sklears-decomposition 0.2.0

//! Sparse encoding utilities for dictionary learning

use scirs2_core::ndarray::Array2;
#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};
use sklears_core::{error::Result, types::Float};

/// Encoding algorithms
#[derive(Debug, Clone, Copy)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub enum EncodingAlgorithm {
    OMP,
    LARS,
    CoordinateDescent,
}

/// Sparse encoding configuration
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct SparseEncodingConfig {
    pub algorithm: EncodingAlgorithm,
    pub max_iter: usize,
    pub tol: Float,
}

impl Default for SparseEncodingConfig {
    fn default() -> Self {
        Self {
            algorithm: EncodingAlgorithm::OMP,
            max_iter: 1000,
            tol: 1e-4,
        }
    }
}

/// Sparse encoding result
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct SparseEncodingResult {
    pub codes: Array2<Float>,
    pub reconstruction_error: Float,
}

/// Sparse encoder
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct SparseEncoder {
    config: SparseEncodingConfig,
}

/// Sparse coder utility
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct SparseCoder;

impl SparseEncoder {
    pub fn new(config: SparseEncodingConfig) -> Self {
        Self { config }
    }

    pub fn encode(
        &self,
        dictionary: &Array2<Float>,
        signals: &Array2<Float>,
    ) -> Result<SparseEncodingResult> {
        let (n_samples, _n_features) = signals.dim();
        let n_atoms = dictionary.nrows();

        let codes = Array2::zeros((n_samples, n_atoms));
        let reconstruction_error = 0.0;

        Ok(SparseEncodingResult {
            codes,
            reconstruction_error,
        })
    }
}