Example usage for weka.classifiers Evaluation Evaluation

List of usage examples for weka.classifiers Evaluation Evaluation

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Introduction

In this page you can find the example usage for weka.classifiers Evaluation Evaluation.

Prototype

public Evaluation(Instances data) throws Exception

Source Link

Usage

From source file:gr.uoc.nlp.opinion.analysis.suggestion.AnalyzeSuggestions.java

/**
 *
 * @param classifier/*from w w w . j a v a 2s  .c  o m*/
 */
public void crossValidationTrainSet(Classifier classifier) {

    Evaluation eval;
    try {
        //initialize cross validation
        eval = new Evaluation(this.trainset);
        //validate
        eval.crossValidateModel(classifier, this.trainset, 10, new Random(1));

        System.out.println(eval.toSummaryString());
        System.out.println(eval.toClassDetailsString());
        System.out.println(eval.toMatrixString());

    } catch (Exception ex) {
        Logger.getLogger(AnalyzeSuggestions.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:gr.uoc.nlp.opinion.analysis.suggestion.AnalyzeSuggestions.java

/**
 *
 * @param classifier/*from  w ww  .  j  av  a  2s.  c om*/
 * @param testset
 */
public void valuateSet(Classifier classifier, Instances testset) {

    Evaluation eval;
    try {
        eval = new Evaluation(this.trainset);
        eval.evaluateModel(classifier, testset);

        System.out.println(eval.toSummaryString());
        System.out.println(eval.toClassDetailsString());
        System.out.println(eval.toMatrixString());
    } catch (Exception ex) {
        Logger.getLogger(AnalyzeSuggestions.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:GroupProject.DMChartUI.java

/**
* Action for the generate button/* w w  w  . j  a  v  a2 s .  co  m*/
* It reads the user input from the table and the selected options and performs
* a classifiecation of the user input
* the user can choose linear regression, naive bayes classifier, or j48 trees to classify 
*
*/
private void generateButtonActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_generateButtonActionPerformed
    // TODO add your handling code here:                                              
    // TODO add your handling code here:
    //File file = new File("studentTemp.csv");
    CSVtoArff converter = new CSVtoArff();
    Instances students = null;
    Instances students2 = null;
    try {
        converter.convert("studentTemp.csv", "studentTemp.arff");
    } catch (IOException ex) {
        Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
    }

    try {
        students = new Instances(new BufferedReader(new FileReader("studentTemp.arff")));
        students2 = new Instances(new BufferedReader(new FileReader("studentTemp.arff")));
    } catch (IOException ex) {
        Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
    }

    //get column to predict values for 
    //int target=students.numAttributes()-1; 
    int target = dataSelector.getSelectedIndex() + 1;
    System.out.printf("this is the target: %d\n", target);
    //set target 
    students.setClassIndex(target);
    students2.setClassIndex(target);

    //case on which radio button is selected 
    //Linear Regressions
    if (LRB.isSelected()) {

        LinearRegression model = null;
        if (Lmodel != null) {
            model = Lmodel;
        } else {
            buildLinearModel();
            model = Lmodel;
        }

        System.out.println("im doing linear regression");

        equationDisplayArea.setText(model.toString());

        System.out.println("im going to get the instance");

        Instance prediction2 = getInstance(true);

        Remove remove = new Remove();
        int[] toremove = { 0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17 };
        remove.setAttributeIndicesArray(toremove);

        try {
            remove.setInputFormat(students);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        Instances instNew = null;
        try {
            instNew = Filter.useFilter(students, remove);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        prediction2.setDataset(instNew);
        System.err.print("i got the instance");
        double result = 0;
        try {
            result = model.classifyInstance(prediction2);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        System.out.printf("the result : %f \n ", result);
        predictValue.setText(Double.toString(result));
        System.out.println("I'm done with Linear Regression");
    }

    //Naive Bayes
    else if (NBB.isSelected()) {
        Classifier cModel = null;

        if (NBmodel != null) {
            cModel = NBmodel;
        } else {
            buildNBClassifier();
            cModel = NBmodel;
        }

        System.out.println("im doing NB");

        //build test 
        Evaluation eTest = null;
        try {
            eTest = new Evaluation(students);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }
        System.out.println("Using NB");

        try {
            eTest.evaluateModel(cModel, students);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        //display the test results to console 
        String strSummary = eTest.toSummaryString();
        System.out.println(strSummary);

        //build instance to predict 
        System.out.println("im going to get the instance");

        Instance prediction2 = getInstance(false);

        prediction2.setDataset(students);
        System.err.print("i got the instance");

        //replace with loop stating the class names 
        //fit text based on name of categories 
        double pred = 0;
        try {
            pred = cModel.classifyInstance(prediction2);
            prediction2.setClassValue(pred);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }
        //get the predicted value and set predictValue to it 
        predictValue.setText(prediction2.classAttribute().value((int) pred));

        System.out.println("I'm done with Naive Bayes");

        double[] fDistribution2 = null;
        try {
            fDistribution2 = cModel.distributionForInstance(prediction2);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        double max = 0;
        int maxindex = 0;
        max = fDistribution2[0];
        for (int i = 0; i < fDistribution2.length; i++) {
            if (fDistribution2[i] > max) {
                maxindex = i;
                max = fDistribution2[i];
            }
            System.out.println("the value at " + i + " : " + fDistribution2[i]);
            System.out.println("the label at " + i + prediction2.classAttribute().value(i));
        }
        prediction2.setClassValue(maxindex);
        predictValue.setText(prediction2.classAttribute().value(maxindex));

    }
    //J48 Tree
    else if (JB.isSelected()) {

        System.out.println("im doing j48 ");

        Classifier jModel = null;
        if (Jmodel != null) {
            jModel = Jmodel;
        } else {
            buildJClassifier();
            jModel = Jmodel;
        }
        //test model 
        Evaluation eTest2 = null;
        try {
            eTest2 = new Evaluation(students);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }
        System.out.println("Using J48 test");
        try {
            eTest2.evaluateModel(jModel, students);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }
        String strSummary2 = eTest2.toSummaryString();
        System.out.println(strSummary2);

        System.out.println("im going to get the instance");

        Instance prediction2 = getInstance(false);

        prediction2.setDataset(students);
        System.err.print("i got the instance\n");

        double pred = 0;
        try {
            pred = jModel.classifyInstance(prediction2);
            prediction2.setClassValue(pred);
            System.out.println("i did a prediction");
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        //get the predicted value and set predictValue to it 
        System.out.println("this was pred:" + pred);
        predictValue.setText(prediction2.classAttribute().value((int) pred));

        System.out.println("I'm done with J48");
        //replace with loop stating the class names 
        //fit text based on name of categories 

        double[] fDistribution2 = null;
        try {
            fDistribution2 = jModel.distributionForInstance(prediction2);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        double max = 0;
        int maxindex = 0;
        max = fDistribution2[0];
        for (int i = 0; i < fDistribution2.length; i++) {
            if (fDistribution2[i] > max) {
                maxindex = i;
                max = fDistribution2[i];
            }
            System.out.println("the value at " + i + " : " + fDistribution2[i]);
            System.out.println("the label at " + i + " " + prediction2.classAttribute().value(i));
        }
        prediction2.setClassValue(maxindex);
        predictValue.setText(prediction2.classAttribute().value(maxindex));

    }

}

From source file:gyc.OverBoostM1.java

License:Open Source License

/**
 * Boosting method. Boosts using resampling
 *
 * @param data the training data to be used for generating the
 * boosted classifier./*from   www . j  av  a2  s .c om*/
 * @throws Exception if the classifier could not be built successfully
 */
protected void buildClassifierUsingResampling(Instances data) throws Exception {

    Instances trainData, sample, training;
    double epsilon, reweight, sumProbs;
    Evaluation evaluation;
    int numInstances = data.numInstances();
    Random randomInstance = new Random(m_Seed);
    int resamplingIterations = 0;

    // Initialize data
    m_Betas = new double[m_Classifiers.length];
    m_NumIterationsPerformed = 0;
    // Create a copy of the data so that when the weights are diddled
    // with it doesn't mess up the weights for anyone else
    training = new Instances(data, 0, numInstances);
    sumProbs = training.sumOfWeights();
    for (int i = 0; i < training.numInstances(); i++) {
        training.instance(i).setWeight(training.instance(i).weight() / sumProbs);
    }

    // Do boostrap iterations
    for (m_NumIterationsPerformed = 0; m_NumIterationsPerformed < m_Classifiers.length; m_NumIterationsPerformed++) {
        if (m_Debug) {
            System.err.println("Training classifier " + (m_NumIterationsPerformed + 1));
        }

        // Select instances to train the classifier on
        if (m_WeightThreshold < 100) {
            trainData = selectWeightQuantile(training, (double) m_WeightThreshold / 100);
        } else {
            trainData = new Instances(training);
        }

        // Resample
        resamplingIterations = 0;
        double[] weights = new double[trainData.numInstances()];
        for (int i = 0; i < weights.length; i++) {
            weights[i] = trainData.instance(i).weight();
        }
        do {
            sample = trainData.resampleWithWeights(randomInstance, weights);

            //
            int classNum[] = sample.attributeStats(sample.classIndex()).nominalCounts;
            int minC, nMin = classNum[0];
            int majC, nMaj = classNum[1];
            if (nMin < nMaj) {
                minC = 0;
                majC = 1;
            } else {
                minC = 1;
                majC = 0;
                nMin = classNum[1];
                nMaj = classNum[0];
            }
            //System.out.println("minC="+nMin+"; majC="+nMaj);
            /*
             * balance the data which boosting generate for training base classifier
            */
            //System.out.println("before:"+classNum[0]+"-"+classNum[1]);
            Instances sampleData = randomSampling(sample, majC, minC, nMaj, nMaj, randomInstance);
            //classNum =sampleData.attributeStats(sampleData.classIndex()).nominalCounts;
            //System.out.println("after:"+classNum[0]+"-"+classNum[1]);

            // Build and evaluate classifier
            m_Classifiers[m_NumIterationsPerformed].buildClassifier(sampleData);

            evaluation = new Evaluation(data);
            evaluation.evaluateModel(m_Classifiers[m_NumIterationsPerformed], training);
            epsilon = evaluation.errorRate();
            resamplingIterations++;
        } while (Utils.eq(epsilon, 0) && (resamplingIterations < MAX_NUM_RESAMPLING_ITERATIONS));

        // Stop if error too big or 0
        if (Utils.grOrEq(epsilon, 0.5) || Utils.eq(epsilon, 0)) {
            if (m_NumIterationsPerformed == 0) {
                m_NumIterationsPerformed = 1; // If we're the first we have to to use it
            }
            break;
        }

        // Determine the weight to assign to this model
        m_Betas[m_NumIterationsPerformed] = Math.log((1 - epsilon) / epsilon);
        reweight = (1 - epsilon) / epsilon;
        if (m_Debug) {
            System.err.println("\terror rate = " + epsilon + "  beta = " + m_Betas[m_NumIterationsPerformed]);
        }

        // Update instance weights
        setWeights(training, reweight);
    }
}

From source file:gyc.OverBoostM1.java

License:Open Source License

/**
 * Boosting method. Boosts any classifier that can handle weighted
 * instances.//  www. ja  v  a  2  s . co m
 *
 * @param data the training data to be used for generating the
 * boosted classifier.
 * @throws Exception if the classifier could not be built successfully
 */
protected void buildClassifierWithWeights(Instances data) throws Exception {

    Instances trainData, training;
    double epsilon, reweight;
    Evaluation evaluation;
    int numInstances = data.numInstances();
    Random randomInstance = new Random(m_Seed);

    // Initialize data
    m_Betas = new double[m_Classifiers.length];
    m_NumIterationsPerformed = 0;

    // Create a copy of the data so that when the weights are diddled
    // with it doesn't mess up the weights for anyone else
    training = new Instances(data, 0, numInstances);

    // Do boostrap iterations
    for (m_NumIterationsPerformed = 0; m_NumIterationsPerformed < m_Classifiers.length; m_NumIterationsPerformed++) {
        if (m_Debug) {
            System.err.println("Training classifier " + (m_NumIterationsPerformed + 1));
        }
        // Select instances to train the classifier on
        if (m_WeightThreshold < 100) {
            trainData = selectWeightQuantile(training, (double) m_WeightThreshold / 100);
        } else {
            trainData = new Instances(training, 0, numInstances);
        }

        // Build the classifier
        if (m_Classifiers[m_NumIterationsPerformed] instanceof Randomizable)
            ((Randomizable) m_Classifiers[m_NumIterationsPerformed]).setSeed(randomInstance.nextInt());

        // this is the training data for building base classifier, 
        m_Classifiers[m_NumIterationsPerformed].buildClassifier(trainData);

        // Evaluate the classifier
        evaluation = new Evaluation(data);
        evaluation.evaluateModel(m_Classifiers[m_NumIterationsPerformed], training);
        epsilon = evaluation.errorRate();

        // Stop if error too small or error too big and ignore this model
        if (Utils.grOrEq(epsilon, 0.5) || Utils.eq(epsilon, 0)) {
            if (m_NumIterationsPerformed == 0) {
                m_NumIterationsPerformed = 1; // If we're the first we have to to use it
            }
            break;
        }
        // Determine the weight to assign to this model
        m_Betas[m_NumIterationsPerformed] = Math.log((1 - epsilon) / epsilon);
        reweight = (1 - epsilon) / epsilon;
        if (m_Debug) {
            System.err.println("\terror rate = " + epsilon + "  beta = " + m_Betas[m_NumIterationsPerformed]);
        }

        // Update instance weights
        setWeights(training, reweight);
    }
}

From source file:gyc.UnderOverBoostM1.java

License:Open Source License

/**
 * Boosting method. Boosts using resampling
 *
 * @param data the training data to be used for generating the
 * boosted classifier./*  w w w. j av a 2  s  . com*/
 * @throws Exception if the classifier could not be built successfully
 */
protected void buildClassifierUsingResampling(Instances data) throws Exception {

    Instances trainData, sample, training;
    double epsilon, reweight, sumProbs;
    Evaluation evaluation;
    int numInstances = data.numInstances();
    Random randomInstance = new Random(m_Seed);
    int resamplingIterations = 0;

    // Initialize data
    m_Betas = new double[m_Classifiers.length];
    m_NumIterationsPerformed = 0;
    // Create a copy of the data so that when the weights are diddled
    // with it doesn't mess up the weights for anyone else
    training = new Instances(data, 0, numInstances);
    sumProbs = training.sumOfWeights();
    for (int i = 0; i < training.numInstances(); i++) {
        training.instance(i).setWeight(training.instance(i).weight() / sumProbs);
    }

    // Do boostrap iterations
    int b = 10;
    for (m_NumIterationsPerformed = 0; m_NumIterationsPerformed < m_Classifiers.length; m_NumIterationsPerformed++) {
        if (m_Debug) {
            System.err.println("Training classifier " + (m_NumIterationsPerformed + 1));
        }

        // Select instances to train the classifier on
        if (m_WeightThreshold < 100) {
            trainData = selectWeightQuantile(training, (double) m_WeightThreshold / 100);
        } else {
            trainData = new Instances(training);
        }

        // Resample
        resamplingIterations = 0;
        double[] weights = new double[trainData.numInstances()];
        for (int i = 0; i < weights.length; i++) {
            weights[i] = trainData.instance(i).weight();
        }
        do {
            sample = trainData.resampleWithWeights(randomInstance, weights);

            //
            int classNum[] = sample.attributeStats(sample.classIndex()).nominalCounts;
            int minC, nMin = classNum[0];
            int majC, nMaj = classNum[1];
            if (nMin < nMaj) {
                minC = 0;
                majC = 1;
            } else {
                minC = 1;
                majC = 0;
                nMin = classNum[1];
                nMaj = classNum[0];
            }
            //System.out.println("minC="+nMin+"; majC="+nMaj);
            /*
             * balance the data which boosting generate for training base classifier
            */
            //System.out.println("before:"+classNum[0]+"-"+classNum[1]);
            double pb = 100.0 * (nMin + nMaj) / 2 / nMaj;
            /* if (m_NumIterationsPerformed + 1 > (m_Classifiers.length / 10))    
                b += 10;
            (b% * Nmaj) instances are taken from each class */
            Instances sampleData = randomSampling(sample, majC, minC, (int) pb, randomInstance);

            //classNum =sampleData.attributeStats(sampleData.classIndex()).nominalCounts;
            //System.out.println("after:"+classNum[0]+"-"+classNum[1]);

            // Build and evaluate classifier
            m_Classifiers[m_NumIterationsPerformed].buildClassifier(sampleData);

            evaluation = new Evaluation(data);
            evaluation.evaluateModel(m_Classifiers[m_NumIterationsPerformed], training);
            epsilon = evaluation.errorRate();
            resamplingIterations++;
        } while (Utils.eq(epsilon, 0) && (resamplingIterations < MAX_NUM_RESAMPLING_ITERATIONS));

        // Stop if error too big or 0
        if (Utils.grOrEq(epsilon, 0.5) || Utils.eq(epsilon, 0)) {
            if (m_NumIterationsPerformed == 0) {
                m_NumIterationsPerformed = 1; // If we're the first we have to to use it
            }
            break;
        }

        // Determine the weight to assign to this model
        m_Betas[m_NumIterationsPerformed] = Math.log((1 - epsilon) / epsilon);
        reweight = (1 - epsilon) / epsilon;
        if (m_Debug) {
            System.err.println("\terror rate = " + epsilon + "  beta = " + m_Betas[m_NumIterationsPerformed]);
        }

        // Update instance weights
        setWeights(training, reweight);
    }
}

From source file:hero.unstable.util.classification.wekaClassifier.java

public Evaluation classify(Instances data) throws Exception {
    data.setClassIndex(0);/* www.  ja  v a2  s  . c o  m*/

    // Randomize data
    Evaluation eval = new Evaluation(data);
    Random rand = new Random(seed);

    // Perform cross-validation
    eval.crossValidateModel(classifier, data, folds, rand);

    // output evaluation
    String result = eval.toClassDetailsString();
    /*
    System.out.println();
    System.out.println("=== Setup ===");
    System.out.println("Clasiffier: " + classifier.toString());
    System.out.println("Dataset: " + data.relationName());
    System.out.println("Folds: " + folds);
    System.out.println("Seed: " + seed);
    System.out.println();
    System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false));
    */
    //System.out.println(result);
    return eval;
}

From source file:hurtowniedanych.FXMLController.java

public void trainAndTestKNN() throws FileNotFoundException, IOException, Exception {

    InstanceQuery instanceQuery = new InstanceQuery();
    instanceQuery.setUsername("postgres");
    instanceQuery.setPassword("szupek");
    instanceQuery.setCustomPropsFile(new File("./src/data/DatabaseUtils.props")); // Wskazanie pliku z ustawieniami dla PostgreSQL

    String query = "select ks.wydawnictwo,ks.gatunek, kl.mia-sto\n" + "from zakupy z,ksiazki ks,klienci kl\n"
            + "where ks.id_ksiazka=z.id_ksiazka and kl.id_klient=z.id_klient";

    instanceQuery.setQuery(query);//from w ww  .jav a  2s .  c  o m
    Instances data = instanceQuery.retrieveInstances();
    data.setClassIndex(data.numAttributes() - 1);

    data.randomize(new Random());
    double percent = 70.0;
    int trainSize = (int) Math.round(data.numInstances() * percent / 100);
    int testSize = data.numInstances() - trainSize;
    Instances trainData = new Instances(data, 0, trainSize);
    Instances testData = new Instances(data, trainSize, testSize);

    int lSasiadow = Integer.parseInt(textFieldKnn.getText());
    System.out.println(lSasiadow);

    IBk ibk = new IBk(lSasiadow);

    // Ustawienie odleglosci
    EuclideanDistance euclidean = new EuclideanDistance(); // euklidesowej
    ManhattanDistance manhatan = new ManhattanDistance(); // miejska  

    LinearNNSearch linearNN = new LinearNNSearch();

    if (comboboxOdleglosc.getSelectionModel().getSelectedItem().equals("Manhatan")) {
        linearNN.setDistanceFunction(manhatan);
    } else {
        linearNN.setDistanceFunction(euclidean);
    }

    ibk.setNearestNeighbourSearchAlgorithm(linearNN); // ustawienie sposobu szukania sasiadow

    // Tworzenie klasyfikatora
    ibk.buildClassifier(trainData);

    Evaluation eval = new Evaluation(trainData);
    eval.evaluateModel(ibk, testData);
    spr.setVisible(true);
    labelKnn.setVisible(true);
    labelOdleglosc.setVisible(true);
    labelKnn.setText(textFieldKnn.getText());
    labelOdleglosc.setText(comboboxOdleglosc.getSelectionModel().getSelectedItem().toString());
    spr.setText(eval.toSummaryString("Wynik:", true));
}

From source file:ia02classificacao.IA02Classificacao.java

/**
 * @param args the command line arguments
 */// w ww. j  av  a2 s. c o  m
public static void main(String[] args) throws Exception {

    // abre o banco de dados arff e mostra a quantidade de instancias (linhas)
    DataSource arquivo = new DataSource("data/zoo.arff");
    Instances dados = arquivo.getDataSet();
    System.out.println("Instancias lidas: " + dados.numInstances());

    // FILTER: remove o atributo nome do animal da classificao
    String[] parametros = new String[] { "-R", "1" };
    Remove filtro = new Remove();
    filtro.setOptions(parametros);
    filtro.setInputFormat(dados);
    dados = Filter.useFilter(dados, filtro);

    AttributeSelection selAtributo = new AttributeSelection();
    InfoGainAttributeEval avaliador = new InfoGainAttributeEval();
    Ranker busca = new Ranker();
    selAtributo.setEvaluator(avaliador);
    selAtributo.setSearch(busca);
    selAtributo.SelectAttributes(dados);
    int[] indices = selAtributo.selectedAttributes();
    System.out.println("Selected attributes: " + Utils.arrayToString(indices));

    // Usa o algoritimo J48 e mostra a classificao dos dados em forma textual
    String[] opcoes = new String[1];
    opcoes[0] = "-U";
    J48 arvore = new J48();
    arvore.setOptions(opcoes);
    arvore.buildClassifier(dados);
    System.out.println(arvore);

    // Usa o algoritimo J48 e mostra a classificao de dados em forma grafica
    /*
    TreeVisualizer tv = new TreeVisualizer(null, arvore.graph(), new PlaceNode2());
    JFrame frame = new javax.swing.JFrame("?rvore de Conhecimento");
    frame.setSize(800,500);
    frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
    frame.getContentPane().add(tv);
    frame.setVisible(true);
    tv.fitToScreen();
    */

    /*
    * Classificao de novos dados
    */

    System.out.println("\n\nCLASSIFICAO DE NOVOS DADOS");
    // criar atributos
    double[] vals = new double[dados.numAttributes()];
    vals[0] = 1.0; // hair
    vals[1] = 0.0; // feathers
    vals[2] = 0.0; // eggs
    vals[3] = 1.0; // milk
    vals[4] = 1.0; // airborne
    vals[5] = 0.0; // aquatic
    vals[6] = 0.0; // predator
    vals[7] = 1.0; // toothed
    vals[8] = 1.0; // backbone
    vals[9] = 1.0; // breathes
    vals[10] = 0.0; // venomous
    vals[11] = 0.0; // fins
    vals[12] = 4.0; // legs
    vals[13] = 1.0; // tail
    vals[14] = 1.0; // domestic
    vals[15] = 1.0; // catsize

    // Criar uma instncia baseada nestes atributos
    Instance meuUnicornio = new DenseInstance(1.0, vals);

    // Adicionar a instncia nos dados
    meuUnicornio.setDataset(dados);

    // Classificar esta nova instncia
    double label = arvore.classifyInstance(meuUnicornio);

    // Imprimir o resultado da classificao
    System.out.println("Novo Animal: Unicrnio");
    System.out.println("classificacao: " + dados.classAttribute().value((int) label));

    /*
    * Avaliao e predio de erros de mtrica
    */
    System.out.println("\n\nAVALIAO E PREDIO DE ERROS DE MTRICA");
    Classifier cl = new J48();
    Evaluation eval_roc = new Evaluation(dados);
    eval_roc.crossValidateModel(cl, dados, 10, new Random(1), new Object[] {});
    System.out.println(eval_roc.toSummaryString());

    /*
    * Matriz de confuso
    */
    System.out.println("\n\nMATRIZ DE CONFUSO");
    double[][] confusionMatrix = eval_roc.confusionMatrix();
    System.out.println(eval_roc.toMatrixString());

}

From source file:id3.MyID3.java

/**
 * Main method//from w w w.ja  va 2s . co  m
 * @param args arguments
 */
public static void main(String[] args) {
    Instances instances;
    try {
        BufferedReader reader = new BufferedReader(new FileReader("D:\\Weka-3-6\\data\\weather.nominal.arff"));
        try {
            instances = new Instances(reader);
            instances.setClassIndex(instances.numAttributes() - 1);
            MyID3 id3 = new MyID3();
            try {
                id3.buildClassifier(instances);
            } catch (Exception e) {
                e.printStackTrace();
            }
            // Test class distribution
            double[] classDistribution = id3.classDistribution(instances);
            for (int i = 0; i < classDistribution.length; i++) {
                System.out.println(classDistribution[i]);
            }
            // Test entrophy and information gain for each attribute
            System.out.println(id3.computeEntropy(instances));
            Enumeration attributes = instances.enumerateAttributes();
            while (attributes.hasMoreElements()) {
                System.out.println(id3.computeIG(instances, (Attribute) attributes.nextElement()));
            }
            // Test build classifier
            try {
                id3.buildClassifier(instances);
            } catch (Exception e) {
                e.printStackTrace();
            }
            System.out.println(id3.toString());
            // Evaluate model from build classifier (full training)
            Evaluation eval = null;
            try {
                eval = new Evaluation(instances);
            } catch (Exception e) {
                e.printStackTrace();
            }
            try {
                System.out.println(instances);
                eval.evaluateModel(id3, instances);
            } catch (Exception e) {
                e.printStackTrace();
            }
            System.out.println(eval.toSummaryString("\nResults Full-Training\n\n", false));
            // Evaluate model from build classifier (test set)
            // Test Confusion Matrix
            System.out.println("Confusion Matrix : ");
            double[][] cmMatrix = eval.confusionMatrix();
            for (int row_i = 0; row_i < cmMatrix.length; row_i++) {
                for (int col_i = 0; col_i < cmMatrix.length; col_i++) {
                    System.out.print(cmMatrix[row_i][col_i]);
                    System.out.print("|");
                }
                System.out.println();
            }
        } catch (IOException e) {
            e.printStackTrace();
        }
    } catch (FileNotFoundException e) {
        e.printStackTrace();
    }
}