org.ai_heuristic.functs.metric

Class MinkowskiDistance

java.lang.Object

org.ai_heuristic.functs.Function

org.ai_heuristic.functs.FunctionMetric

org.ai_heuristic.functs.metric.MinkowskiDistance

All Implemented Interfaces:

FunctionDef, FunctionMetricDef

public class MinkowskiDistance
extends FunctionMetric

This measures the Minkowski Distance between two lists of objects. This is added as part of the som package, copied from the related source code. The power value can be set through the initialisation config parameters, using AiHeuristicConst.MKP as the key. The metric measures the sum[(ds1 - ds2)^p]^(1/p). The MetricDataset needs to use an indexed list instead of an ordered one.
If each dataset has only one (list) object, it is passed to the evaluation function as it is. If the objects are double arrays, then they can be processed as is, without using the metric objects. The desired result is a larger distance, but you can use the isBetter() method to determine what value is better. Null can be returned for invalid data.

Author:

Kieran Greer

Field Summary

Fields inherited from class org.ai_heuristic.functs.Function

config, mathCompare, valueType

Constructor Summary

Constructors

Constructor and Description
MinkowskiDistance() Create a new instance of MinkowskiDistance.
MinkowskiDistance(java.lang.String thisValueType, java.util.HashMap<java.lang.String,?> thisConfig) Create a new instance of MinkowskiDistance.

Method Summary

Modifier and Type	Method and Description
double	evaluate(double[] ds1, double[] ds2, int pow) Evaluate the comparison between the two data lists and return the result.
ReplySet	evaluate(MetricDataset ds1, MetricDataset ds2) Evaluate the comparison between the two data lists and return the result.
protected void	initialise() Initialise the function values, setting the config parameters or other.
boolean	isBetter(java.lang.String valueType, java.lang.Object value1, java.lang.Object value2) Return true if value2 is better than value1, as determined by the measurements of this evaluation function.
FunctionMetric	newInstance() Create and return a new instance of the function, initialised with this function's value type valueType and mathOperation evaluator mathCompare.
boolean	sib() Return true if a smaller distance between the two vectors is better.

Methods inherited from class org.ai_heuristic.functs.FunctionMetric

evaluateCompare, lib

Methods inherited from class org.ai_heuristic.functs.Function

checkValueType, createFunction, createFunction, createFunction, evaluate, getConfigParams, innerObject, isLegalNumber, setConfigParams, setEvaluator, setValueType

Methods inherited from class java.lang.Object

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

Constructor Detail

MinkowskiDistance

public MinkowskiDistance()
                  throws java.lang.Exception

Create a new instance of MinkowskiDistance.

Throws:

java.lang.Exception - any error.

MinkowskiDistance

public MinkowskiDistance(java.lang.String thisValueType,
                         java.util.HashMap<java.lang.String,?> thisConfig)
                  throws java.lang.Exception

Create a new instance of MinkowskiDistance.

Parameters:

thisValueType - the type of object being evaluated.

thisConfig - list of initialisation function-specific parameters.

Throws:

java.lang.Exception - any error.

Method Detail

initialise

protected void initialise()
                   throws java.lang.Exception

Initialise the function values, setting the config parameters or other.

Specified by:

initialise in class Function

Throws:

java.lang.Exception - any error.

evaluate

public ReplySet evaluate(MetricDataset ds1,
                         MetricDataset ds2)
                  throws java.lang.Exception

Evaluate the comparison between the two data lists and return the result. If each list has only one object, then apply to the objects directly and not the lists.

Specified by:

evaluate in interface FunctionMetricDef

Specified by:

evaluate in class FunctionMetric

Parameters:

ds1 - first value dataset.

ds2 - second value dataset.

Returns:

the result of applying the operator to the values.

Throws:

java.lang.Exception - any error.

evaluate

public double evaluate(double[] ds1,
                       double[] ds2,
                       int pow)
                throws java.lang.Exception

Evaluate the comparison between the two data lists and return the result. If each list has only one object, then apply to the objects directly and not the lists.

Parameters:

ds1 - first value dataset.

ds2 - second value dataset.

pow - the power and fraction power value.

Returns:

the result of applying the operator to the values.

Throws:

java.lang.Exception - any error.

isBetter

public boolean isBetter(java.lang.String valueType,
                        java.lang.Object value1,
                        java.lang.Object value2)
                 throws java.lang.Exception

Return true if value2 is better than value1, as determined by the measurements of this evaluation function. A smaller value is better.

Specified by:

isBetter in class FunctionMetric

Parameters:

valueType - the java type of the values to be evaluated.

value1 - the first value type.

value2 - the second value type.

Returns:

true if value2 is better than value1, as determined by this evaluation function.

Throws:

java.lang.Exception - any error.

sib

public boolean sib()

Return true if a smaller distance between the two vectors is better.

Specified by:

sib in class FunctionMetric

Returns:

true if a smaller evaluation is better.

newInstance

public FunctionMetric newInstance()
                           throws java.lang.Exception

Create and return a new instance of the function, initialised with this function's value type valueType and mathOperation evaluator mathCompare.

Specified by:

newInstance in class FunctionMetric

Returns:

a new instance of this function.

Throws:

java.lang.Exception - any error.