Optimize nested loop efficiency in the Anomaly Attention class #63
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Refactored the existing nested loop structure within the Anomaly Attention class used for finding the distance between the indexes. Replaced the original dual for-loops, which scaled with window size, with a more efficient implementation. Performance Improvement: - Original execution time for a window size of 250: ~50 seconds. - New execution time for the same window size: ~0.01 seconds. This enhancement significantly boosts the model's performance, especially for larger window sizes, making it more practical for use in high-performance environments.
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Refactored the existing nested loop structure within the Anomaly Attention class used for finding the distance between the indexes. Replaced the original dual for-loops, which scaled with window size, with a more efficient implementation.
Performance Improvement:
I was experimenting with different window sizes to see their effects on my dataset. However, in my current setup where I use a high-performance workstation, it started to take too much time when I increased the window size. This new approach takes much more lower time to achieve the same matrix.