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[linear] einsums

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mzwiessele 2014-08-19 10:41:53 -07:00
parent 22de3ab676
commit 3972b4bd9a
1 changed files with 9 additions and 6 deletions
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15
GPy/kern/_src/linear.py
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@ -51,7 +51,7 @@ class Linear(Kern):
self.variances = Param('variances', variances, Logexp())
self.add_parameter(self.variances)
self.psicomp = PSICOMP_Linear()
@Cache_this(limit=2)
def K(self, X, X2=None):
if self.ARD:
@ -76,10 +76,12 @@ class Linear(Kern):
def update_gradients_full(self, dL_dK, X, X2=None):
if self.ARD:
if X2 is None:
self.variances.gradient = np.array([np.sum(dL_dK * tdot(X[:, i:i + 1])) for i in range(self.input_dim)])
#self.variances.gradient = np.array([np.sum(dL_dK * tdot(X[:, i:i + 1])) for i in range(self.input_dim)])
self.variances.gradient = np.einsum('ij,iq,jq->q', dL_dK, X, X)
else:
product = X[:, None, :] * X2[None, :, :]
self.variances.gradient = (dL_dK[:, :, None] * product).sum(0).sum(0)
#product = X[:, None, :] * X2[None, :, :]
#self.variances.gradient = (dL_dK[:, :, None] * product).sum(0).sum(0)
self.variances.gradient = np.einsum('ij,iq,jq->q', dL_dK, X, X2)
else:
self.variances.gradient = np.sum(self._dot_product(X, X2) * dL_dK)
@ -93,9 +95,10 @@ class Linear(Kern):
def gradients_X(self, dL_dK, X, X2=None):
if X2 is None:
return np.einsum('mq,nm->nq',X*self.variances,dL_dK)+np.einsum('nq,nm->mq',X*self.variances,dL_dK)
return np.einsum('jq,q,ij->iq', X, 2*self.variances, dL_dK)
else:
return (((X2[None,:, :] * self.variances)) * dL_dK[:, :, None]).sum(1)
#return (((X2[None,:, :] * self.variances)) * dL_dK[:, :, None]).sum(1)
return np.einsum('jq,q,ij->iq', X2, self.variances, dL_dK)
def gradients_X_diag(self, dL_dKdiag, X):
return 2.*self.variances*dL_dKdiag[:,None]*X