Struct AmplifiedDecayAwareSalienceObjective 

pub struct AmplifiedDecayAwareSalienceObjective {
    pub alpha: f64,
}

Scores a NoteCandidate by salience with exponential decay and a non-linear amplification exponent applied after decay.

Formula: (salience * exp(-decay_factor * age_days)) ^ alpha

With alpha > 1.0, high-salience memories rank more clearly above low-salience ones when relevance is similar. At alpha = 1.5 (the memory-recall default), salience 0.9 → 0.854 and salience 0.3 → 0.164 — a ~5.2× spread vs the ~3× linear spread. Keep alpha ≤ 2.0; values above 2 compress near-zero salience toward 0.

Used by the memory recall pipeline to make salience a meaningful tiebreaker without dominating relevance at the default weight of 0.20.

Fields

alpha: f64

Power applied to the decayed salience value. Must be > 0.

Implementations

impl AmplifiedDecayAwareSalienceObjective

pub fn new(alpha: f64) -> Self

Create with the given amplification exponent.

pub fn default_memory() -> Self

Default memory alpha from the memory recall handler: 1.5.

Trait Implementations

impl Objective<NoteCandidate> for AmplifiedDecayAwareSalienceObjective

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.