Example usage for weka.core Instance value

List of usage examples for weka.core Instance value

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Introduction

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

Prototype

public double value(Attribute att);

Source Link

Document

Returns an instance's attribute value in internal format.

Usage

From source file:newdtl.NewID3.java

/**
 * Classifies a given test instance using the decision tree.
 *
 * @param instance the instance to be classified
 * @return the classification//from  ww w.  j a v  a  2s. c o m
 * @throws NoSupportForMissingValuesException if instance has missing values
 */
@Override
public double classifyInstance(Instance instance) throws NoSupportForMissingValuesException {

    if (instance.hasMissingValue()) {
        throw new NoSupportForMissingValuesException("NewID3: Cannot handle missing values");
    }
    if (splitAttribute == null) {
        return label;
    } else {
        return children[(int) instance.value(splitAttribute)].classifyInstance(instance);
    }
}

From source file:newdtl.NewID3.java

/**
 * Computes class distribution for instance using decision tree.
 *
 * @param instance the instance for which distribution is to be computed
 * @return the class distribution for the given instance
 * @throws NoSupportForMissingValuesException if instance has missing values
 *///ww  w .java 2  s . co  m
@Override
public double[] distributionForInstance(Instance instance) throws NoSupportForMissingValuesException {

    if (instance.hasMissingValue()) {
        throw new NoSupportForMissingValuesException("NewID3: Cannot handle missing values");
    }
    if (splitAttribute == null) {
        return classDistributions;
    } else {
        return children[(int) instance.value(splitAttribute)].distributionForInstance(instance);
    }
}

From source file:newdtl.NewJ48.java

/**
 * Classifies a given test instance using the decision tree.
 *
 * @param instance the instance to be classified
 * @return the classification// w w w .j  a v a  2s . c o m
 */
@Override
public double classifyInstance(Instance instance) throws NoSupportForMissingValuesException {
    if (instance.hasMissingValue()) {
        throw new NoSupportForMissingValuesException("NewID3: Cannot handle missing values");
    }

    if (splitAttribute == null) {
        return label;
    } else {
        if (splitAttribute.isNumeric()) {
            if (Double.compare(instance.value(splitAttribute), splitThreshold) <= 0) {
                return children[0].classifyInstance(instance);
            } else {
                return children[1].classifyInstance(instance);
            }
        } else {
            return children[(int) instance.value(splitAttribute)].classifyInstance(instance);
        }
    }
}

From source file:newdtl.NewJ48.java

/**
 * Computes class distribution for instance using decision tree.
 *
 * @param instance the instance for which distribution is to be computed
 * @return the class distribution for the given instance
 *///from  w w  w .  ja  va2  s . com
@Override
public double[] distributionForInstance(Instance instance) throws NoSupportForMissingValuesException {
    if (instance.hasMissingValue()) {
        throw new NoSupportForMissingValuesException("NewID3: Cannot handle missing values");
    }

    if (splitAttribute == null) {
        return normalize(classDistributions);
    } else {
        if (splitAttribute.isNumeric()) {
            if (Double.compare(instance.value(splitAttribute), splitThreshold) <= 0) {
                return children[0].distributionForInstance(instance);
            } else {
                return children[1].distributionForInstance(instance);
            }
        } else {
            return children[(int) instance.value(splitAttribute)].distributionForInstance(instance);
        }
    }
}

From source file:newdtl.NewJ48.java

/**
 * search index of attribute that has most common value
 *
 * @param data the data for searching/*from  w ww . j av a2s  . co  m*/
 * @param att the attribute for searching
 * @return index of attribute that has most common value
 */
private int modusIndex(Instances data, Attribute att) {
    // cari modus
    int[] modus = new int[att.numValues()];
    Enumeration dataEnumeration = data.enumerateInstances();

    while (dataEnumeration.hasMoreElements()) {
        Instance inst = (Instance) dataEnumeration.nextElement();
        if (!inst.isMissing(att)) {
            modus[(int) inst.value(att)]++;
        }
    }

    // cari modus terbesar
    int indexMax = 0;
    for (int i = 1; i < modus.length; ++i) {
        if (modus[i] > modus[indexMax]) {
            indexMax = i;
        }
    }
    return indexMax;
}

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  w  w.  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 .  ja  v  a2  s  .co 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));

    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);//from w w w.j a v  a  2s.  co  m
    }

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

    return newInst;
}

From source file:org.if4071.myann.TopologyModel.java

public void insertDataToInputNodes(Instance inputData) {
    for (int i = 0; i < inputData.numAttributes() - 1; i++) {
        nodes.get(i).setOutput(inputData.value(i));
    }/*  w ww  .ja  v a 2 s.  c om*/
}

From source file:org.iobserve.analysis.behavior.clustering.birch.model.ClusteringFeature.java

License:Apache License

/** Constructor for a clustering feature
 * representing an instance vector./*w  w w. j av  a  2s  .c om*/
 * @param instance the instance to represent.
 */
public ClusteringFeature(final Instance instance) {
    this.number = 1;
    this.linearSum = new double[instance.numAttributes()];
    this.squareSum = new double[instance.numAttributes()];

    for (int i = 0; i < instance.numAttributes(); i++) {
        this.linearSum[i] = instance.value(i);
        this.squareSum[i] = this.linearSum[i] * this.linearSum[i];
    }
}