gov.sandia.cognition.learning.function.scalar

Class LinearFunction

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.math.AbstractScalarFunction<java.lang.Double>

gov.sandia.cognition.math.AbstractUnivariateScalarFunction

gov.sandia.cognition.math.AbstractDifferentiableUnivariateScalarFunction

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

All Implemented Interfaces:

Evaluator<java.lang.Double,java.lang.Double>, DifferentiableEvaluator<java.lang.Double,java.lang.Double,java.lang.Double>, DifferentiableUnivariateScalarFunction, ScalarFunction<java.lang.Double>, UnivariateScalarFunction, CloneableSerializable, java.io.Serializable, java.lang.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

Fields

Modifier and Type	Field and Description
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

Constructors

Constructor and Description
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

Modifier and Type	Method and Description
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 java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.math.DifferentiableUnivariateScalarFunction

differentiate

Methods inherited from interface gov.sandia.cognition.math.UnivariateScalarFunction

evaluate, evaluateAsDouble

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.