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Merge branch 'master' of github.com:SheffieldML/GPy

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This commit is contained in:
Nicolo Fusi 2013-03-13 09:30:30 +00:00
commit e812368a87
4 changed files with 48 additions and 19 deletions
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12
GPy/kern/periodic_Matern32.py
View file

@ -1,6 +1,11 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
import numpy as np
from GPy.util.linalg import mdot, pdinv
from GPy.util.decorators import silence_errors
class periodic_Matern32(kernpart):
"""
@ -39,12 +44,16 @@ class periodic_Matern32(kernpart):
def f(x):
return alpha*np.cos(omega*x+phase)
return f
@silence_errors
def _cos_factorization(self,alpha,omega,phase):
r1 = np.sum(alpha*np.cos(phase),axis=1)[:,None]
r2 = np.sum(alpha*np.sin(phase),axis=1)[:,None]
r = np.sqrt(r1**2 + r2**2)
psi = np.where(r1 != 0, (np.arctan(r2/r1) + (r1<0.)*np.pi),np.arcsin(r2))
return r,omega[:,0:1], psi
@silence_errors
def _int_computation(self,r1,omega1,phi1,r2,omega2,phi2):
Gint1 = 1./(omega1+omega2.T)*( np.sin((omega1+omega2.T)*self.upper+phi1+phi2.T) - np.sin((omega1+omega2.T)*self.lower+phi1+phi2.T)) + 1./(omega1-omega2.T)*( np.sin((omega1-omega2.T)*self.upper+phi1-phi2.T) - np.sin((omega1-omega2.T)*self.lower+phi1-phi2.T) )
Gint2 = 1./(omega1+omega2.T)*( np.sin((omega1+omega2.T)*self.upper+phi1+phi2.T) - np.sin((omega1+omega2.T)*self.lower+phi1+phi2.T)) + np.cos(phi1-phi2.T)*(self.upper-self.lower)
@ -55,6 +64,7 @@ class periodic_Matern32(kernpart):
def _get_params(self):
"""return the value of the parameters."""
return np.hstack((self.variance,self.lengthscale,self.period))
def _set_params(self,x):
"""set the value of the parameters."""
assert x.size==3
@ -101,6 +111,7 @@ class periodic_Matern32(kernpart):
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is NxMxNparam)"""
if X2 is None: X2 = X
@ -172,6 +183,7 @@ class periodic_Matern32(kernpart):
#np.add(target[:,:,2],dK_dper, target[:,:,2])
target[2] += np.sum(dK_dper*dL_dK)
@silence_errors
def dKdiag_dtheta(self,dL_dKdiag,X,target):
"""derivative of the diagonal covariance matrix with respect to the parameters"""
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)

9
GPy/kern/periodic_Matern52.py
View file

@ -1,6 +1,11 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
import numpy as np
from GPy.util.linalg import mdot, pdinv
from GPy.util.decorators import silence_errors
class periodic_Matern52(kernpart):
"""
@ -40,6 +45,7 @@ class periodic_Matern52(kernpart):
return alpha*np.cos(omega*x+phase)
return f
@silence_errors
def _cos_factorization(self,alpha,omega,phase):
r1 = np.sum(alpha*np.cos(phase),axis=1)[:,None]
r2 = np.sum(alpha*np.sin(phase),axis=1)[:,None]
@ -57,6 +63,7 @@ class periodic_Matern52(kernpart):
def _get_params(self):
"""return the value of the parameters."""
return np.hstack((self.variance,self.lengthscale,self.period))
def _set_params(self,x):
"""set the value of the parameters."""
assert x.size==3
@ -105,6 +112,7 @@ class periodic_Matern52(kernpart):
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is NxMxNparam)"""
if X2 is None: X2 = X
@ -184,6 +192,7 @@ class periodic_Matern52(kernpart):
#np.add(target[:,:,2],dK_dper, target[:,:,2])
target[2] += np.sum(dK_dper*dL_dK)
@silence_errors
def dKdiag_dtheta(self,dL_dKdiag,X,target):
"""derivative of the diagonal of the covariance matrix with respect to the parameters"""
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)

10
GPy/kern/periodic_exponential.py
View file

@ -1,6 +1,11 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
import numpy as np
from GPy.util.linalg import mdot, pdinv
from GPy.util.decorators import silence_errors
class periodic_exponential(kernpart):
"""
@ -40,6 +45,7 @@ class periodic_exponential(kernpart):
return alpha*np.cos(omega*x+phase)
return f
@silence_errors
def _cos_factorization(self,alpha,omega,phase):
r1 = np.sum(alpha*np.cos(phase),axis=1)[:,None]
r2 = np.sum(alpha*np.sin(phase),axis=1)[:,None]
@ -57,6 +63,7 @@ class periodic_exponential(kernpart):
def _get_params(self):
"""return the value of the parameters."""
return np.hstack((self.variance,self.lengthscale,self.period))
def _set_params(self,x):
"""set the value of the parameters."""
assert x.size==3
@ -101,6 +108,7 @@ class periodic_exponential(kernpart):
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is NxMxNparam)"""
if X2 is None: X2 = X
@ -166,6 +174,7 @@ class periodic_exponential(kernpart):
target[1] += np.sum(dK_dlen*dL_dK)
target[2] += np.sum(dK_dper*dL_dK)
@silence_errors
def dKdiag_dtheta(self,dL_dKdiag,X,target):
"""derivative of the diagonal of the covariance matrix with respect to the parameters"""
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
@ -225,4 +234,3 @@ class periodic_exponential(kernpart):
target[0] += np.sum(np.diag(dK_dvar)*dL_dKdiag)
target[1] += np.sum(np.diag(dK_dlen)*dL_dKdiag)
target[2] += np.sum(np.diag(dK_dper)*dL_dKdiag)

36
GPy/models/sparse_GP.py
View file

@ -153,14 +153,26 @@ class sparse_GP(GP):
#self.partial_for_likelihood += -np.diag(np.dot((self.C - 0.5 * mdot(self.C,self.psi2_beta_scaled,self.C) ) , self.psi1VVpsi1 ))*self.likelihood.precision #dD
else:
#likelihood is not heterscedatic
beta = self.likelihood.precision
dbeta = 0.5 * self.N*self.D/beta - 0.5 * np.sum(np.square(self.likelihood.Y))
dbeta += - 0.5 * self.D * (self.psi0.sum() - np.trace(self.A)/beta*sf2)
dbeta += - 0.5 * self.D * trace_dot(self.Bi,self.A)/beta
dbeta += np.trace(self.Cpsi1VVpsi1)/beta - 0.5 * trace_dot(np.dot(self.C,self.psi2_beta_scaled) , self.Cpsi1VVpsi1 )/beta
self.partial_for_likelihood = -dbeta*self.likelihood.precision**2
self.partial_for_likelihood = - 0.5 * self.N*self.D*self.likelihood.precision + 0.5 * np.sum(np.square(self.likelihood.Y))*self.likelihood.precision**2
self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum()*self.likelihood.precision**2 - np.trace(self.A)*self.likelihood.precision*sf2)
self.partial_for_likelihood += 0.5 * self.D * trace_dot(self.Bi,self.A)*self.likelihood.precision
self.partial_for_likelihood += self.likelihood.precision*(0.5*trace_dot(self.psi2_beta_scaled,self.E*sf2) - np.trace(self.Cpsi1VVpsi1))
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """
sf2 = self.scale_factor**2
if self.likelihood.is_heteroscedastic:
A = -0.5*self.N*self.D*np.log(2.*np.pi) +0.5*np.sum(np.log(self.likelihood.precision)) -0.5*np.sum(self.V*self.likelihood.Y)
B = -0.5*self.D*(np.sum(self.likelihood.precision.flatten()*self.psi0) - np.trace(self.A)*sf2)
else:
A = -0.5*self.N*self.D*(np.log(2.*np.pi) + np.log(self.likelihood._variance)) -0.5*self.likelihood.precision*self.likelihood.trYYT
B = -0.5*self.D*(np.sum(self.likelihood.precision*self.psi0) - np.trace(self.A)*sf2)
C = -0.5*self.D * (self.B_logdet + self.M*np.log(sf2))
D = 0.5*np.trace(self.Cpsi1VVpsi1)
return A+B+C+D
def _set_params(self, p):
self.Z = p[:self.M*self.Q].reshape(self.M, self.Q)
self.kern._set_params(p[self.Z.size:self.Z.size+self.kern.Nparam])
@ -188,18 +200,6 @@ class sparse_GP(GP):
#self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
self._set_params(self._get_params()) # update the GP
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """
sf2 = self.scale_factor**2
if self.likelihood.is_heteroscedastic:
A = -0.5*self.N*self.D*np.log(2.*np.pi) +0.5*np.sum(np.log(self.likelihood.precision)) -0.5*np.sum(self.V*self.likelihood.Y)
B = -0.5*self.D*(np.sum(self.likelihood.precision.flatten()*self.psi0) - np.trace(self.A)*sf2)
else:
A = -0.5*self.N*self.D*(np.log(2.*np.pi) - np.log(self.likelihood.precision)) -0.5*self.likelihood.precision*self.likelihood.trYYT
B = -0.5*self.D*(np.sum(self.likelihood.precision*self.psi0) - np.trace(self.A)*sf2)
C = -0.5*self.D * (self.B_logdet + self.M*np.log(sf2))
D = 0.5*np.trace(self.Cpsi1VVpsi1)
return A+B+C+D
def _log_likelihood_gradients(self):
return np.hstack((self.dL_dZ().flatten(), self.dL_dtheta(), self.likelihood._gradients(partial=self.partial_for_likelihood)))