Remove xrange
@@ -69,7 +69,7 @@ class StochasticLQ(object):
Ts = self.cls(batch, num_vectors, k, k)
for i in range(num_vectors):
- for j in xrange(batch):
+ for j in range(batch):
Ts[j, i, :, :] = torch.diag(alpha[j, i, :]) + torch.diag(beta[j, i, :], 1) + \
torch.diag(beta[j, i, :], -1)
@@ -241,7 +241,7 @@ class StochasticLQ(object):
for j in range(self.num_random_probes):
if Ts.ndimension() == 4:
- for k in xrange(Ts.size(0)):
+ for k in range(Ts.size(0)):
T = Ts[k, j, :, :]
[f, Y] = T.symeig(eigenvectors=True)
