ed3f6c56c22a7fafa4aa7e4aefd2639e00c0a668,GPy/likelihoods/likelihood.py,Likelihood,ep_gradients,#Likelihood#Any#Any#Any#Any#Any#Any#Any#,230
Before Change
Y_metadata_list.append(Y_metadata_i)
val = self.site_derivatives_ep(Y[index], tau[index], v[index], Y_metadata_i)
dlik_dtheta[:, index] = val.ravel()
f = partial(self.integrate)
quads = zip(*map(f, Y.flatten(), mu.flatten(), np.sqrt(sigma2.flatten())))
After Change
Y_metadata_i[key] = Y_metadata[key][index,:]
Y_metadata_list.append(Y_metadata_i)
if quad_mode == "gk":
f = partial(self.integrate_gk)
quads = zip(*map(f, Y.flatten(), mu.flatten(), np.sqrt(sigma2.flatten()), Y_metadata_list))
quads = np.vstack(quads)
quads.reshape(self.size, shape[0], shape[1])
elif quad_mode == "gh":
f = partial(self.integrate_gh)
quads = zip(*map(f, Y.flatten(), mu.flatten(), np.sqrt(sigma2.flatten())))
quads = np.hstack(quads)
quads = quads.T
else:
raise Exception("no other quadrature mode available")
// do a gaussian-hermite integration
dL_dtheta_avg = boost_grad * np.nanmean(quads, axis=1)
dL_dtheta = boost_grad * np.nansum(quads, axis=1)
// dL_dtheta = boost_grad * np.nansum(dlik_dtheta, axis=1)
else:
In pattern: SUPERPATTERN
Frequency: 4
Non-data size: 7
Instances
Project Name: SheffieldML/GPy
Commit Name: ed3f6c56c22a7fafa4aa7e4aefd2639e00c0a668
Time: 2017-07-03
Author: akash.dhaka@aalto.fi
File Name: GPy/likelihoods/likelihood.py
Class Name: Likelihood
Method Name: ep_gradients
Project Name: tensorlayer/tensorlayer
Commit Name: b7628060e6ae3c23f37a21d47d625609074dda24
Time: 2018-03-13
Author: dhsig552@163.com
File Name: tests/test_layers_convolution.py
Class Name:
Method Name:
Project Name: dmlc/dgl
Commit Name: 18a26fcfb1983af7fba69db9bdce7ba5e6a9945f
Time: 2020-06-29
Author: VoVAllen@users.noreply.github.com
File Name: python/dgl/data/graph_serialize.py
Class Name:
Method Name: load_graphs
Project Name: SheffieldML/GPy
Commit Name: 8b621a409cd07d0f5610a2648b6413322c39a822
Time: 2017-08-10
Author: akash.dhaka@aalto.fi
File Name: GPy/likelihoods/likelihood.py
Class Name: Likelihood
Method Name: ep_gradients