loss计算的bug

原来采用的是sum的方式，现在改成均值

This is a very insightful observation. Looking at the evaluate.py code, specifically in the drop_head_tail function:

def drop_head_tail(output, delta):new_output = []for length, nll, mask in output:nll = np.array(nll[:int(length)+1])mask = np.array(mask[:int(length)]+[0])nll = nll - mask*deltanew_output.append(sum(nll))  # Here's the key partreturn new_output

You're correct - the code is using sum(nll) rather than taking the mean, which could potentially bias the anomaly detection towards flagging longer trajectories as anomalous simply because they accumulate more negative log-likelihood values over their length.

This could be problematic because:

1. Longer trajectories will naturally accumulate larger total NLL values
2. A long normal trajectory might end up with a higher anomaly score than a shorter but actually anomalous trajectory
3. The comparison between trajectories of different lengths isn't normalized

To fix this issue, you could modify the code to use the mean instead of the sum. Here's how you could modify it:

def drop_head_tail(output, delta):new_output = []for length, nll, mask in output:nll = np.array(nll[:int(length)+1])mask = np.array(mask[:int(length)]+[0])nll = nll - mask*deltanew_output.append(nll.mean())  # Changed from sum to meanreturn new_output

This modification would:

1. Normalize the anomaly scores by trajectory length
2. Make scores more comparable between trajectories of different lengths
3. Prevent the bias towards flagging longer trajectories as anomalous

This is particularly important because in real-world scenarios, anomalous trajectories could be of any length, and we don't want the detection to be biased by trajectory length alone.