Small changes for new 2024 edition

.gitignore

@@ -1,5 +1,6 @@
 /target
 /Cargo.lock
 
+/temp
 .justfile
 *.svg

Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "trueskill-tt"
 version = "0.1.0"
-edition = "2021"
+edition = "2024"
 
 [lib]
 bench = false

README.md

@@ -15,6 +15,7 @@ Rust port of [TrueSkillThroughTime.py](https://github.com/glandfried/TrueSkillTh
 
 - [x] Implement approx for Gaussian
 - [x] Add more tests from `TrueSkillThroughTime.jl`
+- [ ] Add tests for `quality()` (Use [sublee/trueskill](https://github.com/sublee/trueskill/tree/master) as reference)
 - [ ] Benchmark Batch::iteration()
 - [ ] Time needs to be an enum so we can have multiple states (see `batch::compute_elapsed()`)
 - [ ] Add examples (use same TrueSkillThroughTime.(py|jl))

src/game.rs

@@ -23,12 +23,12 @@ impl<'a> Game<'a> {
         weights: &'a [Vec<f64>],
         p_draw: f64,
     ) -> Self {
-        assert!(
+        debug_assert!(
             (result.len() == teams.len()),
             "result must have the same length as teams"
         );
 
-        assert!(
+        debug_assert!(
             weights
                 .iter()
                 .zip(teams.iter())
@@ -36,12 +36,12 @@ impl<'a> Game<'a> {
             "weights must have the same dimensions as teams"
         );
 
-        assert!(
+        debug_assert!(
             (0.0..1.0).contains(&p_draw),
             "draw probability.must be >= 0.0 and < 1.0"
         );
 
-        assert!(
+        debug_assert!(
             p_draw > 0.0 || {
                 let mut r = result.to_vec();
                 r.sort_unstable_by(|a, b| a.partial_cmp(b).unwrap());

src/lib.rs

@@ -82,7 +82,7 @@ where
     pub fn key(&self, idx: Index) -> Option<&K> {
         self.0
             .iter()
-            .find(|(_, &value)| value == idx)
+            .find(|&(_, value)| *value == idx)
             .map(|(key, _)| key)
     }
 
@@ -115,11 +115,7 @@ fn erfc(x: f64) -> f64 {
 
     let r = t * (-z * z - 1.26551223 + t * h).exp();
 
-    if x >= 0.0 {
-        r
-    } else {
-        2.0 - r
-    }
+    if x >= 0.0 { r } else { 2.0 - r }
 }
 
 fn erfc_inv(mut y: f64) -> f64 {
@@ -147,11 +143,7 @@ fn erfc_inv(mut y: f64) -> f64 {
         x += err / (FRAC_2_SQRT_PI * (-(x.powi(2))).exp() - x * err)
     }
 
-    if y < 1.0 {
-        x
-    } else {
-        -x
-    }
+    if y < 1.0 { x } else { -x }
 }
 
 fn ppf(p: f64, mu: f64, sigma: f64) -> f64 {
@@ -239,9 +231,9 @@ pub(crate) fn sort_time(xs: &[i64], reverse: bool) -> Vec<usize> {
     let mut x = xs.iter().enumerate().collect::<Vec<_>>();
 
     if reverse {
-        x.sort_by_key(|(_, &x)| Reverse(x));
+        x.sort_by_key(|&(_, x)| Reverse(x));
     } else {
-        x.sort_by_key(|(_, &x)| x);
+        x.sort_by_key(|&(_, x)| x);
     }
 
     x.into_iter().map(|(i, _)| i).collect()
@@ -256,6 +248,7 @@ pub(crate) fn evidence(d: &[DiffMessage], margin: &[f64], tie: &[bool], e: usize
     }
 }
 
+/// Calculates the match quality of the given rating groups. A result is the draw probability in the association
 pub fn quality(rating_groups: &[&[Gaussian]], beta: f64) -> f64 {
     let flatten_ratings = rating_groups
         .iter()
@@ -319,6 +312,8 @@ pub fn quality(rating_groups: &[&[Gaussian]], beta: f64) -> f64 {
 
 #[cfg(test)]
 mod tests {
+    use ::approx::assert_ulps_eq;
+
     use super::*;
 
     #[test]
@@ -330,4 +325,14 @@ mod tests {
     fn test_sort_time() {
         assert_eq!(sort_time(&[0, 1, 2, 0], true), vec![2, 1, 0, 3]);
     }
+
+    #[test]
+    fn test_quality() {
+        let a = Gaussian::from_ms(25.0, 3.0);
+        let b = Gaussian::from_ms(25.0, 3.0);
+
+        let q = quality(&[&[a], &[b]], 25.0 / 3.0 / 2.0);
+
+        assert_ulps_eq!(q, 0.8115343414514944, epsilon = 1e-6)
+    }
 }