LazyAlgorithm

java.lang.Object
- keel.Algorithms.Lazy_Learning.LazyAlgorithm

Direct Known Subclasses:

CamNN, CenterNN, CPW, CW, Deeps, DeepsNN, IDIBL, KNN, KNNAdaptive, KSNN, KStar, LazyDT, LBR, NM, NSC, NumericalNaiveBayes, PW
```
public abstract class LazyAlgorithm
extends java.lang.Object
```
File: LazyAlgorithm.java A general framework for Lazy Learning Algorithms. This class contains all common operations in the development of a Lazy Learning algorithm. Any Lazy algorithm can extend this class and, by implementing the abstract "evaluate" and "readParameters" method, getting most of its work already done.

Since:

JDK1.5

Version:

1.1

Author:

Written by Joaquin Derrac (University of Granada) 13/11/2008, Modified by Joaquin Derrac (University of Granada) 10/18/2008

Field Summary

Fields
Modifier and Type	Field and Description
`protected int[][]`	`confMatrix` Test Confusion matrix.
`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` Predited classes of the test instances.
`int[]`	`predictions` Class predictions for the examples.
`double[][]`	`probabilities` Class probabilities for the examples.
`protected int[][]`	`realClass` Real classes of the test instances.
`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 double`	`testTime` Test prediction time.
`protected InstanceSet`	`train` Training dataset.
`protected int[][]`	`trainConfMatrix` Training Confusion matrix.
`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` Predited classes of the training instances.
`protected int[][]`	`trainRealClass` Real classes of the training instances.
`protected int`	`trainUnclassified` Number of unclassified training instances.
`protected int`	`unclassified` Number of unclassified test instances.

Constructor Summary

Constructors
Constructor and Description

LazyAlgorithm()

Constructors
Constructor and Description
`LazyAlgorithm()`

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.
`protected double[]`	`evaluate2(double[] example)` Evaluates a instance to predict its class probabilities.
`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(double[][] train, int[] clasesTrain, double[][] test, int[] clasesTest, int clases)` Set the training and the test dataset with their classes given as argument.
`protected void`	`readDataFiles(java.lang.String script)` Read the configuration and data files, and process it.
`protected void`	`readDataFiles(java.lang.String script, InstanceSet train, InstanceSet test, InstanceSet reference)` Read the configuration and data files, 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

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.
  - predictions
```
public int[] predictions
```
    Class predictions for the examples.
  - probabilities
```
public double[][] probabilities
```
    Class probabilities for the examples.
  - 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
```
    Test Confusion matrix.
  - unclassified
```
protected int unclassified
```
    Number of unclassified test instances.
  - realClass
```
protected int[][] realClass
```
    Real classes of the test instances.
  - prediction
```
protected int[][] prediction
```
    Predited classes of the test instances.
  - trainConfMatrix
```
protected int[][] trainConfMatrix
```
    Training Confusion matrix.
  - trainUnclassified
```
protected int trainUnclassified
```
    Number of unclassified training instances.
  - trainRealClass
```
protected int[][] trainRealClass
```
    Real classes of the training instances.
  - trainPrediction
```
protected int[][] trainPrediction
```
    Predited classes of the training instances.
- Constructor Detail
  - LazyAlgorithm
```
public LazyAlgorithm()
```
- Method Detail
  - readDataFiles
```
protected void readDataFiles(double[][] train,
                             int[] clasesTrain,
                             double[][] test,
                             int[] clasesTest,
                             int clases)
```
    Set the training and the test dataset with their classes given as argument.
    
    Parameters:
    
    train - training dataset given.
    
    clasesTrain - training examples classes.
    
    test - test dataset given.
    
    clasesTest - test examples classes.
    
    clases - number of classes.
  - readDataFiles
```
protected void readDataFiles(java.lang.String script,
                             InstanceSet train,
                             InstanceSet test,
                             InstanceSet reference)
```
    Read the configuration and data files, process it.
    
    Parameters:
    
    script - Name of the configuration script
    
    train - training dataset given as InstanceSet object.
    
    test - test dataset given as InstanceSet object.
    
    reference - reference dataset given as InstanceSet object.
  - 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 appropriate for problem.
  - 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 appropriate for problem.
  - 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 appropriate for problem.
  - 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"
  - evaluate2
```
protected double[] evaluate2(double[] example)
```
    Evaluates a instance to predict its class probabilities.
    
    Parameters:
    
    example - Instance evaluated
    
    Returns:
    
    Vector with the probability for each class.
  - 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

Class LazyAlgorithm

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

predictions

probabilities

nClasses

nInstances

initialTime

modelTime

trainingTime

testTime

name

seed

confMatrix

unclassified

realClass

prediction

trainConfMatrix

trainUnclassified

trainRealClass

trainPrediction

Constructor Detail

LazyAlgorithm

Method Detail

readDataFiles

readDataFiles

readDataFiles

readConfiguracion

readParameters

normalizeTrain

normalizeTest

normalizeReference

execute

executeReference

evaluate

evaluate2

euclideanDistance

manhattanDistance

same

printInstance

setInitialTime