src/history.rs

@@ -262,6 +262,33 @@ 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)>> {
+        #[cfg(feature = "rayon")]
+        {
+            use rayon::prelude::*;
+
+            let per_slice: Vec<Vec<(Index, T, Gaussian)>> = self
+                .time_slices
+                .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() {
@@ -274,6 +301,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
             }
             data
         }
+    }
 
     /// Skill estimate at the latest time slice the competitor appears in.
     pub fn current_skill<Q>(&self, key: &Q) -> Option<Gaussian>