1 files changed, 156 insertions, 0 deletions
diff --git a/src/intuition.rs b/src/intuition.rs
new file mode 100644
index 0000000..f994029
--- /dev/null
+++ b/src/intuition.rs
@@ -0,0 +1,156 @@
+use std::collections::{HashMap, HashSet};
+
+use rand::{self, seq::IndexedRandom};
+use serde::{Deserialize, Serialize};
+
+use crate::{config::Config, trajectory::Action};
+
+fn compute_weight(past: &HashSet<String>, now: &HashSet<&String>) -> f32 {
+    let inter = now.iter().filter(|&&p| past.contains(p)).count();
+    if inter == 0 {
+        return 0.0;
+    }
+    (inter as f32) / ((now.len() as f32).sqrt() * (past.len() as f32).sqrt())
+}
+
+fn softmax_sample(items: &[(String, f32)], temperature: f32) -> Option<(String, f32)> {
+    if items.is_empty() {
+        return None;
+    }
+    let mut rng = rand::rng();
+
+    let max_val = items
+        .iter()
+        .map(|(_, v)| *v)
+        .fold(f32::NEG_INFINITY, f32::max);
+
+    let exp_values: Vec<_> = items
+        .iter()
+        .map(|(k, val)| (k, ((val - max_val) / temperature).exp()))
+        .collect();
+
+    let sum: f32 = exp_values.iter().map(|(_, v)| *v).sum();
+
+    let norm_values: Vec<_> = exp_values.iter().map(|&(k, v)| (k, v / sum)).collect();
+
+    norm_values
+        .choose_weighted(&mut rng, |item| item.1)
+        .map(|p| (p.0.clone(), p.1))
+        .ok()
+}
+
+#[derive(Serialize, Deserialize)]
+enum Episode {
+    Continue {
+        group: HashSet<String>,
+        avoid: String,
+        committed: String,
+    },
+    Escape {
+        from: HashSet<String>,
+        to: String,
+        committed: String,
+    },
+}
+
+#[derive(Serialize, Deserialize, Default)]
+pub struct Intuition {
+    history: Vec<Episode>,
+}
+
+pub enum Learning {
+    SkipToMore(Vec<String>, String),
+    MoreToSkip(Vec<String>, String),
+}
+
+impl Intuition {
+    pub fn learn(&mut self, learning: &Learning) {
+        let stamp = chrono::Utc::now().format("%+").to_string();
+        match learning {
+            Learning::SkipToMore(trajectory, new) => {
+                self.history.push(Episode::Escape {
+                    from: trajectory.iter().map(String::clone).collect(),
+                    to: new.clone(),
+                    committed: stamp,
+                });
+            }
+            Learning::MoreToSkip(trajectory, new) => {
+                self.history.push(Episode::Continue {
+                    group: trajectory.iter().map(String::clone).collect(),
+                    avoid: new.clone(),
+                    committed: stamp,
+                });
+            }
+        }
+    }
+
+    pub fn sample(
+        &self,
+        trajectory: &[String],
+        action: &Action,
+        candidates: &HashSet<String>,
+        config: &Config,
+    ) -> Option<(String, f32)> {
+        let trajectory: HashSet<_> = trajectory.iter().collect();
+
+        let candidates: Vec<_> = candidates
+            .iter()
+            .filter(|c| !trajectory.contains(c))
+            .collect();
+
+        if candidates.is_empty() {
+            return None;
+        }
+
+        let mut items: HashMap<String, f32> =
+            candidates.into_iter().map(|c| (c.clone(), 0.0)).collect();
+
+        for episode in &self.history {
+            match (episode, action) {
+                (Episode::Escape { from, to, .. }, Action::Skip) => {
+                    let w = compute_weight(from, &trajectory);
+                    if let Some(to_w) = items.get_mut(to) {
+                        *to_w += config.max_weight * w;
+                    }
+                    for f in from {
+                        if let Some(from_weight) = items.get_mut(f) {
+                            *from_weight -= config.mid_weight * w;
+                        }
+                    }
+                }
+                (Episode::Escape { from, to, .. }, Action::More) => {
+                    let w = compute_weight(from, &trajectory);
+                    if trajectory.contains(to) {
+                        for f in from {
+                            if let Some(f_w) = items.get_mut(f) {
+                                *f_w -= config.low_weight * w;
+                            }
+                        }
+                    }
+                }
+                (Episode::Continue { group, avoid, .. }, Action::More) => {
+                    let w = compute_weight(group, &trajectory);
+                    for g in group {
+                        if let Some(g_w) = items.get_mut(g) {
+                            *g_w += config.max_weight * w;
+                        }
+                    }
+                    if let Some(a_w) = items.get_mut(avoid) {
+                        *a_w -= config.low_weight * w;
+                    }
+                }
+                (Episode::Continue { group, .. }, Action::Skip) => {
+                    let w = compute_weight(group, &trajectory);
+                    for g in group {
+                        if let Some(v) = items.get_mut(g) {
+                            *v -= config.high_weight * w;
+                        }
+                    }
+                }
+            }
+        }
+
+        let pairs: Vec<_> = items.into_iter().collect();
+        softmax_sample(&pairs, config.temperature)
+    }
+}