Struct ThresholdFlatModel

pub struct ThresholdFlatModel;

The ThresholdFlat model filters candidates based on a minimum flat weight threshold. Only candidates whose weight meets or exceeds the threshold are included in the output.

Concept: Minimum Weight Filtering

This is useful in elections or ranking systems where very low-contributing candidates should be excluded from consideration.

Characteristics:

• Threshold-based: Filters out candidates below a specified weight.
• Self-inclusive: Candidate’s own weight counts toward the threshold.
• Deterministic ordering: Relative ordering of remaining candidates is preserved from the input (or can be further sorted in downstream models).
• Context-driven: Uses an external [ThresholdModelConfig] struct to provide the threshold.

Applications:

• Removing candidates with insufficient stake or support.
• Pre-filtering before proportional or ranked elections.
• Reward distribution where a minimum contribution is required.

Example:

let input = vec![
    ("Alice", 90),
    ("Bob", 50),
    ("Charlie", 30),
];
let config = ThresholdModelConfig { threshold: 50 };
let output = ThresholdModel::compute(input, Some(config));
assert_eq!(output, vec!["Alice", "Bob"]);

References:

• Thresholding and Filtering - Wikipedia
• Standard minimum weight / eligibility filtering in proportional systems.

Trait Implementations

impl Debug for ThresholdFlatModel

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ThresholdFlatModel

fn default() -> ThresholdFlatModel

Returns the “default value” for a type.

impl<Candidate, FlatWeight, Input, Output> PurePluginModel<Input, ThresholdFlatModelConfig<FlatWeight>, Output> for ThresholdFlatModel

where Input: IntoIterator<Item = (Candidate, FlatWeight)> + Clone, Output: FromIterator<Candidate>, FlatWeight: Ord + Clone, Candidate: Clone,

fn compute( &self, input: Input, ctx: &ThresholdFlatModelConfig<FlatWeight>, ) -> Output

Filters candidates below a configurable flat weight threshold.

• Iterates over all (Candidate, FlatWeight) pairs.
• Keeps only candidates where weight >= ctx.threshold.
• Returns an iterator over the remaining candidates.