sklears-decomposition 0.2.0

//! K-SVD algorithms for dictionary learning

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

/// Configuration for K-SVD algorithm
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct KSVDConfig {
    /// Maximum number of iterations
    pub max_iter: usize,
    /// Convergence tolerance
    pub tol: Float,
    /// Number of non-zero coefficients for sparse coding
    pub n_nonzero_coefs: usize,
}

impl Default for KSVDConfig {
    fn default() -> Self {
        Self {
            max_iter: 100,
            tol: 1e-6,
            n_nonzero_coefs: 10,
        }
    }
}

/// K-SVD algorithm result
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct KSVDResult {
    /// Updated dictionary
    pub dictionary: Array2<Float>,
    /// Sparse codes
    pub codes: Array2<Float>,
    /// Reconstruction error
    pub reconstruction_error: Float,
    /// Number of iterations
    pub n_iter: usize,
}

/// K-SVD encoder
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct KSVDEncoder {
    config: KSVDConfig,
}

impl KSVDEncoder {
    pub fn new(config: KSVDConfig) -> Self {
        Self { config }
    }

    /// Perform K-SVD dictionary learning
    pub fn encode(
        &self,
        data: &Array2<Float>,
        initial_dictionary: &Array2<Float>,
    ) -> Result<KSVDResult> {
        let (n_samples, _n_features) = data.dim();
        let n_atoms = initial_dictionary.nrows();

        // Placeholder implementation
        let dictionary = initial_dictionary.clone();
        let codes = Array2::zeros((n_atoms, n_samples));
        let reconstruction_error = 0.0;

        Ok(KSVDResult {
            dictionary,
            codes,
            reconstruction_error,
            n_iter: 0,
        })
    }
}