Struct IncrementalMultiOutputRegression 

pub struct IncrementalMultiOutputRegression<S = Untrained> { /* private fields */ }

Incremental Multi-Output Regressor

Online learning algorithm that can learn from data streams with multiple outputs. Uses stochastic gradient descent with optional adaptive learning rates.

Examples

use sklears_multioutput::streaming::{IncrementalMultiOutputRegression, IncrementalMultiOutputRegressionConfig};
// Use SciRS2-Core for arrays and random number generation (SciRS2 Policy)
use scirs2_core::ndarray::array;
use sklears_core::traits::{Fit, Predict};

let X = array![[1.0, 2.0], [2.0, 3.0], [3.0, 4.0]];
let y = array![[1.0, 2.0], [2.0, 3.0], [3.0, 4.0]];

let mut model = IncrementalMultiOutputRegression::new();
let trained = model.fit(&X.view(), &y.view()).unwrap();

// Continue learning with new data
let X_new = array![[4.0, 5.0]];
let y_new = array![[4.0, 5.0]];
let updated = trained.partial_fit(&X_new.view(), &y_new.view()).unwrap();

let predictions = updated.predict(&X.view()).unwrap();
assert_eq!(predictions.dim(), (3, 2));

Implementations

impl IncrementalMultiOutputRegression<Untrained>

pub fn new() -> Self

Create a new incremental multi-output regressor

pub fn config(self, config: IncrementalMultiOutputRegressionConfig) -> Self

Set the configuration

pub fn learning_rate(self, lr: Float) -> Self

Set the learning rate

pub fn alpha(self, alpha: Float) -> Self

Set the regularization parameter

pub fn fit_intercept(self, fit_intercept: bool) -> Self

Set whether to fit intercept

impl IncrementalMultiOutputRegression<IncrementalMultiOutputRegressionTrained>

pub fn partial_fit( self, X: &ArrayView2<'_, Float>, y: &ArrayView2<'_, Float>, ) -> SklResult<Self>

Partial fit on new data (incremental learning)

pub fn coef(&self) -> &Array2<Float>

Get the current coefficients

pub fn intercept(&self) -> &Array1<Float>

Get the current intercept

pub fn n_samples_seen(&self) -> usize

Get number of samples seen

pub fn current_learning_rate(&self) -> Float

Get current learning rate

Trait Implementations

impl<S: Clone> Clone for IncrementalMultiOutputRegression<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for IncrementalMultiOutputRegression<S>

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

Formats the value using the given formatter.

impl Default for IncrementalMultiOutputRegression<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for IncrementalMultiOutputRegression<Untrained>

type Config = IncrementalMultiOutputRegressionConfig

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 IncrementalMultiOutputRegression<IncrementalMultiOutputRegressionTrained>

type Config = IncrementalMultiOutputRegressionConfig

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; 2]>>> for IncrementalMultiOutputRegression<Untrained>

type Fitted = IncrementalMultiOutputRegression<IncrementalMultiOutputRegressionTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, Float>, y: &ArrayView2<'_, Float>, ) -> SklResult<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; 2]>>> for IncrementalMultiOutputRegression<IncrementalMultiOutputRegressionTrained>

fn predict(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>>

Make predictions on the provided data

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

Make predictions with confidence intervals