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The warnings are now handeled properly in the periodic kernels

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Nicolas 2013-03-12 16:50:12 +00:00
parent c4162a4bf7
commit 7a9b6ad113
3 changed files with 22 additions and 1 deletions
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4
GPy/kern/periodic_Matern32.py
View file

@ -1,3 +1,7 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart from kernpart import kernpart
import numpy as np import numpy as np
from GPy.util.linalg import mdot, pdinv from GPy.util.linalg import mdot, pdinv

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 from kernpart import kernpart
import numpy as np import numpy as np
from GPy.util.linalg import mdot, pdinv from GPy.util.linalg import mdot, pdinv
from GPy.util.decorators import silence_errors
class periodic_Matern52(kernpart): class periodic_Matern52(kernpart):
""" """
@ -40,6 +45,7 @@ class periodic_Matern52(kernpart):
return alpha*np.cos(omega*x+phase) return alpha*np.cos(omega*x+phase)
return f return f
@silence_errors
def _cos_factorization(self,alpha,omega,phase): def _cos_factorization(self,alpha,omega,phase):
r1 = np.sum(alpha*np.cos(phase),axis=1)[:,None] r1 = np.sum(alpha*np.cos(phase),axis=1)[:,None]
r2 = np.sum(alpha*np.sin(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): def _get_params(self):
"""return the value of the parameters.""" """return the value of the parameters."""
return np.hstack((self.variance,self.lengthscale,self.period)) return np.hstack((self.variance,self.lengthscale,self.period))
def _set_params(self,x): def _set_params(self,x):
"""set the value of the parameters.""" """set the value of the parameters."""
assert x.size==3 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) 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) np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target): def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is NxMxNparam)""" """derivative of the covariance matrix with respect to the parameters (shape is NxMxNparam)"""
if X2 is None: X2 = X if X2 is None: X2 = X
@ -184,6 +192,7 @@ class periodic_Matern52(kernpart):
#np.add(target[:,:,2],dK_dper, target[:,:,2]) #np.add(target[:,:,2],dK_dper, target[:,:,2])
target[2] += np.sum(dK_dper*dL_dK) target[2] += np.sum(dK_dper*dL_dK)
@silence_errors
def dKdiag_dtheta(self,dL_dKdiag,X,target): def dKdiag_dtheta(self,dL_dKdiag,X,target):
"""derivative of the diagonal of the covariance matrix with respect to the parameters""" """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) 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 from kernpart import kernpart
import numpy as np import numpy as np
from GPy.util.linalg import mdot, pdinv from GPy.util.linalg import mdot, pdinv
from GPy.util.decorators import silence_errors
class periodic_exponential(kernpart): class periodic_exponential(kernpart):
""" """
@ -40,6 +45,7 @@ class periodic_exponential(kernpart):
return alpha*np.cos(omega*x+phase) return alpha*np.cos(omega*x+phase)
return f return f
@silence_errors
def _cos_factorization(self,alpha,omega,phase): def _cos_factorization(self,alpha,omega,phase):
r1 = np.sum(alpha*np.cos(phase),axis=1)[:,None] r1 = np.sum(alpha*np.cos(phase),axis=1)[:,None]
r2 = np.sum(alpha*np.sin(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): def _get_params(self):
"""return the value of the parameters.""" """return the value of the parameters."""
return np.hstack((self.variance,self.lengthscale,self.period)) return np.hstack((self.variance,self.lengthscale,self.period))
def _set_params(self,x): def _set_params(self,x):
"""set the value of the parameters.""" """set the value of the parameters."""
assert x.size==3 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) 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) np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target): def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is NxMxNparam)""" """derivative of the covariance matrix with respect to the parameters (shape is NxMxNparam)"""
if X2 is None: X2 = X if X2 is None: X2 = X
@ -166,6 +174,7 @@ class periodic_exponential(kernpart):
target[1] += np.sum(dK_dlen*dL_dK) target[1] += np.sum(dK_dlen*dL_dK)
target[2] += np.sum(dK_dper*dL_dK) target[2] += np.sum(dK_dper*dL_dK)
@silence_errors
def dKdiag_dtheta(self,dL_dKdiag,X,target): def dKdiag_dtheta(self,dL_dKdiag,X,target):
"""derivative of the diagonal of the covariance matrix with respect to the parameters""" """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) 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[0] += np.sum(np.diag(dK_dvar)*dL_dKdiag)
target[1] += np.sum(np.diag(dK_dlen)*dL_dKdiag) target[1] += np.sum(np.diag(dK_dlen)*dL_dKdiag)
target[2] += np.sum(np.diag(dK_dper)*dL_dKdiag) target[2] += np.sum(np.diag(dK_dper)*dL_dKdiag)