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. |
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| Cargo.toml.orig | ||
| README.md | ||
ruvector-crv
CRV (Coordinate Remote Viewing) protocol integration for ruvector.
Maps the 6-stage CRV signal line methodology to ruvector's subsystems:
| CRV Stage | Data Type | ruvector Component |
|---|---|---|
| Stage I (Ideograms) | Gestalt primitives | Poincaré ball hyperbolic embeddings |
| Stage II (Sensory) | Textures, colors, temps | Multi-head attention vectors |
| Stage III (Dimensional) | Spatial sketches | GNN graph topology |
| Stage IV (Emotional) | AOL, intangibles | SNN temporal encoding |
| Stage V (Interrogation) | Signal line probing | Differentiable search |
| Stage VI (3D Model) | Composite model | MinCut partitioning |
Quick Start
use ruvector_crv::{CrvConfig, CrvSessionManager, GestaltType, StageIData};
// Create session manager with default config (384 dimensions)
let config = CrvConfig::default();
let mut manager = CrvSessionManager::new(config);
// Create a session for a target coordinate
manager.create_session("session-001".to_string(), "1234-5678".to_string()).unwrap();
// Add Stage I ideogram data
let stage_i = StageIData {
stroke: vec![(0.0, 0.0), (1.0, 0.5), (2.0, 1.0), (3.0, 0.5)],
spontaneous_descriptor: "angular rising".to_string(),
classification: GestaltType::Manmade,
confidence: 0.85,
};
let embedding = manager.add_stage_i("session-001", &stage_i).unwrap();
assert_eq!(embedding.len(), 384);
Architecture
The Poincaré ball embedding for Stage I gestalts encodes the hierarchical gestalt taxonomy (root → manmade/natural/movement/energy/water/land) with exponentially less distortion than Euclidean space.
For AOL (Analytical Overlay) separation, the spiking neural network temporal encoding models signal-vs-noise discrimination: high-frequency spike bursts correlate with AOL contamination, while sustained low-frequency patterns indicate clean signal line data.
MinCut partitioning in Stage VI identifies natural cluster boundaries in the accumulated session graph, separating distinct target aspects.
Cross-Session Convergence
Multiple sessions targeting the same coordinate can be analyzed for convergence — agreement between independent viewers strengthens the signal validity:
// After adding data to multiple sessions for "1234-5678"...
let convergence = manager.find_convergence("1234-5678", 0.75).unwrap();
// convergence.scores contains similarity values for converging entries
License
MIT OR Apache-2.0