179 lines
6.4 KiB
Rust
179 lines
6.4 KiB
Rust
//! # ruvector-crv
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//!
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//! CRV (Coordinate Remote Viewing) protocol integration for ruvector.
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//!
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//! Maps the 6-stage CRV signal line methodology to ruvector's subsystems:
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//!
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//! | CRV Stage | Data Type | ruvector Component |
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//! |-----------|-----------|-------------------|
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//! | Stage I (Ideograms) | Gestalt primitives | Poincaré ball hyperbolic embeddings |
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//! | Stage II (Sensory) | Textures, colors, temps | Multi-head attention vectors |
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//! | Stage III (Dimensional) | Spatial sketches | GNN graph topology |
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//! | Stage IV (Emotional) | AOL, intangibles | SNN temporal encoding |
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//! | Stage V (Interrogation) | Signal line probing | Differentiable search |
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//! | Stage VI (3D Model) | Composite model | MinCut partitioning |
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//!
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//! ## Quick Start
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//!
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//! ```rust,no_run
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//! use ruvector_crv::{CrvConfig, CrvSessionManager, GestaltType, StageIData};
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//!
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//! // Create session manager with default config (384 dimensions)
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//! let config = CrvConfig::default();
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//! let mut manager = CrvSessionManager::new(config);
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//!
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//! // Create a session for a target coordinate
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//! manager.create_session("session-001".to_string(), "1234-5678".to_string()).unwrap();
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//!
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//! // Add Stage I ideogram data
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//! let stage_i = StageIData {
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//! stroke: vec![(0.0, 0.0), (1.0, 0.5), (2.0, 1.0), (3.0, 0.5)],
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//! spontaneous_descriptor: "angular rising".to_string(),
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//! classification: GestaltType::Manmade,
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//! confidence: 0.85,
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//! };
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//!
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//! let embedding = manager.add_stage_i("session-001", &stage_i).unwrap();
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//! assert_eq!(embedding.len(), 384);
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//! ```
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//!
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//! ## Architecture
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//!
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//! The Poincaré ball embedding for Stage I gestalts encodes the hierarchical
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//! gestalt taxonomy (root → manmade/natural/movement/energy/water/land) with
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//! exponentially less distortion than Euclidean space.
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//!
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//! For AOL (Analytical Overlay) separation, the spiking neural network temporal
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//! encoding models signal-vs-noise discrimination: high-frequency spike bursts
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//! correlate with AOL contamination, while sustained low-frequency patterns
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//! indicate clean signal line data.
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//!
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//! MinCut partitioning in Stage VI identifies natural cluster boundaries in the
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//! accumulated session graph, separating distinct target aspects.
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//!
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//! ## Cross-Session Convergence
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//!
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//! Multiple sessions targeting the same coordinate can be analyzed for
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//! convergence — agreement between independent viewers strengthens the
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//! signal validity:
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//!
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//! ```rust,no_run
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//! # use ruvector_crv::{CrvConfig, CrvSessionManager};
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//! # let mut manager = CrvSessionManager::new(CrvConfig::default());
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//! // After adding data to multiple sessions for "1234-5678"...
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//! let convergence = manager.find_convergence("1234-5678", 0.75).unwrap();
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//! // convergence.scores contains similarity values for converging entries
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//! ```
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pub mod error;
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pub mod session;
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pub mod stage_i;
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pub mod stage_ii;
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pub mod stage_iii;
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pub mod stage_iv;
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pub mod stage_v;
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pub mod stage_vi;
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pub mod types;
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// Re-export main types
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pub use error::{CrvError, CrvResult};
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pub use session::CrvSessionManager;
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pub use stage_i::StageIEncoder;
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pub use stage_ii::StageIIEncoder;
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pub use stage_iii::StageIIIEncoder;
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pub use stage_iv::StageIVEncoder;
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pub use stage_v::StageVEngine;
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pub use stage_vi::StageVIModeler;
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pub use types::{
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AOLDetection, ConvergenceResult, CrossReference, CrvConfig, CrvSessionEntry, GeometricKind,
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GestaltType, SensoryModality, SignalLineProbe, SketchElement, SpatialRelationType,
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SpatialRelationship, StageIData, StageIIData, StageIIIData, StageIVData, StageVData,
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StageVIData, TargetPartition,
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};
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/// Library version.
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pub const VERSION: &str = env!("CARGO_PKG_VERSION");
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn test_version() {
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assert!(!VERSION.is_empty());
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}
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#[test]
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fn test_end_to_end_session() {
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let config = CrvConfig {
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dimensions: 32,
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..CrvConfig::default()
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};
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let mut manager = CrvSessionManager::new(config);
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// Create two sessions for the same coordinate
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manager
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.create_session("viewer-a".to_string(), "target-001".to_string())
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.unwrap();
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manager
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.create_session("viewer-b".to_string(), "target-001".to_string())
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.unwrap();
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// Viewer A: Stage I
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let s1_a = StageIData {
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stroke: vec![(0.0, 0.0), (1.0, 1.0), (2.0, 0.5), (3.0, 0.0)],
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spontaneous_descriptor: "tall angular".to_string(),
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classification: GestaltType::Manmade,
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confidence: 0.85,
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};
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manager.add_stage_i("viewer-a", &s1_a).unwrap();
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// Viewer B: Stage I (similar gestalt)
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let s1_b = StageIData {
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stroke: vec![(0.0, 0.0), (0.5, 1.2), (1.5, 0.8), (2.5, 0.0)],
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spontaneous_descriptor: "structured upward".to_string(),
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classification: GestaltType::Manmade,
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confidence: 0.78,
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};
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manager.add_stage_i("viewer-b", &s1_b).unwrap();
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// Viewer A: Stage II
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let s2_a = StageIIData {
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impressions: vec![
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(SensoryModality::Texture, "rough stone".to_string()),
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(SensoryModality::Temperature, "cool".to_string()),
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(SensoryModality::Color, "gray".to_string()),
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],
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feature_vector: None,
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};
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manager.add_stage_ii("viewer-a", &s2_a).unwrap();
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// Viewer B: Stage II (overlapping sensory)
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let s2_b = StageIIData {
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impressions: vec![
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(SensoryModality::Texture, "grainy rough".to_string()),
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(SensoryModality::Color, "dark gray".to_string()),
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(SensoryModality::Luminosity, "dim".to_string()),
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],
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feature_vector: None,
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};
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manager.add_stage_ii("viewer-b", &s2_b).unwrap();
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// Verify entries
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assert_eq!(manager.session_entry_count("viewer-a"), 2);
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assert_eq!(manager.session_entry_count("viewer-b"), 2);
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// Both sessions should have embeddings
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let entries_a = manager.get_session_embeddings("viewer-a").unwrap();
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let entries_b = manager.get_session_embeddings("viewer-b").unwrap();
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assert_eq!(entries_a.len(), 2);
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assert_eq!(entries_b.len(), 2);
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// All embeddings should be 32-dimensional
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for entry in entries_a.iter().chain(entries_b.iter()) {
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assert_eq!(entry.embedding.len(), 32);
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}
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}
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}
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