Struct StreamingKAN 

pub struct StreamingKAN { /* private fields */ }

Streaming KAN model with online B-spline learning.

Processes one sample at a time. Each edge in the network learns a univariate B-spline activation function via per-sample SGD. Online Welford normalization ensures stable input distribution.

Example

use irithyll::kan::{StreamingKAN, KANConfig};
use irithyll::StreamingLearner;

let config = KANConfig::builder()
    .layer_sizes(vec![3, 10, 1])
    .lr(0.01)
    .build()
    .unwrap();
let mut model = StreamingKAN::new(config);
model.train(&[1.0, 2.0, 3.0], 4.0);
let pred = model.predict(&[1.0, 2.0, 3.0]);

Implementations

impl StreamingKAN

pub fn new(config: KANConfig) -> Self

Create a new StreamingKAN from config.

pub fn config(&self) -> &KANConfig

Access the config.

pub fn n_layers(&self) -> usize

Number of KAN layers (edges between node layers).

pub fn n_params(&self) -> usize

Total number of learnable parameters (B-spline coefficients).

pub fn layer_sizes(&self) -> &[usize]

The layer sizes from the config.

Trait Implementations

impl Debug for StreamingKAN

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

Formats the value using the given formatter.

impl DiagnosticSource for StreamingKAN

fn config_diagnostics(&self) -> Option<ConfigDiagnostics>

Return config diagnostics, or None if not supported.

impl StreamingLearner for StreamingKAN

fn train_one(&mut self, features: &[f64], target: f64, weight: f64)

Train on a single observation with explicit sample weight.

fn predict(&self, features: &[f64]) -> f64

Predict the target for the given feature vector.

fn n_samples_seen(&self) -> u64

Total number of observations trained on since creation or last reset.

fn reset(&mut self)

Reset the model to its initial (untrained) state.

fn train(&mut self, features: &[f64], target: f64)

Train on a single observation with unit weight.

fn predict_batch(&self, feature_matrix: &[&[f64]]) -> Vec<f64>

Predict for each row in a feature matrix.