apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-24 14:15:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge branch 'devel' of github.com:SheffieldML/GPy into devel

Browse source
This commit is contained in:
Alan Saul 2013-12-11 15:48:07 +00:00
commit 990dab77ff
2 changed files with 33 additions and 1 deletions
Download patch file Download diff file Expand all files Collapse all files

4
GPy/kern/parts/ODE_1.py
View file

@ -138,6 +138,10 @@ class ODE_1(Kernpart):
k3 = np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 ) k3 = np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
dkdvar = k1+k2+k3 dkdvar = k1+k2+k3
#target[0] dk dvarU
#target[1] dk dvarY
#target[2] dk d theta1
#target[3] dk d theta2
target[0] += np.sum(self.varianceY*dkdvar * dL_dK) target[0] += np.sum(self.varianceY*dkdvar * dL_dK)
target[1] += np.sum(self.varianceU*dkdvar * dL_dK) target[1] += np.sum(self.varianceU*dkdvar * dL_dK)
target[2] += np.sum(dktheta1*(-np.sqrt(3)*self.lengthscaleU**(-2)) * dL_dK) target[2] += np.sum(dktheta1*(-np.sqrt(3)*self.lengthscaleU**(-2)) * dL_dK)

28
GPy/kern/parts/ODE_UY.py
View file

@ -95,6 +95,8 @@ class ODE_UY(Kernpart):
def K(self, X, X2, target): def K(self, X, X2, target):
"""Compute the covariance matrix between X and X2.""" """Compute the covariance matrix between X and X2."""
# model : a * dy/dt + b * y = U
#lu=sqrt(3)/theta1 ly=1/theta2 theta2= a/b :thetay sigma2=1/(2ab) :sigmay
X,slices = X[:,:-1],index_to_slices(X[:,-1]) X,slices = X[:,:-1],index_to_slices(X[:,-1])
if X2 is None: if X2 is None:
@ -187,6 +189,13 @@ class ODE_UY(Kernpart):
if X2 is None: X2 = X if X2 is None: X2 = X
dist = np.abs(X - X2.T) dist = np.abs(X - X2.T)
X,slices = X[:,:-1],index_to_slices(X[:,-1])
if X2 is None:
X2,slices2 = X,slices
else:
X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
ly=1/self.lengthscaleY ly=1/self.lengthscaleY
lu=np.sqrt(3)/self.lengthscaleU lu=np.sqrt(3)/self.lengthscaleU
#ly=self.lengthscaleY #ly=self.lengthscaleY
@ -230,6 +239,25 @@ class ODE_UY(Kernpart):
k3 = lambda dist: np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 ) k3 = lambda dist: np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
dkdvar = k1+k2+k3 dkdvar = k1+k2+k3
for i, s1 in enumerate(slices):
for j, s2 in enumerate(slices2):
for ss1 in s1:
for ss2 in s2:
if i==0 and j==0:
#target[ss1,ss2] = kuu(np.abs(rdist[ss1,ss2]))
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]]) ) )
elif i==1 and j==1:
#target[ss1,ss2] = kyy(np.abs(rdist[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]]) ) )
target[0] += np.sum(self.varianceY*dkdvar * dL_dK) target[0] += np.sum(self.varianceY*dkdvar * dL_dK)
target[1] += np.sum(self.varianceU*dkdvar * dL_dK) target[1] += np.sum(self.varianceU*dkdvar * dL_dK)
target[2] += np.sum(dktheta1*(-np.sqrt(3)*self.lengthscaleU**(-2)) * dL_dK) target[2] += np.sum(dktheta1*(-np.sqrt(3)*self.lengthscaleU**(-2)) * dL_dK)