60e0e6d3c4
Implement full CRV (Coordinate Remote Viewing) signal-line protocol mapping to WiFi CSI sensing via ruvector-crv: - Stage I: CsiGestaltClassifier (6 gestalt types from amplitude/phase) - Stage II: CsiSensoryEncoder (texture/color/temperature/sound/luminosity/dimension) - Stage III: Mesh topology encoding (AP nodes/links → GNN graph) - Stage IV: Coherence gate → AOL detection (signal vs noise separation) - Stage V: Pose interrogation via differentiable search - Stage VI: Person partitioning via MinCut clustering - Cross-session convergence for cross-room identity New files: - crv/mod.rs: 1,430 lines, 43 tests - crv_bench.rs: 8 criterion benchmarks (gestalt, sensory, pipeline, convergence) - ADR-033: 740-line architecture decision with 30+ acceptance criteria - patches/ruvector-crv: Fix ruvector-gnn 2.0.5 API mismatch Dependencies: ruvector-crv 0.1.1, ruvector-gnn 2.0.5 Co-Authored-By: claude-flow <ruv@ruv.net>
69 lines
2.4 KiB
Markdown
69 lines
2.4 KiB
Markdown
# 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
|
|
|
|
```rust
|
|
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:
|
|
|
|
```rust
|
|
// 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
|