Example usage for weka.core Instance numAttributes

Introduction

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

Prototype

public int numAttributes();

Source Link

Document

Returns the number of attributes.

Usage

From source file:myid3andc45classifier.Model.MyC45.java

@Override
public double classifyInstance(Instance instance) {
    int i = 0;//from   w  ww  .  java  2  s .c  om
    if (attribute == null) {
        return label;
    } else {
        boolean numeric = false;
        for (int j = 0; j < instance.numAttributes(); j++) {
            if (instance.attribute(j).isNumeric()) {
                if (instance.attribute(j).name().equalsIgnoreCase(attribute.name().split(" ")[0])) {
                    numeric = true;
                    break;
                }
                i++;
            }
        }
        if (numeric) {
            double threshold = Double.parseDouble(attribute.name().split(" ")[1]);
            //System.out.println("WOWW!!! " + attribute.name() + " threshold is " + threshold);
            double val = (double) instance.value(i);
            if (val <= threshold) {
                return successors[(int) attribute.indexOfValue("<=" + threshold)].classifyInstance(instance);
                //instance.setValue(attribute, "<="+threshold);
            } else {
                return successors[(int) attribute.indexOfValue(">" + threshold)].classifyInstance(instance);
                //instance.setValue(attribute, ">"+threshold);
            }
        }

        return successors[(int) instance.value(attribute)].classifyInstance(instance);
    }

}

---

From source file:NaiveBayes.NaiveBayes.java

@Override
public double classifyInstance(Instance last) {
    double prob[] = new double[last.classAttribute().numValues()];
    for (int classIndex = 0; classIndex < last.attribute(last.classIndex()).numValues(); classIndex++) {//classifikasi
        double temp = 1;
        int i = 0;

        for (Atribut attr : getList()) {
            if (i == last.classIndex())
                i++;/*from   ww  w  .  j a v  a 2s  .c  om*/
            //System.out.println(attr.getName()+"="+last.attribute(i).name());
            temp *= attr.getFrekuensiNilai(last.attribute(last.classIndex()).value(classIndex),
                    last.toString(i), last.value(i), last.attribute(i).isNumeric()) / numEachClass[classIndex];
            i++;
        }
        double res;
        res = numEachClass[classIndex] / last.numAttributes() * temp;
        prob[classIndex] = res;
    }
    return maxIndex(prob);
}

---

From source file:NaiveBayesPckge.mushClass.java

@Override
public double classifyInstance(Instance instance) throws java.lang.Exception {
    double classify = 0;
    // banyaknya kesimpulan. Misal T dan F berati ada 2
    int numClasses = instance.numClasses();
    double[] out = new double[numClasses];
    //banyaknya kelas yang diuji
    int class_index = instance.classIndex();
    //banyaknya atribut
    int num_attributes = instance.numAttributes();
    double inputs[] = new double[num_attributes];

    for (int i = 0; i < numClasses; i++) {
        out[i] = probabConclusion[i];/*from w w  w  .  j  a  va 2s.c  o  m*/
        for (int j = 0; j < num_attributes - 1; j++) {
            int indexLabel = searchIndexLabel(j, instance.stringValue(j));
            out[i] *= (double) atribNom[j].getAttribObjectType(indexLabel, i);
        }
    }

    classify = getIndexBiggestProbability(out);

    return classify;
}

---

From source file:NaiveBayesPckge.mushClass.java

@Override
public double[] distributionForInstance(Instance instance) throws Exception {

    // banyaknya kesimpulan. Misal T dan F berati ada 2
    int numClasses = instance.numClasses();

    double[] out = new double[numClasses];
    //banyaknya kelas yang diuji
    int class_index = instance.classIndex();
    //banyaknya atribut
    int num_attributes = instance.numAttributes();
    double inputs[] = new double[num_attributes];

    for (int i = 0; i < numClasses; i++) {

        out[i] = probabConclusion[i];//from   w w  w  . j  av a 2 s.c om

        //            System.out.print("\n" + maxIterasi +". out["+i+"] = ");
        for (int j = 1; j < num_attributes; j++) {
            int indexLabel = searchIndexLabel(j, instance.stringValue(j));

            out[i] *= (double) atribNom[j].getAttribObjectType(indexLabel, i);

            //                System.out.print(atribNom[j].getAttribObjectType(indexLabel, i) + "*");
        }

        //            System.out.println("\nout["+i+"] = "+out[i]);
        //            System.out.println(instance.toString());
    }

    //        maxIterasi++;
    return out;
}

---

From source file:NaiveBayesPckge.NaiveBayesCode.java

@Override
public double[] distributionForInstance(Instance instance) throws Exception {

    // banyaknya kesimpulan. Misal T dan F berati ada 2
    int numClasses = instance.numClasses();
    double[] out = new double[numClasses];
    //banyaknya kelas yang diuji
    int class_index = instance.classIndex();
    //banyaknya atribut
    int num_attributes = instance.numAttributes();
    double inputs[] = new double[num_attributes];

    for (int i = 0; i < numClasses; i++) {

        out[i] = probabConclusion[i];//from www. j  a  va  2s  .  c om
        //            System.out.print("\n" + maxIterasi +". out["+i+"] = ");
        for (int j = 0; j < num_attributes - 1; j++) {
            int indexLabel = searchIndexLabel(j, instance.stringValue(j));
            out[i] *= (double) atribNom[j].getAttribObjectType(indexLabel, i);
            //                System.out.print(atribNom[j].getAttribObjectType(indexLabel, i) + "*");
        }
        //            System.out.println("\nout["+i+"] = "+out[i]);
        //            System.out.println(instance.toString());
    }
    //        maxIterasi++;
    return out;
}

---

From source file:net.paudan.evosvm.LibLINEAR.java

License:Open Source License

/**
* returns an instance into a sparse liblinear array
*
* @param instance the instance to work on
* @return the liblinear array//from w  w w  .  java2  s.  c  om
* @throws Exception if setup of array fails
*/
protected FeatureNode[] instanceToArray(Instance instance) throws Exception {
    // determine number of non-zero attributes
    int count = 0;

    for (int i = 0; i < instance.numValues(); i++) {
        if (instance.index(i) == instance.classIndex())
            continue;
        if (instance.valueSparse(i) != 0)
            count++;
    }

    if (m_Bias >= 0) {
        count++;
    }

    // fill array
    FeatureNode[] nodes = new FeatureNode[count];
    int index = 0;
    for (int i = 0; i < instance.numValues(); i++) {

        int idx = instance.index(i);
        double val = instance.valueSparse(i);

        if (idx == instance.classIndex())
            continue;
        if (val == 0)
            continue;

        nodes[index] = new FeatureNode(idx + 1, val);
        index++;
    }

    // add bias term
    if (m_Bias >= 0) {
        nodes[index] = new FeatureNode(instance.numAttributes() + 1, m_Bias);
    }

    return nodes;
}

---

From source file:nl.bioinf.roelen.thema11.classifier_tools.ClassifierUser.java

License:Open Source License

/**
 * use the classifier to test the sequences in a genbank or fasta file for boundaries
 * @param fileLocation the location of the genbank of fasta file
 * @param classifier the classifier to use
 * @return //from w ww  .ja v a  2  s. c o m
 */
public static ArrayList<ClassifiedNucleotide> getPossibleBoundaries(String fileLocation,
        Classifier classifier) {
    ArrayList<Gene> genesFromFile = new ArrayList<>();
    ArrayList<ClassifiedNucleotide> classifiedNucleotides = new ArrayList<>();
    //read from fasta
    if (fileLocation.toUpperCase().endsWith(".FASTA") || fileLocation.toUpperCase().endsWith(".FA")
            || fileLocation.toUpperCase().endsWith(".FAN")) {
        genesFromFile.addAll(readFasta(fileLocation));
    }
    //read from genbank
    else if (fileLocation.toUpperCase().endsWith(".GENBANK") || fileLocation.toUpperCase().endsWith(".GB")) {
        GenBankReader gbr = new GenBankReader();
        gbr.readFile(fileLocation);
        GenbankResult gbresult = gbr.getResult();
        genesFromFile = gbresult.getGenes();
    }
    //get the test data
    HashMap<String, ArrayList<IntronExonBoundaryTesterResult>> geneTestResults;
    geneTestResults = TestGenes.testForIntronExonBoundaries(genesFromFile, 1);
    ArrayList<InstanceToClassify> instanceNucs = new ArrayList<>();
    try {
        //write our results to a temporary file
        File tempArrf = File.createTempFile("realSet", ".arff");
        ArffWriter.write(tempArrf.getAbsolutePath(), geneTestResults, null);
        //get data
        ConverterUtils.DataSource source = new ConverterUtils.DataSource(tempArrf.getAbsolutePath());
        //SET DATA AND OPTIONS
        Instances data = source.getDataSet();
        for (int i = 0; i < data.numInstances(); i++) {
            Instance in = data.instance(i);
            //get the name of the gene or sequence tested
            String nameOfInstance = in.stringValue(in.numAttributes() - 3);
            //get the tested position
            int testedPosition = (int) in.value(in.numAttributes() - 2);
            //set the class as missing, because we want to find it
            in.setMissing((in.numAttributes() - 1));

            Instance instanceNoExtras = new Instance(in);

            //delete the name and position, they are irrelevant for classifying
            instanceNoExtras.deleteAttributeAt(instanceNoExtras.numAttributes() - 2);
            instanceNoExtras.deleteAttributeAt(instanceNoExtras.numAttributes() - 2);
            InstanceToClassify ic = new InstanceToClassify(instanceNoExtras, testedPosition, nameOfInstance);
            instanceNucs.add(ic);
        }
        for (InstanceToClassify ic : instanceNucs) {
            Instance in = ic.getInstance();
            in.setDataset(data);
            data.setClassIndex(data.numAttributes() - 1);
            //classify our instance
            classifier.classifyInstance(in);
            //save the likelyhood something is part of something
            double likelyhoodBoundary = classifier.distributionForInstance(in)[0];
            double likelyhoodNotBoundary = classifier.distributionForInstance(in)[1];

            //create a classified nucleotide and give it the added data
            ClassifiedNucleotide cn = new ClassifiedNucleotide(likelyhoodBoundary, likelyhoodNotBoundary,
                    ic.getName(), ic.getPosition());
            classifiedNucleotides.add(cn);
        }

    } catch (IOException ex) {
        Logger.getLogger(ClassifierUser.class.getName()).log(Level.SEVERE, null, ex);
    } catch (Exception ex) {
        Logger.getLogger(ClassifierUser.class.getName()).log(Level.SEVERE, null, ex);
    }
    return classifiedNucleotides;
}

---

From source file:org.deidentifier.arx.ARFF2ARX.java

License:Open Source License

/**
 *
 * @param instance//from  w w w . j  a  v a2  s.  c o  m
 * @return
 */
protected String[] convertRow(Instance instance) {
    String[] row = new String[instance.numAttributes()];
    for (int i = 0; i < instance.numAttributes(); i++) {
        if (instance.attribute(i).type() == Attribute.NOMINAL
                || instance.attribute(i).type() == Attribute.STRING) {
            row[i] = instance.stringValue(i);
        } else {
            row[i] = String.valueOf((int) instance.value(i));
        }
    }
    return row;
}

---

From source file:org.dkpro.similarity.ml.filters.LogFilter.java

License:Open Source License

@Override
protected Instance process(Instance inst) throws Exception {
    Instance newInst = new DenseInstance(inst.numAttributes());

    newInst.setValue(0, inst.value(0));/* ww w .j av  a  2 s  .c  om*/

    for (int i = 1; i < inst.numAttributes() - 1; i++) {
        double newVal = Math.log(inst.value(i) + 1);
        // double newVal = inst.value(i);               // Passthrough

        newInst.setValue(i, newVal);
    }

    newInst.setValue(inst.numAttributes() - 1, inst.value(inst.numAttributes() - 1));

    return newInst;
}

---

From source file:org.hypknowsys.wumprep.WUMprepWrapper.java

License:Open Source License

/**
 * Creates a dummy dataset from the input format, sends it to the script and
 * reads the script output's ARFF information that in turn is used to set
 * <code>this</code>' output format.
 * //from   w  w  w  .  ja  va 2s .  c om
 * This mechanism allows a WUMprep script to alter the recordset layout as
 * long as this change is documented by the output ARFF header. For example,
 * the <tt>dnsLookup.pl</tt> script changes the <code>host_ip</code> field
 * to <code>host_dns</code> when performing IP lookups.
 * 
 * @param instanceInfo
 *          The input format.
 * @return Object containing the output instance structure.
 */
public Instances getScriptOutputFormat(Instances instanceInfo) {
    Instances outputFormat = instanceInfo;
    Instances testData = new Instances(instanceInfo);
    Instance testInstance = new Instance(testData.numAttributes());

    testData.delete();
    testInstance.setDataset(testData);

    // Initialize the testInstance's attribute values
    for (int i = 0; i < testInstance.numAttributes(); i++) {
        String aName = testInstance.attribute(i).name();
        if (aName.equals("host_ip"))
            testInstance.setValue(i, "127.0.0.1");
        else if (aName.equals("ts_day"))
            testInstance.setValue(i, "01");
        else if (aName.equals("ts_month"))
            testInstance.setValue(i, "Jan");
        else if (aName.equals("ts_year"))
            testInstance.setValue(i, "2005");
        else if (aName.equals("ts_hour"))
            testInstance.setValue(i, "11");
        else if (aName.equals("ts_minutes"))
            testInstance.setValue(i, "55");
        else if (aName.equals("ts_seconds"))
            testInstance.setValue(i, "00");
        else if (aName.equals("tz"))
            testInstance.setValue(i, "+0200");
        else
            testInstance.setValue(i, aName + "-dummy");
    }

    testData.add(testInstance);

    WUMprepWrapper testWrapper = new WUMprepWrapper(m_scriptName, m_args);
    testWrapper.start();
    testWrapper.push(testData.toString());
    testWrapper.push((Instance) null);

    class ErrorReader extends Thread implements Serializable {
        /**  */
        private static final long serialVersionUID = -488779846603045891L;
        PipedReader m_input = null;

        /**
         * Helper class for reading stderr output from the WUMprep script
         * 
         * @param input The script's wrapper's stderr pipe reader
         */
        ErrorReader(PipedReader input) {
            m_input = input;
            this.start();
        }

        public void run() {
            try {
                while (m_input.read() >= 0)
                    ;
            } catch (IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
        }
    }

    // read the stderr output
    new ErrorReader(testWrapper.getErrorPipe());

    try {
        // ignore the stderr output
        outputFormat = new org.hypknowsys.wumprep4weka.core.Instances(testWrapper.getOutputPipe());

    } catch (IOException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }

    return outputFormat;
}

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