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:myID3.MyId3.java

/**
* Classifies a given test instance using the decision tree.
*
* @param instance the instance to be classified
* @return the classification//  ww  w  .  j a  v a  2 s  . co m
* @throws NoSupportForMissingValuesException if instance has missing values
*/
public double classifyInstance(Instance instance) throws NoSupportForMissingValuesException {
    if (instance.hasMissingValue()) {
        throw new NoSupportForMissingValuesException("Id3: no missing values, " + "please.");
    }
    if (currentAttribute == null) {
        return classValue;
    } else {
        return nodes[(int) instance.value(currentAttribute)].classifyInstance(instance);
    }
}

From source file:myID3.MyId3.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
 *//*  w  w w .j a v a2  s  . c  o m*/
public double[] distributionForInstance(Instance instance) throws NoSupportForMissingValuesException {
    if (instance.hasMissingValue()) {
        throw new NoSupportForMissingValuesException("Id3: no missing values, " + "please.");
    }
    if (currentAttribute == null) {
        return classDistribution;
    } else {
        return nodes[(int) instance.value(currentAttribute)].distributionForInstance(instance);
    }
}

From source file:myid3andc45classifier.Model.MyC45.java

@Override
public double classifyInstance(Instance instance) {
    int i = 0;/*from ww  w  .  j  av a  2 s .com*/
    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:myid3andc45classifier.Model.MyC45.java

public Instances[] splitInstancesByAttribute(Instances data, Attribute attr) throws Exception {
    //Split data menjadi beberapa instances sesuai dengan jumlah jenis data pada atribut
    Instances[] splitData = new Instances[attr.numValues()];

    for (int i = 0; i < attr.numValues(); i++) {
        splitData[i] = new Instances(data, data.numInstances());
    }//from   ww  w  .j a v a 2s .  c  om

    Enumeration instanceEnum = data.enumerateInstances();
    while (instanceEnum.hasMoreElements()) {
        Instance inst = (Instance) instanceEnum.nextElement();
        splitData[(int) inst.value(attr)].add(inst);
    }

    for (int i = 0; i < splitData.length; i++) {
        splitData[i].compactify();
    }

    return splitData;
}

From source file:myid3andc45classifier.Model.MyID3.java

@Override
public double classifyInstance(Instance instance) throws NoSupportForMissingValuesException {

    //Periksa apakah instance memiliki missing value
    if (instance.hasMissingValue()) {
        throw new NoSupportForMissingValuesException("MyID3: no missing values, please");
    }/*from  ww w  .j  a va 2 s . c  om*/

    if (attribute == null) {
        return label;
    } else {
        return successors[(int) instance.value(attribute)].classifyInstance(instance);
    }

}

From source file:myJ48.MyJ48.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
 *///from  w w  w .j  a v  a 2 s .  c o  m
public double[] distributionForInstance(Instance instance) throws NoSupportForMissingValuesException {
    if (instance.hasMissingValue()) {
        throw new NoSupportForMissingValuesException("Missing value error");
    }
    if (currentAttribute == null) {
        return classDistribution;
    } else {
        return nodes[(int) instance.value(currentAttribute)].distributionForInstance(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++;//w  w  w  .  j  a v a 2  s .  co  m
            //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:NaiveBayes.NaiveBayes13514004.java

@Override
public void buildClassifier(Instances i) {
    //Algoritma/* w w  w.  j a va  2 s.com*/
    origin = new Instances(i);
    //Menghitung jumlah attribute dan kelas
    numAtt = i.numAttributes() - 1;
    numClass = i.numClasses();

    //Inisialisasi matrix 3 dimensi
    data = new int[numAtt][numClass][0];
    prob = new double[numAtt][numClass][0];
    kelasdata = new int[numClass];
    kelasprob = new double[numClass];

    Enumeration<Instance> enu1 = i.enumerateInstances();
    while (enu1.hasMoreElements()) {
        Instance ins = enu1.nextElement();
        Enumeration<Attribute> enu_t = i.enumerateAttributes();
        int x = 0;
        while (enu_t.hasMoreElements()) {
            Attribute att = enu_t.nextElement();
            numDis = att.numValues();
            data[x][(int) ins.classValue()] = new int[numDis];
            prob[x][(int) ins.classValue()] = new double[numDis];
            x++;
        }
    }

    //Mengisi matriks Frekuensi
    Enumeration<Instance> enu2 = i.enumerateInstances();
    while (enu2.hasMoreElements()) {
        Instance ins = enu2.nextElement();
        Enumeration<Attribute> enu_t = i.enumerateAttributes();
        int x = 0;
        while (enu_t.hasMoreElements()) {
            Attribute att = enu_t.nextElement();
            data[x][(int) ins.classValue()][(int) ins.value(att)]++;
            x++;
        }
        kelasdata[(int) ins.classValue()]++;
    }

    //Menghitung probabilitas kelas
    double numInstances = (double) i.numInstances();
    for (int y = 0; y < numClass; y++) {
        kelasprob[y] = (double) kelasdata[y] / numInstances;
    }

    //Mengisi matriks probabilitas
    Enumeration<Instance> enu3 = i.enumerateInstances();
    while (enu3.hasMoreElements()) {
        Instance ins = enu3.nextElement();
        Enumeration<Attribute> enu_t = i.enumerateAttributes();
        int x = 0;
        while (enu_t.hasMoreElements()) {
            Attribute att = enu_t.nextElement();
            int sumDis = Utils.sum(data[x][(int) ins.classValue()]);
            numDis = att.numValues();
            for (int z = 0; z < numDis; z++) {
                int y = (int) ins.classValue();
                prob[x][y][z] = ((double) data[x][y][z] / (double) sumDis);
            }
            x++;
        }
    }

}

From source file:NaiveBayes.NaiveBayes13514004.java

@Override
public double[] distributionForInstance(Instance instnc) throws Exception {
    Attribute ins;//from w w w .  j a  v  a 2  s  .  c  o m
    double[] result = new double[numClass];

    //Algoritma
    //Menghitung probabilitas
    for (int j = 0; j < numClass; j++) {
        result[j] = kelasprob[j];
        Enumeration<Attribute> e2 = instnc.enumerateAttributes();
        int i = 0;
        while (e2.hasMoreElements()) {
            ins = e2.nextElement();
            result[j] = result[j] * prob[i][j][(int) instnc.value(ins)];
            i++;
        }
    }

    return result;
}

From source file:net.sf.bddbddb.order.MyId3.java

License:LGPL

/**
 * Classifies a given test instance using the decision tree.
 * //from ww w  .j  a  v a  2  s  .c  om
 * @param instance
 *            the instance to be classified
 * @return the classification
 */
public double classifyInstance(Instance instance) {
    if (m_Attribute == null) {
        return m_ClassValue;
    } else if (instance.isMissing(m_Attribute)) {
        try {
            // Use superclass implementation, which uses distributionForInstance.
            return super.classifyInstance(instance);
        } catch (Exception x) {
            x.printStackTrace();
            Assert.UNREACHABLE();
            return 0.;
        }
    } else {
        return m_Successors[(int) instance.value(m_Attribute)].classifyInstance(instance);
    }
}