Trait DistributedEstimator 

pub trait DistributedEstimator: Send + Sync {
    type TrainingData;
    type PredictionInput;
    type PredictionOutput;
    type Parameters: Serialize + for<'de> Deserialize<'de>;

    // Required methods
    fn fit_distributed<'a>(
        &'a mut self,
        cluster: &'a dyn DistributedCluster,
        training_data: &Self::TrainingData,
    ) -> BoxFuture<'a, Result<()>>;
    fn predict_distributed<'a>(
        &'a self,
        cluster: &dyn DistributedCluster,
        input: &'a Self::PredictionInput,
    ) -> BoxFuture<'a, Result<Self::PredictionOutput>>;
    fn get_parameters(&self) -> Result<Self::Parameters>;
    fn set_parameters(&mut self, params: Self::Parameters) -> Result<()>;
    fn sync_parameters(
        &mut self,
        cluster: &dyn DistributedCluster,
    ) -> BoxFuture<'_, Result<()>>;
    fn training_progress(&self) -> DistributedTrainingProgress;
}

Core trait for distributed machine learning estimators

Required Associated Types

type TrainingData

Associated type for training data

type PredictionInput

Associated type for prediction input

type PredictionOutput

Associated type for prediction output

type Parameters: Serialize + for<'de> Deserialize<'de>

Associated type for model parameters

Required Methods

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

Implementors

impl DistributedEstimator for DistributedLinearRegression

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

type PredictionInput = Vec<Vec<f64>>

type PredictionOutput = Vec<f64>

type Parameters = Vec<f64>