ca.uottawa.balie
Class WekaLearner

java.lang.Object
  ca.uottawa.balie.WekaLearner

All Implemented Interfaces:

java.io.Serializable

public class WekaLearner
extends java.lang.Object
implements java.io.Serializable

Methods to create, train and test a classification algorithm.

Author:

nadeaud

See Also:

Serialized Form

Constructor Summary

WekaLearner(weka.core.FastVector attrsMerged, java.lang.String[] attrlblMerged, java.lang.String[] classList, weka.core.Instances trainMerged)

WekaLearner(WekaAttribute[] pi_Attributes, java.lang.String[] pi_ClassAttributes)
Creates a new classification algorithm.

Method Summary

void	AddTestInstance(java.lang.Object[] pi_Instance, java.lang.String pi_Class) Adds an instance in the test set.
void	AddTrainInstance(java.lang.Object[] pi_Instance, java.lang.String pi_Class) Adds an instance in the train set.
void	AddUnlabeledTrainInstance(java.lang.Object[] pi_Instance)
double	Classify(java.lang.Object[] pi_Instance) Classify an unseen instance using the learned classifier.
double	Cluster(java.lang.Object[] pi_Instance) Cluster an unseen instance using the learned clusterizer.
double[][]	ConfusionMatrix() Gets the confusion matrix that plots precision and recall for each class.
void	CreateModel(weka.classifiers.Classifier pi_Classifier) Creates the classification model by learning from the training set.
void	CreateModel(weka.clusterers.Clusterer pi_Clusterer) Creates the cluster model by learning from the training set.
weka.core.Instance	CreateUnlabeledInstance(java.lang.Object[] pi_Instance)
weka.classifiers.Evaluation	EstimateConfidence() Approximate training set error.
weka.core.FastVector	GetAttribute()
java.lang.String[]	GetAttributeList() Gets the list of attributes.
java.lang.String[]	GetClassList() Gets the list of classes.
weka.core.Instances	GetClusterCentroid()
double[]	GetDistribution(java.lang.Object[] pi_Instance) Classify an unseen instance using the learned classifier.
weka.core.Instances	GetTestingSet() Get the training instances
weka.core.Instances	GetTrainingSet() Get the training instances
int	GetTrainingSetSize() Get the number of training instances
double	Likelihood(java.lang.Object[] pi_Instance, int pi_PositiveClassIndex) Classify an unseen instance using the learned classifier.
static void	main(java.lang.String[] args) Test routine
void	SetDoubleOnly(boolean pi_Flag)
void	Shrink() Reduces the size of a classifier by deleting the corpora.
java.lang.String	TestModel() Test the learned model.

Methods inherited from class java.lang.Object

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

Constructor Detail

WekaLearner

public WekaLearner(WekaAttribute[] pi_Attributes,
                   java.lang.String[] pi_ClassAttributes)

Creates a new classification algorithm.

Parameters:

pi_Attributes - Array of attributes

pi_ClassAttributes - Class attribute

WekaLearner

public WekaLearner(weka.core.FastVector attrsMerged,
                   java.lang.String[] attrlblMerged,
                   java.lang.String[] classList,
                   weka.core.Instances trainMerged)

Method Detail

AddTrainInstance

public void AddTrainInstance(java.lang.Object[] pi_Instance,
                             java.lang.String pi_Class)

Adds an instance in the train set.

Parameters:

pi_Instance - The instance, an array of objects (can mix numeric and nominal attributes - see WekaAttribute)

pi_Class - The class of this instance

See Also:

WekaAttribute

AddUnlabeledTrainInstance

public void AddUnlabeledTrainInstance(java.lang.Object[] pi_Instance)

AddTestInstance

public void AddTestInstance(java.lang.Object[] pi_Instance,
                            java.lang.String pi_Class)

Adds an instance in the test set.

Parameters:

pi_Instance - The instance, an array of objects (can mix numeric and nominal attributes - see WekaAttribute)

pi_Class - The class of this instance

See Also:

WekaAttribute

SetDoubleOnly

public void SetDoubleOnly(boolean pi_Flag)

CreateUnlabeledInstance

public weka.core.Instance CreateUnlabeledInstance(java.lang.Object[] pi_Instance)

CreateModel

public void CreateModel(weka.classifiers.Classifier pi_Classifier)

Creates the classification model by learning from the training set.

Parameters:

pi_Classifier - The classification algorithm (from the Weka library, ex.: NaiveBayes, J48, ...)

CreateModel

public void CreateModel(weka.clusterers.Clusterer pi_Clusterer)

Creates the cluster model by learning from the training set.

Parameters:

pi_Clusterer - The clusterization algorithm (from the Weka library, ex.: k-Mean, ...)

EstimateConfidence

public weka.classifiers.Evaluation EstimateConfidence()

Approximate training set error.

Returns:

evaluation module from which many types of errors are exposed (e.g.: mean absolute error)

TestModel

public java.lang.String TestModel()

Test the learned model.

Returns:

A summary string of the performance of the classifier

ConfusionMatrix

public double[][] ConfusionMatrix()

Gets the confusion matrix that plots precision and recall for each class.

Returns:

2-dimension array (X=Ground thruth, Y=Guesses)

Classify

public double Classify(java.lang.Object[] pi_Instance)

Classify an unseen instance using the learned classifier. Output the most probable class.

Parameters:

pi_Instance - The instance, an array of objects (can mix numeric and nominal attributes - see WekaAttribute)

Returns:

The index of the most probable class

See Also:

WekaAttribute

Cluster

public double Cluster(java.lang.Object[] pi_Instance)

Cluster an unseen instance using the learned clusterizer. Output the most probable cluster.

Parameters:

pi_Instance - The instance, an array of objects (can mix numeric and nominal attributes - see WekaAttribute)

Returns:

The index of the most probable cluster

See Also:

WekaAttribute

GetDistribution

public double[] GetDistribution(java.lang.Object[] pi_Instance)

Classify an unseen instance using the learned classifier. Ouput the likelihood of each class.

Parameters:

pi_Instance - The instance, an array of objects (can mix numeric and nominal attributes - see WekaAttribute)

Returns:

An array of probabilities, parallel to the possible classes

See Also:

WekaAttribute

Likelihood

public double Likelihood(java.lang.Object[] pi_Instance,
                         int pi_PositiveClassIndex)

Classify an unseen instance using the learned classifier. Get the likelihood of a given class.

Parameters:

pi_Instance - The instance, an array of objects (can mix numeric and nominal attributes - see WekaAttribute)

pi_PositiveClassIndex - The index of a class

Returns:

the likelihood of the given class

See Also:

WekaAttribute

GetAttributeList

public java.lang.String[] GetAttributeList()

Gets the list of attributes.

Returns:

String array

GetAttribute

public weka.core.FastVector GetAttribute()

GetClassList

public java.lang.String[] GetClassList()

Gets the list of classes.

Returns:

String array

GetTrainingSet

public weka.core.Instances GetTrainingSet()

Get the training instances

Returns:

training instances

GetTestingSet

public weka.core.Instances GetTestingSet()

Get the training instances

Returns:

training instances

GetTrainingSetSize

public int GetTrainingSetSize()

Get the number of training instances

Returns:

training set size

GetClusterCentroid

public weka.core.Instances GetClusterCentroid()

Shrink

public void Shrink()

Reduces the size of a classifier by deleting the corpora. Only keep the learned model and information on classes and attributes.

main

public static void main(java.lang.String[] args)

Test routine

Parameters:

args -