apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-08 11:32:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

simplified the debug classes

Browse source
This commit is contained in:
James Hensman 2013-01-15 09:34:47 +00:00
parent 0ff5cb8447
commit 4241a7c24f
1 changed files with 5 additions and 37 deletions
Download patch file Download diff file Expand all files Collapse all files

42
GPy/models/sparse_GP_regression.py
View file

@ -37,6 +37,7 @@ class sparse_GP_regression(GP_regression):
""" """
def __init__(self,X,Y,kernel=None, X_uncertainty=None, beta=100., Z=None,Zslices=None,M=10,normalize_X=False,normalize_Y=False): def __init__(self,X,Y,kernel=None, X_uncertainty=None, beta=100., Z=None,Zslices=None,M=10,normalize_X=False,normalize_Y=False):
self.stabilit_multiplier = 1.
self.beta = beta self.beta = beta
if Z is None: if Z is None:
self.Z = np.random.permutation(X.copy())[:M] self.Z = np.random.permutation(X.copy())[:M]
@ -88,7 +89,7 @@ class sparse_GP_regression(GP_regression):
self.psi1VVpsi1 = np.dot(self.psi1V, self.psi1V.T) self.psi1VVpsi1 = np.dot(self.psi1V, self.psi1V.T)
self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm) self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm)
self.A = mdot(self.Lmi, self.beta*self.psi2, self.Lmi.T) self.A = mdot(self.Lmi, self.beta*self.psi2, self.Lmi.T)
self.B = np.eye(self.M) + self.A self.B = np.eye(self.M)*self.stabilit_multiplier + self.A
self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B) self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B)
self.LLambdai = np.dot(self.LBi, self.Lmi) self.LLambdai = np.dot(self.LBi, self.Lmi)
self.trace_K = self.psi0 - np.trace(self.A)/self.beta self.trace_K = self.psi0 - np.trace(self.A)/self.beta
@ -200,18 +201,7 @@ class sparse_GP_regression(GP_regression):
class sgp_debugB(sparse_GP_regression): class sgp_debugB(sparse_GP_regression):
def _computations(self): def _computations(self):
self.V = self.beta*self.Y sparse_GP_regression._computations(self)
self.psi1V = np.dot(self.psi1, self.V)
self.psi1VVpsi1 = np.dot(self.psi1V, self.psi1V.T)
self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm)
self.A = mdot(self.Lmi, self.beta*self.psi2, self.Lmi.T)
self.B = np.eye(self.M) + self.A
self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B)
self.LLambdai = np.dot(self.LBi, self.Lmi)
self.trace_K = self.psi0 - np.trace(self.A)/self.beta
self.LBL_inv = mdot(self.Lmi.T, self.Bi, self.Lmi)
self.C = mdot(self.LLambdai, self.psi1V)
self.G = mdot(self.LBL_inv, self.psi1VVpsi1, self.LBL_inv.T)
# Compute dL_dpsi # Compute dL_dpsi
self.dL_dpsi0 = - 0.5 * self.D * self.beta * np.ones(self.N) self.dL_dpsi0 = - 0.5 * self.D * self.beta * np.ones(self.N)
@ -241,18 +231,7 @@ class sgp_debugB(sparse_GP_regression):
class sgp_debugC(sparse_GP_regression): class sgp_debugC(sparse_GP_regression):
def _computations(self): def _computations(self):
self.V = self.beta*self.Y sparse_GP_regression._computations(self)
self.psi1V = np.dot(self.psi1, self.V)
self.psi1VVpsi1 = np.dot(self.psi1V, self.psi1V.T)
self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm)
self.A = mdot(self.Lmi, self.beta*self.psi2, self.Lmi.T)
self.B = np.eye(self.M) + self.A
self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B)
self.LLambdai = np.dot(self.LBi, self.Lmi)
self.trace_K = self.psi0 - np.trace(self.A)/self.beta
self.LBL_inv = mdot(self.Lmi.T, self.Bi, self.Lmi)
self.C = mdot(self.LLambdai, self.psi1V)
self.G = mdot(self.LBL_inv, self.psi1VVpsi1, self.LBL_inv.T)
# Compute dL_dpsi # Compute dL_dpsi
self.dL_dpsi0 = np.zeros(self.N) self.dL_dpsi0 = np.zeros(self.N)
@ -282,18 +261,7 @@ class sgp_debugC(sparse_GP_regression):
class sgp_debugE(sparse_GP_regression): class sgp_debugE(sparse_GP_regression):
def _computations(self): def _computations(self):
self.V = self.beta*self.Y sparse_GP_regression._computations(self)
self.psi1V = np.dot(self.psi1, self.V)
self.psi1VVpsi1 = np.dot(self.psi1V, self.psi1V.T)
self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm)
self.A = mdot(self.Lmi, self.beta*self.psi2, self.Lmi.T)
self.B = np.eye(self.M) + self.A
self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B)
self.LLambdai = np.dot(self.LBi, self.Lmi)
self.trace_K = self.psi0 - np.trace(self.A)/self.beta
self.LBL_inv = mdot(self.Lmi.T, self.Bi, self.Lmi)
self.C = mdot(self.LLambdai, self.psi1V)
self.G = mdot(self.LBL_inv, self.psi1VVpsi1, self.LBL_inv.T)
# Compute dL_dpsi # Compute dL_dpsi
self.dL_dpsi0 = np.zeros(self.N) self.dL_dpsi0 = np.zeros(self.N)