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reimplemented caching in prod_orthogonal...

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James Hensman 2013-04-28 22:32:37 +01:00
parent 7d9352c733
commit 6dced9ac39
1 changed files with 31 additions and 28 deletions
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59
GPy/kern/prod_orthogonal.py
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@ -21,44 +21,35 @@ class prod_orthogonal(kernpart):
self.name = k1.name + '<times>' + k2.name
self.k1 = k1
self.k2 = k2
self._X, self._X2, self._params = np.empty(shape=(3,1))
self._set_params(np.hstack((k1._get_params(),k2._get_params())))
def _get_params(self):
"""return the value of the parameters."""
return self.params
return np.hstack((self.k1._get_params(), self.k2._get_params()))
def _set_params(self,x):
"""set the value of the parameters."""
self.k1._set_params(x[:self.k1.Nparam])
self.k2._set_params(x[self.k1.Nparam:])
self.params = x
def _get_param_names(self):
"""return parameter names."""
return [self.k1.name + '_' + param_name for param_name in self.k1._get_param_names()] + [self.k2.name + '_' + param_name for param_name in self.k2._get_param_names()]
def K(self,X,X2,target):
"""Compute the covariance matrix between X and X2."""
target1 = np.zeros_like(target)
target2 = np.zeros_like(target)
if X2 is None:
self.k1.K(X[:,:self.k1.D],None,target1)
self.k2.K(X[:,self.k1.D:],None,target2)
else:
self.k1.K(X[:,:self.k1.D],X2[:,:self.k1.D],target1)
self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],target2)
target += target1 * target2
self._K_computations(X,X2)
target += self._K1 * self._K2
def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
K1 = np.zeros((X.shape[0],X2.shape[0]))
K2 = np.zeros((X.shape[0],X2.shape[0]))
self.k1.K(X[:,:self.k1.D],X2[:,:self.k1.D],K1)
self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],K2)
self.k1.dK_dtheta(dL_dK*K2, X[:,:self.k1.D], X2[:,:self.k1.D], target[:self.k1.Nparam])
self.k2.dK_dtheta(dL_dK*K1, X[:,self.k1.D:], X2[:,self.k1.D:], target[self.k1.Nparam:])
self._K_computations(X,X2)
if X2 is None:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,:self.k1.D], None, target[:self.k1.Nparam])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.k1.D:], None, target[self.k1.Nparam:])
else:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,:self.k1.D], X2[:,:self.k1.D], target[:self.k1.Nparam])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.k1.D:], X2[:,self.k1.D:], target[self.k1.Nparam:])
def Kdiag(self,X,target):
"""Compute the diagonal of the covariance matrix associated to X."""
@ -78,14 +69,9 @@ class prod_orthogonal(kernpart):
def dK_dX(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None: X2 = X
K1 = np.zeros((X.shape[0],X2.shape[0]))
K2 = np.zeros((X.shape[0],X2.shape[0]))
self.k1.K(X[:,0:self.k1.D],X2[:,0:self.k1.D],K1)
self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],K2)
self.k1.dK_dX(dL_dK*K2, X[:,:self.k1.D], X2[:,:self.k1.D], target)
self.k2.dK_dX(dL_dK*K1, X[:,self.k1.D:], X2[:,self.k1.D:], target)
self._K_computations(X,X2)
self.k1.dK_dX(dL_dK*self._K2, X[:,:self.k1.D], X2[:,:self.k1.D], target)
self.k2.dK_dX(dL_dK*self._K1, X[:,self.k1.D:], X2[:,self.k1.D:], target)
def dKdiag_dX(self, dL_dKdiag, X, target):
K1 = np.zeros(X.shape[0])
@ -96,3 +82,20 @@ class prod_orthogonal(kernpart):
self.k1.dK_dX(dL_dKdiag*K2, X[:,:self.k1.D], target)
self.k2.dK_dX(dL_dKdiag*K1, X[:,self.k1.D:], target)
def _K_computations(self,X,X2):
if not (np.array_equal(X,self._X) and np.array_equal(X2,self._X2) and np.array_equal(self._params , self._get_params())):
self._X = X.copy()
self._params == self._get_params().copy()
if X2 is None:
self._X2 = None
self._K1 = np.zeros((X.shape[0],X.shape[0]))
self._K2 = np.zeros((X.shape[0],X.shape[0]))
self.k1.K(X[:,:self.k1.D],None,self._K1)
self.k2.K(X[:,self.k1.D:],None,self._K2)
else:
self._X2 = X2.copy()
self._K1 = np.zeros((X.shape[0],X2.shape[0]))
self._K2 = np.zeros((X.shape[0],X2.shape[0]))
self.k1.K(X[:,:self.k1.D],X2[:,:self.k1.D],self._K1)
self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],self._K2)