996804c052aca22d54a5206aab5c6f33552b16f7,bindsnet/pipeline/action.py,,select_softmax,#Any#,50

Before Change

    if _sum == 0:
        action = np.random.choice(pipeline.env.action_space.n)
    else:
        action = torch.multinomial((torch.exp(spikes.float()) / _sum).view(-1), 1)[0]

    return action

After Change

    spikes = torch.sum(pipeline.spike_record[output], dim=0)
    probabilities = torch.softmax(spikes, dim=0)
    return torch.multinomial(probabilities, num_samples=1).item()


def select_random(pipeline: EnvironmentPipeline, **kwargs) -> int:
    // language=rst

Instances