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https://github.com/SheffieldML/GPy.git
synced 2026-05-06 02:24:17 +02:00
removed keyname partial
This commit is contained in:
Alan Saul
parent
f881e65761
commit
12d6f5056b
17 changed files with 235 additions and 235 deletions
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@ -271,10 +271,10 @@ class kern(parameterised):
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[p.K(X[s1,i_s],X2[s2,i_s],target=target[s1,s2]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
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return target
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def dK_dtheta(self,partial,X,X2=None,slices1=None,slices2=None):
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def dK_dtheta(self,dL_dK,X,X2=None,slices1=None,slices2=None):
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"""
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:param partial: An array of partial derivaties, dL_dK
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:type partial: Np.ndarray (N x M)
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:param dL_dK: An array of dL_dK derivaties, dL_dK
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:type dL_dK: Np.ndarray (N x M)
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:param X: Observed data inputs
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:type X: np.ndarray (N x D)
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:param X2: Observed dara inputs (optional, defaults to X)
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@ -288,16 +288,16 @@ class kern(parameterised):
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if X2 is None:
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X2 = X
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target = np.zeros(self.Nparam)
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[p.dK_dtheta(partial[s1,s2],X[s1,i_s],X2[s2,i_s],target[ps]) for p,i_s,ps,s1,s2 in zip(self.parts, self.input_slices, self.param_slices, slices1, slices2)]
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[p.dK_dtheta(dL_dK[s1,s2],X[s1,i_s],X2[s2,i_s],target[ps]) for p,i_s,ps,s1,s2 in zip(self.parts, self.input_slices, self.param_slices, slices1, slices2)]
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return self._transform_gradients(target)
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def dK_dX(self,partial,X,X2=None,slices1=None,slices2=None):
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def dK_dX(self,dL_dK,X,X2=None,slices1=None,slices2=None):
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if X2 is None:
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X2 = X
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slices1, slices2 = self._process_slices(slices1,slices2)
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target = np.zeros_like(X)
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[p.dK_dX(partial[s1,s2],X[s1,i_s],X2[s2,i_s],target[s1,i_s]) for p, i_s, s1, s2 in zip(self.parts, self.input_slices, slices1, slices2)]
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[p.dK_dX(dL_dK[s1,s2],X[s1,i_s],X2[s2,i_s],target[s1,i_s]) for p, i_s, s1, s2 in zip(self.parts, self.input_slices, slices1, slices2)]
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return target
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def Kdiag(self,X,slices=None):
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@ -307,20 +307,20 @@ class kern(parameterised):
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[p.Kdiag(X[s,i_s],target=target[s]) for p,i_s,s in zip(self.parts,self.input_slices,slices)]
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return target
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def dKdiag_dtheta(self,partial,X,slices=None):
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def dKdiag_dtheta(self,dL_dKdiag,X,slices=None):
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assert X.shape[1]==self.D
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assert len(partial.shape)==1
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assert partial.size==X.shape[0]
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assert len(dL_dKdiag.shape)==1
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assert dL_dKdiag.size==X.shape[0]
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slices = self._process_slices(slices,False)
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target = np.zeros(self.Nparam)
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[p.dKdiag_dtheta(partial[s],X[s,i_s],target[ps]) for p,i_s,s,ps in zip(self.parts,self.input_slices,slices,self.param_slices)]
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[p.dKdiag_dtheta(dL_dKdiag[s],X[s,i_s],target[ps]) for p,i_s,s,ps in zip(self.parts,self.input_slices,slices,self.param_slices)]
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return self._transform_gradients(target)
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def dKdiag_dX(self, partial, X, slices=None):
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def dKdiag_dX(self, dL_dKdiag, X, slices=None):
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assert X.shape[1]==self.D
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slices = self._process_slices(slices,False)
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target = np.zeros_like(X)
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[p.dKdiag_dX(partial[s],X[s,i_s],target[s,i_s]) for p,i_s,s in zip(self.parts,self.input_slices,slices)]
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[p.dKdiag_dX(dL_dKdiag[s],X[s,i_s],target[s,i_s]) for p,i_s,s in zip(self.parts,self.input_slices,slices)]
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return target
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def psi0(self,Z,mu,S,slices=None):
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@ -329,16 +329,16 @@ class kern(parameterised):
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[p.psi0(Z,mu[s],S[s],target[s]) for p,s in zip(self.parts,slices)]
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return target
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def dpsi0_dtheta(self,partial,Z,mu,S,slices=None):
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def dpsi0_dtheta(self,dL_dpsi0,Z,mu,S,slices=None):
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slices = self._process_slices(slices,False)
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target = np.zeros(self.Nparam)
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[p.dpsi0_dtheta(partial[s],Z,mu[s],S[s],target[ps]) for p,ps,s in zip(self.parts, self.param_slices,slices)]
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[p.dpsi0_dtheta(dL_dpsi0[s],Z,mu[s],S[s],target[ps]) for p,ps,s in zip(self.parts, self.param_slices,slices)]
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return self._transform_gradients(target)
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def dpsi0_dmuS(self,partial,Z,mu,S,slices=None):
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def dpsi0_dmuS(self,dL_dpsi0,Z,mu,S,slices=None):
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slices = self._process_slices(slices,False)
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target_mu,target_S = np.zeros_like(mu),np.zeros_like(S)
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[p.dpsi0_dmuS(partial,Z,mu[s],S[s],target_mu[s],target_S[s]) for p,s in zip(self.parts,slices)]
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[p.dpsi0_dmuS(dL_dpsi0,Z,mu[s],S[s],target_mu[s],target_S[s]) for p,s in zip(self.parts,slices)]
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return target_mu,target_S
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def psi1(self,Z,mu,S,slices1=None,slices2=None):
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@ -348,25 +348,25 @@ class kern(parameterised):
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[p.psi1(Z[s2],mu[s1],S[s1],target[s1,s2]) for p,s1,s2 in zip(self.parts,slices1,slices2)]
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return target
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def dpsi1_dtheta(self,partial,Z,mu,S,slices1=None,slices2=None):
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def dpsi1_dtheta(self,dL_dpsi1,Z,mu,S,slices1=None,slices2=None):
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"""N,M,(Ntheta)"""
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slices1, slices2 = self._process_slices(slices1,slices2)
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target = np.zeros((self.Nparam))
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[p.dpsi1_dtheta(partial[s2,s1],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[ps]) for p,ps,s1,s2,i_s in zip(self.parts, self.param_slices,slices1,slices2,self.input_slices)]
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[p.dpsi1_dtheta(dL_dpsi1[s2,s1],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[ps]) for p,ps,s1,s2,i_s in zip(self.parts, self.param_slices,slices1,slices2,self.input_slices)]
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return self._transform_gradients(target)
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def dpsi1_dZ(self,partial,Z,mu,S,slices1=None,slices2=None):
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def dpsi1_dZ(self,dL_dpsi1,Z,mu,S,slices1=None,slices2=None):
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"""N,M,Q"""
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slices1, slices2 = self._process_slices(slices1,slices2)
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target = np.zeros_like(Z)
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[p.dpsi1_dZ(partial[s2,s1],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)]
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[p.dpsi1_dZ(dL_dpsi1[s2,s1],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)]
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return target
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def dpsi1_dmuS(self,partial,Z,mu,S,slices1=None,slices2=None):
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def dpsi1_dmuS(self,dL_dpsi1,Z,mu,S,slices1=None,slices2=None):
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"""return shapes are N,M,Q"""
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slices1, slices2 = self._process_slices(slices1,slices2)
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target_mu, target_S = np.zeros((2,mu.shape[0],mu.shape[1]))
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[p.dpsi1_dmuS(partial[s2,s1],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)]
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[p.dpsi1_dmuS(dL_dpsi1[s2,s1],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)]
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return target_mu, target_S
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def psi2(self,Z,mu,S,slices1=None,slices2=None):
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@ -416,11 +416,11 @@ class kern(parameterised):
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return target + crossterms
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def dpsi2_dtheta(self,partial,partial1,Z,mu,S,slices1=None,slices2=None):
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def dpsi2_dtheta(self,dL_dpsi2,partial1,Z,mu,S,slices1=None,slices2=None):
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"""Returns shape (N,M,M,Ntheta)"""
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slices1, slices2 = self._process_slices(slices1,slices2)
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target = np.zeros(self.Nparam)
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[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)]
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[p.dpsi2_dtheta(dL_dpsi2[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)]
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#compute the "cross" terms
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#TODO: better looping
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@ -434,11 +434,11 @@ class kern(parameterised):
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pass
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#rbf X bias
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elif p1.name=='bias' and p2.name=='rbf':
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p2.dpsi1_dtheta(partial.sum(1)*p1.variance,Z,mu,S,target[ps2])
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p1.dpsi1_dtheta(partial.sum(1)*p2._psi1,Z,mu,S,target[ps1])
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p2.dpsi1_dtheta(dL_dpsi2.sum(1)*p1.variance,Z,mu,S,target[ps2])
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p1.dpsi1_dtheta(dL_dpsi2.sum(1)*p2._psi1,Z,mu,S,target[ps1])
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elif p2.name=='bias' and p1.name=='rbf':
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p1.dpsi1_dtheta(partial.sum(1)*p2.variance,Z,mu,S,target[ps1])
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p2.dpsi1_dtheta(partial.sum(1)*p1._psi1,Z,mu,S,target[ps2])
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p1.dpsi1_dtheta(dL_dpsi2.sum(1)*p2.variance,Z,mu,S,target[ps1])
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p2.dpsi1_dtheta(dL_dpsi2.sum(1)*p1._psi1,Z,mu,S,target[ps2])
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#rbf X linear
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elif p1.name=='linear' and p2.name=='rbf':
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raise NotImplementedError #TODO
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@ -469,10 +469,10 @@ class kern(parameterised):
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# target += (partial.sum(0)[:,:,None] * (tmp[:, None] + tmp[:,:,None]).sum(0)).sum(0).sum(0)
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return self._transform_gradients(target)
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def dpsi2_dZ(self,partial,Z,mu,S,slices1=None,slices2=None):
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def dpsi2_dZ(self,dL_dpsi2,Z,mu,S,slices1=None,slices2=None):
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slices1, slices2 = self._process_slices(slices1,slices2)
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target = np.zeros_like(Z)
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[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)]
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[p.dpsi2_dZ(dL_dpsi2[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)]
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#compute the "cross" terms
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#TODO: slices (need to iterate around the input slices also...)
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@ -482,9 +482,9 @@ class kern(parameterised):
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pass
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#rbf X bias
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elif p1.name=='bias' and p2.name=='rbf':
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target += p2.dpsi1_dX(partial.sum(1)*p1.variance,Z,mu,S)
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target += p2.dpsi1_dX(dL_dpsi2.sum(1)*p1.variance,Z,mu,S)
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elif p2.name=='bias' and p1.name=='rbf':
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target += p1.dpsi1_dZ(partial.sum(2)*p2.variance,Z,mu,S)
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target += p1.dpsi1_dZ(dL_dpsi2.sum(2)*p2.variance,Z,mu,S)
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#rbf X linear
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elif p1.name=='linear' and p2.name=='rbf':
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raise NotImplementedError #TODO
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@ -496,11 +496,11 @@ class kern(parameterised):
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return target
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def dpsi2_dmuS(self,partial,Z,mu,S,slices1=None,slices2=None):
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def dpsi2_dmuS(self,dL_dpsi2,Z,mu,S,slices1=None,slices2=None):
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"""return shapes are N,M,M,Q"""
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slices1, slices2 = self._process_slices(slices1,slices2)
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target_mu, target_S = np.zeros((2,mu.shape[0],mu.shape[1]))
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[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)]
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[p.dpsi2_dmuS(dL_dpsi2[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)]
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#TODO: there are some extra terms to compute here!
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return target_mu, target_S
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