Added more functions to History

README.md

@@ -1,3 +1,7 @@
 # TrueSkill - Through Time
 
 Rust port of [TrueSkillThroughTime.py](https://github.com/glandfried/TrueSkillThroughTime.py).
+
+## Todo
+
+- [ ] Change `time` to always be a u64 (or usize)?

src/batch.rs

@@ -61,7 +61,7 @@ pub struct Team {
 #[derive(Clone)]
 pub struct Event {
     teams: Vec<Team>,
-    evidence: f64,
+    pub evidence: f64,
 }
 
 impl Event {
@@ -94,8 +94,8 @@ fn compute_elapsed(last_time: f64, actual_time: f64) -> f64 {
 #[derive(Clone)]
 pub struct Batch {
     pub skills: HashMap<String, Skill>,
-    events: Vec<Event>,
+    pub events: Vec<Event>,
-    time: f64,
+    pub time: f64,
     p_draw: f64,
 }
 

src/gaussian.rs

@@ -1,17 +1,7 @@
 use std::fmt;
 use std::ops;
 
-use crate::utils;
+use crate::{utils, MU, SIGMA};
-
-pub const BETA: f64 = 1.0;
-pub const MU: f64 = 0.0;
-pub const SIGMA: f64 = BETA * 6.0;
-pub const GAMMA: f64 = BETA * 0.03;
-
-pub const N01: Gaussian = Gaussian::new(0.0, 1.0);
-pub const N00: Gaussian = Gaussian::new(0.0, 0.0);
-pub const N_INF: Gaussian = Gaussian::new(0.0, f64::INFINITY);
-pub const N_MS: Gaussian = Gaussian::new(MU, SIGMA);
 
 #[derive(Clone, Copy, PartialEq, Debug)]
 pub struct Gaussian {

src/history.rs

@@ -170,7 +170,7 @@ impl History {
     pub fn convergence(&mut self) -> ((f64, f64), usize) {
         let epsilon = 1e-6;
         let iterations = 30;
-        let verbose = false;
+        let verbose = true;
 
         let mut step = (f64::INFINITY, f64::INFINITY);
         let mut i = 0;
@@ -196,12 +196,30 @@ impl History {
         (step, i)
     }
 
-    fn learning_curves(&self) {
+    pub fn learning_curves(&self) -> HashMap<String, Vec<(f64, Gaussian)>> {
-        todo!()
+        let mut data: HashMap<String, Vec<(f64, Gaussian)>> = HashMap::new();
+
+        for b in &self.batches {
+            for agent in b.skills.keys() {
+                let point = (b.time, b.posterior(agent));
+
+                if let Some(entry) = data.get_mut(agent) {
+                    entry.push(point);
+                } else {
+                    data.insert(agent.to_string(), vec![point]);
+                }
+            }
+        }
+
+        data
     }
 
-    fn log_evidence(&self) {
+    pub fn log_evidence(&self) -> f64 {
-        todo!()
+        self.batches
+            .iter()
+            .flat_map(|batch| batch.events.iter())
+            .map(|event| event.evidence.ln())
+            .sum()
     }
 }
 
@@ -444,4 +462,71 @@ mod tests {
             epsilon = 0.000001
         );
     }
+
+    #[test]
+    fn test_learning_curves() {
+        let composition = vec![
+            vec![vec!["aj"], vec!["bj"]],
+            vec![vec!["bj"], vec!["cj"]],
+            vec![vec!["cj"], vec!["aj"]],
+        ];
+        let results = vec![vec![1, 0], vec![1, 0], vec![1, 0]];
+        let times = vec![5, 6, 7];
+
+        let mut priors = HashMap::new();
+
+        for k in ["aj", "bj", "cj"] {
+            let player = Player::new(
+                Gaussian::new(25.0, 25.0 / 3.0),
+                25.0 / 6.0,
+                25.0 / 300.0,
+                N_INF,
+            );
+
+            priors.insert(k.to_string(), player);
+        }
+
+        let mut h = History::new(
+            composition,
+            results,
+            times,
+            priors,
+            MU,
+            BETA,
+            SIGMA,
+            GAMMA,
+            P_DRAW,
+        );
+
+        h.convergence();
+
+        let lc = h.learning_curves();
+
+        let aj_e = lc["aj"].len();
+        let cj_e = lc["cj"].len();
+
+        assert_eq!(lc["aj"][0].0, 5.0);
+        assert_eq!(lc["aj"][aj_e - 1].0, 7.0);
+
+        assert_ulps_eq!(
+            lc["aj"][aj_e - 1].1.mu(),
+            24.99999999569006,
+            epsilon = 0.000001
+        );
+        assert_ulps_eq!(
+            lc["aj"][aj_e - 1].1.sigma(),
+            5.419212002171145,
+            epsilon = 0.000001
+        );
+        assert_ulps_eq!(
+            lc["cj"][cj_e - 1].1.mu(),
+            24.999999998686533,
+            epsilon = 0.000001
+        );
+        assert_ulps_eq!(
+            lc["cj"][cj_e - 1].1.sigma(),
+            5.419212002245715,
+            epsilon = 0.000001
+        );
+    }
 }

src/main.rs

@@ -36,5 +36,5 @@ fn main() {
         P_DRAW,
     );
 
-    let (step, i) = h2.convergence();
+    let (_step, _i) = h2.convergence();
 }