feat(history): parallel learning_curves under rayon feature

Per-slice posterior collection runs in parallel via par_iter; merge
into the per-key HashMap is sequential in slice order so iteration
order and HashMap insertion order are identical to the sequential
impl. Preserves deterministic output across thread counts.

Default-feature (no rayon) build unchanged — uses the T2 sequential
impl.

Part of T3.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/history.rs

@@ -262,17 +262,45 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     /// Note: `key(idx)` is O(n) per lookup; this method is therefore O(n²)
     /// in the number of competitors. Acceptable for T2; T3 may optimize.
     pub fn learning_curves(&self) -> HashMap<K, Vec<(T, Gaussian)>> {
-        let mut data: HashMap<K, Vec<(T, Gaussian)>> = HashMap::new();
+        #[cfg(feature = "rayon")]
-        for slice in &self.time_slices {
+        {
-            for (idx, skill) in slice.skills.iter() {
+            use rayon::prelude::*;
-                if let Some(key) = self.keys.key(idx).cloned() {
+
-                    data.entry(key)
+            let per_slice: Vec<Vec<(Index, T, Gaussian)>> = self
-                        .or_default()
+                .time_slices
-                        .push((slice.time, skill.posterior()));
+                .par_iter()
+                .map(|ts| {
+                    ts.skills
+                        .iter()
+                        .map(|(idx, sk)| (idx, ts.time, sk.posterior()))
+                        .collect()
+                })
+                .collect();
+
+            let mut data: HashMap<K, Vec<(T, Gaussian)>> = HashMap::new();
+            for slice_contrib in per_slice {
+                for (idx, t, g) in slice_contrib {
+                    if let Some(key) = self.keys.key(idx).cloned() {
+                        data.entry(key).or_default().push((t, g));
+                    }
                 }
             }
+            data
+        }
+        #[cfg(not(feature = "rayon"))]
+        {
+            let mut data: HashMap<K, Vec<(T, Gaussian)>> = HashMap::new();
+            for slice in &self.time_slices {
+                for (idx, skill) in slice.skills.iter() {
+                    if let Some(key) = self.keys.key(idx).cloned() {
+                        data.entry(key)
+                            .or_default()
+                            .push((slice.time, skill.posterior()));
+                    }
+                }
+            }
+            data
         }
-        data
     }
 
     /// Skill estimate at the latest time slice the competitor appears in.