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ODE UY dkdtheta

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mu 2013-12-13 14:10:03 +00:00
parent 054b98d55b
commit fa08d20f58
1 changed files with 12 additions and 9 deletions
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21
GPy/kern/parts/ODE_UY.py
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@ -209,7 +209,8 @@ class ODE_UY(Kernpart):
rd=rdist.shape[0]
dktheta1 = np.zeros([rd,rd])
dktheta2 = np.zeros([rd,rd])
dkdvar = np.zeros([rd,rd])
dkUdvar = np.zeros([rd,rd])
dkYdvar = np.zeros([rd,rd])
# dk dtheta for UU
UUdtheta1 = lambda dist: np.exp(-lu* dist)*dist + (-dist)*np.exp(-lu* dist)*(1+lu*dist)
@ -287,7 +288,8 @@ class ODE_UY(Kernpart):
#target[ss1,ss2] = kuu(np.abs(rdist[ss1,ss2]))
dktheta1[ss1,ss2] = self.varianceU*self.varianceY*UUdtheta1(np.abs(rdist[ss1,ss2]))
dktheta2[ss1,ss2] = 0
dkdvar[ss1,ss2] = UUdvar(np.abs(rdist[ss1,ss2]))
dkUdvar[ss1,ss2] = UUdvar(np.abs(rdist[ss1,ss2]))
dkYdvar[ss1,ss2] = 0
elif i==0 and j==1:
#target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
#dktheta1[ss1,ss2] =
@ -295,23 +297,24 @@ class ODE_UY(Kernpart):
#dkdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , dkcrtheta1(np.abs(rdist[ss1,ss2])) ,self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))) )
dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , dkcrtheta2(np.abs(rdist[ss1,ss2])) ,self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2]))+dk2theta2(np.abs(rdist[ss1,ss2]))) )
dkdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyu3(np.abs(rdist[ss1,ss2])) ,k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2])) )
#stop
dkUdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyu3(np.abs(rdist[ss1,ss2])) ,k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2])) )
dkYdvar[ss1,ss2] = dkUdvar[ss1,ss2]
elif i==1 and j==1:
#target[ss1,ss2] = kyy(np.abs(rdist[ss1,ss2]))
dktheta1[ss1,ss2] = self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))+dk3theta1(np.abs(rdist[ss1,ss2])))
dktheta2[ss1,ss2] = self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2])) + dk2theta2(np.abs(rdist[ss1,ss2])) +dk3theta2(np.abs(rdist[ss1,ss2])))
dkdvar[ss1,ss2] = (k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2])) )
dkUdvar[ss1,ss2] = (k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2])) )
dkYdvar[ss1,ss2] = dkUdvar[ss1,ss2]
else:
#target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[s1[0],s2[0]]) ) )
dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 ,self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))) , dkcrtheta1(np.abs(rdist[ss1,ss2])) )
dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 ,self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2]))+dk2theta2(np.abs(rdist[ss1,ss2]))) , dkcrtheta2(np.abs(rdist[ss1,ss2])) )
dkdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2])), kyu3(np.abs(rdist[ss1,ss2])) )
#stop
dkUdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2])), kyu3(np.abs(rdist[ss1,ss2])) )
dkYdvar[ss1,ss2] = dkUdvar[ss1,ss2]
target[0] += np.sum(self.varianceY*dkdvar * dL_dK)
target[1] += np.sum(self.varianceU*dkdvar * dL_dK)
target[0] += np.sum(self.varianceY*dkUdvar * dL_dK)
target[1] += np.sum(self.varianceU*dkYdvar * dL_dK)
target[2] += np.sum(dktheta1*(-np.sqrt(3)*self.lengthscaleU**(-2)) * dL_dK)
target[3] += np.sum(dktheta2*(-self.lengthscaleY**(-2)) * dL_dK)