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Added the derivatives of the state space matrices for likelihood gradient evaluation.

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This commit is contained in:
Arno Solin 2014-01-30 18:51:42 +02:00
parent c7a09de80c
commit 388ba68dcf
1 changed files with 32 additions and 8 deletions
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40
GPy/kern/parts/Matern32.py
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@ -142,13 +142,37 @@ class Matern32(Kernpart):
"""
Return the state space representation of the covariance.
"""
foo = np.sqrt(3)/self.lengthscale
F = np.array([[0, 1], [-foo**2, -2*foo]])
L = np.array([[0], [1]])
Qc = np.array([12*np.sqrt(3) / self.lengthscale**3 * self.variance])
H = np.array([[1, 0]])
foo = np.sqrt(3.)/self.lengthscale
F = np.array([[0, 1], [-foo**2, -2*foo]])
L = np.array([[0], [1]])
Qc = np.array([12.*np.sqrt(3) / self.lengthscale**3 * self.variance])
H = np.array([[1, 0]])
Pinf = np.array([[self.variance, 0],
[0, 3*self.variance/(self.lengthscale**2)]])
# TODO: return the derivatives as well
return (F, L, Qc, H, Pinf)
[0, 3.*self.variance/(self.lengthscale**2)]])
# Allocate space for the derivatives
dF = np.empty([F.shape[0],F.shape[1],2])
dQc = np.empty([Qc.shape[0],Qc.shape[1],2])
dPinf = np.empty([Pinf.shape[0],Pinf.shape[1],2])
# The partial derivatives
dFvariance = np.zeros([2,2])
dFlengthscale = np.array([[0,0],
[6./self.lengthscale**3,2*np.sqrt(3)/self.lengthscale**2]])
dQcvariance = np.array([12.*np.sqrt(3)/self.lengthscale**3])
dQclengthscale = np.array([-3*12*np.sqrt(3)/self.lengthscale**4*self.variance])
dPinfvariance = np.array([[1,0],[0,3./self.lengthscale**2]])
dPinflengthscale = np.array([[0,0],
[0,-6*self.variance/self.lengthscale**3]])
# Combine the derivatives
dF[:,:,0] = dFvariance
dF[:,:,1] = dFlengthscale
dQc[:,:,0] = dQcvariance
dQc[:,:,1] = dQclengthscale
dPinf[:,:,0] = dPinfvariance
dPinf[:,:,1] = dPinflengthscale
# TODO: return the derivatives as well
return (F, L, Qc, H, Pinf, dF, dQc, dPinf)