test_bottom = fold * test_size
test_top = (1 + fold) * test_size
test_indices = shuffidx[test_bottom:test_top]
train_indices = shuffidx[0:test_bottom] + shuffidx[test_top:]
loss_func = []
bfs_params = []
test_loss = []
torch.set_rng_state(rng_state) #fix init state
torch.manual_seed(0)
pht = MP(rbf=bfs, filtername='f')
optimizer_filter = optim.SGD([{'params': pht.rbfweights}], lr=lr)
for epoch in range(max_epoch):
pht.train()
np.random.shuffle(train_indices)
lss = 0
for b in range(train_batches):
bfs_params.append(pht.rbfweights.detach().clone().numpy())
pht.train()
train_indices_batch = train_indices[b * batch_size:(b + 1) *
batch_size]
optimizer_filter.zero_grad()
loss = pht([data[i] for i in train_indices_batch])
loss.backward()
optimizer_filter.step()
