Copyright	(c) Alexander Ignatyev 2016-2017
License	BSD-3
Stability	experimental
Portability	POSIX
Safe Haskell	None
Language	Haskell2010

MachineLearning.Classification.MultiClass

Description

MultiClass Classification.

Synopsis

Documentation

class Classifier a where Source #

Classifier type class represents Multi-class classification models.

Minimal complete definition

cscore, chypothesis, ccost, cgradient, cnumClasses

Methods

cscore :: a -> Matrix -> Matrix -> Matrix Source #

Score function

chypothesis :: a -> Matrix -> Matrix -> Vector Source #

Hypothesis function Takes X (m x n) and theta (n x k), returns y (m x k).

ccost :: a -> Regularization -> Matrix -> Vector -> Matrix -> R Source #

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

cgradient :: a -> Regularization -> Matrix -> Vector -> Matrix -> Matrix Source #

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

cnumClasses :: a -> Int Source #

Returns Number of Classes

Instances

Classifier SoftmaxClassifier Source #
Methods cscore :: SoftmaxClassifier -> Matrix -> Matrix -> Matrix Source # chypothesis :: SoftmaxClassifier -> Matrix -> Matrix -> Vector Source # ccost :: SoftmaxClassifier -> Regularization -> Matrix -> Vector -> Matrix -> R Source # cgradient :: SoftmaxClassifier -> Regularization -> Matrix -> Vector -> Matrix -> Matrix Source # cnumClasses :: SoftmaxClassifier -> Int Source #
Classifier MultiSvmClassifier Source #
Methods cscore :: MultiSvmClassifier -> Matrix -> Matrix -> Matrix Source # chypothesis :: MultiSvmClassifier -> Matrix -> Matrix -> Vector Source # ccost :: MultiSvmClassifier -> Regularization -> Matrix -> Vector -> Matrix -> R Source # cgradient :: MultiSvmClassifier -> Regularization -> Matrix -> Vector -> Matrix -> Matrix Source # cnumClasses :: MultiSvmClassifier -> Int Source #

data MultiClassModel m Source #

MultiClassModel is Model wrapper class around Classifier

Constructors

MultiClass m

Instances

Classifier a => Model (MultiClassModel a) Source #
Methods hypothesis :: MultiClassModel a -> Matrix -> Vector -> Vector Source # cost :: MultiClassModel a -> Regularization -> Matrix -> Vector -> Vector -> R Source # gradient :: MultiClassModel a -> Regularization -> Matrix -> Vector -> Vector -> Vector Source #

processOutput :: Classifier c => c -> Vector -> Matrix Source #

Process outputs for MultiClass Classification. Takes Classifier and output vector y. Returns matrix of binary outputs. It is supposed that labels are integerets start at 0.

data Regularization Source #

Regularization

Constructors

RegNone	No regularization
L2 R	L2 Regularization

ccostReg :: Regularization -> Matrix -> R Source #

Calculates regularization for Classifier.ccost. It takes regularization parameter and theta.

cgradientReg :: Regularization -> Matrix -> Matrix Source #

Calculates regularization for Classifier.cgradient. It takes regularization parameter and theta.