Begin working on batch.

src/batch.rs

@@ -0,0 +1,284 @@
+use std::collections::{HashMap, HashSet};
+
+use crate::{Game, Gaussian, Player, N_INF};
+
+pub struct Skill {
+    pub forward: Gaussian,
+    pub backward: Gaussian,
+    pub likelihood: Gaussian,
+    pub elapsed: f64,
+}
+
+impl Default for Skill {
+    fn default() -> Self {
+        Self {
+            forward: N_INF,
+            backward: N_INF,
+            likelihood: N_INF,
+            elapsed: 0.0,
+        }
+    }
+}
+
+pub struct Agent {
+    pub player: Player,
+    pub message: Gaussian,
+    pub last_time: f64,
+}
+
+impl Agent {
+    pub fn new(player: Player, message: Gaussian, last_time: f64) -> Self {
+        Self {
+            player,
+            message,
+            last_time,
+        }
+    }
+
+    pub fn receive(&self, elapsed: f64) -> Gaussian {
+        if self.message != N_INF {
+            self.message.forget(self.player.gamma, elapsed)
+        } else {
+            self.player.prior
+        }
+    }
+}
+
+pub struct Item {
+    name: String,
+    likelihood: Gaussian,
+}
+
+pub struct Team {
+    items: Vec<Item>,
+    output: u16,
+}
+
+pub struct Event {
+    teams: Vec<Team>,
+    evidence: f64,
+}
+
+impl Event {
+    pub fn names(&self) -> Vec<&str> {
+        self.teams
+            .iter()
+            .flat_map(|team| team.items.iter())
+            .map(|item| item.name.as_str())
+            .collect::<Vec<_>>()
+    }
+
+    pub fn result(&self) -> Vec<u16> {
+        self.teams
+            .iter()
+            .map(|team| team.output)
+            .collect::<Vec<_>>()
+    }
+}
+
+fn compute_elapsed(last_time: f64, actual_time: f64) -> f64 {
+    if last_time == f64::NEG_INFINITY {
+        0.0
+    } else if last_time == f64::INFINITY {
+        1.0
+    } else {
+        actual_time - last_time
+    }
+}
+
+pub struct Batch {
+    skills: HashMap<String, Skill>,
+    events: Vec<Event>,
+    time: f64,
+    agents: HashMap<String, Agent>,
+    p_draw: f64,
+}
+
+impl Batch {
+    pub fn new(
+        composition: Vec<Vec<Vec<&str>>>,
+        results: Vec<Vec<u16>>,
+        time: f64,
+        agents: HashMap<String, Agent>,
+        p_draw: f64,
+    ) -> Self {
+        let mut this = Self {
+            skills: HashMap::new(),
+            events: Vec::new(),
+            time,
+            agents,
+            p_draw,
+        };
+
+        this.add_events(composition, results);
+
+        this
+    }
+
+    pub fn add_events(&mut self, composition: Vec<Vec<Vec<&str>>>, results: Vec<Vec<u16>>) {
+        let this_agent = composition
+            .iter()
+            .flat_map(|teams| teams.iter())
+            .flat_map(|team| team.iter())
+            .cloned()
+            .collect::<HashSet<_>>();
+
+        for a in this_agent {
+            let elapsed = compute_elapsed(self.agents[a].last_time, self.time);
+
+            if let Some(skill) = self.skills.get_mut(a) {
+                skill.elapsed = elapsed;
+                skill.forward = self.agents[a].receive(elapsed);
+            } else {
+                self.skills.insert(
+                    a.to_string(),
+                    Skill {
+                        forward: self.agents[a].receive(elapsed),
+                        ..Default::default()
+                    },
+                );
+            }
+        }
+
+        let from = self.events.len() + 1;
+
+        for e in 0..composition.len() {
+            let teams = (0..composition[e].len())
+                .map(|t| {
+                    let items = (0..composition[e][t].len())
+                        .map(|a| Item {
+                            name: composition[e][t][a].to_string(),
+                            likelihood: N_INF,
+                        })
+                        .collect::<Vec<_>>();
+
+                    Team {
+                        items,
+                        output: results[e][t],
+                    }
+                })
+                .collect::<Vec<_>>();
+
+            let event = Event {
+                teams,
+                evidence: 0.0,
+            };
+
+            self.events.push(event);
+        }
+
+        self.iteration(from);
+    }
+
+    fn posterior(&self, agent: &str) -> Gaussian {
+        let skill = &self.skills[agent];
+
+        skill.likelihood * skill.backward * skill.forward
+    }
+
+    fn posteriors(&self) -> HashMap<&str, Gaussian> {
+        self.skills
+            .keys()
+            .map(|a| (a.as_str(), self.posterior(a)))
+            .collect::<HashMap<_, _>>()
+    }
+
+    fn within_prior(&self, item: &Item) -> Player {
+        let r = &self.agents[&item.name].player;
+        let g = self.posterior(&item.name) / item.likelihood;
+
+        Player::new(g, r.beta, r.gamma, N_INF)
+    }
+
+    fn within_priors(&self, event: usize) -> Vec<Vec<Player>> {
+        self.events[event]
+            .teams
+            .iter()
+            .map(|team| {
+                team.items
+                    .iter()
+                    .map(|item| self.within_prior(item))
+                    .collect::<Vec<_>>()
+            })
+            .collect::<Vec<_>>()
+    }
+
+    fn iteration(&mut self, from: usize) {
+        for e in from..self.events.len() {
+            let teams = self.within_priors(e);
+            let result = self.events[e].result();
+
+            let g = Game::new(teams, result, self.p_draw);
+
+            for (t, team) in self.events[e].teams.iter_mut().enumerate() {
+                for (i, item) in team.items.iter_mut().enumerate() {
+                    self.skills.get_mut(&item.name).unwrap().likelihood =
+                        (self.skills[&item.name].likelihood / item.likelihood)
+                            * g.likelihoods[t][i];
+
+                    item.likelihood = g.likelihoods[t][i];
+                }
+            }
+
+            self.events[e].evidence = g.evidence;
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_one_event_each() {
+        let mut agents = HashMap::new();
+
+        for k in ["a", "b", "c", "d", "e", "f"] {
+            let agent = Agent::new(
+                Player::new(
+                    Gaussian::new(25.0, 25.0 / 3.0),
+                    25.0 / 6.0,
+                    25.0 / 300.0,
+                    N_INF,
+                ),
+                N_INF,
+                f64::NEG_INFINITY,
+            );
+
+            agents.insert(k.to_string(), agent);
+        }
+
+        let b = Batch::new(
+            vec![
+                vec![vec!["a"], vec!["b"]],
+                vec![vec!["c"], vec!["d"]],
+                vec![vec!["e"], vec!["f"]],
+            ],
+            vec![vec![1, 0], vec![0, 1], vec![1, 0]],
+            0.0,
+            agents,
+            0.0,
+        );
+
+        let post = b.posteriors();
+
+        assert_eq!(post["a"].mu(), 29.205);
+        assert_eq!(post["a"].sigma(), 7.194)
+
+        /*
+        agents = dict()
+        for k in ["a", "b", "c", "d", "e", "f"]:
+            agents[k] = ttt.Agent(ttt.Player(ttt.Gaussian(25., 25.0/3), 25.0/6, 25.0/300 ) , ttt.Ninf, -ttt.inf)
+        b = ttt.Batch(composition=[ [["a"],["b"]], [["c"],["d"]] , [["e"],["f"]] ], results= [[1,0],[0,1],[1,0]], time = 0, agents=agents)
+        post = b.posteriors()
+        self.assertAlmostEqual(post["a"].mu,29.205,3)
+        self.assertAlmostEqual(post["a"].sigma,7.194,3)
+
+        self.assertAlmostEqual(post["b"].mu,20.795,3)
+        self.assertAlmostEqual(post["b"].sigma,7.194,3)
+        self.assertAlmostEqual(post["c"].mu,20.795,3)
+        self.assertAlmostEqual(post["c"].sigma,7.194,3)
+        self.assertEqual(b.convergence(),1)
+        */
+    }
+}

src/gaussian.rs

@@ -40,11 +40,8 @@ impl Gaussian {
         self.sigma.powi(-2)
     }
 
-    pub fn forget(&self, gamma: f64, t: u32) -> Self {
-        Self::new(
-            self.mu,
-            (self.sigma().powi(2) + t as f64 * gamma.powi(2)).sqrt(),
-        )
+    pub fn forget(&self, gamma: f64, t: f64) -> Self {
+        Self::new(self.mu, (self.sigma().powi(2) + t * gamma.powi(2)).sqrt())
     }
 
     pub fn delta(&self, m: Gaussian) -> (f64, f64) {

src/lib.rs

@@ -1,3 +1,4 @@
+mod batch;
 mod game;
 mod gaussian;
 mod history;
@@ -6,6 +7,7 @@ mod player;
 mod utils;
 mod variable;
 
+pub use batch::*;
 pub use game::*;
 pub use gaussian::*;
 pub use history::*;

src/player.rs

@@ -6,7 +6,7 @@ use crate::{Gaussian, BETA, GAMMA, N_INF};
 pub struct Player {
     pub prior: Gaussian,
     pub beta: f64,
-    gamma: f64,
+    pub gamma: f64,
     prior_draw: Gaussian,
 }