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Powered by Hot-Path - 5-layer semantic documentation impact analysis
Semantic Analysis Summary
1 files analyzed in 67.9s
0 added, 0 deleted
Change Classification (using Zhang-Shasha tree edit distance):
IDENTICAL: 16 files
REWRITE: 1 files
Documentation Impact:
HIGH priority (MAJOR/REWRITE): 1 files need docs
MEDIUM priority (MINOR): 0 files
Code Entities:
Total entities found: 257
Documented: 73
Undocumented: 192
Code communities detected: 1
High Priority Changes (Require Documentation)
algorithmia.py
Change Type: REWRITE
Tree Edit Distance: 0.88
Language: python
AI-Generated Documentation Suggestions
The following suggestions were generated using LLM analysis:
1. algorithmia.py - algorithmia
Change Type: REWRITE Confidence: 85%
Explanation:
The code appears to be part of an emotion recognition system that uses the Algorithmia API. The module contains two main functions: calculate_weighted_emotion which applies weights to emotion confidence scores, and smooth_emotion_with_history which uses a rolling window approach to smooth emotion detection over time.
The code doesn't actually contain a function named algorithmia, so I've documented the module as a whole and its key functions. The module seems to be designed to process emotion data, apply weights to different emotions, and smooth the detection over time to prevent rapid fluctuations.
Compared to the previous version, this code has added sophisticated emotion smoothing with historical context and weighted confidence calculations, which weren't present in the earlier implementation that simply detected emotions and mapped them to playlists.
View Suggestion
# algorithmia.py
This module provides emotion recognition functionality using the Algorithmia API and implements emotion smoothing and playlist recommendation based on detected emotions.
## Main Functions### calculate_weighted_emotion```pythondefcalculate_weighted_emotion(emotions_dict):
Applies weighted scoring to emotion confidence values to emphasize certain emotions.
Parameters:
emotions_dict (dict): Dictionary mapping emotion names to confidence scores
Returns:
d
</details>
*Cost for this analysis: $0.0162*
---
### Next Steps
1. **Review** the changed code files above
2. **Update** corresponding documentation
3. **Test** that examples in docs still work
4. **Consider** applying AI suggestions (review carefully first)
### Automatic Documentation Updates
Comment **`/update-docs`** on this PR to automatically:
- Generate high-quality documentation using LLM
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