RSTAlgorithm

java.lang.Object
- keel.Algorithms.RST_Learning.RSTAlgorithm

Direct Known Subclasses:

EFS_RPS, EIS_RFS
```
public abstract class RSTAlgorithm
extends java.lang.Object
```
File: RSTAlgorithm.java Main class for RST methods. Provides a basic framework for reading and preprocessing data, conduct the experiment and report results

Since:

JDK1.5

Version:

1.0

Author:

Written by Joaquín Derrac (University of Granada) 20/04/2010

Field Summary

Fields
Modifier and Type	Field and Description
`protected int`	`inputAtt` Number of input attributes
`protected Attribute[]`	`inputs` Inputs attributes
`protected java.lang.String`	`name` Naming.
`protected int`	`nClasses` Number of classes.
`protected int[]`	`nInstances` Number of instances of each classes.
`protected boolean[]`	`nominal` If an attribute is nominal or not.
`protected boolean[]`	`nulls` Missing values of a instance
`protected java.lang.String[]`	`outFile` Output files names
`protected Attribute`	`output` Output attribute
`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 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 int[]`	`testPrediction` Test predicted classes
`protected InstanceSet`	`train` Training dataset
`protected double[][]`	`trainData` Training input data.
`protected java.lang.String`	`trainFile` Train file name
`protected int[]`	`trainOutput` Training output data.
`protected int[]`	`trainPrediction` Training predicted classes
`protected int`	`trainSize` Number of instances on training dataset.

Constructor Summary

Constructors
Constructor and Description

RSTAlgorithm()

Constructors
Constructor and Description
`RSTAlgorithm()`

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`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
`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 void`	`writeOutput(java.lang.String filename, int[] realClass, int[] prediction)` Prints KEEL standard output files.

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
  - trainSize
```
protected int trainSize
```
    Number of instances on training dataset.
  - inputs
```
protected Attribute[] inputs
```
    Inputs attributes
  - output
```
protected Attribute output
```
    Output attribute
  - nulls
```
protected boolean[] nulls
```
    Missing values of a instance
  - nominal
```
protected boolean[] nominal
```
    If an attribute is nominal or not.
  - 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.
  - name
```
protected java.lang.String name
```
    Naming.
  - seed
```
protected long seed
```
    Random seed.
  - trainPrediction
```
protected int[] trainPrediction
```
    Training predicted classes
  - testPrediction
```
protected int[] testPrediction
```
    Test predicted classes
- Constructor Detail
  - RSTAlgorithm
```
public RSTAlgorithm()
```
- 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 dataset is not appropiate for the algorithm
  - normalizeTest
```
protected void normalizeTest()
                      throws DataException
```
    This function builds the data matrix for test data and normalizes inputs values
    
    Throws:
    
    DataException - if the dataset is not appropiate for the algorithm
  - normalizeReference
```
protected void normalizeReference()
                           throws DataException
```
    This function builds the data matrix for reference data and normalizes inputs values
    
    Throws:
    
    DataException - if the dataset is not appropiate for the algorithm
  - writeOutput
```
protected void writeOutput(java.lang.String filename,
                           int[] realClass,
                           int[] prediction)
```
    Prints KEEL standard output files.
    
    Parameters:
    
    filename - Name of output file
    
    realClass - Real output of instances
    
    prediction - Predicted output for instances

Class RSTAlgorithm

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

outFile

testFile

trainFile

referenceFile

train

test

reference

temp

inputAtt

trainSize

inputs

output

nulls

nominal

trainData

trainOutput

testData

testOutput

referenceData

referenceOutput

relation

nClasses

nInstances

name

seed

trainPrediction

testPrediction

Constructor Detail

RSTAlgorithm

Method Detail

readDataFiles

readConfiguracion

readParameters

normalizeTrain

normalizeTest

normalizeReference

writeOutput