quantrs2_device/ml_optimization/
ensemble.rs

1//! Ensemble Learning Configuration Types
2
3use serde::{Deserialize, Serialize};
4
5/// Ensemble configuration
6#[derive(Debug, Clone, Serialize, Deserialize)]
7pub struct EnsembleConfig {
8    /// Enable ensemble methods
9    pub enable_ensemble: bool,
10    /// Ensemble methods
11    pub ensemble_methods: Vec<EnsembleMethod>,
12    /// Number of models in ensemble
13    pub num_models: usize,
14    /// Voting strategy
15    pub voting_strategy: VotingStrategy,
16    /// Diversity measures
17    pub diversity_measures: Vec<DiversityMeasure>,
18    /// Dynamic ensemble selection
19    pub dynamic_selection: bool,
20}
21
22/// Ensemble methods
23#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
24pub enum EnsembleMethod {
25    Bagging,
26    Boosting,
27    Stacking,
28    VotingClassifier,
29    RandomSubspace,
30    DynamicSelection,
31}
32
33/// Voting strategies
34#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
35pub enum VotingStrategy {
36    Majority,
37    Weighted,
38    Stacking,
39    BayesianAveraging,
40    PerformanceBased,
41}
42
43/// Diversity measures
44#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
45pub enum DiversityMeasure {
46    PairwiseDisagreement,
47    EntropyMeasure,
48    CorrelationCoefficient,
49    QStatistic,
50    KappaDiversity,
51}
quantrs2_device/ml_optimization/ensemble.rs

quantrs2_device/ml_optimization/
ensemble.rs
