mulan.classifier.neural.model
Class Neuron

java.lang.Object
  mulan.classifier.neural.model.Neuron

All Implemented Interfaces:

Serializable

public class Neuron
extends Object
implements Serializable

Implementation of a neuron unit. The neurons are used as processing elements in NeuralNet.

Version:

2012.02.27

Author:

Jozef Vilcek

See Also:

Serialized Form

Constructor Summary

Neuron(ActivationFunction function, int inputDim, double biasValue)
Creates a new Neuron instance.

Neuron(ActivationFunction function, int inputDim, double biasValue, Collection<Neuron> nextNeurons)
Creates a new Neuron instance.

Neuron(ActivationFunction function, int inputDim, double biasValue, Random random)
Creates a new Neuron instance.

Method Summary

boolean	addAllNeurons(Collection<Neuron> neurons) Adds connections to all specified Neuron instances.
boolean	addNeuron(Neuron neuron) Adds a connection to a specified Neuron.
ActivationFunction	getActivationFunction() Returns the ActivationFunction used by the Neuron.
double	getBiasInput() Returns a bias input value.
protected int	getConnectedNeuronsCount() Gets the count of neurons connected to the output of this neuron instance.
double[]	getDeltas() Returns deltas of the Neuron.
double	getError() Returns error term of the Neuron.
double	getNeuronInput() Returns an input value of the Neuron.
double	getOutput() Returns the output of the Neuron.
double[]	getWeights() Returns weights of the Neuron.
double	processInput(double[] inputs) Process an input pattern vector and returns the response of the Neuron.
boolean	removeNeuron(Neuron neuron) Removes a connection to a specified Neuron.
void	reset() Performs reset, re-initialization of the Neuron.
void	setError(double error) Sets the error term of the Neuron.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Neuron

public Neuron(ActivationFunction function,
              int inputDim,
              double biasValue)

Creates a new Neuron instance.

Parameters:

function - the activation function of the neuron

inputDim - the dimension of input pattern vector the neuron can process (the bias not included). The input dimension must be greater than zero.

biasValue - the bias input value

Neuron

public Neuron(ActivationFunction function,
              int inputDim,
              double biasValue,
              Random random)

Creates a new Neuron instance.

Parameters:

function - the activation function of the neuron

inputDim - the dimension of input pattern vector the neuron can process (the bias not included). The input dimension must be greater than zero.

biasValue - the bias input value

random - the pseudo-random generator to be used for computations involving randomness. This parameter can be null. In this case, new random instance with default seed will be constructed where needed.

Neuron

public Neuron(ActivationFunction function,
              int inputDim,
              double biasValue,
              Collection<Neuron> nextNeurons)

Creates a new Neuron instance.

Parameters:

function - the activation function of the neuron

inputDim - the dimension of input pattern vector the neuron can process (the bias not included) The input dimension must be greater than zero.

biasValue - the bias input value

nextNeurons - collection of neurons for which this neuron will be an input.

Method Detail

getActivationFunction

public ActivationFunction getActivationFunction()

Returns the ActivationFunction used by the Neuron.

Returns:

the activation function

getWeights

public double[] getWeights()

Returns weights of the Neuron.
The index of returned array corresponds to input pattern dimension + 1 for a bias. Weight for a bias is at the end of returned array.

Returns:

weights of the Neuron

getError

public double getError()

Returns error term of the Neuron.

Returns:

error term

setError

public void setError(double error)

Sets the error term of the Neuron.

Parameters:

error - the error value

getDeltas

public double[] getDeltas()

Returns deltas of the Neuron. Deltas are terms, which are used to update weights. Here are returned deltas which were computed and used to update weights in previous learning iteration.
The index of returned array corresponds to input pattern dimension + 1 for a bias. Delta for the bias is at the end of returned array.

Returns:

delta values

processInput

public double processInput(double[] inputs)

Process an input pattern vector and returns the response of the Neuron.

Parameters:

inputs - input pattern vector

Returns:

the output of the Neuron

getOutput

public double getOutput()

Returns the output of the Neuron. The output value is cached from processing of last input.

Returns:

output of the Neuron or 0 if no pattern was processed yet or neuron is after reset.

getNeuronInput

public double getNeuronInput()

Returns an input value of the Neuron. The value is input pattern multiplied with weights and summed across all weights of particular neuron. The output of the neuron is then input transformed by activation function.
The input values are cached from last processed input pattern.

Returns:

the input value of the Neuron or 0 if no pattern was processed yet or neuron is after reset.

getBiasInput

public double getBiasInput()

Returns a bias input value.

Returns:

the input bias

addNeuron

public boolean addNeuron(Neuron neuron)

Adds a connection to a specified Neuron.
The passed instance is assumed to be connected to the output of this instance (forward connections only).

Parameters:

neuron - the neuron which is connected to the output of this instance.

Returns:

true if specified neuron was successfully connected; false if connection already exists

Throws:

IllegalArgumentException - in neuron is null

addAllNeurons

public boolean addAllNeurons(Collection<Neuron> neurons)

Adds connections to all specified Neuron instances.
Each instance of the collection is assumed to be connected to the output of this instance (forward connections only).

Parameters:

neurons - the collection of neurons which have to be connected to the output of this instance.

Returns:

true if at least one of specified neurons was successfully connected; false if no connection was made. This means that all instances are already connected.

Throws:

IllegalArgumentException - if neurons collection is null

removeNeuron

public boolean removeNeuron(Neuron neuron)

Removes a connection to a specified Neuron.
The passed instance is assumed to be connected to the output of this instance (forward connections only).

Parameters:

neuron - the neuron which is connected to the output of this instance.

Returns:

true if connection to specified neuron was successfully removed; false if connection did not exist

Throws:

IllegalArgumentException - if neuron is null

reset

public void reset()

Performs reset, re-initialization of the Neuron. The weights are randomly initialized, all state variables (error term, neuron output, neuron input and deltas) are discarded.

getConnectedNeuronsCount

protected int getConnectedNeuronsCount()

Gets the count of neurons connected to the output of this neuron instance. Support for unit tests ...

Returns:

number of connected neurons