AdalineGD Class Reference

#include <AdalineGD.h>

Inheritance diagram for AdalineGD:

Public Member Functions
	AdalineGD (double _eta=0.01, int iter=10)
void	fit (const Matrix< double > &X, const Matrix< double > &y) override
Matrix< double >	netInput (const Matrix< double > &X) override
Matrix< double >	activation (const Matrix< double > &X) override
Matrix< double >	predict (const Matrix< double > &X) override
double	costFunction (const Matrix< double > &X) override
Public Member Functions inherited from ANNClassifier
	ANNClassifier (double _eta, int _n_iter)
virtual Matrix< double >	activation (const Matrix< double > &)=0
virtual Matrix< double >	netInput (const Matrix< double > &)=0
virtual double	costFunction (const Matrix< double > &)=0
Public Member Functions inherited from Classifier
	Classifier ()
void	initialize_weights (size_t numRows, size_t numColumns=1)
void	update_weights (const Matrix< double > &update, const Matrix< double > &delta)
Matrix< double >	transform (const Matrix< double > &in) override
virtual void	fit (const Matrix< double > &X, const Matrix< double > &y)=0
virtual Matrix< double >	predict (const Matrix< double > &)=0
virtual Matrix< double >	transform (const Matrix< double > &)=0

Additional Inherited Members
Public Attributes inherited from Classifier
Matrix< double >	weights
	Vector holding weights. More...
Matrix< double >	costs
	Vector holding classification error per epoch. More...
Protected Attributes inherited from ANNClassifier
double	eta
	Learning rate. More...
int	n_iter
	number epochs More...
Protected Attributes inherited from Classifier
bool	w_initialized = false
	flag to initialize weights only once More...

Detailed Description

Adaline neuron classifier using gradient decent method

Examples

ds/TestAdalineGD.cpp.

Constructor & Destructor Documentation

AdalineGD()

AdalineGD::AdalineGD ( double  _eta = 0.01, int  iter = 10  )

inlineexplicit

default constructor

Parameters

_eta	learning rate
iter	max number iterations

Member Function Documentation

activation()

Matrix< double > AdalineGD::activation ( const Matrix< double > &  X )

inlineoverridevirtual

activates given input

Parameters

X

input

Returns

activated input

Implements ANNClassifier.

costFunction()

double AdalineGD::costFunction ( const Matrix< double > &  X )

inlineoverridevirtual

calculates cost

$$\frac{\sum X^2}{2}

Parameters

X	input values

Returns

squared cost

Implements ANNClassifier.

fit()

void AdalineGD::fit ( const Matrix< double > &  X, const Matrix< double > &  y  )

inlineoverridevirtual

fit method to train the classifier using gradient decent method

Parameters

X	input
y	target output

Implements Predictor.

netInput()

Matrix< double > AdalineGD::netInput ( const Matrix< double > &  X )

inlineoverridevirtual

calculates $$ X \cdot w + b

Parameters

X	input values

Returns

Implements ANNClassifier.

predict()

Matrix< double > AdalineGD::predict ( const Matrix< double > &  X )

inlineoverridevirtual

predicts class of given input

Parameters

X	input values

Returns

prediction

Implements Predictor.

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The documentation for this class was generated from the following file:

• include/math/ds/AdalineGD.h