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:srl.recognition.handwriting.StrokePixelator.java

License:BSD License

/**
 * Returns a Weka instance of a sketch (using the pixelize function))
 * //  w w w .  j a v a  2s .  c  o m
 * @param sk
 * @param heightWidth
 * @return
 */
public static Instance getInstance(Sketch sk, int heightWidth, Instances dataSet) {

    int[][] pixels = pixelizeSketch(sk, heightWidth);

    Instance inst = new Instance(dataSet.numAttributes());

    inst.setDataset(dataSet);

    int count = 0;

    for (int i = 0; i < pixels.length; i++) {
        String holder = "";
        for (int j = 0; j < pixels.length; j++) {
            if (pixels[j][i] == 1)
                inst.setValue(dataSet.attribute("Pixel" + count), 1);
            else
                inst.setValue(dataSet.attribute("Pixel" + count), -1);
            holder += String.valueOf(pixels[j][i]);
            count++;
        }
        log.debug(holder);
    }
    return inst;
}

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From source file:srl.recognition.handwriting.StrokePixelator.java

License:BSD License

/**
 * Returns a Weka instance of a sketch for CivilSketch attributes
 * //from   w  w w  . ja v a 2s.  co m
 * @param sk
 * @param heightWidth
 * @return
 */
public static Instance getInstanceCivilSketch(Sketch sk, Instances dataSet) {

    Instance inst = new Instance(8);
    inst.setDataset(dataSet);

    return inst;
}

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From source file:statechum.analysis.learning.experiments.PairSelection.WekaDataCollector.java

License:Open Source License

/**  Constructs a Weka {@link Instance} for a pair of interest.
 * //from  w  ww . ja  va 2s .c  om
 * @param comparisonResults metrics related to the considered pair.
 * @param classification whether this is a correct pair
 * @return an instance of a test or a training sample. 
 */
Instance constructInstance(int[] comparisonResults, boolean classification) {
    if (comparisonResults.length != instanceLength)
        throw new IllegalArgumentException("results' length does not match the number of comparators");

    double[] instanceValues = new double[instanceLength + 1];
    for (int i = 0; i < instanceLength; ++i)
        instanceValues[i] = convertAssessmentResultToString(comparisonResults[i], attributesOfAnInstance[i]);

    instanceValues[instanceLength] = trainingData.classAttribute()
            .indexOfValue(Boolean.toString(classification));
    Instance outcome = new Instance(1, instanceValues);
    outcome.setDataset(trainingData);
    return outcome;
}

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From source file:Statistics.WekaFunctions.java

public Instance selectTestInstance() {
    //Select last instance from loaded set of Test Instances

    //Create test instance
    //Instance testInstance = new Instance(newDataTest.firstInstance());
    //Instance testInstance = new Instance(newDataTest.instance(0));
    Instance testInstance = new Instance(RF_testInstances.lastInstance());
    //System.out.println("-selecting last instance in test set RF_testInstances, done");

    // Specify that the instance belong to the training set 
    // in order to inherit from the set description                                
    testInstance.setDataset(trainParameters);
    System.out.println("-selected last instance in test set: " + testInstance.toString());

    return testInstance;
}

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From source file:SupervisedMetablocking.AbstractSupervisedMetablocking.java

License:Open Source License

protected Instance getFeatures(int match, List<Integer> commonBlockIndices, Comparison comparison) {
    double[] instanceValues = new double[noOfAttributes];

    int entityId2 = comparison.getEntityId2() + entityIndex.getDatasetLimit();

    double ibf1 = Math.log(noOfBlocks / entityIndex.getNoOfEntityBlocks(comparison.getEntityId1(), 0));
    double ibf2 = Math.log(noOfBlocks / entityIndex.getNoOfEntityBlocks(comparison.getEntityId2(), 1));
    instanceValues[0] = commonBlockIndices.size() * ibf1 * ibf2;

    double raccb = 0;
    for (Integer index : commonBlockIndices) {
        raccb += 1.0 / comparisonsPerBlock[index];
    }//from w w  w .j  a v a2s  .c  o m
    if (raccb < 1.0E-6) {
        raccb = 1.0E-6;
    }
    instanceValues[1] = raccb;

    instanceValues[2] = commonBlockIndices.size()
            / (redundantCPE[comparison.getEntityId1()] + redundantCPE[entityId2] - commonBlockIndices.size());
    instanceValues[3] = nonRedundantCPE[comparison.getEntityId1()];
    instanceValues[4] = nonRedundantCPE[entityId2];
    instanceValues[5] = match;

    Instance newInstance = new DenseInstance(1.0, instanceValues);
    newInstance.setDataset(trainingInstances);
    return newInstance;
}

---

From source file:tclass.clusteralg.EMClusterer.java

License:Open Source License

public float prob(Instance inst, int cluster) {
    try {/*from  w  ww  . ja va  2s  . c o m*/
        inst.setDataset(stuff);
        float retval = (float) em.probInstanceInCluster(inst, cluster);
        return retval;
    } catch (Exception e) {
        System.err.println("AAAARGH!!! Could not get distribution f for instance!");
        e.printStackTrace();
    }
    return 0;
}

---

From source file:tclass.clusteralg.EMClusterer.java

License:Open Source License

public ClusterMem findBestLabel(EventI ev) {
    // System.out.println("Trying to find best label for " + ev); 

    Instance inst = WekaBridge.makeInstance(ev, edi, ignoreTime);
    inst.setDataset(stuff);
    try {/*w  ww  . j a v  a  2s  .  com*/
        int cluster = em.clusterInstance(inst);
        float conf = (float) em.probInstanceInCluster(inst, cluster);
        // if(conf > 1){
        //    System.out.println("WTF?? Label is " + (new ClusterMem(elAt(cluster), conf)).toString() );               //  }
        return new ClusterMem(elAt(cluster), conf);

    } catch (Exception e) {
        System.out.println("Label finding failed. AAAAAARGH!!!");
        e.printStackTrace();
        return null;
    }
}

---

From source file:tclass.WekaBridge.java

License:Open Source License

/** 
 *  Makes an instances objects. /*from ww w .j  a  v a2  s . c o m*/
 */

public static Instances makeInstances(ClassStreamEventsVecI csevi, ClassDescVecI cdvi, EventDescVecI edvi,
        int evPos, boolean ignoreClasses, boolean ignoreTime) throws Exception {
    // System.out.println("Asked to ignore classes."); 

    Instances retval;
    // Ok, first convert attributes. 
    StreamEventsVecI sevi = csevi.getStreamEventsVec();
    ClassificationVecI cvi = csevi.getClassVec();
    // System.out.println("Event Desc Vec = " + edvi); 
    int numAtts = edvi.elAt(evPos).numParams();
    if (ignoreTime) {
        numAtts = numAtts - 1;
    }
    FastVector atts = makeAttVector(edvi.elAt(evPos), ignoreTime);
    if (!ignoreClasses) {
        int size = cdvi.size();
        FastVector classes = new FastVector(size);
        for (int i = 0; i < size; i++) {
            classes.addElement(cdvi.getClassLabel(i));
        }
        atts.addElement(new Attribute("class", classes));
    }
    int size = csevi.size();
    retval = new Instances(edvi.elName(evPos), atts, size);
    if (!ignoreClasses) {
        retval.setClassIndex(numAtts);
    } else {
        retval.setClassIndex(-1);
    }
    for (int i = 0; i < size; i++) {
        // Get events of this type: 
        EventVecI evi = sevi.elAt(i).getEvents(evPos);
        int numEvents = evi.size();
        for (int j = 0; j < numEvents; j++) {
            // System.out.println("Adding event " + j + " of stream " + i); 
            Instance thisInst = new DenseInstance(atts.size());
            thisInst.setDataset(retval);
            EventI thisEvent = evi.elAt(j);

            for (int k = (ignoreTime ? 1 : 0); k < edvi.elAt(evPos).numParams(); k++) {
                thisInst.setValue(k - (ignoreTime ? 1 : 0), thisEvent.valOf(k));
            }
            if (!ignoreClasses) {
                thisInst.setValue(numAtts, cdvi.getClassLabel(cvi.elAt(i).getRealClass()));
            }
            retval.add(thisInst);
        }
    }
    return retval;
}

---

From source file:tclass.WekaBridge.java

License:Open Source License

public static Instances makeInstances(ClassStreamAttValVecI csavvi, String name) throws Exception {
    StreamAttValVecI origData = csavvi.getStreamAttValVec();
    AttDescVecI format = origData.getDescription();
    ClassificationVecI classes = csavvi.getClassVec();
    ClassDescVecI classInfo = classes.getClassDescVec();
    FastVector instanceDesc = makeAttVector(format, classInfo);
    int numInstances = origData.size();
    int numAtts = format.size();
    Instances retval = new Instances(name, instanceDesc, numInstances);
    retval.setClassIndex(numAtts); // Set class to last attribute. 

    for (int i = 0; i < numInstances; i++) {
        Instance thisInst = new DenseInstance(numAtts + 1); // To include the class.  
        thisInst.setDataset(retval);
        StreamAttValI thisStream = origData.elAt(i);
        for (int j = 0; j < numAtts; j++) {
            thisInst.setValue(j, thisStream.getAtt(j));
        }/*from  w w  w.j  a va2  s  . c o m*/
        thisInst.setValue(numAtts, classInfo.getClassLabel(classes.elAt(i).getRealClass()));
        retval.add(thisInst);
    }
    return retval;
}

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From source file:tr.gov.ulakbim.jDenetX.streams.clustering.RandomRBFGeneratorEvents.java

License:Open Source License

public Instance nextInstance() {
    numGeneratedInstances++;// ww w .jav a2s.co m
    eventScheduler();

    //make room for thge classlabel
    double[] values_new = new double[numAttsOption.getValue() + 1];
    double[] values = null;
    int clusterChoice = -1;

    if (instanceRandom.nextDouble() > noiseLevelOption.getValue()) {
        clusterChoice = chooseWeightedElement();
        values = kernels.get(clusterChoice).generator.sample(instanceRandom).toDoubleArray();
    } else {
        //get ranodm noise point
        values = getNewSample();
    }

    if (Double.isNaN(values[0])) {
        System.out.println("Instance corrupted:" + numGeneratedInstances);
    }
    System.arraycopy(values, 0, values_new, 0, values.length);

    Instance inst = new DenseInstance(1.0, values_new);
    inst.setDataset(getHeader());
    if (clusterChoice == -1) {
        inst.setClassValue(-1);
    } else {
        inst.setClassValue(kernels.get(clusterChoice).generator.getId());
        //Do we need micro cluster representation if have overlapping clusters?
        //if(!overlappingOption.isSet())
        kernels.get(clusterChoice).addInstance(inst);
    }
    //        System.out.println(numGeneratedInstances+": Overlap is"+updateOverlaps());

    return inst;
}

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