelihood using it
Uses the Faa di Bruno's formula for the chain rule
@ -320,44 +324,44 @@ class Likelihood(Parameterized):
:type f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in student t distribution - not used
:param Y_metadata: Y_metadata which is not used in student t distribution - not used
:returns: third derivative of log likelihood evaluated for this point
:rtype: float
"""
link_f = self.gp_link.transf(f)
d3logpdf_dlink3 = self.d3logpdf_dlink3(link_f, y, extra_data=extra_data)
d3logpdf_dlink3 = self.d3logpdf_dlink3(link_f, y, Y_metadata=Y_metadata)
dlink_df = self.gp_link.dtransf_df(f)
d2logpdf_dlink2 = self.d2logpdf_dlink2(link_f, y, extra_data=extra_data)
d2logpdf_dlink2 = self.d2logpdf_dlink2(link_f, y, Y_metadata=Y_metadata)
d2link_df2 = self.gp_link.d2transf_df2(f)
dlogpdf_dlink = self.dlogpdf_dlink(link_f, y, extra_data=extra_data)
dlogpdf_dlink = self.dlogpdf_dlink(link_f, y, Y_metadata=Y_metadata)
d3link_df3 = self.gp_link.d3transf_df3(f)
return chain_3(d3logpdf_dlink3, dlink_df, d2logpdf_dlink2, d2link_df2, dlogpdf_dlink, d3link_df3)
def dlogpdf_dtheta(self, f, y, extra_data=None):
def dlogpdf_dtheta(self, f, y, Y_metadata=None):
"""
TODO: Doc strings
"""
if self.size > 0:
link_f = self.gp_link.transf(f)
return self.dlogpdf_link_dtheta(link_f, y, extra_data=extra_data)
return self.dlogpdf_link_dtheta(link_f, y, Y_metadata=Y_metadata)
else:
#Is no parameters so return an empty array for its derivatives
return np.zeros([1, 0])
def dlogpdf_df_dtheta(self, f, y, extra_data=None):
def dlogpdf_df_dtheta(self, f, y, Y_metadata=None):
"""
TODO: Doc strings
"""
if self.size > 0:
link_f = self.gp_link.transf(f)
dlink_df = self.gp_link.dtransf_df(f)
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(link_f, y, extra_data=extra_data)
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(link_f, y, Y_metadata=Y_metadata)
return chain_1(dlogpdf_dlink_dtheta, dlink_df)
else:
#Is no parameters so return an empty array for its derivatives
return np.zeros([f.shape[0], 0])
def d2logpdf_df2_dtheta(self, f, y, extra_data=None):
def d2logpdf_df2_dtheta(self, f, y, Y_metadata=None):
"""
TODO: Doc strings
"""
@ -365,17 +369,17 @@ class Likelihood(Parameterized):
link_f = self.gp_link.transf(f)
dlink_df = self.gp_link.dtransf_df(f)
d2link_df2 = self.gp_link.d2transf_df2(f)
d2logpdf_dlink2_dtheta = self.d2logpdf_dlink2_dtheta(link_f, y, extra_data=extra_data)
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(link_f, y, extra_data=extra_data)
d2logpdf_dlink2_dtheta = self.d2logpdf_dlink2_dtheta(link_f, y, Y_metadata=Y_metadata)
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(link_f, y, Y_metadata=Y_metadata)
return chain_2(d2logpdf_dlink2_dtheta, dlink_df, dlogpdf_dlink_dtheta, d2link_df2)
else:
#Is no parameters so return an empty array for its derivatives
return np.zeros([f.shape[0], 0])
def _laplace_gradients(self, f, y, extra_data=None):
dlogpdf_dtheta = self.dlogpdf_dtheta(f, y, extra_data=extra_data)
dlogpdf_df_dtheta = self.dlogpdf_df_dtheta(f, y, extra_data=extra_data)
d2logpdf_df2_dtheta = self.d2logpdf_df2_dtheta(f, y, extra_data=extra_data)
def _laplace_gradients(self, f, y, Y_metadata=None):
dlogpdf_dtheta = self.dlogpdf_dtheta(f, y, Y_metadata=Y_metadata)
dlogpdf_df_dtheta = self.dlogpdf_df_dtheta(f, y, Y_metadata=Y_metadata)
d2logpdf_df2_dtheta = self.d2logpdf_df2_dtheta(f, y, Y_metadata=Y_metadata)
#Parameters are stacked vertically. Must be listed in same order as 'get_param_names'
# ensure we have gradients for every parameter we want to optimize
@ -390,7 +394,7 @@ class Likelihood(Parameterized):
def predictive_values(self, mu, var, full_cov=False, Y_metadata=None):
"""
Compute mean, variance and conficence interval (percentiles 5 and 95) of the prediction.
Compute mean, variance of the predictive distibution.
:param mu: mean of the latent variable, f, of posterior
:param var: variance of the latent variable, f, of posterior

9
GPy/likelihoods/mixed_noise.py
View file

@ -24,10 +24,11 @@ class MixedNoise(Likelihood):
variance = np.zeros(ind.size)
for lik, j in zip(self.likelihoods_list, range(len(self.likelihoods_list))):
variance[ind==j] = lik.variance
return variance[:,None]
return variance
def betaY(self,Y,Y_metadata):
return Y/self.gaussian_variance(Y_metadata=Y_metadata)
#TODO not here.
return Y/self.gaussian_variance(Y_metadata=Y_metadata)[:,None]
def update_gradients(self, gradients):
self.gradient = gradients
@ -60,10 +61,6 @@ class MixedNoise(Likelihood):
Q[ind==j,:] = np.hstack(q)
return [q[:,None] for q in Q.T]
def covariance_matrix(self, Y, Y_metadata):
#TODO make more general, to allow non-gaussian likelihoods
return np.diag(self.gaussian_variance(Y_metadata).flatten())
def samples(self, gp, Y_metadata):
"""
Returns a set of samples of observations based on a given value of the latent variable.

27
GPy/likelihoods/poisson.py
View file

@ -25,10 +25,13 @@ class Poisson(Likelihood):
super(Poisson, self).__init__(gp_link, name='Poisson')
def _preprocess_values(self,Y):
return Y
def _conditional_mean(self, f):
"""
the expected value of y given a value of f
"""
return self.gp_link.transf(gp)
def pdf_link(self, link_f, y, extra_data=None):
def pdf_link(self, link_f, y, Y_metadata=None):
"""
Likelihood function given link(f)
@ -39,14 +42,14 @@ class Poisson(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in poisson distribution
:param Y_metadata: Y_metadata which is not used in poisson distribution
:returns: likelihood evaluated for this point
:rtype: float
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
return np.prod(stats.poisson.pmf(y,link_f))
def logpdf_link(self, link_f, y, extra_data=None):
def logpdf_link(self, link_f, y, Y_metadata=None):
"""
Log Likelihood Function given link(f)
@ -57,7 +60,7 @@ class Poisson(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in poisson distribution
:param Y_metadata: Y_metadata which is not used in poisson distribution
:returns: likelihood evaluated for this point
:rtype: float
@ -65,7 +68,7 @@ class Poisson(Likelihood):
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
return np.sum(-link_f + y*np.log(link_f) - special.gammaln(y+1))
def dlogpdf_dlink(self, link_f, y, extra_data=None):
def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
"""
Gradient of the log likelihood function at y, given link(f) w.r.t link(f)
@ -76,7 +79,7 @@ class Poisson(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in poisson distribution
:param Y_metadata: Y_metadata which is not used in poisson distribution
:returns: gradient of likelihood evaluated at points
:rtype: Nx1 array
@ -84,7 +87,7 @@ class Poisson(Likelihood):
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
return y/link_f - 1
def d2logpdf_dlink2(self, link_f, y, extra_data=None):
def d2logpdf_dlink2(self, link_f, y, Y_metadata=None):
"""
Hessian at y, given link(f), w.r.t link(f)
i.e. second derivative logpdf at y given link(f_i) and link(f_j) w.r.t link(f_i) and link(f_j)
@ -97,7 +100,7 @@ class Poisson(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in poisson distribution
:param Y_metadata: Y_metadata which is not used in poisson distribution
:returns: Diagonal of hessian matrix (second derivative of likelihood evaluated at points f)
:rtype: Nx1 array
@ -112,7 +115,7 @@ class Poisson(Likelihood):
#transf = self.gp_link.transf(gp)
#return obs * ((self.gp_link.dtransf_df(gp)/transf)**2 - d2_df/transf) + d2_df
def d3logpdf_dlink3(self, link_f, y, extra_data=None):
def d3logpdf_dlink3(self, link_f, y, Y_metadata=None):
"""
Third order derivative log-likelihood function at y given link(f) w.r.t link(f)
@ -123,7 +126,7 @@ class Poisson(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in poisson distribution
:param Y_metadata: Y_metadata which is not used in poisson distribution
:returns: third derivative of likelihood evaluated at points f
:rtype: Nx1 array
"""

51
GPy/likelihoods/student_t.py
View file

@ -43,10 +43,10 @@ class StudentT(Likelihood):
Pull out the gradients, be careful as the order must match the order
in which the parameters are added
"""
self.sigma2.gradient = grads[0]
self.v.gradient = grads[1]
self.sigma2.gradient = derivatives[0]
self.v.gradient = derivatives[1]
def pdf_link(self, link_f, y, extra_data=None):
def pdf_link(self, link_f, y, Y_metadata=None):
"""
Likelihood function given link(f)
@ -57,7 +57,7 @@ class StudentT(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in student t distribution
:param Y_metadata: Y_metadata which is not used in student t distribution
:returns: likelihood evaluated for this point
:rtype: float
"""
@ -70,7 +70,7 @@ class StudentT(Likelihood):
)
return np.prod(objective)
def logpdf_link(self, link_f, y, extra_data=None):
def logpdf_link(self, link_f, y, Y_metadata=None):
"""
Log Likelihood Function given link(f)
@ -81,7 +81,7 @@ class StudentT(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in student t distribution
:param Y_metadata: Y_metadata which is not used in student t distribution
:returns: likelihood evaluated for this point
:rtype: float
@ -99,7 +99,7 @@ class StudentT(Likelihood):
)
return np.sum(objective)
def dlogpdf_dlink(self, link_f, y, extra_data=None):
def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
"""
Gradient of the log likelihood function at y, given link(f) w.r.t link(f)
@ -110,7 +110,7 @@ class StudentT(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in student t distribution
:param Y_metadata: Y_metadata which is not used in student t distribution
:returns: gradient of likelihood evaluated at points
:rtype: Nx1 array
@ -120,7 +120,7 @@ class StudentT(Likelihood):
grad = ((self.v + 1) * e) / (self.v * self.sigma2 + (e**2))
return grad
def d2logpdf_dlink2(self, link_f, y, extra_data=None):
def d2logpdf_dlink2(self, link_f, y, Y_metadata=None):
"""
Hessian at y, given link(f), w.r.t link(f)
i.e. second derivative logpdf at y given link(f_i) and link(f_j) w.r.t link(f_i) and link(f_j)
@ -133,7 +133,7 @@ class StudentT(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in student t distribution
:param Y_metadata: Y_metadata which is not used in student t distribution
:returns: Diagonal of hessian matrix (second derivative of likelihood evaluated at points f)
:rtype: Nx1 array
@ -146,7 +146,7 @@ class StudentT(Likelihood):
hess = ((self.v + 1)*(e**2 - self.v*self.sigma2)) / ((self.sigma2*self.v + e**2)**2)
return hess
def d3logpdf_dlink3(self, link_f, y, extra_data=None):
def d3logpdf_dlink3(self, link_f, y, Y_metadata=None):
"""
Third order derivative log-likelihood function at y given link(f) w.r.t link(f)
@ -157,7 +157,7 @@ class StudentT(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in student t distribution
:param Y_metadata: Y_metadata which is not used in student t distribution
:returns: third derivative of likelihood evaluated at points f
:rtype: Nx1 array
"""
@ -168,7 +168,7 @@ class StudentT(Likelihood):
)
return d3lik_dlink3
def dlogpdf_link_dvar(self, link_f, y, extra_data=None):
def dlogpdf_link_dvar(self, link_f, y, Y_metadata=None):
"""
Gradient of the log-likelihood function at y given f, w.r.t variance parameter (t_noise)
@ -179,7 +179,7 @@ class StudentT(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in student t distribution
:param Y_metadata: Y_metadata which is not used in student t distribution
:returns: derivative of likelihood evaluated at points f w.r.t variance parameter
:rtype: float
"""
@ -188,7 +188,7 @@ class StudentT(Likelihood):
dlogpdf_dvar = self.v*(e**2 - self.sigma2)/(2*self.sigma2*(self.sigma2*self.v + e**2))
return np.sum(dlogpdf_dvar)
def dlogpdf_dlink_dvar(self, link_f, y, extra_data=None):
def dlogpdf_dlink_dvar(self, link_f, y, Y_metadata=None):
"""
Derivative of the dlogpdf_dlink w.r.t variance parameter (t_noise)
@ -199,7 +199,7 @@ class StudentT(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in student t distribution
:param Y_metadata: Y_metadata which is not used in student t distribution
:returns: derivative of likelihood evaluated at points f w.r.t variance parameter
:rtype: Nx1 array
"""
@ -208,7 +208,7 @@ class StudentT(Likelihood):
dlogpdf_dlink_dvar = (self.v*(self.v+1)*(-e))/((self.sigma2*self.v + e**2)**2)
return dlogpdf_dlink_dvar
def d2logpdf_dlink2_dvar(self, link_f, y, extra_data=None):
def d2logpdf_dlink2_dvar(self, link_f, y, Y_metadata=None):
"""
Gradient of the hessian (d2logpdf_dlink2) w.r.t variance parameter (t_noise)
@ -219,7 +219,7 @@ class StudentT(Likelihood):
:type link_f: Nx1 array
:param y: data
:type y: Nx1 array
:param extra_data: extra_data which is not used in student t distribution
:param Y_metadata: Y_metadata which is not used in student t distribution
:returns: derivative of hessian evaluated at points f and f_j w.r.t variance parameter
:rtype: Nx1 array
"""
@ -230,25 +230,22 @@ class StudentT(Likelihood):
)
return d2logpdf_dlink2_dvar
def dlogpdf_link_dtheta(self, f, y, extra_data=None):
dlogpdf_dvar = self.dlogpdf_link_dvar(f, y, extra_data=extra_data)
def dlogpdf_link_dtheta(self, f, y, Y_metadata=None):
dlogpdf_dvar = self.dlogpdf_link_dvar(f, y, Y_metadata=Y_metadata)
dlogpdf_dv = np.zeros_like(dlogpdf_dvar) #FIXME: Not done yet
return np.hstack((dlogpdf_dvar, dlogpdf_dv))
def dlogpdf_dlink_dtheta(self, f, y, extra_data=None):
dlogpdf_dlink_dvar = self.dlogpdf_dlink_dvar(f, y, extra_data=extra_data)
def dlogpdf_dlink_dtheta(self, f, y, Y_metadata=None):
dlogpdf_dlink_dvar = self.dlogpdf_dlink_dvar(f, y, Y_metadata=Y_metadata)
dlogpdf_dlink_dv = np.zeros_like(dlogpdf_dlink_dvar) #FIXME: Not done yet
return np.hstack((dlogpdf_dlink_dvar, dlogpdf_dlink_dv))
def d2logpdf_dlink2_dtheta(self, f, y, extra_data=None):
d2logpdf_dlink2_dvar = self.d2logpdf_dlink2_dvar(f, y, extra_data=extra_data)
def d2logpdf_dlink2_dtheta(self, f, y, Y_metadata=None):
d2logpdf_dlink2_dvar = self.d2logpdf_dlink2_dvar(f, y, Y_metadata=Y_metadata)
d2logpdf_dlink2_dv = np.zeros_like(d2logpdf_dlink2_dvar) #FIXME: Not done yet
return np.hstack((d2logpdf_dlink2_dvar, d2logpdf_dlink2_dv))
def predictive_mean(self, mu, sigma, Y_metadata=None):
"""
Compute mean of the prediction
"""
return self.gp_link.transf(mu) # only true in link is monotoci, which it is.
def predictive_variance(self, mu,variance, predictive_mean=None, Y_metadata=None):

2
GPy/models/sparse_gp_coregionalized_regression.py
View file

@ -50,7 +50,7 @@ class SparseGPCoregionalizedRegression(SparseGP):
#Inducing inputs list
if len(Z_list):
assert len(Z_list) == self.output_dim, 'Number of outputs do not match length of inducing inputs list.'
assert len(Z_list) == Ny, 'Number of outputs do not match length of inducing inputs list.'
else:
if isinstance(num_inducing,np.int):
num_inducing = [num_inducing] * Ny

38
GPy/testing/likelihood_tests.py
View file

@ -255,25 +255,25 @@ class TestNoiseModels(object):
"Y": self.binary_Y,
"ep": False # FIXME: Should be True when we have it working again
},
#"Exponential_default": {
#"model": GPy.likelihoods.exponential(),
#"link_f_constraints": [constrain_positive],
#"Y": self.positive_Y,
#"laplace": True,
#},
#"Poisson_default": {
#"model": GPy.likelihoods.poisson(),
#"link_f_constraints": [constrain_positive],
#"Y": self.integer_Y,
#"laplace": True,
#"ep": False #Should work though...
#},
#"Gamma_default": {
#"model": GPy.likelihoods.gamma(),
#"link_f_constraints": [constrain_positive],
#"Y": self.positive_Y,
#"laplace": True
#}
"Exponential_default": {
"model": GPy.likelihoods.Exponential(),
"link_f_constraints": [constrain_positive],
"Y": self.positive_Y,
"laplace": True,
},
"Poisson_default": {
"model": GPy.likelihoods.Poisson(),
"link_f_constraints": [constrain_positive],
"Y": self.integer_Y,
"laplace": True,
"ep": False #Should work though...
},
"Gamma_default": {
"model": GPy.likelihoods.Gamma(),
"link_f_constraints": [constrain_positive],
"Y": self.positive_Y,
"laplace": True
}
}
for name, attributes in noise_models.iteritems():

0
GPy/testing/gp_transformation_tests.py → GPy/testing/old_tests/gp_transformation_tests.py
View file

0
GPy/testing/gplvm_tests.py → GPy/testing/old_tests/gplvm_tests.py
View file

0
GPy/testing/psi_stat_gradient_tests.py → GPy/testing/old_tests/psi_stat_gradient_tests.py
View file

0
GPy/testing/sparse_gplvm_tests.py → GPy/testing/old_tests/sparse_gplvm_tests.py
View file

2
GPy/util/multioutput.py
View file

@ -56,8 +56,6 @@ def ICM(input_dim, num_outputs, kernel, W_rank=1,W=None,kappa=None,name='X'):
warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.")
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()
return K