Struct DeepGaussianProcess 

pub struct DeepGaussianProcess {
    pub layer_dims: Vec<usize>,
    pub num_inducing: usize,
    pub epochs: usize,
    pub learning_rate: f64,
    pub noise_variance: f64,
    pub random_state: Option<u64>,
    /* private fields */
}

Deep Gaussian Process for semi-supervised learning

This implements a multi-layer composition of Gaussian processes that can learn complex non-linear mappings while providing principled uncertainty quantification for semi-supervised learning scenarios.

Fields

layer_dims: Vec<usize>

layer_dims

num_inducing: usize

num_inducing

epochs: usize

epochs

learning_rate: f64

learning_rate

noise_variance: f64

noise_variance

random_state: Option<u64>

random_state

Implementations

impl DeepGaussianProcess

pub fn new() -> Self

pub fn layer_dims(self, layer_dims: Vec<usize>) -> Result<Self>

pub fn num_inducing(self, num_inducing: usize) -> Result<Self>

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

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

pub fn noise_variance(self, noise_variance: f64) -> Result<Self>

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

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

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

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

pub fn predict_with_uncertainty( &self, X: &ArrayView2<'_, f64>, ) -> Result<(Array1<i32>, Array2<f64>)>

Trait Implementations

impl Clone for DeepGaussianProcess

fn clone(&self) -> DeepGaussianProcess

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for DeepGaussianProcess

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

Formats the value using the given formatter.

impl Default for DeepGaussianProcess

fn default() -> Self

Returns the “default value” for a type.

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

type Fitted = FittedDeepGaussianProcess

The fitted model type

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

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