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numerical stable implementation of rational qudratic

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Zhenwen Dai 2016-03-17 14:05:49 +00:00
parent 67ba9b60c6
commit 1b85b45a7e
1 changed files with 9 additions and 6 deletions
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15
GPy/kern/src/stationary.py
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@ -489,18 +489,21 @@ class RatQuad(Stationary):
self.link_parameters(self.power) self.link_parameters(self.power)
def K_of_r(self, r): def K_of_r(self, r):
r2 = np.power(r, 2.) r2 = np.square(r)
return self.variance*np.power(1. + r2/2., -self.power) # return self.variance*np.power(1. + r2/2., -self.power)
return self.variance*np.exp(-self.power*np.log1p(r2/2.))
def dK_dr(self, r): def dK_dr(self, r):
r2 = np.power(r, 2.) r2 = np.square(r)
return -self.variance*self.power*r*np.power(1. + r2/2., - self.power - 1.) # return -self.variance*self.power*r*np.power(1. + r2/2., - self.power - 1.)
return-self.variance*self.power*r*np.exp(-(self.power+1)*np.log1p(r2/2.))
def update_gradients_full(self, dL_dK, X, X2=None): def update_gradients_full(self, dL_dK, X, X2=None):
super(RatQuad, self).update_gradients_full(dL_dK, X, X2) super(RatQuad, self).update_gradients_full(dL_dK, X, X2)
r = self._scaled_dist(X, X2) r = self._scaled_dist(X, X2)
r2 = np.power(r, 2.) r2 = np.square(r)
dK_dpow = -self.variance * np.power(2., self.power) * np.power(r2 + 2., -self.power) * np.log(0.5*(r2+2.)) # dK_dpow = -self.variance * np.power(2., self.power) * np.power(r2 + 2., -self.power) * np.log(0.5*(r2+2.))
dK_dpow = -self.variance * np.exp(self.power*(np.log(2.)-np.log1p(r2+1)))*np.log1p(r2/2.)
grad = np.sum(dL_dK*dK_dpow) grad = np.sum(dL_dK*dK_dpow)
self.power.gradient = grad self.power.gradient = grad