test: Add empirical validation of semantic mixing formula #37
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- Add test_mixing_formula.py: Empirical testing framework for universal semantic mixing - Add MIXING_FORMULA_REPORT.md: Comprehensive validation report with real data Results: - ✅ Primary concepts are perfectly pure (1.000 purity) - ✅ Simple 50/50 mixtures work perfectly (100% success, 0.000 error) - ✅ Formula validated: weighted averaging works as predicted -⚠️ Vocabulary coverage limitation identified (113 keywords) Key findings: 1. The four primaries (Love, Justice, Power, Wisdom) are orthogonal 2. Weighted averaging correctly predicts concept combinations 3. Engine already implements the universal mixing formula 4. Need to expand vocabulary coverage for complex phrases This validates the theoretical framework with real engine data, not simulations.
BruinGrowly
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Nov 5, 2025
Major new feature that provides intelligent function name suggestions based on execution semantics using the validated LJWP mixing formula. Key Changes: - Integrated semantic naming engine into main CLI tool - Added 200+ action verbs with LJWP coordinate mappings - Implemented cosine similarity matching in 4D semantic space - Added --suggest-names and --top-suggestions CLI flags - Comprehensive test coverage (35 new tests, 59 total) - Updated documentation and version to 1.5 Technical Details: - SemanticNamingEngine uses validated mixing formula from PR #37 - Vocabulary expanded from 50 to 200+ verbs covering: * Love domain: connection, communication, care * Justice domain: validation, authorization, rules * Power domain: transformation, CRUD, control flow * Wisdom domain: analysis, search, metrics - Context-aware suggestions extract noun from function name - Performance optimized with efficient similarity calculations Testing: - 35 new tests for semantic naming engine - All 59 tests passing - Verified CLI integration end-to-end - Tests validate coordinate normalization and accuracy Documentation: - Updated README with v1.5 features - Updated version badges (tests: 59, version: 1.5) - Added usage examples for new CLI flags This feature transforms the Harmonizer from a detector to a teacher, providing actionable suggestions for better function names based on what the code actually does.
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Results:
Key findings:
This validates the theoretical framework with real engine data, not simulations.