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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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10
GPy/kern/periodic_exponential.py
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@ -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)