MachineLearning.Model

Regression Model type class.

Documentation

class Model a where

Minimal complete definition

hypothesis, cost, gradient

Methods

hypothesis :: a -> Matrix -> Vector -> Vector

Hypothesis function, a.k.a. score function (for lassifition problem) Takes X (m x n) and theta (n x 1), returns y (m x 1).

cost :: a -> Regularization -> Matrix -> Vector -> Vector -> R

Cost function J(Theta), a.k.a. loss function. It takes regularizarion parameter, matrix X (m x n), vector y (m x 1) and vector theta (n x 1).

gradient :: a -> Regularization -> Matrix -> Vector -> Vector -> Vector

Gradient function. It takes regularizarion parameter, X (m x n), y (m x 1) and theta (n x 1). Returns vector of gradients (n x 1).

Instances

Model LeastSquaresModel

Methods

hypothesis :: LeastSquaresModel -> Matrix -> Vector -> Vector

cost :: LeastSquaresModel -> Regularization -> Matrix -> Vector -> Vector -> R

gradient :: LeastSquaresModel -> Regularization -> Matrix -> Vector -> Vector -> Vector

Model LogisticModel

Methods

hypothesis :: LogisticModel -> Matrix -> Vector -> Vector

cost :: LogisticModel -> Regularization -> Matrix -> Vector -> Vector -> R

gradient :: LogisticModel -> Regularization -> Matrix -> Vector -> Vector -> Vector

Model NeuralNetworkModel

Methods

hypothesis :: NeuralNetworkModel -> Matrix -> Vector -> Vector

cost :: NeuralNetworkModel -> Regularization -> Matrix -> Vector -> Vector -> R

gradient :: NeuralNetworkModel -> Regularization -> Matrix -> Vector -> Vector -> Vector

Classifier a => Model (MultiClassModel a)

Methods

hypothesis :: MultiClassModel a -> Matrix -> Vector -> Vector

cost :: MultiClassModel a -> Regularization -> Matrix -> Vector -> Vector -> R

gradient :: MultiClassModel a -> Regularization -> Matrix -> Vector -> Vector -> Vector