Struct DistributedLinearRegression 

pub struct DistributedLinearRegression { /* private fields */ }

Example distributed linear regression implementation

Implementations

impl DistributedLinearRegression

pub fn new() -> Self

Create a new distributed linear regression model

pub fn with_config(self, config: DistributedTrainingConfig) -> Self

Configure the distributed training parameters

Trait Implementations

impl Debug for DistributedLinearRegression

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

Formats the value using the given formatter.

impl Default for DistributedLinearRegression

fn default() -> Self

Returns the “default value” for a type.

impl DistributedEstimator for DistributedLinearRegression

type TrainingData = (Vec<Vec<f64>>, Vec<f64>)

Associated type for training data

type PredictionInput = Vec<Vec<f64>>

Associated type for prediction input

type PredictionOutput = Vec<f64>

Associated type for prediction output

type Parameters = Vec<f64>

Associated type for model parameters

fn fit_distributed<'a>( &'a mut self, _cluster: &'a dyn DistributedCluster, training_data: &Self::TrainingData, ) -> BoxFuture<'a, Result<()>>

Fit the model using distributed training

fn predict_distributed<'a>( &'a self, _cluster: &dyn DistributedCluster, input: &'a Self::PredictionInput, ) -> BoxFuture<'a, Result<Self::PredictionOutput>>

Make predictions using the distributed model

fn get_parameters(&self) -> Result<Self::Parameters>

Get current model parameters

fn set_parameters(&mut self, params: Self::Parameters) -> Result<()>

Set model parameters

fn sync_parameters( &mut self, _cluster: &dyn DistributedCluster, ) -> BoxFuture<'_, Result<()>>

Synchronize parameters across cluster nodes

fn training_progress(&self) -> DistributedTrainingProgress

Get training progress information