logaritmisk/kickscore
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Small clean up

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This commit is contained in:
Anders Olsson
2022-06-07 10:01:01 +02:00
parent a5c6e8b8a2
commit 5a54d460c9
3 changed files with 14 additions and 13 deletions
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20
examples/abcdef.rs
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@@ -5,11 +5,11 @@ use kickscore as ks;
fn main() { fn main() {
let mut model = ks::model::Binary::probit(); let mut model = ks::model::Binary::probit();
for player in &["A", "B", "C", "D", "E", "F"] { for player in ["A", "B", "C", "D", "E", "F"] {
let kernel: [Box<dyn ks::Kernel>; 2] = [ let kernel = (
Box::new(ks::kernel::Constant::new(1.0)), ks::kernel::Constant::new(1.0),
Box::new(ks::kernel::Matern52::new(0.5, 1.0)), ks::kernel::Matern52::new(0.5, 1.0),
]; );
model.add_item(player, kernel); model.add_item(player, kernel);
} }
@@ -18,7 +18,7 @@ fn main() {
model.fit(); model.fit();
for player in &["A", "B", "C", "D", "E", "F"] { for player in ["A", "B", "C", "D", "E", "F"] {
let (mu, sigma) = model.item_score(player, 1.25); let (mu, sigma) = model.item_score(player, 1.25);
println!("{}: mu={} sigma={}", player, mu, sigma); println!("{}: mu={} sigma={}", player, mu, sigma);
@@ -28,7 +28,7 @@ fn main() {
model.fit(); model.fit();
for player in &["A", "B", "C", "D", "E", "F"] { for player in ["A", "B", "C", "D", "E", "F"] {
let (mu, sigma) = model.item_score(player, 1.25); let (mu, sigma) = model.item_score(player, 1.25);
println!("{}: mu={} sigma={}", player, mu, sigma); println!("{}: mu={} sigma={}", player, mu, sigma);
@@ -38,7 +38,7 @@ fn main() {
model.fit(); model.fit();
for player in &["A", "B", "C", "D", "E", "F"] { for player in ["A", "B", "C", "D", "E", "F"] {
let (mu, sigma) = model.item_score(player, 1.25); let (mu, sigma) = model.item_score(player, 1.25);
println!("{}: mu={} sigma={}", player, mu, sigma); println!("{}: mu={} sigma={}", player, mu, sigma);
@@ -48,7 +48,7 @@ fn main() {
model.fit(); model.fit();
for player in &["A", "B", "C", "D", "E", "F"] { for player in ["A", "B", "C", "D", "E", "F"] {
let (mu, sigma) = model.item_score(player, 1.25); let (mu, sigma) = model.item_score(player, 1.25);
println!("{}: mu={} sigma={}", player, mu, sigma); println!("{}: mu={} sigma={}", player, mu, sigma);
@@ -58,7 +58,7 @@ fn main() {
model.fit(); model.fit();
for player in &["A", "B", "C", "D", "E", "F"] { for player in ["A", "B", "C", "D", "E", "F"] {
let (mu, sigma) = model.item_score(player, 1.25); let (mu, sigma) = model.item_score(player, 1.25);
println!("{}: mu={} sigma={}", player, mu, sigma); println!("{}: mu={} sigma={}", player, mu, sigma);

5
src/fitter/recursive.rs
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@@ -7,6 +7,7 @@ use crate::kernel::Kernel;
use super::Fitter; use super::Fitter;
#[derive(Clone)]
pub struct Recursive<K> { pub struct Recursive<K> {
ts_new: Vec<f64>, ts_new: Vec<f64>,
kernel: K, kernel: K,
@@ -232,7 +233,7 @@ impl<K: Kernel> Fitter for Recursive<K> {
self.ts self.ts
.iter() .iter()
.position(|tc| t <= tc) .position(|tc| t <= tc)
.unwrap_or_else(|| self.ts.len()) .unwrap_or(self.ts.len())
}); });
for (i, nxt) in locations.into_iter().enumerate() { for (i, nxt) in locations.into_iter().enumerate() {
@@ -415,7 +416,7 @@ mod tests {
} }
fn data_loglik() -> f64 { fn data_loglik() -> f64 {
-17.357282245711051 -17.35728224571105
} }
fn data_ts_pred() -> &'static [f64] { fn data_ts_pred() -> &'static [f64] {

2
src/storage.rs
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@@ -3,7 +3,7 @@ use std::collections::HashMap;
use crate::item::Item; use crate::item::Item;
/* /*
pub trait Storage { pub trait Backend {
type Key; type Key;
type IterKeys: type IterKeys: