Example usage for weka.core Instance value

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:id3classifier.ID3Classifiers.java

private Map<Instance, Double> valuesByAttribute(List<Instance> instances, Attribute attribute) {

    HashMap<Instance, Double> map = new HashMap<>();

    for (Instance instance : instances) {

        if (!Double.isNaN(instance.value(attribute))) {

            map.put(instance, instance.value(attribute));
        }//from ww w  . ja  va  2  s. co m
    }

    return map;
}

---

From source file:id3classifier.ID3Classifiers.java

public double getClassification(Instance instance, Leaf node) {

    // set the maxCounts and max values
    int maxCount = -1;
    double maxValue = 0;

    // if the node is indeed a leaf...
    if (node.isLeaf()) {

        // go ahead are return the node's (leaf) value
        return node.getLeafValue();
    } else {//from  ww  w  . j  a  v a  2 s.co  m

        // else... create a new attribute from node's attribute
        Attribute attribute = node.getAttribute();

        // if this attribute is a number
        if (!Double.isNaN(instance.value(attribute))) {

            // set a new double called val equal to the value of instance's
            // node's attribute's value... mouthful
            Double val = instance.value(node.getAttribute());

            // if this is not a number... set val equal to zero
            if (Double.isNaN(val)) {

                val = 0.0;
            }

            // is if node's attribute is not nominal... and if val is < 0.5
            if (!node.getAttribute().isNominal()) {

                if (val < 0.5) {

                    // set that val to 0
                    val = 0.0;
                } else {

                    // if that val was >= to 0.5... reset it to 1
                    val = 1.0;
                }
            }

            // overright child with node's value
            Leaf child = node.get(val);

            // if child is now null... retunr the classification based on k
            // nearest neighbors
            if (child == null) {

                return KNNClassifier.getClassification(node.getInstances());
            } else {

                // else return the classification based on getClassificaion
                // using instance and node's value
                return getClassification(instance, node.get(val));
            }
        } else {

            // else if the attribute is not a number... create a new map of 
            // type double and integer to hold the class counts
            Map<Double, Integer> classToCount = new HashMap<>();

            // for each child of node
            for (Leaf child : node.getChildren()) {

                // get the value of the classification using instance and child
                Double value = getClassification(instance, child);

                // if classes to count does not contain the key matching value,
                // and if classes to count's value is not null put the value in
                // to classes to count at location matching it's value + 1
                if (!classToCount.containsKey(value) && classToCount.get(value) != null) {

                    classToCount.put(value, classToCount.get(value) + 1);
                } else {

                    // else just put value in the slot "1" of classes to count
                    classToCount.put(value, 1);
                }
            }

            // for each double value in classes to counts set of keys... IF
            // classes to count's current key is greater than the max count,
            // and set the max value equal to the current double value
            for (Double doubVal : classToCount.keySet()) {

                if (classToCount.get(doubVal) > maxCount) {

                    maxCount = classToCount.get(doubVal);
                    maxValue = doubVal;
                }
            }

            return maxValue;
        }
    }
}

---

From source file:imba.classifier.FFNNTubes.java

private void feedForward(Instance instance) {
    input = new double[nAttribute + 1];
    input[0] = 1.0;/* w ww .  j  av a 2 s  .c om*/
    for (int i = 1; i <= nAttribute; i++) {
        input[i] = (double) instance.value(i - 1);
    }
    if (nHidden == 0) {
        output = new double[nOutput];
        for (int i = 0; i < nOutput; i++) {
            output[i] = 0.0;
            double sum = 0.0;
            for (int j = 0; j <= nAttribute; j++) {
                sum = sum + (Weight1[i][j] * input[j]);
            }
            output[i] = activationFunction(sum);
        }
    } else {
        hidden = new double[nNeuron + 1];
        output = new double[nOutput];
        for (int i = 0; i <= nNeuron; i++) {
            hidden[i] = 0.0;
            double sum = 0.0;
            for (int j = 0; j <= nAttribute; j++) {
                sum = sum + (Weight1[i][j] * input[j]);
            }
            hidden[i] = activationFunction(sum);
        }

        for (int i = 0; i < nOutput; i++) {
            output[i] = 0.0;
            double sum = 0.0;
            for (int j = 0; j <= nNeuron; j++) {
                sum = sum + (Weight2[i][j] * hidden[j]);
            }
            output[i] = activationFunction(sum);
        }
    }
}

---

From source file:iris.ID3.java

private Instances[] makeBins(Instances instances, Attribute att) {
    // Create array of bins based on numValues in Attribute parameter
    Instances[] bins = new Instances[att.numValues()];

    for (int i = 0; i < att.numValues(); i++) {
        bins[i] = new Instances(instances, instances.numInstances());
    }/*from   www. ja va 2  s . co  m*/

    // Create pointer to first instance
    Enumeration instanceEnum = instances.enumerateInstances();

    while (instanceEnum.hasMoreElements()) {
        // Create new instance from the one pointer is pointing at
        Instance oneInstance = (Instance) instanceEnum.nextElement();
        // Add instance to the proper bin
        bins[(int) oneInstance.value(att)].add(oneInstance);
    }

    // Compactify
    for (int i = 0; i < bins.length; i++) {
        bins[i].compactify();
    }
    return bins;
}

---

From source file:iris.ID3.java

public double classifyInstance(Instance instance) throws Exception {
    // Assign class value
    if (highestInfoGain == null) {
        return classValue;
    }/*from   w  ww  .  j a  v a2s.  co m*/
    // Else, keep going
    else {
        return children[(int) instance.value(highestInfoGain)].classifyInstance(instance);
    }
}

---

From source file:iris.Network.java

@Override
public double classifyInstance(Instance newInstance) throws Exception {
    List<Double> values = new ArrayList<>();
    List<Double> finalValues = new ArrayList<>();

    for (int i = 0; i < newInstance.numAttributes() - 1; i++) {
        values.add(newInstance.value(i));
    }//from   www.j  a  va 2 s  . c  om

    finalValues = getOutputs(values);

    double maxIndex = 0;

    //sets maxIndex to the highest value we calculated, i.e., what we think
    //the classification of the flower is according to our calculations.
    for (int i = 0; i < finalValues.size(); i++) {
        if (finalValues.get(i) > finalValues.get((int) maxIndex)) {
            maxIndex = i;
        }
    }

    return maxIndex;
}

---

From source file:irisdriver.IrisDriver.java

/**
 * @param args the command line arguments
 *///  ww w .  j  a  v  a2s.com
public static void main(String[] args) {
    //As an example of arguments: sepallength=5.1 sepalwidth=3.5 petallength=1.4 petalwidth=0.2    
    try {
        Hashtable<String, String> values = new Hashtable<String, String>();
        /*Iris irisModel = new Iris();
                
        for(int i = 0; i < args.length; i++) {
        String[] tokens = args[i].split("=");
                
        values.put(tokens[0], tokens[1]);
        }
                
        System.out.println("Classification: " + irisModel.classifySpecies(values));*/

        //Loading the model
        String pathModel = "";
        String pathTestSet = "";
        JFileChooser chooserModel = new JFileChooser();
        chooserModel.setCurrentDirectory(new java.io.File("."));
        chooserModel.setDialogTitle("Choose the model");
        chooserModel.setFileSelectionMode(JFileChooser.FILES_AND_DIRECTORIES);
        chooserModel.setAcceptAllFileFilterUsed(true);

        if (chooserModel.showOpenDialog(null) == JFileChooser.APPROVE_OPTION) {
            File filePathModel = chooserModel.getSelectedFile();
            pathModel = filePathModel.getPath();

            Iris irisModel = new Iris(pathModel);

            //Loading the model
            JFileChooser chooserTestSet = new JFileChooser();
            chooserTestSet.setDialogTitle("Choose TEST SET");
            chooserTestSet.setFileSelectionMode(JFileChooser.FILES_AND_DIRECTORIES);
            chooserTestSet.setAcceptAllFileFilterUsed(true);

            //Loading the testing dataset
            if (chooserTestSet.showOpenDialog(null) == JFileChooser.APPROVE_OPTION) {
                File filePathTestSet = chooserTestSet.getSelectedFile();
                pathTestSet = filePathTestSet.getPath();

                //WRITTING THE OUTPUT:
                BufferedWriter writer = new BufferedWriter(new FileWriter("D:\\output_file.txt"));

                //Transforming the data set into pairs attribute-value
                ConverterUtils.DataSource unlabeledSource = new ConverterUtils.DataSource(pathTestSet);
                Instances unlabeledData = unlabeledSource.getDataSet();
                if (unlabeledData.classIndex() == -1) {
                    unlabeledData.setClassIndex(unlabeledData.numAttributes() - 1);
                }

                for (int i = 0; i < unlabeledData.numInstances(); i++) {
                    Instance ins = unlabeledData.instance(i);

                    //ins.numAttributes()-1 --> not to include the label
                    for (int j = 0; j < ins.numAttributes() - 1; j++) {

                        String attrib = ins.attribute(j).name();
                        double val = ins.value(ins.attribute(j));

                        values.put(attrib, String.valueOf(val));

                    }

                    String predictedLabel = irisModel.classifySpecies(values);
                    System.out.println("Classification: " + predictedLabel);
                    values.clear();

                    //Writting the results in a txt
                    writer.write("The label is: " + predictedLabel);

                    //writer.newLine();

                    //writers.write("The error rate of the prediction is : " + eval.errorRate());

                    //writer.newLine();

                }

                writer.flush();
                writer.close();

            }

        }

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

}

---

From source file:j48.BinC45Split.java

License:Open Source License

/**
 * Creates split on enumerated attribute.
 *
 * @exception Exception if something goes wrong
 *///from   w w w .  ja  v  a 2 s.  c  o m
private void handleEnumeratedAttribute(Instances trainInstances) throws Exception {

    Distribution newDistribution, secondDistribution;
    int numAttValues;
    double currIG, currGR;
    Instance instance;
    int i;

    numAttValues = trainInstances.attribute(m_attIndex).numValues();
    newDistribution = new Distribution(numAttValues, trainInstances.numClasses());

    // Only Instances with known values are relevant.
    Enumeration enu = trainInstances.enumerateInstances();
    while (enu.hasMoreElements()) {
        instance = (Instance) enu.nextElement();
        if (!instance.isMissing(m_attIndex))
            newDistribution.add((int) instance.value(m_attIndex), instance);
    }
    m_distribution = newDistribution;

    // For all values
    for (i = 0; i < numAttValues; i++) {

        if (Utils.grOrEq(newDistribution.perBag(i), m_minNoObj)) {
            secondDistribution = new Distribution(newDistribution, i);

            // Check if minimum number of Instances in the two
            // subsets.
            if (secondDistribution.check(m_minNoObj)) {
                m_numSubsets = 2;
                currIG = m_infoGainCrit.splitCritValue(secondDistribution, m_sumOfWeights);
                currGR = m_gainRatioCrit.splitCritValue(secondDistribution, m_sumOfWeights, currIG);
                if ((i == 0) || Utils.gr(currGR, m_gainRatio)) {
                    m_gainRatio = currGR;
                    m_infoGain = currIG;
                    m_splitPoint = (double) i;
                    m_distribution = secondDistribution;
                }
            }
        }
    }
}

---

From source file:j48.BinC45Split.java

License:Open Source License

/**
 * Sets split point to greatest value in given data smaller or equal to
 * old split point./*from w  w w .j a  v a 2 s  .  co  m*/
 * (C4.5 does this for some strange reason).
 */
public final void setSplitPoint(Instances allInstances) {

    double newSplitPoint = -Double.MAX_VALUE;
    double tempValue;
    Instance instance;

    if ((!allInstances.attribute(m_attIndex).isNominal()) && (m_numSubsets > 1)) {
        Enumeration enu = allInstances.enumerateInstances();
        while (enu.hasMoreElements()) {
            instance = (Instance) enu.nextElement();
            if (!instance.isMissing(m_attIndex)) {
                tempValue = instance.value(m_attIndex);
                if (Utils.gr(tempValue, newSplitPoint) && Utils.smOrEq(tempValue, m_splitPoint))
                    newSplitPoint = tempValue;
            }
        }
        m_splitPoint = newSplitPoint;
    }
}

---

From source file:j48.BinC45Split.java

License:Open Source License

/**
 * Returns index of subset instance is assigned to.
 * Returns -1 if instance is assigned to more than one subset.
 *
 * @exception Exception if something goes wrong
 *///w ww.j a  v  a2s. co m

public final int whichSubset(Instance instance) throws Exception {

    if (instance.isMissing(m_attIndex))
        return -1;
    else {
        if (instance.attribute(m_attIndex).isNominal()) {
            if ((int) m_splitPoint == (int) instance.value(m_attIndex))
                return 0;
            else
                return 1;
        } else if (Utils.smOrEq(instance.value(m_attIndex), m_splitPoint))
            return 0;
        else
            return 1;
    }
}

---

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