org.encog.neural.activation
Class ActivationBiPolar

java.lang.Object
  org.encog.neural.activation.BasicActivationFunction
      org.encog.neural.activation.ActivationBiPolar

All Implemented Interfaces:

java.io.Serializable, ActivationFunction, EncogPersistedObject

public class ActivationBiPolar
extends BasicActivationFunction

BiPolar activation function. This will scale the neural data into the bipolar range. Greater than zero becomes 1, less than or equal to zero becomes -1.

Author:

jheaton

See Also:

Serialized Form

Constructor Summary

ActivationBiPolar()

Method Summary

void	activationFunction(double[] d) Implements the activation function.
java.lang.Object	clone()
Persistor	createPersistor() Create a Persistor for this activation function.
void	derivativeFunction(double[] d) Implements the activation function derivative.
boolean	hasDerivative()

Methods inherited from class org.encog.neural.activation.BasicActivationFunction

getDescription, getName, setDescription, setName

Methods inherited from class java.lang.Object

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

Constructor Detail

ActivationBiPolar

public ActivationBiPolar()

Method Detail

activationFunction

public void activationFunction(double[] d)

Implements the activation function. The array is modified according to the activation function being used. See the class description for more specific information on this type of activation function.

Parameters:

d - The input array to the activation function.

clone

public java.lang.Object clone()

Specified by:

clone in class BasicActivationFunction

Returns:

The object cloned.

createPersistor

public Persistor createPersistor()

Create a Persistor for this activation function.

Specified by:

createPersistor in interface EncogPersistedObject

Specified by:

createPersistor in class BasicActivationFunction

Returns:

The persistor.

derivativeFunction

public void derivativeFunction(double[] d)

Implements the activation function derivative. The array is modified according derivative of the activation function being used. See the class description for more specific information on this type of activation function. Propagation training requires the derivative. Some activation functions do not support a derivative and will throw an error.

Parameters:

d - The input array to the activation function.

hasDerivative

public boolean hasDerivative()

Returns:

Return false, bipolar has no derivative.