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precomputations for linear psi statistics

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Nicolo Fusi 2013-02-06 17:58:42 +00:00
parent 7d8e2183a2
commit 2e948d888a
1 changed files with 28 additions and 14 deletions
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42
GPy/kern/linear.py
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@ -44,6 +44,10 @@ class linear(kernpart):
variances = np.ones(self.D) variances = np.ones(self.D)
self._set_params(variances) self._set_params(variances)
#initialize cache
self._Z, self._mu, self._S = np.empty(shape=(3,1))
self._X, self._X2, self._params = np.empty(shape=(3,1))
def _get_params(self): def _get_params(self):
return self.variances return self.variances
@ -86,12 +90,12 @@ class linear(kernpart):
#---------------------------------------# #---------------------------------------#
def psi0(self,Z,mu,S,target): def psi0(self,Z,mu,S,target):
expected = np.square(mu) + S self._psi_computations(Z,mu,S)
target += np.sum(self.variances*expected) target += np.sum(self.variances*self.mu2_S)
def dpsi0_dtheta(self,partial,Z,mu,S,target): def dpsi0_dtheta(self,partial,Z,mu,S,target):
expected = np.square(mu) + S self._psi_computations(Z,mu,S)
target += (partial[:, None] * (np.sum(expected,0))).sum() target += (partial[:, None] * (np.sum(self.mu2_S,0))).sum()
def dpsi0_dmuS(self,partial, Z,mu,S,target_mu,target_S): def dpsi0_dmuS(self,partial, Z,mu,S,target_mu,target_S):
target_mu += partial[:, None] * (2.0*mu*self.variances) * mu.shape[0] target_mu += partial[:, None] * (2.0*mu*self.variances) * mu.shape[0]
@ -110,7 +114,8 @@ class linear(kernpart):
def dpsi1_dmuS(self,partial,Z,mu,S,target_mu,target_S): def dpsi1_dmuS(self,partial,Z,mu,S,target_mu,target_S):
"""Do nothing for S, it does not affect psi1""" """Do nothing for S, it does not affect psi1"""
target_mu += (partial.T[:,:, None]*(Z*self.variances)).sum(1) self._psi_computations(Z,mu,S)
target_mu += (partial.T[:,:, None]*(Z*self.variances)).sum(1)
def dpsi1_dZ(self,partial,Z,mu,S,target): def dpsi1_dZ(self,partial,Z,mu,S,target):
self.dK_dX(partial.T,Z,mu,target) self.dK_dX(partial.T,Z,mu,target)
@ -119,25 +124,24 @@ class linear(kernpart):
""" """
returns N,M,M matrix returns N,M,M matrix
""" """
mu2_S = np.square(mu)+S# N,Q, self._psi_computations(Z,mu,S)
ZZ = Z[:,None,:]*Z[None,:,:] # M,M,Q psi2 = self.ZZ*np.square(self.variances)*self.mu2_S[:, None, None, :]
psi2 = ZZ*np.square(self.variances)*mu2_S[:, None, None, :]
target += psi2.sum(-1) target += psi2.sum(-1)
def dpsi2_dtheta(self,partial,Z,mu,S,target): def dpsi2_dtheta(self,partial,Z,mu,S,target):
mu2_S = np.square(mu)+S# N,Q, self._psi_computations(Z,mu,S)
ZZ = Z[:,None,:]*Z[None,:,:] # M,M,Q target += (partial[:,:,:,None]*(2.*self.ZZ*self.mu2_S[:,None,None,:]*self.variances)).sum()
target += (partial[:,:,:,None]*(2.*ZZ*mu2_S[:,None,None,:]*self.variances)).sum()
def dpsi2_dmuS(self,partial,Z,mu,S,target_mu,target_S): def dpsi2_dmuS(self,partial,Z,mu,S,target_mu,target_S):
"""Think N,M,M,Q """ """Think N,M,M,Q """
ZZ = Z[:,None,:]*Z[None,:,:] # M,M,Q self._psi_computations(Z,mu,S)
tmp = ZZ*np.square(self.variances) # M,M,Q tmp = self.ZZ*np.square(self.variances) # M,M,Q
target_mu += (partial[:,:,:,None]*tmp*2.*mu[:,None,None,:]).sum(1).sum(1) target_mu += (partial[:,:,:,None]*tmp*2.*mu[:,None,None,:]).sum(1).sum(1)
target_S += (partial[:,:,:,None]*tmp).sum(1).sum(1) target_S += (partial[:,:,:,None]*tmp).sum(1).sum(1)
def dpsi2_dZ(self,partial,Z,mu,S,target): def dpsi2_dZ(self,partial,Z,mu,S,target):
mu2_S = np.sum(np.square(mu)+S,0)# Q, self._psi_computations(Z,mu,S)
mu2_S = np.sum(self.mu2_S,0)# Q,
target += (partial[:,:,:,None]* (Z * mu2_S * np.square(self.variances))).sum(0).sum(1) target += (partial[:,:,:,None]* (Z * mu2_S * np.square(self.variances))).sum(0).sum(1)
#---------------------------------------# #---------------------------------------#
@ -154,3 +158,13 @@ class linear(kernpart):
else: else:
# print "Cache hit!" # print "Cache hit!"
pass # TODO: insert debug message here (logging framework) pass # TODO: insert debug message here (logging framework)
def _psi_computations(self,Z,mu,S):
#here are the "statistics" for psi1 and psi2
if not np.all(Z==self._Z):
#Z has changed, compute Z specific stuff
self.ZZ = Z[:,None,:]*Z[None,:,:] # M,M,Q
self._Z = Z
if not (np.all(Z==self._Z) and np.all(mu==self._mu) and np.all(S==self._S)):
self.mu2_S = np.square(mu)+S
self._Z, self._mu, self._S = Z, mu,S