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[basisfunckern] gradients for non ard adjusted

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mzwiessele 2015-04-21 13:33:09 +02:00
parent f50ad8b378
commit 9b54f831b3
1 changed files with 5 additions and 7 deletions
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12
GPy/kern/_src/basis_funcs.py
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@ -26,7 +26,7 @@ class BasisFuncKernel(Kern):
def parameters_changed(self): def parameters_changed(self):
self.alpha = np.sqrt(self.variance) self.alpha = np.sqrt(self.variance)
#self.beta = 1./self.variance self.beta = 1./self.variance
@Cache_this(limit=3, ignore_args=()) @Cache_this(limit=3, ignore_args=())
def phi(self, X): def phi(self, X):
@ -50,14 +50,14 @@ class BasisFuncKernel(Kern):
phi2 = self.phi(X2) phi2 = self.phi(X2)
self.variance.gradient = np.einsum('ij,iq,jq->q', dL_dK, phi1, phi2) self.variance.gradient = np.einsum('ij,iq,jq->q', dL_dK, phi1, phi2)
else: else:
self.variance.gradient = np.einsum('ij,ij', dL_dK, self._K(X, X2)) self.variance.gradient = np.einsum('ij,ij', dL_dK, self._K(X, X2)) * self.beta
def update_gradients_diag(self, dL_dKdiag, X): def update_gradients_diag(self, dL_dKdiag, X):
if self.ARD: if self.ARD:
phi1 = self.phi(X) phi1 = self.phi(X)
self.variance.gradient = np.einsum('i,iq,iq->q', dL_dKdiag, phi1, phi1) self.variance.gradient = np.einsum('i,iq,iq->q', dL_dKdiag, phi1, phi1)
else: else:
self.variance.gradient = np.einsum('i,i', dL_dKdiag, self.Kdiag(X)) self.variance.gradient = np.einsum('i,i', dL_dKdiag, self.Kdiag(X)) * self.beta
def concatenate_offset(self, X): def concatenate_offset(self, X):
return np.c_[np.ones((X.shape[0], 1)), X] return np.c_[np.ones((X.shape[0], 1)), X]
@ -118,7 +118,6 @@ class LinearSlopeBasisFuncKernel(BasisFuncKernel):
phi = np.where(X < self.start, self.start, X) phi = np.where(X < self.start, self.start, X)
phi = np.where(phi > self.stop, self.stop, phi) phi = np.where(phi > self.stop, self.stop, phi)
return ((phi-(self.stop+self.start)/2.))#/(.5*(self.stop-self.start)))-1. return ((phi-(self.stop+self.start)/2.))#/(.5*(self.stop-self.start)))-1.
return self.concatenate_offset(phi) # ((phi-self.start)/(self.stop-self.start))-.5
class ChangePointBasisFuncKernel(BasisFuncKernel): class ChangePointBasisFuncKernel(BasisFuncKernel):
def __init__(self, input_dim, changepoint, variance=1., active_dims=None, ARD=False, name='changepoint'): def __init__(self, input_dim, changepoint, variance=1., active_dims=None, ARD=False, name='changepoint'):
@ -127,7 +126,7 @@ class ChangePointBasisFuncKernel(BasisFuncKernel):
@Cache_this(limit=3, ignore_args=()) @Cache_this(limit=3, ignore_args=())
def _phi(self, X): def _phi(self, X):
return self.concatenate_offset(np.where((X < self.changepoint), -1, 1)) return np.where((X < self.changepoint), -1, 1)
class DomainKernel(LinearSlopeBasisFuncKernel): class DomainKernel(LinearSlopeBasisFuncKernel):
def __init__(self, input_dim, start, stop, variance=1., active_dims=None, ARD=False, name='constant_domain'): def __init__(self, input_dim, start, stop, variance=1., active_dims=None, ARD=False, name='constant_domain'):
@ -135,6 +134,5 @@ class DomainKernel(LinearSlopeBasisFuncKernel):
@Cache_this(limit=3, ignore_args=()) @Cache_this(limit=3, ignore_args=())
def _phi(self, X): def _phi(self, X):
phi = np.where((X>self.start)*(X<self.stop), 1., 0.) phi = np.where((X>self.start)*(X<self.stop), 1, 0)
return phi#((phi-self.start)/(self.stop-self.start))-.5 return phi#((phi-self.start)/(self.stop-self.start))-.5
return self.concatenate_offset(phi) # ((phi-self.start)/(self.stop-self.start))-.5