Struct DecayAwareSalienceObjective 

pub struct DecayAwareSalienceObjective { /* private fields */ }

Scores a NoteCandidate by salience with configurable temporal decay.

Uses the fixed exponential rate supplied at construction: salience * exp(-decay_rate * age_days). It does not read the candidate’s per-note decay_factor. The memory recall pipeline applies its configured DecayModel before scoring and uses AmplifiedDecayAwareSalienceObjective over the resulting effective_salience instead.

This objective remains useful for callers that want one decay policy across every candidate in a WeightedObjective composition.

Implementations

impl DecayAwareSalienceObjective

pub fn new(decay_rate: f64) -> Self

Create a new objective with the given exponential decay rate. Panics if decay_rate is negative or non-finite.

decay_rate = 0.01 gives a ~69-day half-life (default for memory notes).

pub fn default_memory() -> Self

Default memory decay rate: 0.01 (~69-day half-life).

Trait Implementations

impl Objective<NoteCandidate> for DecayAwareSalienceObjective

fn score(&self, candidate: &NoteCandidate, _context: &ObjectiveContext) -> f64

Evaluate a single candidate.

fn name(&self) -> &str

Get the name of this objective.

fn precision(&self, _candidate: &T, _context: &ObjectiveContext) -> f64

Precision (inverse variance) of the score estimate; default 1.0 (fully trusted).

fn passes_score(&self, score: f64, context: &ObjectiveContext) -> bool

Check if a score passes the threshold; non-finite scores never pass.

fn passes(&self, candidate: &T, context: &ObjectiveContext) -> bool

Check if a candidate passes the threshold.

fn batch_score( &self, candidates: &[T], context: &ObjectiveContext, ) -> Vec<(usize, f64)>

Score a batch of candidates and return passing (index, score) pairs.

fn select<'a>( &self, candidates: &'a [T], context: &ObjectiveContext, ) -> Vec<Selection<&'a T>>

Select all passing candidates in score-descending order.

fn select_top<'a>( &self, candidates: &'a [T], n: usize, context: &ObjectiveContext, ) -> Vec<Selection<&'a T>>

Select the top N candidates by precision-weighted score.