Added todo to readme, and documentation for quality function

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

@@ -256,6 +256,7 @@ 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()