Struct DistributedSVM 

pub struct DistributedSVM<S> {
    pub c: Float,
    pub kernel: KernelType,
    pub config: DistributedConfig,
    pub strategy: DistributedStrategy,
    pub protocol: CommunicationProtocol,
    pub n_classes: Option<usize>,
    pub support_vectors: Option<Array2<Float>>,
    pub dual_coef: Option<Array1<Float>>,
    pub intercept: Float,
    pub classes: Option<Array1<i32>>,
    pub n_support: Option<Array1<usize>>,
    /* private fields */
}

Distributed SVM classifier

Fields

c: Float

Regularization parameter

kernel: KernelType

Kernel function

config: DistributedConfig

Distributed training configuration

strategy: DistributedStrategy

Training strategy

protocol: CommunicationProtocol

Communication protocol

n_classes: Option<usize>

Number of classes

support_vectors: Option<Array2<Float>>

Support vectors

dual_coef: Option<Array1<Float>>

Dual coefficients

intercept: Float

Intercept

classes: Option<Array1<i32>>

Class labels

n_support: Option<Array1<usize>>

Number of support vectors per class

Implementations

impl DistributedSVM<Untrained>

pub fn new( c: Float, kernel: KernelType, config: DistributedConfig, strategy: DistributedStrategy, protocol: CommunicationProtocol, ) -> Self

Create a new distributed SVM classifier

pub fn builder() -> DistributedSVMBuilder

Builder pattern for configuration

impl DistributedSVM<Untrained>

pub fn fit_distributed( &self, x: ArrayView2<'_, Float>, y: ArrayView1<'_, Float>, ) -> Result<DistributedSVM<Trained>>

Train using distributed approach

Trait Implementations

impl<S: Clone> Clone for DistributedSVM<S>

fn clone(&self) -> DistributedSVM<S>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for DistributedSVM<S>

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

Formats the value using the given formatter.

impl Estimator for DistributedSVM<Untrained>

type Config = DistributedConfig

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Estimator for DistributedSVM<Trained>

type Config = DistributedConfig

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Fit<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<ViewRepr<&f64>, Dim<[usize; 1]>>> for DistributedSVM<Untrained>

type Fitted = DistributedSVM<Trained>

The fitted model type

fn fit( self, x: &ArrayView2<'_, Float>, y: &ArrayView1<'_, Float>, ) -> Result<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl Predict<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for DistributedSVM<Trained>

fn predict(&self, x: &ArrayView2<'_, Float>) -> Result<Array1<Float>>

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals