forked from kent/consciousness
eliminate schema_fit: it's clustering coefficient
schema_fit was algebraically identical to clustering_coefficient (both compute 2E/(d*(d-1)) = fraction of connected neighbor pairs). Remove the redundant function, field, and metrics column. - Delete schema_fit() and schema_fit_all() from graph.rs - Remove schema_fit field from Node struct - Remove avg_schema_fit from MetricsSnapshot (duplicated avg_cc) - Replace all callers with graph.clustering_coefficient() - Rename ReplayItem.schema_fit to .cc - Query: "cc" and "schema_fit" both resolve from graph CC - Low-CC count folded into health report CC line Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
This commit is contained in:
ProofOfConcept
parent
fb7aa46e03
commit
ec8b4b2ed2
5 changed files with 33 additions and 83 deletions
64
src/neuro.rs
64
src/neuro.rs
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@ -19,12 +19,12 @@ const SECS_PER_DAY: f64 = 86400.0;
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/// With spectral data:
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/// priority = spectral_displacement × overdue × emotion
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/// Without:
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/// priority = (1 - schema_fit) × overdue × emotion
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/// priority = (1 - cc) × overdue × emotion
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///
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/// Spectral displacement is the outlier_score clamped and normalized —
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/// it measures how far a node sits from its community center in the
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/// eigenspace. This is a global signal (considers all graph structure)
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/// vs schema_fit which is local (only immediate neighbors).
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/// vs CC which is local (only immediate neighbors).
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pub fn consolidation_priority(
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store: &Store,
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key: &str,
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@ -44,8 +44,8 @@ pub fn consolidation_priority(
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// outlier=5 and outlier=10 matters less than 1 vs 2.
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(outlier / 3.0).min(3.0)
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} else {
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let fit = graph::schema_fit(graph, key) as f64;
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1.0 - fit
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let cc = graph.clustering_coefficient(key) as f64;
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1.0 - cc
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};
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// Spaced repetition: how overdue is this node for replay?
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@ -69,7 +69,7 @@ pub struct ReplayItem {
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pub priority: f64,
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pub interval_days: u32,
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pub emotion: f32,
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pub schema_fit: f32,
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pub cc: f32,
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/// Spectral classification: "bridge", "outlier", "core", "peripheral"
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pub classification: &'static str,
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/// Raw spectral outlier score (distance / median)
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@ -91,8 +91,6 @@ pub fn replay_queue_with_graph(
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graph: &Graph,
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emb: Option<&SpectralEmbedding>,
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) -> Vec<ReplayItem> {
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let fits = graph::schema_fit_all(graph);
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// Build spectral position map if embedding is available
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let positions: HashMap<String, SpectralPosition> = if let Some(emb) = emb {
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let communities = graph.communities().clone();
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@ -116,14 +114,12 @@ pub fn replay_queue_with_graph(
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store, key, graph,
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pos.map(|p| p.outlier_score),
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);
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let fit = fits.get(key).copied().unwrap_or(0.0);
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ReplayItem {
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key: key.clone(),
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priority,
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interval_days: node.spaced_repetition_interval,
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emotion: node.emotion,
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schema_fit: fit,
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cc: graph.clustering_coefficient(key),
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classification,
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outlier_score,
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}
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@ -178,7 +174,7 @@ pub fn detect_interference(
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/// Low fit (<0.2): deep examination needed — new schema seed, bridge, or noise?
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pub fn schema_assimilation(store: &Store, key: &str) -> (f32, &'static str) {
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let graph = store.build_graph();
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let fit = graph::schema_fit(&graph, key);
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let fit = graph.clustering_coefficient(key);
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let recommendation = if fit > 0.5 {
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"auto-integrate"
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@ -267,8 +263,8 @@ fn format_nodes_section(store: &Store, items: &[ReplayItem], graph: &Graph) -> S
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};
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out.push_str(&format!("## {} \n", item.key));
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out.push_str(&format!("Priority: {:.3} Schema fit: {:.3} Emotion: {:.1} ",
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item.priority, item.schema_fit, item.emotion));
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out.push_str(&format!("Priority: {:.3} CC: {:.3} Emotion: {:.1} ",
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item.priority, item.cc, item.emotion));
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out.push_str(&format!("Category: {} Interval: {}d\n",
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node.category.label(), node.spaced_repetition_interval));
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if item.outlier_score > 0.0 {
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@ -485,8 +481,8 @@ pub fn consolidation_batch(store: &Store, count: usize, auto: bool) -> Result<()
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let node_type = store.nodes.get(&item.key)
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.map(|n| if n.key.contains("journal") { "episodic" } else { "semantic" })
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.unwrap_or("?");
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println!(" [{:.3}] {} (fit={:.3}, interval={}d, type={})",
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item.priority, item.key, item.schema_fit, item.interval_days, node_type);
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println!(" [{:.3}] {} (cc={:.3}, interval={}d, type={})",
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item.priority, item.key, item.cc, item.interval_days, node_type);
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}
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// Also show interference pairs
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@ -556,13 +552,12 @@ pub fn agent_prompt(store: &Store, agent: &str, count: usize) -> Result<String,
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let items: Vec<ReplayItem> = episode_keys.iter()
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.filter_map(|k| {
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let node = store.nodes.get(k)?;
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let fit = graph::schema_fit(&graph, k);
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Some(ReplayItem {
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key: k.clone(),
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priority: consolidation_priority(store, k, &graph, None),
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interval_days: node.spaced_repetition_interval,
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emotion: node.emotion,
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schema_fit: fit,
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cc: graph.clustering_coefficient(k),
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classification: "unknown",
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outlier_score: 0.0,
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})
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@ -597,12 +592,7 @@ pub fn consolidation_plan(store: &Store) -> ConsolidationPlan {
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let graph = store.build_graph();
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let alpha = graph.degree_power_law_exponent();
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let gini = graph.degree_gini();
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let avg_fit = {
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let fits = graph::schema_fit_all(&graph);
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if fits.is_empty() { 0.0 } else {
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fits.values().sum::<f32>() / fits.len() as f32
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}
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};
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let avg_cc = graph.avg_clustering_coefficient();
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let interference_pairs = detect_interference(store, &graph, 0.5);
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let interference_count = interference_pairs.len();
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@ -651,17 +641,17 @@ pub fn consolidation_plan(store: &Store) -> ConsolidationPlan {
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gini));
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}
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// Target: avg schema fit ≥ 0.2
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if avg_fit < 0.1 {
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// Target: avg CC ≥ 0.2
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if avg_cc < 0.1 {
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plan.replay_count += 5;
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plan.rationale.push(format!(
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"Schema fit={:.3} (target ≥0.2): very poor integration → +5 replay",
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avg_fit));
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} else if avg_fit < 0.2 {
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"CC={:.3} (target ≥0.2): very poor integration → +5 replay",
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avg_cc));
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} else if avg_cc < 0.2 {
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plan.replay_count += 2;
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plan.rationale.push(format!(
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"Schema fit={:.3} (target ≥0.2): low integration → +2 replay",
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avg_fit));
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"CC={:.3} (target ≥0.2): low integration → +2 replay",
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avg_cc));
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}
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// Interference: >100 pairs is a lot, <10 is clean
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@ -748,21 +738,14 @@ pub fn daily_check(store: &Store) -> String {
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let gini = graph.degree_gini();
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let sigma = graph.small_world_sigma();
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let avg_cc = graph.avg_clustering_coefficient();
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let avg_fit = {
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let fits = graph::schema_fit_all(&graph);
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if fits.is_empty() { 0.0 } else {
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fits.values().sum::<f32>() / fits.len() as f32
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}
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};
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let history = graph::load_metrics_history();
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let prev = history.last();
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let mut out = String::from("Memory daily check\n");
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// Current state
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out.push_str(&format!(" σ={:.1} α={:.2} gini={:.3} cc={:.4} fit={:.3}\n",
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sigma, alpha, gini, avg_cc, avg_fit));
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out.push_str(&format!(" σ={:.1} α={:.2} gini={:.3} cc={:.4}\n",
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sigma, alpha, gini, avg_cc));
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// Trend
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if let Some(p) = prev {
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@ -777,7 +760,7 @@ pub fn daily_check(store: &Store) -> String {
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let mut issues = Vec::new();
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if alpha < 2.0 { issues.push("hub dominance critical"); }
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if gini > 0.5 { issues.push("high inequality"); }
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if avg_fit < 0.1 { issues.push("poor integration"); }
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if avg_cc < 0.1 { issues.push("poor integration"); }
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if d_sigma < -5.0 { issues.push("σ declining"); }
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if d_alpha < -0.1 { issues.push("α declining"); }
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if d_gini > 0.02 { issues.push("inequality increasing"); }
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@ -802,7 +785,6 @@ pub fn daily_check(store: &Store) -> String {
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communities: graph.community_count(),
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sigma, alpha, gini, avg_cc,
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avg_path_length: graph.avg_path_length(),
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avg_schema_fit: avg_fit,
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});
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out
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