Struct MLService 

pub struct MLService { /* private fields */ }

Machine Learning Service

Implementations

impl MLService

pub fn new() -> Self

Create a new ML service instance

pub fn initialize( &mut self, features_dim: usize, model_version: &str, ) -> AnyaResult<()>

Initialize the ML service with a specific model

pub fn analyze_proposal( &self, proposal: &Proposal, ) -> AnyaResult<HashMap<String, f64>>

Analyze a DAO proposal and return metrics

pub async fn train( &mut self, features: Vec<f64>, labels: Vec<f64>, ) -> AnyaResult<()>

Train the model with new data

pub async fn apply_federated_update( &mut self, weights: Vec<f64>, ) -> AnyaResult<()>

Apply federated learning update

pub async fn predict_proposal_metrics( &self, proposal: &Proposal, ) -> AnyaResult<ProposalMetrics>

[AIR-3][AIS-3][BPC-3][RES-3] Predict proposal metrics based on proposal data This follows official Bitcoin Improvement Proposals (BIPs) standards for ML predictions

pub fn export_model(&self) -> AnyaResult<Vec<u8>>

Export model to bytes for sharing

pub fn import_model(&mut self, bytes: &[u8]) -> AnyaResult<()>

Import model from bytes

Trait Implementations

impl Debug for MLService

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

Formats the value using the given formatter.

impl Default for MLService

fn default() -> Self

Returns the “default value” for a type.

impl MLModel for MLService

fn train(&mut self, data: &[u8]) -> AnyaResult<()>

Train the model with the given data

fn predict(&self, input: &[u8]) -> AnyaResult<Vec<u8>>

Predict using the trained model

fn evaluate(&self, test_data: &[u8]) -> AnyaResult<f64>

Evaluate the model performance

fn get_health_metrics(&self) -> HashMap<String, f64>

Get health metrics for the model