ml_kit 1.0.0

A Machine Learning library for Rust
Documentation 
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use matrix_kit::dynamic::matrix::Matrix;

pub type LFI = LossFunctionIdentifier;

#[derive(Clone, Copy, PartialEq, Eq, Debug)]
pub enum LossFunctionIdentifier {
    Squared,
    // Additional loss functions (e.g., CrossEntropy) can be added here.
}

const LOSS_LOOKUP_TABLE: [LossFunctionIdentifier; 1] = [LossFunctionIdentifier::Squared];

impl LossFunctionIdentifier {
    pub fn from_int(n: u64) -> LossFunctionIdentifier {
        LOSS_LOOKUP_TABLE[n as usize]
    }

    pub fn raw_value(self) -> u64 {
        match self {
            LossFunctionIdentifier::Squared => 0,
        }
    }

    pub fn loss(
        self,
        prediction: &matrix_kit::dynamic::matrix::Matrix<f64>,
        target: &matrix_kit::dynamic::matrix::Matrix<f64>,
    ) -> f64 {
        match self {
            LossFunctionIdentifier::Squared => SquaredLoss {}.loss(prediction, target),
        }
    }

    pub fn derivative(
        self,
        prediction: &matrix_kit::dynamic::matrix::Matrix<f64>,
        target: &matrix_kit::dynamic::matrix::Matrix<f64>,
    ) -> Matrix<f64> {
        match self {
            LossFunctionIdentifier::Squared => SquaredLoss {}.derivative(prediction, target),
        }
    }
}

pub trait LossFunction {
    fn loss(&self, prediction: &Matrix<f64>, target: &Matrix<f64>) -> f64;

    fn derivative(&self, prediction: &Matrix<f64>, target: &Matrix<f64>) -> Matrix<f64>;
}

pub struct SquaredLoss;

impl LossFunction for SquaredLoss {
    fn loss(&self, prediction: &Matrix<f64>, target: &Matrix<f64>) -> f64 {
        ((prediction.clone()) - target.clone()).l2_norm_squared()
    }

    fn derivative(&self, prediction: &Matrix<f64>, target: &Matrix<f64>) -> Matrix<f64> {
        (prediction.clone() - target.clone()) * 2.0
    }
}