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Added automatic computation of dKdiag_dtheta in kernpart.py using dK_dtheta.

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This commit is contained in:
Neil Lawrence 2013-08-17 07:13:51 +01:00
parent bba3dc5458
commit a570198029
3 changed files with 33 additions and 19 deletions
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6
GPy/kern/parts/kernpart.py
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@ -29,7 +29,11 @@ class Kernpart(object):
def dK_dtheta(self,dL_dK,X,X2,target):
raise NotImplementedError
def dKdiag_dtheta(self,dL_dKdiag,X,target):
raise NotImplementedError
# In the base case compute this by calling dK_dtheta. Need to
# override for stationary covariances (for example) to save
# time.
for i in range(X.shape[0]):
self.dK_dtheta(dL_dKdiag[i], X[i, :][None, :], X2=None, target=target)
def psi0(self,Z,mu,S,target):
raise NotImplementedError
def dpsi0_dtheta(self,dL_dpsi0,Z,mu,S,target):

27
GPy/kern/parts/mlp.py
View file

@ -96,34 +96,39 @@ class MLP(Kernpart):
vec2 = (X2*X2).sum(1)
target[1] += ((self._K_inner_prod/self._K_denom
-.5*self._K_numer/denom3
*(np.outer((self.weight_variance*vec1+self.bias_variance+1.), vec2) + np.outer(vec1, self.weight_variance*vec2 + self.bias_variance_1.)))*base_cov_grad).sum()
*(np.outer((self.weight_variance*vec1+self.bias_variance+1.), vec2) + np.outer(vec1, self.weight_variance*vec2 + self.bias_variance+1.)))*base_cov_grad).sum()
target[2] += ((1./self._K_denom
-.5*self._K_numer/denom3
*((vec1[None, :]+vec2[:, None])*self.weight_variance
*((vec1[:, None]+vec2[None, :])*self.weight_variance
+ 2*self.bias_variance + 2.))*base_cov_grad).sum()
target[0] += np.sum(self._K_dvar*dL_dK)
def dK_dX(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X"""
self._K_computations(X, X2)
gX = np.zeros((X2.shape[0], X.shape[1], X.shape[0]))
for i in range(X.shape[0]):
gX[:, :, i] = self._dK_dX_point(X[i, :], X2)
def _dK_dX_point(self, x, X2):
gX[:, :, i] = self._dK_dX_point(X, X2, i)
def _dK_dX_point(self, X, X2, i):
"""Gradient with respect to one point of X"""
inner_prod = np.dot(X2,x.T)
numer = inner_prod*self.weight_variance + self.bias_variance
vec1 = (x*x).sum(1)*self.weight_variance + self.bias_variance + 1.
inner_prod = self._K_inner_prod[i, :].T
numer = self._K_numer[i, :].T
denom = self._K_denom[i, :].T
arg = self._K_asin_arg[i, :].T
vec1 = (X[i, :]*X[i, :]).sum()*self.weight_variance + self.bias_variance + 1.
vec2 = (X2*X2).sum(1)*self.weight_variance + self.bias_variance + 1.
denom = np.sqrt(np.outer(vec2,vec1))
arg = numer/denom
#denom = np.sqrt(np.outer(vec2,vec1))
#arg = numer/denom
gX = np.zeros(X2.shape)
denom3 = denom*denom*denom
for j in range(X2.shape[1]):
gX[:, j]=X2[:, j]/denom - vec2*x[:, j]*numer/denom3
gX[:, j]=X2[:, j]/denom - vec2*X[i, j]*numer/denom3
gX[:, j] = four_over_tau*self.weight_variance*self.variance*gX[:, j]/np.sqrt(1-arg*arg)