AdalineSGD Class Reference

#include <AdalineSGD.h>

Inheritance diagram for AdalineSGD:

Public Member Functions
	AdalineSGD (double _eta=0.01, int iter=10, bool _shuffle=false, int _randomState=0)
void	fit (const Matrix< double > &X, const Matrix< double > &y) override
void	partial_fit (const Matrix< double > &X, const Matrix< double > &y)
Matrix< double >	netInput (const Matrix< double > &X) override
double	costFunction (const Matrix< double > &X) override
Matrix< double >	activation (const Matrix< double > &X) override
Matrix< double >	predict (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

Public Attributes
bool	shuffle
	signalizes whether given dataset should be shuffled while fitting More...
int	randomState
	initialize weights with random state More...
SGD	sgd
	algorithmic object to represent fitting algorithm More...
Public Attributes inherited from Classifier
Matrix< double >	weights
	Vector holding weights. More...
Matrix< double >	costs
	Vector holding classification error per epoch. More...

Additional Inherited Members
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 linear neuron implementation using statistic gradient decent for training of a vector of weights and a single bias.

weights: shape [N+1 x 1] bias: shape [1, 1], first element of weights

Note: only allows binary classification and uses a single layer of weights see the description here [1] I found it very easy to understand and implement.

• [1] http://rasbt.github.io/mlxtend/user_guide/classifier/Adaline/

Examples

ds/TestAdalineSGD.cpp.

Constructor & Destructor Documentation

AdalineSGD()

AdalineSGD::AdalineSGD ( double  _eta = 0.01, int  iter = 10, bool  _shuffle = false, int  _randomState = 0  )

inlineexplicit

default constructor

Parameters

_eta	learning rate
iter	number of learning iterations
_shuffle	use shuffled data
_randomState	seed of random state

Member Function Documentation

activation()

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

inlineoverridevirtual

activates given input

Parameters

X	input values

Returns

activated input values

Implements ANNClassifier.

costFunction()

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

inlineoverridevirtual

Do not use

Parameters

X

Returns

Implements ANNClassifier.

fit()

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

inlineoverridevirtual

fit weights using sgd member

Parameters

X	array-like with the shape: [n_samples, n_features]
y	array-like with shape: [n_samples, 1]

Returns

this

Implements Predictor.

netInput()

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

inlineoverridevirtual

alias for sdg.netInput

Parameters

X	input values

Returns

Implements ANNClassifier.

partial_fit()

void AdalineSGD::partial_fit ( const Matrix< double > &  X, const Matrix< double > &  y  )

inline

partially fits the model

Parameters

X	input values
y	target output values

predict()

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

inlineoverridevirtual

predict output class of given input

Parameters

X	input values

Returns

predicted output

Implements Predictor.

Member Data Documentation

randomState

int AdalineSGD::randomState

initialize weights with random state

sgd

SGD AdalineSGD::sgd

algorithmic object to represent fitting algorithm

shuffle

bool AdalineSGD::shuffle

signalizes whether given dataset should be shuffled while fitting

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

• include/math/ds/AdalineSGD.h