Example usage for weka.core Instances numAttributes

Introduction

In this page you can find the example usage for weka.core Instances numAttributes.

Prototype

publicint numAttributes() 

Source Link

Document

Returns the number of attributes.

Usage

From source file:bme.mace.logicdomain.Evaluation.java

License:Open Source License

/**
 * Prints the header for the predictions output into a supplied StringBuffer
 * //from   w w w  . j  a v a 2  s. c  o m
 * @param test structure of the test set to print predictions for
 * @param attributesToOutput indices of the attributes to output
 * @param printDistribution prints the complete distribution for nominal
 *          attributes, not just the predicted value
 * @param text the StringBuffer to print to
 */
protected static void printClassificationsHeader(Instances test, Range attributesToOutput,
        boolean printDistribution, StringBuffer text) {
    // print header
    if (test.classAttribute().isNominal()) {
        if (printDistribution) {
            text.append(" inst#     actual  predicted error distribution");
        } else {
            text.append(" inst#     actual  predicted error prediction");
        }
    } else {
        text.append(" inst#     actual  predicted      error");
    }
    if (attributesToOutput != null) {
        attributesToOutput.setUpper(test.numAttributes() - 1);
        text.append(" (");
        boolean first = true;
        for (int i = 0; i < test.numAttributes(); i++) {
            if (i == test.classIndex()) {
                continue;
            }

            if (attributesToOutput.isInRange(i)) {
                if (!first) {
                    text.append(",");
                }
                text.append(test.attribute(i).name());
                first = false;
            }
        }
        text.append(")");
    }
    text.append("\n");
}

---

From source file:bme.mace.logicdomain.Evaluation.java

License:Open Source License

/**
 * Prints the predictions for the given dataset into a supplied StringBuffer
 * //www . j  a  va  2 s. co  m
 * @param classifier the classifier to use
 * @param train the training data
 * @param testSource the test set
 * @param classIndex the class index (1-based), if -1 ot does not override the
 *          class index is stored in the data file (by using the last
 *          attribute)
 * @param attributesToOutput the indices of the attributes to output
 * @param printDistribution prints the complete distribution for nominal
 *          classes, not just the predicted value
 * @param text StringBuffer to hold the printed predictions
 * @throws Exception if test file cannot be opened
 */
public static void printClassifications(Classifier classifier, Instances train, DataSource testSource,
        int classIndex, Range attributesToOutput, boolean printDistribution, StringBuffer text)
        throws Exception {

    if (testSource != null) {
        Instances test = testSource.getStructure();
        if (classIndex != -1) {
            test.setClassIndex(classIndex - 1);
        } else {
            if (test.classIndex() == -1) {
                test.setClassIndex(test.numAttributes() - 1);
            }
        }

        // print the header
        printClassificationsHeader(test, attributesToOutput, printDistribution, text);

        // print predictions
        int i = 0;
        testSource.reset();
        test = testSource.getStructure(test.classIndex());
        while (testSource.hasMoreElements(test)) {
            Instance inst = testSource.nextElement(test);
            text.append(predictionText(classifier, inst, i, attributesToOutput, printDistribution));
            i++;
        }
    }
    // return text.toString();
}

---

From source file:boosting.classifiers.DecisionStumpWritable.java

License:Open Source License

/**
 * Generates the classifier.//from   www.ja v a2s  .  c  o m
 *
 * @param instances set of instances serving as training data 
 * @throws Exception if the classifier has not been generated successfully
 */
public void buildClassifier(Instances instances) throws Exception {

    double bestVal = Double.MAX_VALUE, currVal;
    double bestPoint = -Double.MAX_VALUE;
    int bestAtt = -1, numClasses;

    // can classifier handle the data?
    getCapabilities().testWithFail(instances);

    // remove instances with missing class
    instances = new Instances(instances);
    instances.deleteWithMissingClass();

    // only class? -> build ZeroR model
    if (instances.numAttributes() == 1) {
        System.err.println(
                "Cannot build model (only class attribute present in data!), " + "using ZeroR model instead!");
        m_ZeroR = new weka.classifiers.rules.ZeroR();
        m_ZeroR.buildClassifier(instances);
        return;
    } else {
        m_ZeroR = null;
    }

    double[][] bestDist = new double[3][instances.numClasses()];

    m_Instances = new Instances(instances);

    if (m_Instances.classAttribute().isNominal()) {
        numClasses = m_Instances.numClasses();
    } else {
        numClasses = 1;
    }

    // For each attribute
    boolean first = true;
    for (int i = 0; i < m_Instances.numAttributes(); i++) {
        if (i != m_Instances.classIndex()) {

            // Reserve space for distribution.
            m_Distribution = new double[3][numClasses];

            // Compute value of criterion for best split on attribute
            if (m_Instances.attribute(i).isNominal()) {
                currVal = findSplitNominal(i);
            } else {
                currVal = findSplitNumeric(i);
            }
            if ((first) || (currVal < bestVal)) {
                bestVal = currVal;
                bestAtt = i;
                bestPoint = m_SplitPoint;
                for (int j = 0; j < 3; j++) {
                    System.arraycopy(m_Distribution[j], 0, bestDist[j], 0, numClasses);
                }
            }

            // First attribute has been investigated
            first = false;
        }
    }

    // Set attribute, split point and distribution.
    m_AttIndex = bestAtt;
    m_SplitPoint = bestPoint;
    m_Distribution = bestDist;
    if (m_Instances.classAttribute().isNominal()) {
        for (int i = 0; i < m_Distribution.length; i++) {
            double sumCounts = Utils.sum(m_Distribution[i]);
            if (sumCounts == 0) { // This means there were only missing attribute values
                System.arraycopy(m_Distribution[2], 0, m_Distribution[i], 0, m_Distribution[2].length);
                Utils.normalize(m_Distribution[i]);
            } else {
                Utils.normalize(m_Distribution[i], sumCounts);
            }
        }
    }

    // Save memory
    m_Instances = new Instances(m_Instances, 0);
}

---

From source file:boostingPL.boosting.AdaBoost.java

License:Open Source License

public static void main(String[] args) throws Exception {
    java.io.File inputFile = new java.io.File(
            "/home/aax/xpShareSpace/dataset/single-class/+winered/winequality-red.datatrain1.arff");
    ArffLoader atf = new ArffLoader();
    atf.setFile(inputFile);//from   w  w  w .  j  a v a2 s .  com
    Instances training = atf.getDataSet();
    training.setClassIndex(training.numAttributes() - 1);

    AdaBoost adaBoost = new AdaBoost(training, 100);
    for (int t = 0; t < 100; t++) {
        adaBoost.run(t);
    }

    java.io.File inputFilet = new java.io.File(
            "/home/aax/xpShareSpace/dataset/single-class/+winered/winequality-red.datatest1.arff");
    ArffLoader atft = new ArffLoader();
    atft.setFile(inputFilet);
    Instances testing = atft.getDataSet();
    testing.setClassIndex(testing.numAttributes() - 1);

    Evaluation eval = new Evaluation(testing);
    for (Instance inst : testing) {
        eval.evaluateModelOnceAndRecordPrediction(adaBoost, inst);
    }
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());

    /*
    int right = 0;
    for (int i = 0; i < testing.numInstances(); i++) {
       Instance inst = testing.instance(i);
       if (adaBoost.classifyInstance(inst) == inst.classValue()) {
    right++;
       }
    }
    System.out.println(right);
    System.out.println((double)right/training.numInstances());
    */
}

---

From source file:boostingPL.boosting.InstancesHelper.java

License:Open Source License

/**
 * create instances header from metadata,
 * the metadata like this://w w  w . j av a2  s  . c o m
 * 
 *   <br/>
 *   <p>attributesNum:100</p>
 *   <p>classes:+1,-1</p>
 *   <br/>
 * 
 * @param in
 * @return
 * @throws IOException
 */
public static Instances createInstancesFromMetadata(LineReader in) throws IOException {
    int attributesNum = 0;
    ArrayList<Attribute> attInfo = null;
    List<String> classItems = null;

    Text line = new Text();
    while (in.readLine(line) > 0) {
        String sline = line.toString();
        if (sline.startsWith("attributesNum:")) {
            attributesNum = Integer.parseInt(sline.substring(14));
            attInfo = new ArrayList<Attribute>(attributesNum + 1);
            for (int i = 0; i < attributesNum; i++) {
                attInfo.add(new Attribute("attr" + i));
            }

            System.out.println("AttributeNum:" + attributesNum);
        } else if (sline.startsWith("classes:")) {
            String classes = sline.substring(8);
            String[] citems = classes.split(",");
            classItems = new ArrayList<String>(citems.length);
            for (String s : citems) {
                classItems.add(s);
            }

            System.out.println("classes:" + classes);
        }
    }

    attInfo.add(new Attribute("class", classItems));
    Instances insts = new Instances("BoostingPL-dataset", attInfo, 0);
    insts.setClassIndex(insts.numAttributes() - 1);

    return insts;
}

---

From source file:boostingPL.boosting.InstancesHelper.java

License:Open Source License

/**
 * create instances header from a instance
 * //ww  w. ja  v  a2s.  com
 * @param instance
 * @return instances
 */
public static Instances createInstances(String text) {
    String[] items = text.split(" ");

    ArrayList<Attribute> attInfo = new ArrayList<Attribute>();
    for (int i = 0; i < items.length - 1; i++) {
        attInfo.add(new Attribute("attr" + i));
    }

    List<String> classItems = new ArrayList<String>(2);
    classItems.add("1");
    classItems.add("-1");
    attInfo.add(new Attribute("class", classItems));
    Instances insts = new Instances("BoostingPL-dataset", attInfo, 0);
    insts.setClassIndex(insts.numAttributes() - 1);

    return insts;
}

---

From source file:boostingPL.boosting.SAMME.java

License:Open Source License

public static void main(String[] args) throws Exception {
    java.io.File inputFile = new java.io.File(args[0]);
    ArffLoader atf = new ArffLoader();
    atf.setFile(inputFile);//w  w  w  . j av a  2 s  . co m
    Instances training = atf.getDataSet();
    training.setClassIndex(training.numAttributes() - 1);
    //Instances testing = new Instances(training);

    int iterationNum = 100;
    SAMME samme = new SAMME(training, iterationNum);
    for (int t = 0; t < iterationNum; t++) {
        samme.run(t);
    }

    java.io.File inputFilet = new java.io.File(args[1]);
    ArffLoader atft = new ArffLoader();
    atft.setFile(inputFilet);
    Instances testing = atft.getDataSet();
    testing.setClassIndex(testing.numAttributes() - 1);

    Evaluation eval = new Evaluation(testing);
    for (Instance inst : testing) {
        eval.evaluateModelOnceAndRecordPrediction(samme, inst);
    }
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());
}

---

From source file:br.com.edu.arff.ConstructArff.java

public static void main(String[] args) throws Exception {
    FastVector atts;// w w w.  j  a va 2  s.co m
    FastVector attsRel;
    FastVector attVals;
    FastVector attValsRel;
    Instances data;
    Instances dataRel;
    double[] vals;
    double[] valsRel;
    int i, j, cont;

    ConnectionLAK conexao = new ConnectionLAK();
    ArrayList<String> uris;
    ArrayList<String> todos_topicos;
    ArrayList<Topicos> valores;

    uris = conexao.buscaURIS();
    todos_topicos = conexao.BuscarTodosTopicos();

    atts = new FastVector();
    for (String topicos : todos_topicos) {
        atts.addElement(new Attribute(topicos));
    }

    data = new Instances("Relacao", atts, 0);

    for (String uri : uris) {
        i = 0;
        vals = new double[data.numAttributes()];
        valores = conexao.BuscarTopicos(uri);
        for (int t = 0; t < data.numAttributes(); t++) {
            for (Topicos topic : valores) {
                String comparacao = topic.getTopico();
                String atributo = data.attribute(t).name();
                if (comparacao.equals(atributo)) {
                    vals[t] = topic.getValor();
                    break;
                }
            }
        }

        data.add(new Instance(1.0, vals));
    }

    System.out.println(data);
}

---

From source file:br.com.edu.arff.LoadArff.java

public ArrayList<Cluster> carregarArff(String caminho) throws FileNotFoundException, IOException {
    BufferedReader reader = new BufferedReader(new FileReader(caminho));
    ArffReader arff = new ArffReader(reader);
    Instances data = arff.getData();
    data.setClassIndex(data.numAttributes() - 1);
    Instance inst = null;/*from w  w  w  .  j  a v a 2 s.  co  m*/
    Attribute att = data.attribute("Cluster");

    ArrayList<String> uris;
    ArrayList<Cluster> lista = new ArrayList<Cluster>();

    Fuseki fuseki = new Fuseki();

    uris = fuseki.buscaURIS();

    for (int i = 0; i <= data.numInstances() - 1; i++) {
        Cluster cluster = new Cluster();
        String clusters = String.valueOf(data.get(i).stringValue(att));
        cluster.setUri(uris.get(i));
        cluster.setGrupo(clusters);
        lista.add(cluster);
    }

    //        for (Cluster c : lista) {
    //              System.out.println(c.getUri());
    //              System.out.println(c.getGrupo());
    //        } 
    return lista;
}

---

From source file:br.com.edu.partition.Tranning.java

public static Double Tranning_JRIP(String test, String tranning) throws IOException, Exception {
    Double result_ = null;//from www . j  av a2s .c o  m
    ArffLoader loader;
    loader = new ArffLoader();
    loader.setFile(new File(tranning));
    loader.getStructure();

    Instances trainingset = loader.getDataSet();
    int classIndex = trainingset.numAttributes() - 1;
    trainingset.setClassIndex(classIndex);

    //J48 j48 = new J48();
    JRip jRip = new JRip();
    //String[] options2 = {"-F", "3", "-N", "2.0", "-O", "2", "-S", "1"};
    //jRip.setOptions(options2);
    //jRip.buildClassifier(trainingset);
    jRip.buildClassifier(trainingset);

    loader = new ArffLoader();
    loader.setFile(new File(test));
    loader.getStructure();

    Instances testset = loader.getDataSet();
    testset.setClassIndex(testset.numAttributes() - 1);
    for (Instance instance : testset) {
        //double[] result = jRip.distributionForInstance(instance);
        double[] result = jRip.distributionForInstance(instance);
        result_ = result[1];
        //System.out.println(test + " " + result[1] + " " + tranning);
    }
    return result_;

}

---

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