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fixed mlp kern

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Max Zwiessele 2014-03-13 12:30:07 +00:00
parent e471ec7a15
commit f77233acf9
1 changed files with 2 additions and 2 deletions
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4
GPy/kern/_src/mlp.py
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@ -96,12 +96,12 @@ class MLP(Kern):
vec = (X*X).sum(1)*self.weight_variance+self.bias_variance + 1. vec = (X*X).sum(1)*self.weight_variance+self.bias_variance + 1.
return 2*four_over_tau*self.weight_variance*self.variance*((X[None, :, :]/denom[:, :, None] - vec[None, :, None]*X[:, None, :]*(numer/denom3)[:, :, None])*(dL_dK/np.sqrt(1-arg*arg))[:, :, None]).sum(1) return 2*four_over_tau*self.weight_variance*self.variance*((X[None, :, :]/denom[:, :, None] - vec[None, :, None]*X[:, None, :]*(numer/denom3)[:, :, None])*(dL_dK/np.sqrt(1-arg*arg))[:, :, None]).sum(1)
def dKdiag_dX(self, dL_dKdiag, X, target): def gradients_X_diag(self, dL_dKdiag, X):
"""Gradient of diagonal of covariance with respect to X""" """Gradient of diagonal of covariance with respect to X"""
self._K_diag_computations(X) self._K_diag_computations(X)
arg = self._K_diag_asin_arg arg = self._K_diag_asin_arg
denom = self._K_diag_denom denom = self._K_diag_denom
numer = self._K_diag_numer #numer = self._K_diag_numer
return four_over_tau*2.*self.weight_variance*self.variance*X*(1./denom*(1. - arg)*dL_dKdiag/(np.sqrt(1-arg*arg)))[:, None] return four_over_tau*2.*self.weight_variance*self.variance*X*(1./denom*(1. - arg)*dL_dKdiag/(np.sqrt(1-arg*arg)))[:, None]