T3: rayon-backed concurrency (opt-in) #2
@@ -262,17 +262,45 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
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/// Note: `key(idx)` is O(n) per lookup; this method is therefore O(n²)
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/// in the number of competitors. Acceptable for T2; T3 may optimize.
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pub fn learning_curves(&self) -> HashMap<K, Vec<(T, Gaussian)>> {
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let mut data: HashMap<K, Vec<(T, Gaussian)>> = HashMap::new();
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for slice in &self.time_slices {
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for (idx, skill) in slice.skills.iter() {
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if let Some(key) = self.keys.key(idx).cloned() {
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data.entry(key)
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.or_default()
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.push((slice.time, skill.posterior()));
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#[cfg(feature = "rayon")]
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{
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use rayon::prelude::*;
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let per_slice: Vec<Vec<(Index, T, Gaussian)>> = self
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.time_slices
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.par_iter()
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.map(|ts| {
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ts.skills
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.iter()
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.map(|(idx, sk)| (idx, ts.time, sk.posterior()))
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.collect()
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})
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.collect();
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let mut data: HashMap<K, Vec<(T, Gaussian)>> = HashMap::new();
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for slice_contrib in per_slice {
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for (idx, t, g) in slice_contrib {
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if let Some(key) = self.keys.key(idx).cloned() {
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data.entry(key).or_default().push((t, g));
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}
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}
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}
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data
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}
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#[cfg(not(feature = "rayon"))]
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{
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let mut data: HashMap<K, Vec<(T, Gaussian)>> = HashMap::new();
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for slice in &self.time_slices {
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for (idx, skill) in slice.skills.iter() {
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if let Some(key) = self.keys.key(idx).cloned() {
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data.entry(key)
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.or_default()
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.push((slice.time, skill.posterior()));
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}
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}
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}
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data
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}
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data
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}
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/// Skill estimate at the latest time slice the competitor appears in.
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