# TASK-012 ## Title Music DNA Vector Encoder --- ## Objective Implement the Music DNA Vector Encoder. The encoder transforms a validated MusicDNA object into a deterministic fixed-size Float32 vector. This vector becomes the canonical numerical representation consumed by future components: - Similarity Engine - Embedding Engine - Recommendation Engine - Playlist Generator - Neural Models The encoder must be completely deterministic. It performs no similarity calculations and no machine learning. --- # Expected Commit Message Add Music DNA Vector Encoder --- # Requirements Read before implementation - docs/00_PROJECT_PRINCIPLES.md - docs/01_SPEC.md - docs/02_ARCHITECTURE.md - docs/03_ANALYZER_API.md - docs/04_MUSIC_DNA_SCHEMA.md - docs/04a_FEATURE_REGISTRY.md - docs/04b_NORMALIZATION_RULES.md - docs/05_AI_DEVELOPER_GUIDE.md --- # Scope Create src/music_dna/ encoder.py layout.py vector.py test_encoder.py --- # MusicDNAVector Implement immutable MusicDNAVector. Fields track_id schema_version dimension values metadata --- ## values numpy.ndarray dtype=float32 shape=(512,) Read-only. The internal array must not be mutable after construction. --- ## metadata Contains encoder_version layout_version created_at feature_count reserved_dimensions --- # Layout Implement a dedicated MusicDNALayout. The layout is the only component responsible for mapping feature identifiers to vector indices. Example signal.rms -> 0 signal.peak -> 1 signal.dc_offset -> 2 spectral.centroid -> 32 spectral.bandwidth -> 33 ... Do NOT hardcode analyzer logic inside the encoder. The encoder must only consume the layout. The layout should expose get_index(feature_identifier) contains(feature_identifier) dimension() list_entries() The layout must be deterministic. --- # MusicDNAEncoder Implement encode() Input MusicDNA Output MusicDNAVector Workflow 1. Load layout. 2. Iterate over MusicDNA values. 3. Validate every feature exists in the layout. 4. Write each normalized value into its assigned vector position. 5. Fill every unused reserved position with 0.0f 6. Return immutable MusicDNAVector. --- # Validation The encoder must detect unknown feature identifier duplicate layout index duplicate feature identifier missing layout entry vector overflow incorrect vector dimension Raise dedicated exceptions. --- # Vector Size The output vector always has 512 dimensions. Never shorter. Never longer. --- # Reserved Dimensions Unused dimensions are always 0.0f Never NaN. Never random. Never omitted. --- # Feature Types Numeric Store directly. Boolean false -> 0.0 true -> 1.0 Categorical Store 0.0 for now. Categorical encoding will be implemented in a later task. --- # Determinism Encoding identical MusicDNA objects must always produce byte-identical vectors. --- # CLI Add musimuse encode Workflow Load MusicDNA Encode Validate Print summary Example Encoding Summary Tracks processed : 42 Encoded : 42 Failed : 0 Vector dimension : 512 Schema version : 1.0 --- # Logging encoder.started encoder.track encoder.validation_failed encoder.completed --- # Forbidden Do NOT implement Similarity Cosine distance Euclidean distance ANN Embeddings Recommendations Persistence Machine Learning Feature weighting --- # Tests Cover immutable vectors float32 dtype shape=(512,) layout validation duplicate indices missing layout entries unknown features reserved dimensions deterministic encoding CLI --- # Documentation Update Architecture. Document MusicDNALayout MusicDNAEncoder MusicDNAVector Explain that the encoder depends only on MusicDNA + MusicDNALayout and is independent from analyzer implementations. --- # Definition of Done ✓ MusicDNAVector implemented ✓ MusicDNALayout implemented ✓ MusicDNAEncoder implemented ✓ Deterministic feature→index mapping ✓ Immutable float32 vectors ✓ Fixed 512-dimensional output ✓ Reserved dimensions filled with zeros ✓ Validation implemented ✓ CLI encode command ✓ Structured logging ✓ Tests pass ✓ Ruff passes ✓ Black passes ✓ isort passes --- ## Important The encoder is the final transformation layer before AI. It converts the canonical MusicDNA representation into a fixed-size numerical vector. Future Similarity, Embedding and Recommendation systems must consume this vector directly without modifying its layout.