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rbf now works in a more memory friendly fashion

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This commit is contained in:
James Hensman 2013-03-27 15:03:46 +00:00
parent 3546a2a729
commit adfa6de1d8
2 changed files with 16 additions and 16 deletions
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2
GPy/examples/regression.py
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@ -66,7 +66,7 @@ def silhouette():
# optimize
m.ensure_default_constraints()
m.optimize()
m.optimize(messages=True)
print(m)
return m

30
GPy/kern/rbf.py
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@ -85,12 +85,10 @@ class rbf(kernpart):
def dK_dtheta(self,dL_dK,X,X2,target):
self._K_computations(X,X2)
target[0] += np.sum(self._K_dvar*dL_dK)
if self.ARD == True:
dl = self._K_dvar[:,:,None]*self.variance*self._K_dist2/self.lengthscale
target[1:] += (dl*dL_dK[:,:,None]).sum(0).sum(0)
if self.ARD:
[np.add(target[1+q:2+q],self.variance/self.lengthscale[q]**3*np.sum(self._K_dvar*dL_dK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.D)]
else:
target[1] += np.sum(self._K_dvar*self.variance*(self._K_dist2.sum(-1))/self.lengthscale*dL_dK)
#np.sum(self._K_dvar*self.variance*self._K_dist2/self.lengthscale*dL_dK)
target[1] += np.sum(self._K_dvar*self.variance*self._K_dist2/self.lengthscale*dL_dK)
def dKdiag_dtheta(self,dL_dKdiag,X,target):
#NB: derivative of diagonal elements wrt lengthscale is 0
@ -98,7 +96,7 @@ class rbf(kernpart):
def dK_dX(self,dL_dK,X,X2,target):
self._K_computations(X,X2)
_K_dist = X[:,None,:]-X2[None,:,:]
_K_dist = X[:,None,:]-X2[None,:,:] #don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = np.transpose(-self.variance*self._K_dvar[:,:,np.newaxis]*_K_dist/self.lengthscale2,(1,0,2))
target += np.sum(dK_dX*dL_dK.T[:,:,None],0)
@ -183,16 +181,18 @@ class rbf(kernpart):
#---------------------------------------#
def _K_computations(self,X,X2):
if not (np.all(X==self._X) and np.all(X2==self._X2)):
self._X = X
self._X2 = X2
if not (np.all(X==self._X) and np.all(X2==self._X2) and np.all(self._params == self._get_params())):
self._X = X.copy()
self._X2 = X2.copy()
self._params == self._get_params().copy()
if X2 is None: X2 = X
self._K_dist = X[:,None,:]-X2[None,:,:] # this can be computationally heavy
self._params = np.empty(shape=(1,0)) #ensure the next section gets called
if not np.all(self._params == self._get_params()):
self._params == self._get_params()
self._K_dist2 = np.square(self._K_dist/self.lengthscale)
self._K_dvar = np.exp(-0.5*self._K_dist2.sum(-1))
#never do this: self._K_dist = X[:,None,:]-X2[None,:,:] # this can be computationally heavy
#_K_dist = X[:,None,:]-X2[None,:,:]
#_K_dist2 = np.square(_K_dist/self.lengthscale)
X = X/self.lengthscale
X2 = X2/self.lengthscale
self._K_dist2 = (-2.*np.dot(X, X2.T) + np.sum(np.square(X),1)[:,None] + np.sum(np.square(X2),1)[None,:])
self._K_dvar = np.exp(-0.5*self._K_dist2)
def _psi_computations(self,Z,mu,S):
#here are the "statistics" for psi1 and psi2