From 1dfe7ed0a8f4f65cd30a88d78d092b9731a85d16 Mon Sep 17 00:00:00 2001 From: durrande Date: Sun, 8 Jun 2014 18:37:07 +0200 Subject: [PATCH] fixed unnecessary warnings when using periodic kernels --- GPy/kern/_src/periodic.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/GPy/kern/_src/periodic.py b/GPy/kern/_src/periodic.py index a8573a05..9f232ab0 100644 --- a/GPy/kern/_src/periodic.py +++ b/GPy/kern/_src/periodic.py @@ -101,6 +101,7 @@ class PeriodicExponential(Periodic): Flower = np.array(self._cos(self.basis_alpha,self.basis_omega,self.basis_phi)(self.lower))[:,None] return(self.lengthscale/(2*self.variance) * Gint + 1./self.variance*np.dot(Flower,Flower.T)) + @silence_errors def update_gradients_full(self, dL_dK, X, X2=None): """derivative of the covariance matrix with respect to the parameters (shape is N x num_inducing x num_params)""" if X2 is None: X2 = X @@ -213,7 +214,7 @@ class PeriodicMatern32(Periodic): return(self.lengthscale**3/(12*np.sqrt(3)*self.variance) * Gint + 1./self.variance*np.dot(Flower,Flower.T) + self.lengthscale**2/(3.*self.variance)*np.dot(F1lower,F1lower.T)) - #@silence_errors + @silence_errors def update_gradients_full(self,dL_dK,X,X2): """derivative of the covariance matrix with respect to the parameters (shape is num_data x num_inducing x num_params)""" if X2 is None: X2 = X