Enum LearningMode 

pub enum LearningMode {
    QTable,
    MetaAdapt,
    Distill,
    Federated,
    Elastic,
    Informal,
    Smart,
}

The active learning paradigms for the HELM engine.

Each variant corresponds to one sub-module of crate::cognition::learning. The Smart variant enables all paradigms simultaneously.

Examples

use lmm_agent::types::LearningMode;
use std::collections::HashSet;

let all = LearningMode::all();
assert!(all.contains(&LearningMode::QTable));
assert!(all.contains(&LearningMode::Informal));

Variants

QTable

Tabular Bellman TD(0) Q-learning.

MetaAdapt

Prototype-based task meta-adaptation.

Distill

Knowledge distillation from cold store to KnowledgeIndex.

Federated

Self-federated Q-table aggregation across agents.

Elastic

Elastic memory guard (Fisher-analog importance pinning).

Informal

Informal PMI co-occurrence mining.

Smart

Enables all paradigms (alias for the full set).

Implementations

impl LearningMode

pub fn all() -> HashSet<LearningMode>

Returns a HashSet containing all seven LearningMode variants.

Trait Implementations

impl Clone for LearningMode

fn clone(&self) -> LearningMode

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for LearningMode

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for LearningMode

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Hash for LearningMode

fn hash<__H: Hasher>(&self, state: &mut __H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl PartialEq for LearningMode

fn eq(&self, other: &LearningMode) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for LearningMode

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Eq for LearningMode

impl StructuralPartialEq for LearningMode