Don't need to normalize sample vectors
@@ -58,7 +58,6 @@ class InterpolatedToeplitzGPMarginalLogLikelihood(Function):
W_right_mat_inv_y.squeeze())
log_det_part = torch.zeros(len(c))
sample_matrix = torch.sign(torch.randn(len(y), self.num_samples))
- sample_matrix.div_(torch.norm(sample_matrix, 2, 0).expand_as(sample_matrix))
left_vectors = torch.dsmm(self.W_left.t(), LinearCG().solve(mv_closure, sample_matrix)).t()
right_vectors = torch.dsmm(self.W_right.t(), sample_matrix).t()
