Dummy example gives non intuitive result

[ashual]

Hello and thank you for this great paper and implementation.

I've run your method with a dummy example:

    fake_features = torch.ones((1024, 4096))
    real_features = torch.ones((1024, 4096))

and would expect 1.0 for both density and coverage but actually got 0.0

There are two changes to the Density metric that might help

1. Add less or equal
   (distance_real_fake < np.expand_dims(real_nearest_neighbour_distances, axis=1)
   =>
   (distance_real_fake <= np.expand_dims(real_nearest_neighbour_distances, axis=1)
2. Add clamp with self.neareset_k and enforce [0,1] result
   (distance_real_fake <= real_nearest_neighbour_distances.unsqueeze(1)).sum(dim=0) => (distance_real_fake <= real_nearest_neighbour_distances.unsqueeze(1)).sum(dim=0).clamp(0, self.nearest_k)

Is it make sense or do I miss something?