org.ai_heuristic.functs.similar

Class JaroWinkler

java.lang.Object

org.ai_heuristic.functs.Function

org.ai_heuristic.functs.FunctionMetric

org.ai_heuristic.functs.similar.JaroWinkler

All Implemented Interfaces:

FunctionDef, FunctionMetricDef

public class JaroWinkler
extends FunctionMetric

An example of the Jaro-Winkler similarity measure. The Jaro-Winkler distance is purposely designed for record linkage and was designed for linking short strings. It calculates a normalised score on the similarity between two strings. The default implementation can be used simply with an empty constructor and calling the evaluate(String compOne, String compTwo) method. As the classes have been made generic, it is also possible to attempt a Jaro-Winkler evaluation over other data types. For this, you might be required to add the evaluation function that evaluates the data comparison. You would then use the evaluate(Object parameter) method to attempt the comparison. The desired result is a larger distance, but you can use the isBetter() method to determine what value is better. The count result is inherently a Double, so cast a reply to that.

This is a modified version of, but using the same algorithm: http://blogs.ucl.ac.uk/chime/2010/06/28/java-example-code-of-common-similarity-algorithms-used-in-data-mining/

Author:

Kieran greer.

Field Summary

Fields inherited from class org.ai_heuristic.functs.Function

config, mathCompare, valueType

Constructor Summary

Constructors

Constructor and Description
JaroWinkler() Create a new instance of JaroWinkler.
JaroWinkler(java.lang.String thisValueType) Create a new instance of JaroWinkler.

Method Summary

Modifier and Type	Method and Description
ReplySet	evaluate(MetricDataset ds1, MetricDataset ds2) Evaluate the comparison between the two data lists and return the result.
ReplySet	evaluate(java.lang.Object dataset) Return a value based on the function evaluation.
double	evaluate(java.lang.String compOne, java.lang.String compTwo) Return a value based on the function evaluation.
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 math 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

JaroWinkler

public JaroWinkler()
            throws java.lang.Exception

Create a new instance of JaroWinkler.

Throws:

java.lang.Exception - any error.

JaroWinkler

public JaroWinkler(java.lang.String thisValueType)
            throws java.lang.Exception

Create a new instance of JaroWinkler.

Parameters:

thisValueType - the type of object being evaluated.

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(java.lang.Object dataset)
                  throws java.lang.Exception

Return a value based on the function evaluation. This returns the similarity evaluation between the two strings.

Parameters:

dataset - the value to pass through the function. This can be either a String[2] or a MetricCompare with only one data object, in each of the data lists.

Returns:

the similarity evaluation integer value.

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 - the first set of data values.

ds2 - the second set of data values.

Returns:

the result of applying the operator to the values.

Throws:

java.lang.Exception - any error.

evaluate

public double evaluate(java.lang.String compOne,
                       java.lang.String compTwo)
                throws java.lang.Exception

Return a value based on the function evaluation. This returns the similarity evaluation between the two strings specifically, which is the traditional use of the function.

Parameters:

compOne - the first comparison string.

compTwo - the second comparison string.

Returns:

the similarity evaluation integer value.

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 larger 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 math evaluator mathCompare.

Specified by:

newInstance in class FunctionMetric

Returns:

a new instance of this function.

Throws:

java.lang.Exception - any error.