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new shape for psi2

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Nicolo Fusi 2013-01-29 16:10:12 +00:00
parent ff1b64022e
commit 10c774e84e
3 changed files with 19 additions and 19 deletions
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10
GPy/kern/kern.py
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@ -259,29 +259,29 @@ class kern(parameterised):
:Z: np.ndarray of inducing inputs (M x Q)
: mu, S: np.ndarrays of means and variacnes (each N x Q)
:returns psi2: np.ndarray (N,M,M,Q) """
target = np.zeros((Z.shape[0],Z.shape[0]))
target = np.zeros((mu.shape[0],Z.shape[0],Z.shape[0]))
slices1, slices2 = self._process_slices(slices1,slices2)
[p.psi2(Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[s2,s2]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
[p.psi2(Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[s1,s2,s2]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
return target
def dpsi2_dtheta(self,partial,Z,mu,S,slices1=None,slices2=None):
"""Returns shape (N,M,M,Ntheta)"""
slices1, slices2 = self._process_slices(slices1,slices2)
target = np.zeros(self.Nparam)
[p.dpsi2_dtheta(partial[s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[ps]) for p,i_s,s1,s2,ps in zip(self.parts,self.input_slices,slices1,slices2,self.param_slices)]
[p.dpsi2_dtheta(partial[s1,s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[ps]) for p,i_s,s1,s2,ps in zip(self.parts,self.input_slices,slices1,slices2,self.param_slices)]
return target
def dpsi2_dZ(self,partial,Z,mu,S,slices1=None,slices2=None):
slices1, slices2 = self._process_slices(slices1,slices2)
target = np.zeros_like(Z)
[p.dpsi2_dZ(partial[s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[s2,i_s]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
[p.dpsi2_dZ(partial[s1,s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[s2,i_s]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
return target
def dpsi2_dmuS(self,partial,Z,mu,S,slices1=None,slices2=None):
"""return shapes are N,M,M,Q"""
slices1, slices2 = self._process_slices(slices1,slices2)
target_mu, target_S = np.zeros((2,mu.shape[0],mu.shape[1]))
[p.dpsi2_dmuS(partial[s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target_mu[s1,i_s],target_S[s1,i_s]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
[p.dpsi2_dmuS(partial[s1,s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target_mu[s1,i_s],target_S[s1,i_s]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
#TODO: there are some extra terms to compute here!
return target_mu, target_S

16
GPy/kern/rbf_ARD.py
View file

@ -106,31 +106,31 @@ class rbf_ARD(kernpart):
def psi2(self,Z,mu,S,target):
self._psi_computations(Z,mu,S)
target += self._psi2.sum(0) #TODO: psi2 should be NxMxM (for het. noise)
target += self._psi2
def dpsi2_dtheta(self,partial,Z,mu,S,target):
"""Shape N,M,M,Ntheta"""
self._psi_computations(Z,mu,S)
d_var = np.sum(2.*self._psi2/self.variance,0)
d_var = 2.*self._psi2/self.variance
d_length = self._psi2[:,:,:,None]*(0.5*self._psi2_Zdist_sq*self._psi2_denom + 2.*self._psi2_mudist_sq + 2.*S[:,None,None,:]/self.lengthscales2)/(self.lengthscales*self._psi2_denom)
d_length = d_length.sum(0)
# d_length = d_length.sum(0)
target[0] += np.sum(partial*d_var)
target[1:] += (d_length*partial[:,:,None]).sum(0).sum(0)
target[1:] += (d_length*partial[:,:,:,None]).sum(0).sum(0).sum(0)
def dpsi2_dZ(self,partial,Z,mu,S,target):
"""Returns shape N,M,M,Q"""
self._psi_computations(Z,mu,S)
term1 = 0.5*self._psi2_Zdist/self.lengthscales2 # M, M, Q
term2 = self._psi2_mudist/self._psi2_denom/self.lengthscales2 # N, M, M, Q
dZ = self._psi2[:,:,:,None] * (term1[None] + term2)
target += (partial[None,:,:,None]*dZ).sum(0).sum(0)
dZ = self._psi2[:,:,:,None] * (term1[None] + term2)
target += (partial[:,:,:,None]*dZ).sum(0).sum(0)
def dpsi2_dmuS(self,partial,Z,mu,S,target_mu,target_S):
"""Think N,M,M,Q """
self._psi_computations(Z,mu,S)
tmp = self._psi2[:,:,:,None]/self.lengthscales2/self._psi2_denom
target_mu += (partial[None,:,:,None]*-tmp*2.*self._psi2_mudist).sum(1).sum(1)
target_S += (partial[None,:,:,None]*tmp*(2.*self._psi2_mudist_sq-1)).sum(1).sum(1)
target_mu += (partial[:,:,:,None]*-tmp*2.*self._psi2_mudist).sum(1).sum(1)
target_S += (partial[:,:,:,None]*tmp*(2.*self._psi2_mudist_sq-1)).sum(1).sum(1)
def _K_computations(self,X,X2):
if not (np.all(X==self._X) and np.all(X2==self._X2)):