gov.sandia.cognition.learning.function.scalar

Class KernelScalarFunction<InputType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.function.kernel.DefaultKernelContainer<InputType>

gov.sandia.cognition.learning.function.scalar.KernelScalarFunction<InputType>

Type Parameters:

InputType - The type of the input to the function. For example, Vector.

All Implemented Interfaces:

Evaluator<InputType,java.lang.Double>, KernelContainer<InputType>, Regressor<InputType>, ScalarFunction<InputType>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

LocallyWeightedKernelScalarFunction

public class KernelScalarFunction<InputType>
extends DefaultKernelContainer<InputType>
implements Regressor<InputType>

The KernelScalarFunction class implements a scalar function that uses a kernel to compute its output value. It is parameterized by a kernel function, a list of examples and their weights, and a bias term. This types of scalar function represents what is learned by algorithms such as Support Vector Regression.

Since:

2.0

Author:

Justin Basilico

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected double	bias The bias term.
static double	DEFAULT_BIAS The default value for the bias is 0.0.
protected java.util.Collection<? extends WeightedValue<? extends InputType>>	examples The list of weighted examples that are used for categorization.

Fields inherited from class gov.sandia.cognition.learning.function.kernel.DefaultKernelContainer

kernel

Constructor Summary

Constructors

Constructor and Description
KernelScalarFunction() Creates a new instance of KernelScalarFunction.
KernelScalarFunction(Kernel<? super InputType> kernel) Creates a new instance of KernelScalarFunction with the given kernel.
KernelScalarFunction(Kernel<? super InputType> kernel, java.util.Collection<? extends WeightedValue<? extends InputType>> examples, double bias) Creates a new instance of KernelScalarFunction with the given kernel, weighted examples, and bias.
KernelScalarFunction(KernelScalarFunction<InputType> other) Creates a new copy of a KernelScalarFunction.

Method Summary

Modifier and Type	Method and Description
java.lang.Double	evaluate(InputType input) Evaluates the given input vector as a double by: sum w_i * k(input, x_i)
double	evaluateAsDouble(InputType input) Evaluates the given input vector as a double by: sum w_i * k(input, x_i)
double	getBias() Gets the bias term.
java.util.Collection<? extends WeightedValue<? extends InputType>>	getExamples() Gets the list of weighted examples that categorizer is using.
void	setBias(double bias) Sets the bias term.
void	setExamples(java.util.Collection<? extends WeightedValue<? extends InputType>> examples) Sets the list of weighted examples that categorizer is using.

Methods inherited from class gov.sandia.cognition.learning.function.kernel.DefaultKernelContainer

clone, getKernel, setKernel

Methods inherited from class java.lang.Object

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

Field Detail

DEFAULT_BIAS

public static final double DEFAULT_BIAS

The default value for the bias is 0.0.

See Also:

Constant Field Values

examples

protected java.util.Collection<? extends WeightedValue<? extends InputType>> examples

The list of weighted examples that are used for categorization.

bias

protected double bias

The bias term.

Constructor Detail

KernelScalarFunction

public KernelScalarFunction()

Creates a new instance of KernelScalarFunction.

KernelScalarFunction

public KernelScalarFunction(Kernel<? super InputType> kernel)

Creates a new instance of KernelScalarFunction with the given kernel.

Parameters:

kernel - The kernel to use.

KernelScalarFunction

public KernelScalarFunction(Kernel<? super InputType> kernel,
                            java.util.Collection<? extends WeightedValue<? extends InputType>> examples,
                            double bias)

Creates a new instance of KernelScalarFunction with the given kernel, weighted examples, and bias.

Parameters:

kernel - The kernel to use.

examples - The weighted examples.

bias - The bias.

KernelScalarFunction

public KernelScalarFunction(KernelScalarFunction<InputType> other)

Creates a new copy of a KernelScalarFunction.

Parameters:

other - The KernelScalarFunction to copy.

Method Detail

evaluate

public java.lang.Double evaluate(InputType input)

Evaluates the given input vector as a double by: sum w_i * k(input, x_i)

Specified by:

evaluate in interface Evaluator<InputType,java.lang.Double>

Specified by:

evaluate in interface ScalarFunction<InputType>

Parameters:

input - The input to categorize.

Returns:

The output categorization as a double where the sign is the categorization.

evaluateAsDouble

public double evaluateAsDouble(InputType input)

Evaluates the given input vector as a double by: sum w_i * k(input, x_i)

Specified by:

evaluateAsDouble in interface ScalarFunction<InputType>

Parameters:

input - The input to categorize.

Returns:

The output categorization as a double where the sign is the categorization.

getExamples

public java.util.Collection<? extends WeightedValue<? extends InputType>> getExamples()

Gets the list of weighted examples that categorizer is using.

Returns:

The list of weighted examples.

setExamples

public void setExamples(java.util.Collection<? extends WeightedValue<? extends InputType>> examples)

Sets the list of weighted examples that categorizer is using.

Parameters:

examples - The list of weighted examples.

getBias

public double getBias()

Gets the bias term.

Returns:

bias The bias term.

setBias

public void setBias(double bias)

Sets the bias term.

Parameters:

bias - The bias term.