HyperrectanglesAlgorithm

java.lang.Object
- keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Direct Known Subclasses:

BNGE, INNER, RISE
```
public abstract class HyperrectanglesAlgorithm
extends java.lang.Object
```
File: HyperrectanglesAlgorithm.java A general framework for Hyperrectangles Algorithms. This class contains all common operations in the development of a Hyperrectangles algorithm. Any Hyperrectangles algorithm can extend this class and, by implementing the abstract methods "readParameters", "evaluate" and "writeRules" getting most of its work already done.

Since:

JDK1.5

Version:

1.2

Author:

Written by Joaquin Derrac (University of Granada) 8/7/2009, Modified by Joaquin Derrac (University of Granada) 17/10/2009

Field Summary

Fields
Modifier and Type	Field and Description
`protected int[][]`	`confMatrix` Confusion matrix for test
`protected long`	`initialTime` Initial time.
`protected int`	`inputAtt` Number of input attributes
`protected Attribute[]`	`inputs` Inputs attributes
`protected double`	`modelTime` Generation model time.
`protected java.lang.String`	`name` Naming.
`protected int`	`nClasses` Number of classes.
`protected int[]`	`nInstances` Number of instances of each classes.
`protected boolean[]`	`nulls` Missing values of a instance
`protected java.lang.String[]`	`outFile` Output files names
`protected Attribute`	`output` Output attribute
`protected int[][]`	`prediction` predictions for test
`protected int[][]`	`realClass` real classes values for test
`protected InstanceSet`	`reference` Reference dataset
`protected double[][]`	`referenceData` Reference input data.
`protected java.lang.String`	`referenceFile` Reference file name
`protected int[]`	`referenceOutput` Reference output data.
`protected java.lang.String`	`relation` Relation string.
`protected java.lang.String`	`ruleSetText` Rules set text
`protected long`	`seed` Random seed.
`protected Instance`	`temp` Temporal instance.
`protected InstanceSet`	`test` Test dataset
`protected double[][]`	`testData` Test input data.
`protected java.lang.String`	`testFile` Test file name
`protected int[]`	`testOutput` Test output data.
`protected double`	`testTime` Test prediction time.
`protected InstanceSet`	`train` Training dataset
`protected int[][]`	`trainConfMatrix` Confusion matrix for training
`protected double[][]`	`trainData` Training input data.
`protected java.lang.String`	`trainFile` Train file name
`protected double`	`trainingTime` Training prediction time.
`protected int[]`	`trainOutput` Training output data.
`protected int[][]`	`trainPrediction` Predictions for training
`protected int[][]`	`trainRealClass` real classes values for training
`protected int`	`trainUnclassified` number of instances unclassified for training
`protected int`	`unclassified` number of instances unclassified for test

Constructor Summary

Constructors
Constructor and Description

HyperrectanglesAlgorithm()

Constructors
Constructor and Description
`HyperrectanglesAlgorithm()`

Method Summary

All Methods Static Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`protected double`	`euclideanDistance(double[] instance1, double[] instance2)` Calculates the Euclidean distance between two instances
`protected abstract int`	`evaluate(double[] example)` Evaluates a instance to predict its class.
`void`	`execute()` Executes the classification of train and test data sets
`void`	`executeReference()` Executes the classification of reference and test data sets
`protected double`	`manhattanDistance(double[] instance1, double[] instance2)` Calculates the Manhattan distance between two instances
`protected void`	`normalizeReference()` This function builds the data matrix for reference data and normalizes inputs values
`protected void`	`normalizeTest()` This function builds the data matrix for test data and normalizes inputs values
`protected void`	`normalizeTrain()` This function builds the data matrix for training data and normalizes inputs values
`static java.lang.String`	`printInstance(int[] instance)` Generates a string with the contents of the instance
`protected void`	`readConfiguracion(java.lang.String script)` Reads configuration script, and extracts its contents.
`protected void`	`readDataFiles(java.lang.String script)` Read the configuration and data files, and process it.
`protected abstract void`	`readParameters(java.lang.String script)` Reads the parameters of the algorithm.
`protected boolean`	`same(double[] a, double[] b)` Checks if two instances are the same
`protected void`	`setInitialTime()` Sets the time counter
`protected abstract int`	`writeRules()` Writes the final ruleset obtained, in the ruleSetText variable.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - outFile
```
protected java.lang.String[] outFile
```
    Output files names
  - testFile
```
protected java.lang.String testFile
```
    Test file name
  - trainFile
```
protected java.lang.String trainFile
```
    Train file name
  - referenceFile
```
protected java.lang.String referenceFile
```
    Reference file name
  - train
```
protected InstanceSet train
```
    Training dataset
  - test
```
protected InstanceSet test
```
    Test dataset
  - reference
```
protected InstanceSet reference
```
    Reference dataset
  - temp
```
protected Instance temp
```
    Temporal instance.
  - inputAtt
```
protected int inputAtt
```
    Number of input attributes
  - inputs
```
protected Attribute[] inputs
```
    Inputs attributes
  - output
```
protected Attribute output
```
    Output attribute
  - nulls
```
protected boolean[] nulls
```
    Missing values of a instance
  - trainData
```
protected double[][] trainData
```
    Training input data.
  - trainOutput
```
protected int[] trainOutput
```
    Training output data.
  - testData
```
protected double[][] testData
```
    Test input data.
  - testOutput
```
protected int[] testOutput
```
    Test output data.
  - referenceData
```
protected double[][] referenceData
```
    Reference input data.
  - referenceOutput
```
protected int[] referenceOutput
```
    Reference output data.
  - relation
```
protected java.lang.String relation
```
    Relation string.
  - nClasses
```
protected int nClasses
```
    Number of classes.
  - nInstances
```
protected int[] nInstances
```
    Number of instances of each classes.
  - initialTime
```
protected long initialTime
```
    Initial time.
  - modelTime
```
protected double modelTime
```
    Generation model time.
  - trainingTime
```
protected double trainingTime
```
    Training prediction time.
  - testTime
```
protected double testTime
```
    Test prediction time.
  - name
```
protected java.lang.String name
```
    Naming.
  - seed
```
protected long seed
```
    Random seed.
  - confMatrix
```
protected int[][] confMatrix
```
    Confusion matrix for test
  - unclassified
```
protected int unclassified
```
    number of instances unclassified for test
  - realClass
```
protected int[][] realClass
```
    real classes values for test
  - prediction
```
protected int[][] prediction
```
    predictions for test
  - trainConfMatrix
```
protected int[][] trainConfMatrix
```
    Confusion matrix for training
  - trainUnclassified
```
protected int trainUnclassified
```
    number of instances unclassified for training
  - trainRealClass
```
protected int[][] trainRealClass
```
    real classes values for training
  - trainPrediction
```
protected int[][] trainPrediction
```
    Predictions for training
  - ruleSetText
```
protected java.lang.String ruleSetText
```
    Rules set text
- Constructor Detail
  - HyperrectanglesAlgorithm
```
public HyperrectanglesAlgorithm()
```
- Method Detail
  - readDataFiles
```
protected void readDataFiles(java.lang.String script)
```
    Read the configuration and data files, and process it.
    
    Parameters:
    
    script - Name of the configuration script
  - readConfiguracion
```
protected void readConfiguracion(java.lang.String script)
```
    Reads configuration script, and extracts its contents.
    
    Parameters:
    
    script - Name of the configuration script
  - readParameters
```
protected abstract void readParameters(java.lang.String script)
```
    Reads the parameters of the algorithm. Must be implemented in the subclass.
    
    Parameters:
    
    script - Configuration script
  - normalizeTrain
```
protected void normalizeTrain()
                       throws DataException
```
    This function builds the data matrix for training data and normalizes inputs values
    
    Throws:
    
    DataException - if the data cant be read correctly
  - normalizeTest
```
protected void normalizeTest()
                      throws DataException
```
    This function builds the data matrix for test data and normalizes inputs values
    
    Throws:
    
    DataException - if the data cant be read correctly
  - normalizeReference
```
protected void normalizeReference()
                           throws DataException
```
    This function builds the data matrix for reference data and normalizes inputs values
    
    Throws:
    
    DataException - if the data cant be read correctly
  - execute
```
public void execute()
```
    Executes the classification of train and test data sets
  - executeReference
```
public void executeReference()
```
    Executes the classification of reference and test data sets
  - evaluate
```
protected abstract int evaluate(double[] example)
```
    Evaluates a instance to predict its class. Must be implemented in the subclass.
    
    Parameters:
    
    example - Instance evaluated
    
    Returns:
    
    The class predicted. -1 if the instance remains "Unclassified"
  - euclideanDistance
```
protected double euclideanDistance(double[] instance1,
                                   double[] instance2)
```
    Calculates the Euclidean distance between two instances
    
    Parameters:
    
    instance1 - First instance
    
    instance2 - Second instance
    
    Returns:
    
    The Euclidean distance
  - manhattanDistance
```
protected double manhattanDistance(double[] instance1,
                                   double[] instance2)
```
    Calculates the Manhattan distance between two instances
    
    Parameters:
    
    instance1 - First instance
    
    instance2 - Second instance
    
    Returns:
    
    The Euclidean distance
  - same
```
protected boolean same(double[] a,
                       double[] b)
```
    Checks if two instances are the same
    
    Parameters:
    
    a - First instance
    
    b - Second instance
    
    Returns:
    
    True if both instances are equal.
  - printInstance
```
public static java.lang.String printInstance(int[] instance)
```
    Generates a string with the contents of the instance
    
    Parameters:
    
    instance - Instance to print.
    
    Returns:
    
    A string, with the values of the instance
  - setInitialTime
```
protected void setInitialTime()
```
    Sets the time counter
  - writeRules
```
protected abstract int writeRules()
```
    Writes the final ruleset obtained, in the ruleSetText variable. Must be implemented in the subclass.
    
    Returns:
    
    The number of rules of the final rule set

Class HyperrectanglesAlgorithm

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

outFile

testFile

trainFile

referenceFile

train

test

reference

temp

inputAtt

inputs

output

nulls

trainData

trainOutput

testData

testOutput

referenceData

referenceOutput

relation

nClasses

nInstances

initialTime

modelTime

trainingTime

testTime

name

seed

confMatrix

unclassified

realClass

prediction

trainConfMatrix

trainUnclassified

trainRealClass

trainPrediction

ruleSetText

Constructor Detail

HyperrectanglesAlgorithm

Method Detail

readDataFiles

readConfiguracion

readParameters

normalizeTrain

normalizeTest

normalizeReference

execute

executeReference

evaluate

euclideanDistance

manhattanDistance

same

printInstance

setInitialTime

writeRules