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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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Nicolas 2013-06-06 11:24:06 +01:00
commit adf2594677
1 changed files with 8 additions and 8 deletions
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16
GPy/kern/rbf.py
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@ -96,13 +96,13 @@ class rbf(Kernpart):
var_len3 = self.variance / np.power(self.lengthscale, 3)
if X2 is None:
# save computation for the symmetrical case
dvardLdK += dvardLdK.T
dvardLdK = dvardLdK + dvardLdK.T
code = """
int q,i,j;
double tmp;
for(q=0; q<input_dim; q++){
tmp = 0;
for(i=0; i<N; i++){
for(i=0; i<num_data; i++){
for(j=0; j<i; j++){
tmp += (X(i,q)-X(j,q))*(X(i,q)-X(j,q))*dvardLdK(i,j);
}
@ -110,14 +110,15 @@ class rbf(Kernpart):
target(q+1) += var_len3(q)*tmp;
}
"""
N, num_inducing, input_dim = X.shape[0], X.shape[0], self.input_dim
num_data, num_inducing, input_dim = X.shape[0], X.shape[0], self.input_dim
weave.inline(code, arg_names=['num_data','num_inducing','input_dim','X','X2','target','dvardLdK','var_len3'], type_converters=weave.converters.blitz, **self.weave_options)
else:
code = """
int q,i,j;
double tmp;
for(q=0; q<input_dim; q++){
tmp = 0;
for(i=0; i<N; i++){
for(i=0; i<num_data; i++){
for(j=0; j<num_inducing; j++){
tmp += (X(i,q)-X2(j,q))*(X(i,q)-X2(j,q))*dvardLdK(i,j);
}
@ -125,10 +126,9 @@ class rbf(Kernpart):
target(q+1) += var_len3(q)*tmp;
}
"""
N, num_inducing, input_dim = X.shape[0], X2.shape[0], self.input_dim
# [np.add(target[1+q:2+q],var_len3[q]*np.sum(dvardLdK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.input_dim)]
weave.inline(code, arg_names=['N','num_inducing','input_dim','X','X2','target','dvardLdK','var_len3'],
type_converters=weave.converters.blitz, **self.weave_options)
num_data, num_inducing, input_dim = X.shape[0], X2.shape[0], self.input_dim
#[np.add(target[1+q:2+q],var_len3[q]*np.sum(dvardLdK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.input_dim)]
weave.inline(code, arg_names=['num_data','num_inducing','input_dim','X','X2','target','dvardLdK','var_len3'], type_converters=weave.converters.blitz, **self.weave_options)
else:
target[1] += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK)