Fix torch.nonzero inconsistency and n-gram range bug in evaluation.py

[Copilot]

Summary

This PR addresses inconsistencies in PyTorch tensor operations and fixes a critical bug in the n-gram evaluation functionality that was causing incorrect predictions.

Issues Fixed

1. torch.nonzero() Inconsistency

The ngram() and update_ngram_scores() functions used different approaches to extract non-zero indices:

# ngram() function (inefficient)
ordering = torch.nonzero(activity[t].view(-1))
if ordering.numel() > 0:
    fire_order += ordering[:, 0].tolist()

# update_ngram_scores() function (efficient)
ordering = torch.nonzero(activity[t]).view(-1)
if ordering.numel() > 0:
    fire_order += ordering.tolist()

Both approaches produce identical results, but the second is more direct and efficient. This PR standardizes on the cleaner approach.

2. Critical N-gram Range Bug

The ngram() function had an off-by-one error in the n-gram sequence processing loop:

# Before (incorrect - misses last n-gram)
for j in range(len(fire_order) - n):

# After (correct - includes all possible n-grams)  
for j in range(len(fire_order) - n + 1):

This bug caused the function to skip the last possible n-gram sequence, potentially leading to incorrect classification predictions.

3. Modernized Tensor Operations

Updated deprecated tensor size access pattern:

# Before
for t in range(activity.size()[0]):

# After  
for t in range(activity.size(0)):

Testing

• All existing tests continue to pass
• Added comprehensive test coverage for n-gram functionality
• Verified edge cases (empty spikes, single spikes, multiple spikes per timestep)
• Confirmed backward compatibility

Impact

• Correctness: Fixes incorrect predictions in n-gram classification
• Performance: Slightly more efficient tensor operations
• Maintainability: Consistent code patterns across functions
• Compatibility: Ensures compatibility with PyTorch 2.7.1

The changes are minimal and surgical, maintaining full backward compatibility while improving correctness and efficiency.

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[Hananel-Hazan]

No need to maintine backwaord compatibility to PyTorch 2.7.1.
The code should work with Python >=3.9

[Hananel-Hazan]

No need to maintine backwaord compatibility to PyTorch 2.7.1.
The code should work with Python >=3.9