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Mus code seems to work on params now

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James Hensman 2014-03-20 11:15:12 +00:00
parent 1a293948f4
commit 6ba0592101
2 changed files with 36 additions and 36 deletions
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2
GPy/kern/__init__.py
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@ -10,5 +10,5 @@ from _src.periodic import PeriodicExponential, PeriodicMatern32, PeriodicMatern5
from _src.independent_outputs import IndependentOutputs, Hierarchical from _src.independent_outputs import IndependentOutputs, Hierarchical
from _src.coregionalize import Coregionalize from _src.coregionalize import Coregionalize
from _src.ssrbf import SSRBF # TODO: ZD: did you remove this? from _src.ssrbf import SSRBF # TODO: ZD: did you remove this?
from _src.ODE_Uy import ODE_UY from _src.ODE_UY import ODE_UY

60
GPy/kern/_src/ODE_UY.py
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@ -7,10 +7,10 @@ from ...core.parameterization.transformations import Logexp
import numpy as np import numpy as np
from independent_outputs import index_to_slices from independent_outputs import index_to_slices
class ODEUY(Kern): class ODE_UY(Kern):
def __init__(self, input_dim, variance_U=3., variance_Y=1., lengthscale_U=1., lengthscale_Y=1., active_dims=None, name='ode_uy'): def __init__(self, input_dim, variance_U=3., variance_Y=1., lengthscale_U=1., lengthscale_Y=1., active_dims=None, name='ode_uy'):
assert input_dim ==2, "only defined for 2 input dims" assert input_dim ==2, "only defined for 2 input dims"
super(ODEUY, self).__init__(input_dim, active_dims, name) super(ODE_UY, self).__init__(input_dim, active_dims, name)
self.variance_Y = Param('variance_Y', variance_Y, Logexp()) self.variance_Y = Param('variance_Y', variance_Y, Logexp())
self.variance_U = Param('variance_U', variance_Y, Logexp()) self.variance_U = Param('variance_U', variance_Y, Logexp())
@ -34,11 +34,11 @@ class ODEUY(Kern):
#rdist = X[:,0][:,None] - X2[:,0][:,None].T #rdist = X[:,0][:,None] - X2[:,0][:,None].T
rdist = X - X2.T rdist = X - X2.T
ly=1/self.lengthscaleY ly=1/self.lengthscale_Y
lu=np.sqrt(3)/self.lengthscaleU lu=np.sqrt(3)/self.lengthscale_U
#iu=self.input_lengthU #dimention of U #iu=self.input_lengthU #dimention of U
Vu=self.varianceU Vu=self.variance_U
Vy=self.varianceY Vy=self.variance_Y
#Vy=ly/2 #Vy=ly/2
#stop #stop
@ -95,11 +95,11 @@ class ODEUY(Kern):
def Kdiag(self, X): def Kdiag(self, X):
"""Compute the diagonal of the covariance matrix associated to X.""" """Compute the diagonal of the covariance matrix associated to X."""
Kdiag = np.zeros(X.shape[0]) Kdiag = np.zeros(X.shape[0])
ly=1/self.lengthscaleY ly=1/self.lengthscale_Y
lu=np.sqrt(3)/self.lengthscaleU lu=np.sqrt(3)/self.lengthscale_U
Vu = self.varianceU Vu = self.variance_U
Vy=self.varianceY Vy=self.variance_Y
k1 = (2*lu+ly)/(lu+ly)**2 k1 = (2*lu+ly)/(lu+ly)**2
k2 = (ly-2*lu + 2*lu-ly ) / (ly-lu)**2 k2 = (ly-2*lu + 2*lu-ly ) / (ly-lu)**2
@ -110,13 +110,13 @@ class ODEUY(Kern):
for i, ss1 in enumerate(slices): for i, ss1 in enumerate(slices):
for s1 in ss1: for s1 in ss1:
if i==0: if i==0:
Kdiag[s1]+= self.varianceU Kdiag[s1]+= self.variance_U
elif i==1: elif i==1:
Kdiag[s1]+= Vu*Vy*(k1+k2+k3) Kdiag[s1]+= Vu*Vy*(k1+k2+k3)
else: else:
raise ValueError, "invalid input/output index" raise ValueError, "invalid input/output index"
#Kdiag[slices[0][0]]+= self.varianceU #matern32 diag #Kdiag[slices[0][0]]+= self.variance_U #matern32 diag
#Kdiag[slices[1][0]]+= self.varianceU*self.varianceY*(k1+k2+k3) # diag #Kdiag[slices[1][0]]+= self.variance_U*self.variance_Y*(k1+k2+k3) # diag
return Kdiag return Kdiag
@ -130,11 +130,11 @@ class ODEUY(Kern):
#rdist = X[:,0][:,None] - X2[:,0][:,None].T #rdist = X[:,0][:,None] - X2[:,0][:,None].T
rdist = X - X2.T rdist = X - X2.T
ly=1/self.lengthscaleY ly=1/self.lengthscale_Y
lu=np.sqrt(3)/self.lengthscaleU lu=np.sqrt(3)/self.lengthscale_U
Vu=self.varianceU Vu=self.variance_U
Vy=self.varianceY Vy=self.variance_Y
Vyu = np.sqrt(Vy*ly*2) Vyu = np.sqrt(Vy*ly*2)
dVdly = 0.5/np.sqrt(ly)*np.sqrt(2*Vy) dVdly = 0.5/np.sqrt(ly)*np.sqrt(2*Vy)
dVdVy = 0.5/np.sqrt(Vy)*np.sqrt(2*ly) dVdVy = 0.5/np.sqrt(Vy)*np.sqrt(2*ly)
@ -164,7 +164,7 @@ class ODEUY(Kern):
dk3theta1 = lambda dist: np.exp(-dist*lu)*(lu+ly)**(-2)*((2*lu+ly+dist*lu**2+lu*ly*dist)*(-dist-2/(lu+ly))+2+2*lu*dist+ly*dist) dk3theta1 = lambda dist: np.exp(-dist*lu)*(lu+ly)**(-2)*((2*lu+ly+dist*lu**2+lu*ly*dist)*(-dist-2/(lu+ly))+2+2*lu*dist+ly*dist)
#dktheta1 = lambda dist: self.varianceU*self.varianceY*(dk1theta1+dk2theta1+dk3theta1) #dktheta1 = lambda dist: self.variance_U*self.variance_Y*(dk1theta1+dk2theta1+dk3theta1)
@ -178,7 +178,7 @@ class ODEUY(Kern):
dk3theta2 = lambda dist: np.exp(-dist*lu) * (-3*lu-ly-dist*lu**2-lu*ly*dist)/(lu+ly)**3 dk3theta2 = lambda dist: np.exp(-dist*lu) * (-3*lu-ly-dist*lu**2-lu*ly*dist)/(lu+ly)**3
#dktheta2 = lambda dist: self.varianceU*self.varianceY*(dk1theta2 + dk2theta2 +dk3theta2) #dktheta2 = lambda dist: self.variance_U*self.variance_Y*(dk1theta2 + dk2theta2 +dk3theta2)
# kyy kernel # kyy kernel
@ -250,22 +250,22 @@ class ODEUY(Kern):
elif i==0 and j==1: 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]]) ) ) ########target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
#np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) ) #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 , self.varianceU*self.varianceY*dkcrtheta1(np.abs(rdist[ss1,ss2])) ,self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))) ) #dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , self.variance_U*self.variance_Y*dkcrtheta1(np.abs(rdist[ss1,ss2])) ,self.variance_U*self.variance_Y*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))) )
#dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , self.varianceU*self.varianceY*dkcrtheta2(np.abs(rdist[ss1,ss2])) ,self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2]))+dk2theta2(np.abs(rdist[ss1,ss2]))) ) #dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , self.variance_U*self.variance_Y*dkcrtheta2(np.abs(rdist[ss1,ss2])) ,self.variance_U*self.variance_Y*(dk1theta2(np.abs(rdist[ss1,ss2]))+dk2theta2(np.abs(rdist[ss1,ss2]))) )
dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vu*Vyu*dkuypdtheta1(rdist[ss1,ss2]),Vu*Vyu*dkuyndtheta1(rdist[ss1,ss2]) ) dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vu*Vyu*dkuypdtheta1(rdist[ss1,ss2]),Vu*Vyu*dkuyndtheta1(rdist[ss1,ss2]) )
dkUdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vyu*kuyp(rdist[ss1,ss2]), Vyu* kuyn(rdist[ss1,ss2]) ) dkUdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vyu*kuyp(rdist[ss1,ss2]), Vyu* kuyn(rdist[ss1,ss2]) )
dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vu*Vyu*dkuypdtheta2(rdist[ss1,ss2])+Vu*dVdly*kuyp(rdist[ss1,ss2]),Vu*Vyu*dkuyndtheta2(rdist[ss1,ss2])+Vu*dVdly*kuyn(rdist[ss1,ss2]) ) dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vu*Vyu*dkuypdtheta2(rdist[ss1,ss2])+Vu*dVdly*kuyp(rdist[ss1,ss2]),Vu*Vyu*dkuyndtheta2(rdist[ss1,ss2])+Vu*dVdly*kuyn(rdist[ss1,ss2]) )
dkYdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vu*dVdVy*kuyp(rdist[ss1,ss2]), Vu*dVdVy* kuyn(rdist[ss1,ss2]) ) dkYdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vu*dVdVy*kuyp(rdist[ss1,ss2]), Vu*dVdVy* kuyn(rdist[ss1,ss2]) )
elif i==1 and j==1: elif i==1 and j==1:
#target[ss1,ss2] = kyy(np.abs(rdist[ss1,ss2])) #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]))) dktheta1[ss1,ss2] = self.variance_U*self.variance_Y*(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]))) dktheta2[ss1,ss2] = self.variance_U*self.variance_Y*(dk1theta2(np.abs(rdist[ss1,ss2])) + dk2theta2(np.abs(rdist[ss1,ss2])) +dk3theta2(np.abs(rdist[ss1,ss2])))
dkUdvar[ss1,ss2] = self.varianceY*(k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2])) ) dkUdvar[ss1,ss2] = self.variance_Y*(k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2])) )
dkYdvar[ss1,ss2] = self.varianceU*(k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2])) ) dkYdvar[ss1,ss2] = self.variance_U*(k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2])) )
else: 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[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]))) , self.varianceU*self.varianceY*dkcrtheta1(np.abs(rdist[ss1,ss2])) ) #dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 ,self.variance_U*self.variance_Y*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))) , self.variance_U*self.variance_Y*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]))) , self.varianceU*self.varianceY*dkcrtheta2(np.abs(rdist[ss1,ss2])) ) #dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 ,self.variance_U*self.variance_Y*(dk1theta2(np.abs(rdist[ss1,ss2]))+dk2theta2(np.abs(rdist[ss1,ss2]))) , self.variance_U*self.variance_Y*dkcrtheta2(np.abs(rdist[ss1,ss2])) )
dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vu*Vyu*dkyupdtheta1(rdist[ss1,ss2]),Vu*Vyu*dkyundtheta1(rdist[ss1,ss2]) ) dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vu*Vyu*dkyupdtheta1(rdist[ss1,ss2]),Vu*Vyu*dkyundtheta1(rdist[ss1,ss2]) )
dkUdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vyu*kyup(rdist[ss1,ss2]),Vyu*kyun(rdist[ss1,ss2])) dkUdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vyu*kyup(rdist[ss1,ss2]),Vyu*kyun(rdist[ss1,ss2]))
dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vu*Vyu*dkyupdtheta2(rdist[ss1,ss2])+Vu*dVdly*kyup(rdist[ss1,ss2]),Vu*Vyu*dkyundtheta2(rdist[ss1,ss2])+Vu*dVdly*kyun(rdist[ss1,ss2]) ) dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , Vu*Vyu*dkyupdtheta2(rdist[ss1,ss2])+Vu*dVdly*kyup(rdist[ss1,ss2]),Vu*Vyu*dkyundtheta2(rdist[ss1,ss2])+Vu*dVdly*kyun(rdist[ss1,ss2]) )
@ -274,9 +274,9 @@ class ODEUY(Kern):
#stop #stop
self.variance_U.gradient = np.sum(dkUdvar * dL_dK) # Vu self.variance_U.gradient = np.sum(dkUdvar * dL_dK) # Vu
self.varaince_Y.gradient = np.sum(dkYdvar * dL_dK) # Vy self.variance_Y.gradient = np.sum(dkYdvar * dL_dK) # Vy
self.lengthscale_U.gradient = np.sum(dktheta1*(-np.sqrt(3)*self.lengthscaleU**(-2))* dL_dK) #lu self.lengthscale_U.gradient = np.sum(dktheta1*(-np.sqrt(3)*self.lengthscale_U**(-2))* dL_dK) #lu
self.lengthscaleY.gradient = np.sum(dktheta2*(-self.lengthscaleY**(-2)) * dL_dK) #ly self.lengthscale_Y.gradient = np.sum(dktheta2*(-self.lengthscale_Y**(-2)) * dL_dK) #ly