AimsSelfOrganizingMap Class Reference

The class for Kohonen's self organizing maps. More...

#include <aims/neuralnet/selforganizingmap.h>

Collaboration diagram for AimsSelfOrganizingMap:

Public Member Functions
Constructors and Destructor
	AimsSelfOrganizingMap (int size, int ninputs, float learningrate)
	Constructor for virgin SOM. More...
	AimsSelfOrganizingMap (const AimsData< float > &weight, float learningrate)
	Constructor for learned SOM. More...
virtual	~AimsSelfOrganizingMap ()
	Destructor deletes the weight data. More...

Protected Attributes
Data
int	_size
	Size of the square grid. More...
int	_nInputs
	Number of inputs. More...
int	_counter
	Number of iterations. More...
float	_learningRate
	Learning rate. More...
AimsData< float > *	_weight
	Weights data. More...

Methods
AimsVector< short, 2 >	winner (const AimsData< float > &input)
	Get the winner neuron for a given input. More...
void	update (const AimsData< float > &input, float learn, int delta)
	Do a learning step. More...
int	size () const
	Return the size of the network. More...
int	nInputs () const
	Return the number of inputs of the network on each neuron. More...
int	counter () const
	Return the number of epochs during the learning step. More...
float	learningRate () const
	Return the learning rate of the network. More...
void	setLearningRate (float learningrate)
	Set the learning rate of the network. More...
void	learn (const AimsData< float > &base, int counter)
	Send a learning step from a data base. More...
AimsData< AimsVector< short, 2 > >	test (const AimsData< float > &base)
	Test a data base and return the result. More...
void	save (const std::string &filename)
	Save all the weights in a GIS file. More...

Detailed Description

The class for Kohonen's self organizing maps.

Definition at line 46 of file selforganizingmap.h.

Constructor & Destructor Documentation

AimsSelfOrganizingMap() [1/2]

AimsSelfOrganizingMap::AimsSelfOrganizingMap	(	int	size,
		int	ninputs,
		float	learningrate
	)

Constructor for virgin SOM.

Parameters

size	size of the square grid of neurons
ninputs	number of inputs on each neuron
learningrate	learning rate

AimsSelfOrganizingMap() [2/2]

AimsSelfOrganizingMap::AimsSelfOrganizingMap	(	const AimsData< float > &	weight,
		float	learningrate
	)

Constructor for learned SOM.

Parameters

weight	weight data for all the neurons
learningrate	learning rate

~AimsSelfOrganizingMap()

virtual AimsSelfOrganizingMap::~AimsSelfOrganizingMap ( )

virtual

Destructor deletes the weight data.

Member Function Documentation

counter()

int AimsSelfOrganizingMap::counter

(

)

const

Return the number of epochs during the learning step.

learn()

void AimsSelfOrganizingMap::learn	(	const AimsData< float > &	base,
		int	counter
	)

Send a learning step from a data base.

Parameters

base	the base is a 2D-data where the x coordinate corresponds to the index of the example and the y coordinate corresponds to the input of the neuron
counter	number of epoch to iterate the process

learningRate()

float AimsSelfOrganizingMap::learningRate

(

)

const

Return the learning rate of the network.

nInputs()

int AimsSelfOrganizingMap::nInputs

(

)

const

Return the number of inputs of the network on each neuron.

save()

void AimsSelfOrganizingMap::save

(

const std::string &

filename

)

Save all the weights in a GIS file.

setLearningRate()

void AimsSelfOrganizingMap::setLearningRate

(

float

learningrate

)

Set the learning rate of the network.

size()

int AimsSelfOrganizingMap::size

(

)

const

Return the size of the network.

test()

AimsData< AimsVector<short,2> > AimsSelfOrganizingMap::test

(

const AimsData< float > &

base

)

Test a data base and return the result.

Parameters

base	the base is a 2D-data where the x coordinate corresponds to the index of the example and the y coordinate corresponds to the input of the neuron

Returns

a 2D-data containing the winner in the grid for each example of the base

update()

void AimsSelfOrganizingMap::update ( const AimsData< float > &  input, float  learn, int  delta  )

protected

Do a learning step.

Parameters

input	input example
learn	learning rate
delta	width of the square neighborhood of neurons to update around the winner

winner()

AimsVector<short,2> AimsSelfOrganizingMap::winner ( const AimsData< float > &  input )

protected

Get the winner neuron for a given input.

Member Data Documentation

_counter

int AimsSelfOrganizingMap::_counter

protected

Number of iterations.

Definition at line 55 of file selforganizingmap.h.

_learningRate

float AimsSelfOrganizingMap::_learningRate

protected

Learning rate.

Definition at line 57 of file selforganizingmap.h.

_nInputs

int AimsSelfOrganizingMap::_nInputs

protected

Number of inputs.

Definition at line 53 of file selforganizingmap.h.

_size

int AimsSelfOrganizingMap::_size

protected

Size of the square grid.

Definition at line 51 of file selforganizingmap.h.

_weight

AimsData<float>* AimsSelfOrganizingMap::_weight

protected

Weights data.

Definition at line 59 of file selforganizingmap.h.

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

• aims/neuralnet/selforganizingmap.h