gov.sandia.cognition.learning.function.scalar
Class SigmoidFunction

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.math.AbstractScalarFunction<Double>
          extended by gov.sandia.cognition.math.AbstractUnivariateScalarFunction
              extended by gov.sandia.cognition.math.AbstractDifferentiableUnivariateScalarFunction
                  extended by gov.sandia.cognition.learning.function.scalar.SigmoidFunction
All Implemented Interfaces:
Evaluator<Double,Double>, DifferentiableEvaluator<Double,Double,Double>, DifferentiableUnivariateScalarFunction, ScalarFunction<Double>, UnivariateScalarFunction, CloneableSerializable, Serializable, Cloneable

@CodeReviews(reviews={@CodeReview(reviewer="Kevin R. Dixon",date="2009-07-06",changesNeeded=false,comments={"Made clone() call super.clone().","Otherwise, class looks fine."}),@CodeReview(reviewer="Justin Basilico",date="2006-10-05",changesNeeded=false,comments="Class looks fine.")})
@PublicationReference(author="Wikipedia",
                      title="Sigmoid function",
                      type=WebPage,
                      year=2008,
                      url="http://en.wikipedia.org/wiki/Sigmoid_function")
public class SigmoidFunction
extends AbstractDifferentiableUnivariateScalarFunction

An implementation of a sigmoid squashing function.

Since:
1.0
Author:
Kevin R. Dixon
See Also:
Serialized Form

Constructor Summary
SigmoidFunction()
          Creates a new instance of SigmoidFunction
 
Method Summary
 SigmoidFunction clone()
          This makes public the clone method on the Object class and removes the exception that it throws.
 double differentiate(double input)
          Differentiates the output of the function about the given input
 double evaluate(double input)
          Evaluates the squashing function on the given input value.
 
Methods inherited from class gov.sandia.cognition.math.AbstractDifferentiableUnivariateScalarFunction
differentiate
 
Methods inherited from class gov.sandia.cognition.math.AbstractUnivariateScalarFunction
evaluate, evaluateAsDouble
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 
Methods inherited from interface gov.sandia.cognition.math.ScalarFunction
evaluateAsDouble
 
Methods inherited from interface gov.sandia.cognition.evaluator.Evaluator
evaluate
 

Constructor Detail

SigmoidFunction

public SigmoidFunction()
Creates a new instance of SigmoidFunction

Method Detail

clone

public SigmoidFunction clone()
Description copied from class: AbstractCloneableSerializable
This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:
clone in interface CloneableSerializable
Overrides:
clone in class AbstractCloneableSerializable
Returns:
A clone of this object.

evaluate

public double evaluate(double input)
Evaluates the squashing function on the given input value.

Parameters:
input - The input value to squash.
Returns:
The output of the sigmoid.

differentiate

public double differentiate(double input)
Description copied from interface: DifferentiableUnivariateScalarFunction
Differentiates the output of the function about the given input

Parameters:
input - Input about which to compute the derivative of the function output
Returns:
Derivative of the output with respect to the input