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small changes to rbf and rbf_inv

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James Hensman 2013-07-18 15:36:04 +01:00
parent 14b8fd0c7d
commit 8131a065cb
3 changed files with 6 additions and 8 deletions
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4
GPy/core/gp.py
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@ -127,7 +127,7 @@ class GP(GPBase):
debug_this # @UndefinedVariable debug_this # @UndefinedVariable
return mu, var return mu, var
def predict(self, Xnew, which_parts='all', full_cov=False): def predict(self, Xnew, which_parts='all', full_cov=False, likelihood_args=dict()):
""" """
Predict the function(s) at the new point(s) Xnew. Predict the function(s) at the new point(s) Xnew.
Arguments Arguments
@ -152,6 +152,6 @@ class GP(GPBase):
mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts) mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts)
# now push through likelihood # now push through likelihood
mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov) mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, **likelihood_args)
return mean, var, _025pm, _975pm return mean, var, _025pm, _975pm

3
GPy/kern/parts/rbf.py
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@ -166,9 +166,8 @@ class RBF(Kernpart):
def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S, target): def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S, target):
self._psi_computations(Z, mu, S) self._psi_computations(Z, mu, S)
denom_deriv = S[:, None, :] / (self.lengthscale ** 3 + self.lengthscale * S[:, None, :])
d_length = self._psi1[:, :, None] * (self.lengthscale * np.square(self._psi1_dist / (self.lengthscale2 + S[:, None, :])) + denom_deriv)
target[0] += np.sum(dL_dpsi1 * self._psi1 / self.variance) target[0] += np.sum(dL_dpsi1 * self._psi1 / self.variance)
d_length = self._psi1[:,:,None] * ((self._psi1_dist_sq - 1.)/(self.lengthscale*self._psi1_denom) +1./self.lengthscale)
dpsi1_dlength = d_length * dL_dpsi1[:, :, None] dpsi1_dlength = d_length * dL_dpsi1[:, :, None]
if not self.ARD: if not self.ARD:
target[1] += dpsi1_dlength.sum() target[1] += dpsi1_dlength.sum()

7
GPy/kern/parts/rbf_inv.py
View file

@ -159,12 +159,11 @@ class RBFInv(RBF):
def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S, target): def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S, target):
self._psi_computations(Z, mu, S) self._psi_computations(Z, mu, S)
##d_length = self._psi1[:, :, None] * (-0.5 * ((np.square((self._psi1_dist)/(self.inv_lengthscale * S[:,None,:] + 1))) + ((S[:, None, :])/(self.inv_lengthscale * S[:, None, :] + 1))))
tmp = 1 + S[:,None,:]*self.inv_lengthscale2 tmp = 1 + S[:,None,:]*self.inv_lengthscale2
#inv_len3 = np.power(self.inv_lengthscale,3) d_inv_length_old = -self._psi1[:,:,None] * ((self._psi1_dist_sq - 1.)/(self.lengthscale*self._psi1_denom) + self.inv_lengthscale)/self.inv_lengthscale2
d_length = -(self._psi1[:, :, None] * ((np.square(self._psi1_dist) * self.inv_lengthscale)/(tmp**2) + (S[:,None,:]*self.inv_lengthscale)/(tmp))) d_inv_length = -self._psi1[:,:,None] * ((self._psi1_dist_sq - 1.)/self._psi1_denom + self.lengthscale)
target[0] += np.sum(dL_dpsi1 * self._psi1 / self.variance) target[0] += np.sum(dL_dpsi1 * self._psi1 / self.variance)
dpsi1_dlength = d_length * dL_dpsi1[:, :, None] dpsi1_dlength = d_inv_length * dL_dpsi1[:, :, None]
if not self.ARD: if not self.ARD:
target[1] += dpsi1_dlength.sum()#*(-self.lengthscale2) target[1] += dpsi1_dlength.sum()#*(-self.lengthscale2)
else: else: