Struct AutoregressiveModel 

pub struct AutoregressiveModel { /* private fields */ }

Autoregressive neural network for semi-supervised learning

This implements an autoregressive model that learns to generate sequences by predicting the next element given previous elements. For semi-supervised learning, it combines generative modeling with discriminative classification.

Implementations

impl AutoregressiveModel

pub fn new() -> Self

Create a new autoregressive model

pub fn hidden_dims(self, dims: Vec<usize>) -> Self

Set the hidden layer dimensions

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

Set the number of classes

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

Set the input dimension

pub fn sequence_length(self, length: usize) -> Self

Set the sequence length

pub fn learning_rate(self, lr: f64) -> Self

Set the learning rate

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

Set the number of epochs

pub fn regularization(self, reg: f64) -> Self

Set the regularization parameter

pub fn temperature(self, temp: f64) -> Self

Set the temperature for sampling

pub fn classification_weight(self, weight: f64) -> Self

Set the classification weight

pub fn generate_sequence( &self, initial_context: &ArrayView1<'_, f64>, length: usize, ) -> Result<Array1<f64>, SklearsError>

Generate a sequence using the autoregressive model

pub fn log_likelihood( &self, sequence: &ArrayView1<'_, f64>, ) -> Result<f64, SklearsError>

Compute log-likelihood of a sequence

Trait Implementations

impl Clone for AutoregressiveModel

fn clone(&self) -> AutoregressiveModel

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for AutoregressiveModel

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

Formats the value using the given formatter.

impl Default for AutoregressiveModel

fn default() -> Self

Returns the “default value” for a type.

impl Fit<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<ViewRepr<&i32>, Dim<[usize; 1]>>> for AutoregressiveModel

type Fitted = AutoregressiveModel

The fitted model type

fn fit( self, X: &ArrayView2<'_, f64>, y: &ArrayView1<'_, i32>, ) -> Result<Self::Fitted, SklearsError>

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<i32>, Dim<[usize; 1]>>> for AutoregressiveModel

fn predict(&self, X: &ArrayView2<'_, f64>) -> Result<Array1<i32>, SklearsError>

Make predictions on the provided data

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

Make predictions with confidence intervals

impl PredictProba<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for AutoregressiveModel

fn predict_proba( &self, X: &ArrayView2<'_, f64>, ) -> Result<Array2<f64>, SklearsError>

Predict class probabilities