5f229ae1c23263175c8b089c481b80229adc64e0,qucumber/nn_states/neural_state.py,NeuralStateBase,gradient,#NeuralStateBase#,275

Before Change

                    samp = samples[indices == i, :]
                    sample_grad = self.rotated_gradient(basis, samp)
                    grad[0] += sample_grad[0]  // Accumulate amplitude RBM gradient
                    grad[1] += sample_grad[1]  // Accumulate phase RBM gradient
                else:
                    sample_grad = [
                        self.rbm_am.effective_energy_gradient(samples[indices == i, :]),

After Change

                bases = np.array(list(bases)).reshape(1, -1)

            unique_bases, indices = np.unique(bases, axis=0, return_inverse=True)
            indices = torch.Tensor(indices).to(samples)

            for i in range(unique_bases.shape[0]):
                basis = unique_bases[i, :]
                rot_sites = np.where(basis != "Z")[0]

Instances