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

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.LinearFunction
All Implemented Interfaces:
Evaluator<Double,Double>, DifferentiableEvaluator<Double,Double,Double>, DifferentiableUnivariateScalarFunction, ScalarFunction<Double>, UnivariateScalarFunction, CloneableSerializable, Serializable, Cloneable

public class LinearFunction
extends AbstractDifferentiableUnivariateScalarFunction

This function acts as a simple linear function of the form f(x) = m*x + b. Here m is known as the slope and b as the offset. Other terms for m and b are scale/bias, beta_1/beta_0.

Since:
2.1
Author:
Justin Basilico, Kevin R. Dixon
See Also:
Serialized Form

Field Summary
static double DEFAULT_OFFSET
          The default offset is 0.0.
static double DEFAULT_SLOPE
          The default slope is 1.0.
protected  double offset
          The offset (b).
protected  double slope
          The slope (m).
 
Constructor Summary
LinearFunction()
          Creates a new LinearFunction with a slope of 1 and offset of 0.
LinearFunction(double slope, double offset)
          Creates a new LinearFunction with the given slope and offset.
LinearFunction(LinearFunction other)
          Creates a copy of a given LinearFunction.
 
Method Summary
 LinearFunction 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)
          Produces a double output for the given double input
 double getOffset()
          Gets the offset of the function, which is the b term in: f(x) = m*x + b.
 double getSlope()
          Gets the slope of the function, which is the m term in: f(x) = m*x + b.
 void setOffset(double offset)
          Sets the offset of the function, which is the b term in: f(x) = m*x + b.
 void setSlope(double slope)
          Sets the slope of the function, which is the m term in: f(x) = m*x + b.
 
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
 

Field Detail

DEFAULT_SLOPE

public static final double DEFAULT_SLOPE
The default slope is 1.0.

See Also:
Constant Field Values

DEFAULT_OFFSET

public static final double DEFAULT_OFFSET
The default offset is 0.0.

See Also:
Constant Field Values

slope

protected double slope
The slope (m).


offset

protected double offset
The offset (b).

Constructor Detail

LinearFunction

public LinearFunction()
Creates a new LinearFunction with a slope of 1 and offset of 0. This makes f(x) = x.


LinearFunction

public LinearFunction(double slope,
                      double offset)
Creates a new LinearFunction with the given slope and offset.

Parameters:
slope - The slope.
offset - The offset.

LinearFunction

public LinearFunction(LinearFunction other)
Creates a copy of a given LinearFunction.

Parameters:
other - The LinearFunction to copy.
Method Detail

clone

public LinearFunction 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)
Description copied from interface: UnivariateScalarFunction
Produces a double output for the given double input

Parameters:
input - Input to the Evaluator
Returns:
output at the given input

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

getSlope

public double getSlope()
Gets the slope of the function, which is the m term in: f(x) = m*x + b.

Returns:
The slope.

setSlope

public void setSlope(double slope)
Sets the slope of the function, which is the m term in: f(x) = m*x + b.

Parameters:
slope - The slope.

getOffset

public double getOffset()
Gets the offset of the function, which is the b term in: f(x) = m*x + b.

Returns:
The offset.

setOffset

public void setOffset(double offset)
Sets the offset of the function, which is the b term in: f(x) = m*x + b.

Parameters:
offset - The offset.