Example usage for weka.core Instance setDataset

Introduction

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

Prototype

public void setDataset(Instances instances);

Source Link

Document

Sets the reference to the dataset.

Usage

From source file:mx.itesm.web2mexadl.mvc.MvcAnalyzer.java

License:Open Source License

/**
 * Generate the architecture document associated to the specified web
 * application data.//from  www  .  j ava  2 s.  c  o  m
 * 
 * @param dependencies
 *            List containing the dependencies for each class to classify.
 * @param internalPackages
 *            Project's internal packages.
 * @return Map containing the classification layer for each class.
 * @throws Exception
 *             If an Exception occurs during classification.
 */
private static Map<String, Layer> generateArchitecture(final List<ClassDependencies> dependencies,
        final Map<String, Set<String>> internalPackages, final File outputDir) throws Exception {
    int viewCount;
    int modelCount;
    int instanceLayer;
    Instance instance;
    boolean valueFound;
    int controllerCount;
    Instances instances;
    String instanceType;
    String[] typeValues;
    Layer componentLayer;
    String[] suffixValues;
    Layer dependencyLayer;
    FastVector attributes;
    String[] externalApiValues;
    Map<String, Layer> returnValue;
    Set<String> currentPackageContent;
    Map<String, Layer> packagesClassification;
    Map<String, String[]> externalApiPackages;
    StringBuilder modelPackages;
    StringBuilder viewPackages;
    StringBuilder controllerPackages;

    // Model variables
    attributes = new FastVector();
    for (Variable variable : Variable.values()) {
        attributes.addElement(variable.getAttribute());
    }

    // Layer variable
    attributes.addElement(Layer.attribute);

    // Set the test instances, the Layer variable is unknown
    instances = new Instances("mvc", attributes, 0);
    instances.setClassIndex(Variable.values().length);

    // Valid suffixes to look for in the class names
    suffixValues = Util.getPropertyValues(Util.Variable.Suffix.getVariableName());

    // Valid file types to look for in the component names
    typeValues = Util.getPropertyValues(Util.Variable.Type.getVariableName());

    // Valid external api packages to look for in the classes dependencies
    externalApiValues = Util.getPropertyValues(Util.Variable.ExternalAPI.getVariableName());
    externalApiPackages = new HashMap<String, String[]>(externalApiValues.length);
    for (int i = 0; i < externalApiValues.length; i++) {
        if (!externalApiValues[i].equals("none")) {
            externalApiPackages.put(externalApiValues[i],
                    Util.getPropertyValues("externalApi." + externalApiValues[i] + ".packages"));
        }
    }

    returnValue = new HashMap<String, Layer>(dependencies.size());
    for (ClassDependencies classDependencies : dependencies) {
        // Variables + Layer
        instance = new Instance(Variable.values().length + 1);

        // Type
        instanceType = "java";
        for (String validType : typeValues) {
            if (classDependencies.getClassName().endsWith("." + validType)) {
                instanceType = validType;
                break;
            }
        }
        instance.setValue(Variable.Type.getAttribute(), instanceType);

        // ExternalAPI
        valueFound = false;
        externalApi: for (String externalApi : externalApiValues) {
            if (externalApi.equals("none")) {
                continue;
            }

            // Check if any of the class' external dependencies match with
            // one of the key external dependencies
            if (classDependencies.getExternalDependencies() != null) {
                for (String externalDependency : classDependencies.getExternalDependencies()) {
                    for (String externalPackage : externalApiPackages.get(externalApi)) {
                        if (externalDependency.toLowerCase().startsWith(externalPackage)) {
                            valueFound = true;
                            instance.setValue(Variable.ExternalAPI.getAttribute(), externalApi);
                            break externalApi;
                        }
                    }
                }
            }
        }

        // No key external dependency found
        if (!valueFound) {
            instance.setValue(Variable.ExternalAPI.getAttribute(), "none");
        }

        // Suffix
        valueFound = false;
        for (String suffix : suffixValues) {
            if (classDependencies.getClassName().toLowerCase().endsWith(suffix)) {
                valueFound = true;
                instance.setValue(Variable.Suffix.getAttribute(), suffix);
                break;
            }
        }

        // No key suffix found
        if (!valueFound) {
            instance.setValue(Variable.Suffix.getAttribute(), "none");
        }

        // Layer, the unknown variable
        instance.setMissing(Layer.attribute);
        instances.add(instance);
        instance.setDataset(instances);

        try {
            instanceLayer = (int) Util.classifier.classifyInstance(instance);
        } catch (Exception e) {
            // Default value
            instanceLayer = 0;
            logger.severe("Unable to classify: " + instance);
        }

        returnValue.put(classDependencies.getClassName(), Layer.values()[instanceLayer]);
        logger.info(
                classDependencies.getClassName() + " : " + returnValue.get(classDependencies.getClassName()));
    }

    // Check for any invalid relation
    viewPackages = new StringBuilder();
    modelPackages = new StringBuilder();
    controllerPackages = new StringBuilder();
    packagesClassification = new HashMap<String, Layer>(internalPackages.size());
    for (String currentPackage : internalPackages.keySet()) {
        modelCount = viewCount = controllerCount = 0;
        currentPackageContent = internalPackages.get(currentPackage);

        for (String component : currentPackageContent) {
            componentLayer = returnValue.get(component);
            if (componentLayer == Layer.Model) {
                modelCount++;
            } else if (componentLayer == Layer.View) {
                viewCount++;
            } else if (componentLayer == Layer.Controller) {
                controllerCount++;
            }
        }

        if ((modelCount > viewCount) && (modelCount > controllerCount)) {
            packagesClassification.put(currentPackage, Layer.Model);
            Util.addImplementationPackage(modelPackages, currentPackage);
        } else if ((viewCount > modelCount) && (viewCount > controllerCount)) {
            packagesClassification.put(currentPackage, Layer.View);
            Util.addImplementationPackage(viewPackages, currentPackage);
        } else if ((controllerCount > viewCount) && (controllerCount > modelCount)) {
            packagesClassification.put(currentPackage, Layer.Controller);
            Util.addImplementationPackage(controllerPackages, currentPackage);
        } else {
            packagesClassification.put(currentPackage, null);
        }
    }

    for (ClassDependencies classDependencies : dependencies) {
        // Code relations
        valueFound = false;
        componentLayer = returnValue.get(classDependencies.getClassName());
        if (classDependencies.getInternalDependencies() != null) {
            for (String internalDependency : classDependencies.getInternalDependencies()) {
                dependencyLayer = returnValue.get(internalDependency);

                if (!componentLayer.isValidRelation(dependencyLayer)) {
                    valueFound = true;
                    returnValue.put(classDependencies.getClassName(),
                            Layer.valueOf("Invalid" + componentLayer));
                    logger.info("Invalid relation detected between: " + classDependencies.getClassName()
                            + " and " + internalDependency);
                }
            }
        }

        // Package relations
        if (!valueFound) {
            dependencyLayer = packagesClassification.get(classDependencies.getPackageName());

            if ((dependencyLayer != null) && (componentLayer != dependencyLayer)) {
                returnValue.put(classDependencies.getClassName(), Layer.valueOf("Invalid" + componentLayer));
            }
        }
    }

    // Export MexADL architecture
    MvcAnalyzer.exportToMexADL(outputDir, modelPackages.toString(), controllerPackages.toString(),
            viewPackages.toString());

    return returnValue;
}

---

From source file:myclassifier.wekaCode.java

public static void classifyUnseenData(String[] attributes, Classifier classifiers, Instances data)
        throws Exception {
    Instance newInstance = new Instance(data.numAttributes());
    newInstance.setDataset(data);
    for (int i = 0; i < data.numAttributes() - 1; i++) {
        if (Attribute.NUMERIC == data.attribute(i).type()) {
            Double value = Double.valueOf(attributes[i]);
            newInstance.setValue(i, value);
        } else {//from ww  w .  j av a  2 s.  c  o  m
            newInstance.setValue(i, attributes[i]);
        }
    }

    double clsLabel = classifiers.classifyInstance(newInstance);
    newInstance.setClassValue(clsLabel);

    String result = data.classAttribute().value((int) clsLabel);

    System.out.println("Hasil Classify Unseen Data Adalah: " + result);
}

---

From source file:myclusterer.WekaCode.java

public static void classifyUnseenData(String[] attributes, Clusterer clusterer, Instances data)
        throws Exception {
    Instance newInstance = new Instance(data.numAttributes());
    newInstance.setDataset(data);
    for (int i = 0; i < data.numAttributes() - 1; i++) {
        if (Attribute.NUMERIC == data.attribute(i).type()) {
            Double value = Double.valueOf(attributes[i]);
            newInstance.setValue(i, value);
        } else {/*from   w  ww .  ja v a2 s.  co  m*/
            newInstance.setValue(i, attributes[i]);
        }
    }

    double clsLabel = clusterer.clusterInstance(newInstance);
    newInstance.setClassValue(clsLabel);

    String result = data.classAttribute().value((int) clsLabel);

    System.out.println("Hasil Classify Unseen Data Adalah: " + result);
}

---

From source file:NaiveBayesPckge.NaiveBayesMain.java

public static void addNewInstance(Instances instances) throws Exception {
    Scanner scan = new Scanner(System.in);
    ArrayList<Attribute> atts = new ArrayList<Attribute>();
    ArrayList<String> classVal = new ArrayList<String>();
    int nConclus = instances.attribute(instances.numAttributes() - 1).numValues();
    int numAttribut = instances.numAttributes();

    //buat nambah kesimpulan. Misal T dan F
    for (int i = 0; i < nConclus; i++) {
        classVal.add(instances.attribute(instances.numAttributes() - 1).value(i));
    }/*ww  w. j  a  v a2s  .  c  o m*/

    //buat nambahin attribut
    for (int i = 0; i < numAttribut - 1; i++) {
        atts.add(new Attribute(instances.attribute(i).name()));
    }
    atts.add(new Attribute(instances.attribute(numAttribut - 1).name(), classVal));

    double[] attValues = new double[numAttribut];
    System.out.print("Masukkan nilai : ");
    for (int i = 0; i < numAttribut - 1; i++) {
        attValues[i] = scan.nextDouble();
    }
    Discretize discretize = new Discretize();
    String s = scan.nextLine();

    Instance instance = new DenseInstance(1.0, attValues);

    instance.setDataset(instances);

    discretize.setInputFormat(instances);
    discretize.input(instance);

    int classify1 = (int) naive.classifyInstance(instance);
    System.out.print("Prediction Class : ");
    System.out.println(classVal.get(classify1));
}

---

From source file:net.sf.bddbddb.OrderClassifier.java

License:LGPL

public double importance(weka.core.Attribute attribute, String attrValue) {//, String classValue){

    int count = 0;
    int goodCount = 0, badCount = 0;
    List newInstances = new LinkedList();

    for (Iterator it = orders.iterator(); it.hasNext();) {
        Instance instance = (Instance) it.next();
        if (//!instance.stringValue(instance.classIndex()).equals(classValue) ||
        !instance.stringValue(attribute).equals(attrValue))
            continue;

        if (goodClusters.contains(instance.stringValue(instance.classIndex())))
            ++goodCount;/*from   w  ww .  j  av  a  2  s. co m*/
        else
            ++badCount;

        Instance newInstance = new Instance(instance);
        newInstance.setDataset(instance.dataset());
        newInstances.add(newInstance);
    }
    goodCount *= attrOptions.size() - 1;
    badCount *= attrOptions.size() - 1;
    for (Iterator it = newInstances.iterator(); it.hasNext();) {
        Instance instance = (Instance) it.next();
        /*      if(//!instance.stringValue(instance.classIndex()).equals(classValue) || 
         !instance.stringValue(attribute).equals(attrValue)) continue;
         */

        String classValue = instance.stringValue(instance.classIndex());
        FastVector newOptions = new FastVector();
        newOptions.appendElements(attrOptions);
        newOptions.removeElementAt(newOptions.indexOf(instance.stringValue(attribute)));
        //int index = Math.abs(LearnedOrder.randomNumGen.nextInt()) % newOptions.size();
        int index = 0;
        while (index < newOptions.size()) {
            instance.setValue(attribute, attrOptions.indexOf(newOptions.elementAt(index)));
            String value = classify(instance);
            if (goodClusters.contains(classValue)) {
                if (goodClusters.contains(value))
                    --goodCount;
            } else if (!goodClusters.contains(classValue)) {
                if (!goodClusters.contains(value))
                    --badCount;
            }
            ++index;
        }
        //if(value.equals(classValue)) --count;
    }

    count = goodCount - badCount;
    count /= attrOptions.size() - 1;

    double importance = ((double) count) / newInstances.size();
    if (Double.isNaN(importance))
        return 0;
    return importance;
}

---

From source file:net.sf.bddbddb.OrderClassifier.java

License:LGPL

public String classify(String varOrder) {
    Instance instance = makeInstance(varOrder);
    instance.setDataset(data);
    return classify(instance);
}

---

From source file:net.sf.jclal.activelearning.multilabel.querystrategy.MultiLabelMMCQueryStrategy.java

License:Open Source License

/**
 * Trains the logistic regresion/* w ww . j a va  2 s.com*/
 */
public void trainLogisticRegresion() {

    try {

        // To clear the current instances in the transformed dataset
        newDataset.clear();

        // Each instance is transformed according to LR-based label
        // prediction method proposed

        Instances labeledSet = getLabelledData().getDataset();

        for (Instance instanceLabeled : labeledSet) {

            Instance newInstance = convertInstance(instanceLabeled);

            newInstance.setDataset(newDataset);

            newDataset.add(newInstance);
        }

        // To train the LR classifier
        logistic = new SimpleLogistic();

        logistic.buildClassifier(newDataset);

    } catch (Exception e) {

        Logger.getLogger(MultiLabelMMCQueryStrategy.class.getName()).log(Level.SEVERE, null, e);
    }

}

---

From source file:net.sf.jclal.activelearning.multilabel.querystrategy.MultiLabelMMCQueryStrategy.java

License:Open Source License

@Override
public double utilityInstance(Instance instance) {

    try {/* www .j av a 2  s.  c o  m*/

        // Convert the current instance according to LR-based method
        // proposed. The classifiersOutputs array stores the probability for the current instance
        Instance newInstance = convertInstance(instance);

        newInstance.setDataset(newDataset);

        // To classify the converted instance according to LR-based method
        double classes = logistic.classifyInstance(newInstance);

        double score = 0;

        int yl = 1;

        // Score calculation
        for (int l = 0; l < getNumLabels(); l++) {

            if (l > classes) {
                yl = -1;
            }

            score += (1 - yl * classifiersOutputs[l]) / 2;

        }

        return score;

    } catch (Exception e) {
        // TODO Auto-generated catch block
        Logger.getLogger(MultiLabelMMCQueryStrategy.class.getName()).log(Level.SEVERE, null, e);
    }

    return 0;
}

---

From source file:net.sf.markov4jmeter.behaviormodelextractor.extraction.transformation.clustering.AbstractClusteringStrategy.java

License:Apache License

/**
 * Returns a instance as vector based on a single BehaviorModelAbsolute.
 * /*from  w  ww  .jav  a 2  s . com*/
 * @param behaviorModelsAbsolute
 * @return a instance, transitions of behaviorModelAbsolute as input vector
 */
protected Instance getInstance(BehaviorModelAbsolute behaviorModelAbsolute, Instances instances) {

    // create new instance with size n x (n + 1)
    int nrVertices = behaviorModelAbsolute.getVertices().size() - 1;
    Instance instance = new Instance((nrVertices * (nrVertices + 1)) + 1);
    final List<Vertex> vertices = behaviorModelAbsolute.getVertices();
    int indexOfAttribute = 0;
    for (final Vertex srcVertex : vertices) {
        if (srcVertex.getUseCase() != null) { // no final state
            // for each transition set the value of the instance vector
            for (final Vertex dstVertex : vertices) {
                final UseCase srcUseCase = srcVertex.getUseCase();
                final UseCase dstUseCase = dstVertex.getUseCase();
                final String srcUseCaseId = srcUseCase.getId();

                // if dstUseCase is null, its vertex denotes the final state
                // (no ID);
                final String dstUseCaseId = (dstUseCase != null) ? dstUseCase.getId() : null;
                Transition transition = this.findTransitionByUseCaseIDs(behaviorModelAbsolute, srcUseCaseId,
                        dstUseCaseId);

                if (transition != null) {
                    instance.setValue(indexOfAttribute, transition.getValue());
                } else {
                    instance.setValue(indexOfAttribute, 0);
                }
                indexOfAttribute++;
            }
        } else {
            continue; // skip final state ("$");
        }
    }

    instance.setDataset(instances);

    // set attribute for classification
    instance.setValue(indexOfAttribute, behaviorModelAbsolute.getTransactionType());
    return instance;
}

---

From source file:Neural_Network.NuralN.java

private static Instance toInstance(String[] instanceData) {
    try {//w w w. j a v  a2 s  .c  o  m
        loadDataTemplate();
        Instance tempInstance = new Instance(temp.numAttributes());
        tempInstance.setDataset(temp);

        for (int index = 0; index < instanceData.length; index++) {
            tempInstance.setValue(index, instanceData[index]);
        }
        temp.add(tempInstance);
    } catch (Exception e) {
        System.err.println(e.toString());
    }
    return temp.lastInstance();
}

---

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