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linear without caching, derivatives done

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Max Zwiessele 2014-02-21 09:14:31 +00:00
parent 1d722c4f28
commit 0c92fca31a
7 changed files with 71 additions and 54 deletions
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56
GPy/kern/_src/linear.py
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@ -119,34 +119,55 @@ class Linear(Kern):
def gradients_X_diag(self, dL_dKdiag, X):
return 2.*self.variances*dL_dKdiag[:,None]*X
#---------------------------------------#
# PSI statistics #
# variational #
#---------------------------------------#
def psi0(self, Z, mu, S, target):
self._psi_computations(Z, mu, S)
target += np.sum(self.variances * self.mu2_S, 1)
def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
# Kmm
grad = self.gradients_X(dL_dKmm, Z, None)
#psi1
grad += self.gradients_X(dL_dpsi1.T, Z, mu)
#psi2
self._weave_dpsi2_dZ(dL_dpsi2, Z, mu, S, grad)
return grad
def gradients_muS_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
target_mu, target_S = np.zeros(mu.shape), np.zeros(mu.shape)
# psi0
target_mu += dL_dpsi0[:, None] * (2.0 * mu * self.variances)
target_S += dL_dpsi0[:, None] * self.variances
# psi1
target_mu += (dL_dpsi1[:, :, None] * (Z * self.variances)).sum(1)
# psi2
self._weave_dpsi2_dmuS(dL_dpsi2, Z, mu, S, target_mu, target_S)
return target_mu, target_S
def psi0(self, Z, mu, S):
self._psi_computations(Z, mu, S)
return np.sum(self.variances * self.mu2_S, 1)
def psi1(self, Z, mu, S):
"""the variance, it does nothing"""
self._psi1 = self.K(mu, Z)
return self._psi1
def psi2(self, Z, mu, S):
self._psi_computations(Z, mu, S)
return self._psi2
def dpsi0_dmuS(self, dL_dpsi0, Z, mu, S, target_mu, target_S):
target_mu += dL_dpsi0[:, None] * (2.0 * mu * self.variances)
target_S += dL_dpsi0[:, None] * self.variances
def psi1(self, Z, mu, S, target):
"""the variance, it does nothing"""
self._psi1 = self.K(mu, Z, target)
def dpsi1_dmuS(self, dL_dpsi1, Z, mu, S, target_mu, target_S):
"""Do nothing for S, it does not affect psi1"""
self._psi_computations(Z, mu, S)
target_mu += (dL_dpsi1[:, :, None] * (Z * self.variances)).sum(1)
def dpsi1_dZ(self, dL_dpsi1, Z, mu, S, target):
self.gradients_X(dL_dpsi1.T, Z, mu, target)
def psi2(self, Z, mu, S, target):
self._psi_computations(Z, mu, S)
target += self._psi2
def psi2_new(self,Z,mu,S,target):
tmp = np.zeros((mu.shape[0], Z.shape[0]))
@ -172,7 +193,7 @@ class Linear(Kern):
Zs_sq = Zs[:,None,:]*Zs[None,:,:]
target_S += (dL_dpsi2[:,:,:,None]*Zs_sq[None,:,:,:]).sum(1).sum(1)
def dpsi2_dmuS(self, dL_dpsi2, Z, mu, S, target_mu, target_S):
def _weave_dpsi2_dmuS(self, dL_dpsi2, Z, mu, S, target_mu, target_S):
"""Think N,num_inducing,num_inducing,input_dim """
self._psi_computations(Z, mu, S)
AZZA = self.ZA.T[:, None, :, None] * self.ZA[None, :, None, :]
@ -226,7 +247,7 @@ class Linear(Kern):
type_converters=weave.converters.blitz,**weave_options)
def dpsi2_dZ(self, dL_dpsi2, Z, mu, S, target):
def _weave_dpsi2_dZ(self, dL_dpsi2, Z, mu, S, target):
self._psi_computations(Z, mu, S)
#psi2_dZ = dL_dpsi2[:, :, :, None] * self.variances * self.ZAinner[:, :, None, :]
#dummy_target = np.zeros_like(target)
@ -261,9 +282,6 @@ class Linear(Kern):
type_converters=weave.converters.blitz,**weave_options)
#---------------------------------------#
# Precomputations #
#---------------------------------------#