sklears-decomposition 0.2.0

Matrix decomposition algorithms for sklears: PCA, ICA, NMF, SVD
Documentation
//! Coordinate Descent algorithms for dictionary learning

use scirs2_core::ndarray::{Array1, Array2};
#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};
use sklears_core::{error::Result, types::Float};

/// Configuration for Coordinate Descent algorithm
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct CDConfig {
    /// Maximum number of iterations
    pub max_iter: usize,
    /// Convergence tolerance
    pub tol: Float,
    /// Regularization parameter
    pub alpha: Float,
}

impl Default for CDConfig {
    fn default() -> Self {
        Self {
            max_iter: 1000,
            tol: 1e-4,
            alpha: 1.0,
        }
    }
}

/// Coordinate Descent result
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct CDResult {
    /// Sparse coefficients
    pub coefficients: Array1<Float>,
    /// Objective value
    pub objective: Float,
    /// Number of iterations
    pub n_iter: usize,
}

/// Soft thresholding operator
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct SoftThresholding {
    pub threshold: Float,
}

impl SoftThresholding {
    pub fn new(threshold: Float) -> Self {
        Self { threshold }
    }

    pub fn apply(&self, x: Float) -> Float {
        if x > self.threshold {
            x - self.threshold
        } else if x < -self.threshold {
            x + self.threshold
        } else {
            0.0
        }
    }
}

/// Coordinate Descent encoder
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct CDEncoder {
    config: CDConfig,
}

impl CDEncoder {
    pub fn new(config: CDConfig) -> Self {
        Self { config }
    }

    /// Perform coordinate descent sparse coding
    pub fn encode(&self, dictionary: &Array2<Float>, _signal: &Array1<Float>) -> Result<CDResult> {
        let n_atoms = dictionary.nrows();
        let coefficients = Array1::zeros(n_atoms);

        // Placeholder implementation
        let objective = 0.0;

        Ok(CDResult {
            coefficients,
            objective,
            n_iter: 0,
        })
    }
}