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https://github.com/ruvnet/RuView
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rUv
f49c722764
The Rust port lived two directories deep (rust-port/wifi-densepose-rs/) without any sibling under rust-port/ that warranted the extra level. Move the whole workspace up to v2/ to match v1/ (Python) at the same depth and shorten every cd / build command across the repo. git mv preserves history for all tracked files. 60 files updated for path references (CI workflows, ADRs, docs, scripts, READMEs, internal .claude-flow state). Two manual fixes for relative-cd paths in CLAUDE.md and ADR-043 that became wrong after the depth change (cd ../.. → cd ..). Validated: - cargo check --workspace --no-default-features → clean (after target/ nuke; the gitignored target/ was carried by the OS rename and had hard-coded old paths in build scripts) - cargo test --workspace --no-default-features → 1,539 passed, 0 failed, 8 ignored (same totals as pre-rename) - ESP32-S3 on COM7 → still streaming live CSI (cb #40300, RSSI -64 dBm) After-merge follow-up: contributors should `rm -rf v2/target` once and let cargo regenerate from the new path.
103 lines
3.2 KiB
Rust
103 lines
3.2 KiB
Rust
//! rUv Neural Embed -- Graph embedding generation for brain connectivity states.
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//!
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//! This crate provides multiple embedding methods to convert brain connectivity
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//! graphs (`BrainGraph`) into fixed-dimensional vector representations suitable
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//! for downstream classification, clustering, and temporal analysis.
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//!
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//! # Embedding Methods
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//!
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//! - **Spectral**: Laplacian eigenvector-based positional encoding
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//! - **Topology**: Hand-crafted topological feature vectors
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//! - **Node2Vec**: Random-walk co-occurrence embeddings
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//! - **Combined**: Weighted concatenation of multiple methods
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//! - **Temporal**: Sliding-window context-enriched embeddings
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//!
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//! # RVF Export
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//!
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//! Embeddings can be serialized to the RuVector `.rvf` format for interoperability
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//! with the broader RuVector ecosystem.
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pub mod combined;
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pub mod distance;
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pub mod node2vec;
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pub mod rvf_export;
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pub mod spectral_embed;
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pub mod temporal;
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pub mod topology_embed;
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// Re-export core types used throughout this crate.
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pub use ruv_neural_core::embedding::{EmbeddingMetadata, EmbeddingTrajectory, NeuralEmbedding};
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pub use ruv_neural_core::graph::{BrainGraph, BrainGraphSequence};
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pub use ruv_neural_core::traits::EmbeddingGenerator;
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/// Helper to build an `EmbeddingMetadata` with just a method name and atlas.
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pub fn default_metadata(
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method: &str,
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atlas: ruv_neural_core::brain::Atlas,
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) -> EmbeddingMetadata {
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EmbeddingMetadata {
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subject_id: None,
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session_id: None,
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cognitive_state: None,
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source_atlas: atlas,
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embedding_method: method.to_string(),
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use ruv_neural_core::brain::Atlas;
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#[test]
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fn test_neural_embedding_new() {
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let meta = default_metadata("test", Atlas::Custom(3));
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let emb = NeuralEmbedding::new(vec![1.0, 2.0, 3.0], 0.0, meta).unwrap();
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assert_eq!(emb.dimension, 3);
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assert_eq!(emb.vector.len(), 3);
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}
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#[test]
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fn test_neural_embedding_empty_fails() {
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let meta = default_metadata("test", Atlas::Custom(1));
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let result = NeuralEmbedding::new(vec![], 0.0, meta);
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assert!(result.is_err());
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}
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#[test]
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fn test_embedding_norm() {
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let meta = default_metadata("test", Atlas::Custom(2));
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let emb = NeuralEmbedding::new(vec![3.0, 4.0], 0.0, meta).unwrap();
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assert!((emb.norm() - 5.0).abs() < 1e-10);
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}
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#[test]
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fn test_trajectory() {
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let traj = EmbeddingTrajectory {
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embeddings: vec![
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NeuralEmbedding::new(
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vec![0.0; 4],
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0.0,
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default_metadata("test", Atlas::Custom(4)),
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)
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.unwrap(),
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NeuralEmbedding::new(
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vec![0.0; 4],
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0.5,
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default_metadata("test", Atlas::Custom(4)),
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)
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.unwrap(),
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NeuralEmbedding::new(
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vec![0.0; 4],
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1.0,
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default_metadata("test", Atlas::Custom(4)),
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)
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.unwrap(),
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],
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timestamps: vec![0.0, 0.5, 1.0],
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};
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assert_eq!(traj.len(), 3);
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assert!((traj.duration_s() - 1.0).abs() < 1e-10);
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}
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}
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