logaritmisk/trueskill-rs
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New structure implemented, test passes. Now I will have to add a new test with different teams to verify that it works.

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This commit is contained in:
Anders Olsson
2018-10-25 17:16:30 +02:00
parent 2b950a0caa
commit eceaf8f6e0
1 changed files with 77 additions and 136 deletions
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213
src/lib.rs
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@@ -72,34 +72,29 @@ fn draw_margin(p: f64, beta: f64, total_players: f64) -> f64 {
math::icdf((p + 1.0) / 2.0) * total_players.sqrt() * beta
}
pub fn rate<R>(rating_groups: &[(R, u16)], ranks: &[u16]) -> Vec<Rating>
pub fn rate<R>(ratings: &[(R, u16)], ranks: &[u16]) -> Vec<Rating>
where
R: Rateable,
{
// TODO Validate rating_groups is orderded in teams.
// TODO Validate that teams are orderd after rank.
let flatten_ratings = rating_groups
.iter()
.map(|group| &group.0)
.collect::<Vec<_>>();
let size = flatten_ratings.len();
let tau_sqr = TAU.powi(2);
let beta_sqr = BETA.powi(2);
let mut variable_arena = VariableArena::new();
// ---------------------------------------
let ratings = rating_groups;
let rating_count = ratings.len();
let team_count = ranks.len();
let rating_vars = (0..rating_count).map(|_| variable_arena.create()).collect::<Vec<_>>();
let perf_vars = (0..rating_count).map(|_| variable_arena.create()).collect::<Vec<_>>();
let perf_vars = ratings
.iter()
.map(|(_, team)| {
(variable_arena.create(), *team)
})
.collect::<Vec<_>>();
let team_perf_vars = (0..team_count).map(|_| variable_arena.create()).collect::<Vec<_>>();
let team_diff_vars = (0..team_count - 1).map(|_| variable_arena.create()).collect::<Vec<_>>();
@@ -107,7 +102,7 @@ where
let rating_layer = rating_vars
.iter()
.zip(flatten_ratings.iter())
.zip(ratings.iter().map(|(rating, _)| rating))
.map(|(rating_var, rating)| {
let gaussian = Gaussian::from_mu_sigma(rating.mu(), (rating.sigma().powi(2) + tau_sqr).sqrt());
@@ -124,7 +119,7 @@ where
let perf_layer = rating_vars
.iter()
.zip(perf_vars.iter())
.zip(perf_vars.iter().map(|(variable, _)| variable))
.map(|(rating_var, perf)| {
factor_id += 1;
@@ -138,165 +133,111 @@ where
})
.collect::<Vec<_>>();
// let team_perf_layer = team_perf_vars.iter();
let team_perf_layer = team_perf_vars
.iter()
.enumerate()
.map(|(i, variable)| {
factor_id += 1;
/*
def build_team_perf_layer():
for team, team_perf_var in enumerate(team_perf_vars):
if team > 0:
start = team_sizes[team - 1]
else:
start = 0
end = team_sizes[team]
child_perf_vars = perf_vars[start:end]
coeffs = flatten_weights[start:end]
yield SumFactor(team_perf_var, child_perf_vars, coeffs)
*/
let team = perf_vars
.iter()
.filter(|(_, team)| *team as usize == i)
.map(|(variable, _)| *variable)
.collect::<Vec<_>>();
// ---------------------------------------
let team_count = team.len();
let mut ss = Vec::new();
let mut ps = Vec::new();
let mut ts = Vec::new();
let mut ds = Vec::new();
SumFactor::new(
&mut variable_arena,
factor_id,
*variable,
team,
vec![1.0; team_count],
)
})
.collect::<Vec<_>>();
for _ in 0..size {
ss.push(variable_arena.create());
ps.push(variable_arena.create());
ts.push(variable_arena.create());
}
let team_diff_layer = team_diff_vars
.iter()
.zip(team_perf_vars.windows(2))
.map(|(variable, teams)| {
factor_id += 1;
for _ in 0..size - 1 {
ds.push(variable_arena.create());
}
SumFactor::new(
&mut variable_arena,
factor_id,
*variable,
teams.to_vec(),
vec![1.0, -1.0],
)
let mut factor_id = 0;
let mut skill = Vec::new();
for (i, rating) in flatten_ratings.iter().enumerate() {
let variable = ss[i];
let gaussian =
Gaussian::from_mu_sigma(rating.mu(), (rating.sigma().powi(2) + tau_sqr).sqrt());
skill.push(PriorFactor::new(
&mut variable_arena,
factor_id,
variable,
gaussian,
));
factor_id += 1;
}
let mut skill_to_perf = Vec::new();;
for i in 0..size {
skill_to_perf.push(LikelihoodFactor::new(
&mut variable_arena,
factor_id,
ss[i],
ps[i],
beta_sqr,
));
factor_id += 1;
}
let mut perf_to_team = Vec::new();;
for i in 0..size {
perf_to_team.push(SumFactor::new(
&mut variable_arena,
factor_id,
ts[i],
vec![ps[i]],
vec![1.0],
));
factor_id += 1;
}
let mut team_diff = Vec::new();;
for i in 0..size - 1 {
team_diff.push(SumFactor::new(
&mut variable_arena,
factor_id,
ds[i],
vec![ts[i], ts[i + 1]],
vec![1.0, -1.0],
));
factor_id += 1;
}
let mut trunc = Vec::new();
})
.collect::<Vec<_>>();
let epsilon = draw_margin(DRAW_PROBABILITY, BETA, 2.0);
for i in 0..size - 1 {
trunc.push(TruncateFactor::new(
&mut variable_arena,
factor_id,
ds[i],
epsilon,
ranks[i] == ranks[i + 1],
));
let trunc_layer = team_diff_vars
.iter()
.enumerate()
.map(|(i, variable)| {
factor_id += 1;
factor_id += 1;
}
TruncateFactor::new(
&mut variable_arena,
factor_id,
*variable,
epsilon,
ranks[i] == ranks[i + 1],
)
for factor in &skill {
})
.collect::<Vec<_>>();;
for factor in &rating_layer {
factor.start(&mut variable_arena);
}
for factor in &skill_to_perf {
for factor in &perf_layer {
factor.update_value(&mut variable_arena);
}
for factor in &perf_to_team {
for factor in &team_perf_layer {
factor.update_sum(&mut variable_arena);
}
for _ in 0..5 {
for factor in &team_diff {
for factor in &team_diff_layer {
factor.update_sum(&mut variable_arena);
}
for factor in &trunc {
for factor in &trunc_layer {
factor.update(&mut variable_arena);
}
for factor in &team_diff {
for factor in &team_diff_layer {
factor.update_term(&mut variable_arena, 0);
factor.update_term(&mut variable_arena, 1);
}
}
for factor in &perf_to_team {
for factor in &team_perf_layer {
factor.update_term(&mut variable_arena, 0);
}
for factor in &skill_to_perf {
for factor in &perf_layer {
factor.update_mean(&mut variable_arena);
}
let mut ratings = Vec::new();
for i in 0..size {
let value = variable_arena
.get(ss[i])
.map(|variable| variable.get_value())
.unwrap();
ratings.push(Rating {
mu: value.mu(),
sigma: value.sigma(),
});
}
ratings
rating_vars
.iter()
.map(|variable| variable_arena.get(*variable).unwrap().get_value())
.map(|value| {
Rating {
mu: value.mu(),
sigma: value.sigma(),
}
})
.collect::<Vec<_>>()
}
pub fn quality<R>(rating_groups: &[&[R]]) -> f64