Clean up

src/game.rs

@@ -82,30 +82,6 @@ impl Game {
     }
 
     fn likelihoods(&mut self) {
-        let m_t_ft = self.likelihood_teams();
-
-        self.likelihoods = self
-            .teams
-            .iter()
-            .zip(self.weights.iter())
-            .zip(m_t_ft)
-            .map(|((p, w), m)| {
-                let performance = p.iter().zip(w.iter()).fold(N00, |p, (player, &weight)| {
-                    p + (player.performance() * weight)
-                });
-
-                p.iter()
-                    .zip(w.iter())
-                    .map(|(p, &w)| {
-                        ((m - performance.exclude(p.performance() * w)) * (1.0 / w))
-                            .forget(p.beta, 1)
-                    })
-                    .collect::<Vec<_>>()
-            })
-            .collect::<Vec<_>>();
-    }
-
-    fn likelihood_teams(&mut self) -> Vec<Gaussian> {
         let o = sort_perm(&self.result, true);
 
         let mut team = o
@@ -143,14 +119,10 @@ impl Game {
         } else {
             o.windows(2)
                 .map(|w| {
-                    if self.p_draw == 0.0 {
-                        0.0
-                    } else {
                     let a: f64 = self.teams[w[0]].iter().map(|a| a.beta.powi(2)).sum();
                     let b: f64 = self.teams[w[1]].iter().map(|a| a.beta.powi(2)).sum();
 
                     compute_margin(self.p_draw, (a + b).sqrt())
-                    }
                 })
                 .collect::<Vec<_>>()
         };
@@ -205,7 +177,27 @@ impl Game {
         team[0].likelihood_win = team[1].posterior_lose() + diff[0].likelihood;
         team[t_end].likelihood_lose = team[t_end - 1].posterior_win() - diff[d_end].likelihood;
 
-        o.iter().map(|&e| team[e].likelihood()).collect::<Vec<_>>()
+        let m_t_ft = o.into_iter().map(|e| team[e].likelihood());
+
+        self.likelihoods = self
+            .teams
+            .iter()
+            .zip(self.weights.iter())
+            .zip(m_t_ft)
+            .map(|((p, w), m)| {
+                let performance = p.iter().zip(w.iter()).fold(N00, |p, (player, &weight)| {
+                    p + (player.performance() * weight)
+                });
+
+                p.iter()
+                    .zip(w.iter())
+                    .map(|(p, &w)| {
+                        ((m - performance.exclude(p.performance() * w)) * (1.0 / w))
+                            .forget(p.beta, 1)
+                    })
+                    .collect::<Vec<_>>()
+            })
+            .collect::<Vec<_>>();
     }
 
     pub fn posteriors(&self) -> Vec<Vec<Gaussian>> {

src/gaussian2.rs

@@ -1,159 +0,0 @@
-use std::ops;
-
-#[derive(Clone, Copy, PartialEq, Debug)]
-pub struct Gaussian {
-    mu: f64,
-    sigma: f64,
-}
-
-impl Gaussian {
-    #[inline(always)]
-    pub const fn from_ms(mu: f64, sigma: f64) -> Self {
-        Self { mu, sigma }
-    }
-
-    #[inline(always)]
-    pub fn from_pt(pi: f64, tau: f64) -> Self {
-        Self::from_ms(tau / pi, (1.0 / pi).sqrt())
-    }
-
-    #[inline(always)]
-    pub fn mu(&self) -> f64 {
-        self.mu
-    }
-
-    #[inline(always)]
-    pub fn sigma(&self) -> f64 {
-        self.sigma
-    }
-
-    #[inline(always)]
-    pub fn pi(&self) -> f64 {
-        if self.sigma > 0.0 {
-            self.sigma.powi(-2)
-        } else {
-            f64::INFINITY
-        }
-    }
-
-    #[inline(always)]
-    pub fn tau(&self) -> f64 {
-        if self.sigma > 0.0 {
-            self.mu * self.pi()
-        } else {
-            f64::INFINITY
-        }
-    }
-}
-
-impl ops::Add<Gaussian> for Gaussian {
-    type Output = Gaussian;
-
-    fn add(self, rhs: Gaussian) -> Self::Output {
-        Self {
-            mu: self.mu + rhs.mu,
-            sigma: (self.sigma.powi(2) + rhs.sigma.powi(2)).sqrt(),
-        }
-    }
-}
-
-impl ops::Sub<Gaussian> for Gaussian {
-    type Output = Gaussian;
-
-    fn sub(self, rhs: Gaussian) -> Self::Output {
-        Self {
-            mu: self.mu - rhs.mu,
-            sigma: (self.sigma.powi(2) + rhs.sigma.powi(2)).sqrt(),
-        }
-    }
-}
-
-impl ops::Mul<Gaussian> for Gaussian {
-    type Output = Gaussian;
-
-    fn mul(self, rhs: Gaussian) -> Self::Output {
-        /*
-        if self.sigma == 0.0 || rhs.sigma == 0.0 {
-            let mu = self.mu / (self.sigma.powi(2) / rhs.sigma.powi(2) + 1.0)
-                + rhs.mu / (rhs.sigma.powi(2) / self.sigma.powi(2) + 1.0);
-
-            let sigma = (1.0 / ((1.0 / self.sigma.powi(2)) + (1.0 / rhs.sigma.powi(2)))).sqrt();
-
-            Self::from_ms(mu, sigma)
-        } else {
-            Self::from_pt(self.pi() + rhs.pi(), self.tau() + rhs.tau())
-        }
-        */
-
-        Self::from_pt(self.pi() + rhs.pi(), self.tau() + rhs.tau())
-    }
-}
-
-impl ops::Div<Gaussian> for Gaussian {
-    type Output = Gaussian;
-
-    fn div(self, rhs: Gaussian) -> Self::Output {
-        /*
-        let (mu, sigma) = if self.sigma == 0.0 || rhs.sigma == 0.0 {
-            let mu = self.mu / (1.0 - self.sigma.powi(2) / rhs.sigma.powi(2))
-                + rhs.mu / (rhs.sigma.powi(2) / self.sigma.powi(2) - 1.0);
-
-            let sigma = (1.0 / ((1.0 / self.sigma.powi(2)) - (1.0 / rhs.sigma.powi(2)))).sqrt();
-
-            Self::from_ms(mu, sigma)
-        } else {
-            Self::from_pt(self.pi() - rhs.pi(), self.tau() - rhs.tau())
-        }
-        */
-
-        Self::from_pt(self.pi() - rhs.pi(), self.tau() - rhs.tau())
-    }
-}
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    #[test]
-    fn test_add() {
-        let n = Gaussian::from_ms(25.0, 25.0 / 3.0);
-        let m = Gaussian::from_ms(0.0, 1.0);
-
-        assert_eq!(n + m, Gaussian::from_ms(25.0, 8.393118874676116));
-    }
-
-    #[test]
-    fn test_sub() {
-        let n = Gaussian::from_ms(25.0, 25.0 / 3.0);
-        let m = Gaussian::from_ms(1.0, 1.0);
-
-        assert_eq!(n - m, Gaussian::from_ms(24.0, 8.393118874676116));
-    }
-
-    #[test]
-    fn test_mul() {
-        let n = Gaussian::from_ms(25.0, 25.0 / 3.0);
-        let m = Gaussian::from_ms(0.0, 1.0);
-
-        assert_eq!(
-            n * m,
-            Gaussian::from_ms(0.35488958990536273, 0.992876838486922)
-        );
-    }
-
-    #[test]
-    fn test_div() {
-        let n = Gaussian::from_ms(25.0, 25.0 / 3.0);
-        let m = Gaussian::from_ms(0.0, 1.0);
-
-        assert_eq!(
-            m / n,
-            Gaussian::from_ms(-0.3652597402597402, 1.0072787050317253)
-        );
-
-        assert_eq!(
-            n / m,
-            Gaussian::from_ms(-0.3652597402597402, 1.0072787050317253)
-        );
-    }
-}