Trait tinguely::model::SupervisedLearn

pub trait SupervisedLearn<T, U> {
    fn predict(&self, input: &T) -> Result<U, ()>;
    fn train<'a, 'b>(&'a mut self, input: &'b T, target: &'b U);
}

Required methods

fn predict(&self, input: &T) -> Result<U, ()>

Predict output from inputs.

fn train<'a, 'b>(&'a mut self, input: &'b T, target: &'b U)

Train the model using inputs and targets.

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Implementors

impl<T> SupervisedLearn<Matrix<T>, Vector<T>> for LogisticRegression<T> where
    T: Real, 

fn predict(&self, x: &Matrix<T>) -> Result<Vector<T>, ()>

fn train(&mut self, x: &Matrix<T>, y: &Vector<T>)

impl<T> SupervisedLearn<Matrix<T>, Vector<T>> for LinearRegression<T> where
    T: Real, 

fn predict(&self, x: &Matrix<T>) -> Result<Vector<T>, ()>

fn train(&mut self, x: &Matrix<T>, y: &Vector<T>)

Train the linear regression model.

Examples

//use tinguely::regression::LinearRegression;
//use mathru::algebra::linear::{Vector, Matrix};
//use mathru::optimization::Gradient;
//use tinguely::SupervisedLearn;

//let optimizer = Gradient::new(0.1, 200);
//let mut lin_mod: LinearRegression<f64> = LinearRegression::new(optimizer);
//let inputs: Matrix<f64> = Matrix::new(3, 1, vec![2.0, 3.0, 4.0]);
//let targets: Vector<f64> = Vector::new_column(3, vec![5.0, 6.0, 7.0]);

//lin_mod.train(&inputs, &targets);

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