Struct MultiOutputMLP 

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

Multi-Layer Perceptron for Multi-Output Learning

This neural network can handle both regression and classification tasks with multiple outputs. It supports configurable architecture, activation functions, and training parameters.

Examples

use sklears_multioutput::mlp::{MultiOutputMLP};
use sklears_multioutput::activation::ActivationFunction;
use sklears_multioutput::loss::LossFunction;
use sklears_core::traits::{Predict, Fit};
// Use SciRS2-Core for arrays and random number generation (SciRS2 Policy)
use scirs2_core::ndarray::array;

let X = array![[1.0, 2.0], [2.0, 3.0], [3.0, 1.0], [4.0, 4.0]];
let y = array![[0.5, 1.2], [1.0, 2.1], [1.5, 0.8], [2.0, 2.5]]; // Multi-output regression

let mlp = MultiOutputMLP::new()
    .hidden_layer_sizes(vec![10, 5])
    .activation(ActivationFunction::ReLU)
    .output_activation(ActivationFunction::Linear)
    .loss_function(LossFunction::MeanSquaredError)
    .learning_rate(0.01)
    .max_iter(1000)
    .random_state(Some(42));

let trained_mlp = mlp.fit(&X.view(), &y).unwrap();
let predictions = trained_mlp.predict(&X.view()).unwrap();

Implementations

impl MultiOutputMLP<Untrained>

pub fn new() -> Self

Create a new MultiOutputMLP instance

pub fn hidden_layer_sizes(self, sizes: Vec<usize>) -> Self

Set hidden layer sizes

pub fn activation(self, activation: ActivationFunction) -> Self

Set activation function for hidden layers

pub fn output_activation(self, activation: ActivationFunction) -> Self

Set activation function for output layer

pub fn loss_function(self, loss_function: LossFunction) -> Self

Set loss function

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

Set learning rate

pub fn max_iter(self, max_iter: usize) -> Self

Set maximum number of iterations

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

Set convergence tolerance

pub fn random_state(self, random_state: Option<u64>) -> Self

Set random state for reproducibility

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

Set L2 regularization parameter

pub fn batch_size(self, batch_size: Option<usize>) -> Self

Set batch size for mini-batch gradient descent

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

Enable early stopping

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

Set validation fraction for early stopping

impl MultiOutputMLP<MultiOutputMLPTrained>

pub fn loss_curve(&self) -> &[Float]

Get the loss curve from training

pub fn n_iter(&self) -> usize

Get the number of iterations performed during training

pub fn weights(&self) -> &[Array2<Float>]

Get the network weights

pub fn biases(&self) -> &[Array1<Float>]

Get the network biases

impl MultiOutputMLP

pub fn new_classifier() -> Self

Create a new classifier with appropriate defaults

impl MultiOutputMLP

pub fn new_regressor() -> Self

Create a new regressor with appropriate defaults

Trait Implementations

impl<S: Clone> Clone for MultiOutputMLP<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for MultiOutputMLP<S>

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

Formats the value using the given formatter.

impl Default for MultiOutputMLP<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for MultiOutputMLP<Untrained>

type Config = ()

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<OwnedRepr<f64>, Dim<[usize; 2]>>> for MultiOutputMLP<Untrained>

type Fitted = MultiOutputMLP<MultiOutputMLPTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, Float>, y: &Array2<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 MultiOutputMLP<MultiOutputMLPTrained>

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