Java 类weka.core.DenseInstance 实例源码

项目：Machine-Learning-End-to-Endguide-for-Java-developers 文件：Main-SVG.java

public Main() {
    try {
        BufferedReader datafile;
        datafile = readDataFile("camping.txt");
        Instances data = new Instances(datafile);
        data.setClassIndex(data.numAttributes() - 1);

        Instances trainingData = new Instances(data, 0, 14);
        Instances testingData = new Instances(data, 14, 5);
        Evaluation evaluation = new Evaluation(trainingData);

        SMO smo = new SMO();
        smo.buildClassifier(data);

        evaluation.evaluateModel(smo, testingData);
        System.out.println(evaluation.toSummaryString());

        // Test instance 
        Instance instance = new DenseInstance(3);
        instance.setValue(data.attribute("age"), 78);
        instance.setValue(data.attribute("income"), 125700);
        instance.setValue(data.attribute("camps"), 1);            
        instance.setDataset(data);
        System.out.println("The instance: " + instance);
        System.out.println(smo.classifyInstance(instance));
    } catch (Exception ex) {
        ex.printStackTrace();
    }
}

项目：parlo 文件：SentenceClassifier.java

public SentenceType classifySentence(Sentence sentence) {
    SpeechActsClassifier.Features features = speechActsClassifier.classifyFeatures(sentence);

    Instance inst = new DenseInstance(6);
    inst.setDataset(dataSet);

    inst.setValue(0, features.getSentenceLength());
    inst.setValue(1, features.getNumberOfNouns());
    inst.setValue(2, (features.isEndingInNounOrAdjective() ? 1 : 0));
    inst.setValue(3, (features.isBeginningInVerb() ? 1 : 0));
    inst.setValue(4, features.getCountOfWhMarkers());
    inst.setValue(5, Utils.missingValue());

    try {
        return SentenceType.valueOf(classifier.classifyInstance(inst));
    } catch (Exception e) {
        throw new RuntimeException("Can't classify");
    }
}

项目：parlo 文件：QuestionClassifier.java

public QuestionType classifyQuestion(Sentence sentence) {
    if (!sentence.isQuestion()) {
        return QuestionType.NA;
    }

    QuestionTypeClassifier.Features features = questionTypeClassifier.classifyFeatures(sentence);

    Instance inst = new DenseInstance(5);
    inst.setDataset(dataSet);

    inst.setValue(0, features.getWhWord());
    inst.setValue(1, features.getWhWordPos());
    inst.setValue(2, features.getPosOfNext());
    inst.setValue(3, features.getRootPos());
    inst.setValue(4, Utils.missingValue());

    try {
        int ndx = (int) classifier.classifyInstance(inst);
         return QuestionType.valueOf(ndx);
    } catch (Exception e) {
        throw new RuntimeException("Not classified");
    }
}

项目：ijcnlp2017-cmaps 文件：FeatureContainer.java

public Instance createInstance(List<String> featureNames, I key) {
    double[] vals = new double[featureNames.size()];
    for (int i = 0; i < featureNames.size(); i++) {
        Feature<Object> f = this.featureValues.get(key).get(featureNames.get(i));
        if (f != null)
            vals[i] = f.getDoubleValue();
        else {
            Class<Object> type = features.get(featureNames.get(i));
            if (type.equals(Double.class) || type.equals(Float.class) || type.equals(Integer.class))
                vals[i] = Double.NaN;
            if (type.equals(Boolean.class) || type.equals(String.class))
                vals[i] = 0;
        }
    }
    return new DenseInstance(1.0, vals);
}

项目：TacTex 文件：RegressionUtils.java

public static Instances createXInstances(XYForRegression xy) {
  // build attributes
  ArrayList<Attribute> attributes = new ArrayList<Attribute>();
  for (String attrName : xy.getAttrNames() ) {
    // attribute name is the polynomial power
    Attribute xattr = new Attribute(attrName); 
    attributes.add(xattr);
  }    

  double[][] Xarr = xy.getX();
  int numInstances = Xarr.length;
  int numAttr = Xarr[0].length;
  Instances Xinst = new Instances("X", attributes, numInstances);

  // fill instances 
  for (int i = 0; i < numInstances; ++i) {
    Instance row = new DenseInstance(numAttr);
    for (int j = 0; j < numAttr; ++j) {
      row.setValue(j, Xarr[i][j]);
    }
    Xinst.add(row);
  }
  return Xinst; 
}

项目：1stclass 文件：NaiveBayesSystem.java

public void train(TrainData data, TermVocabulary vocab) throws Exception {
    this.vocab = vocab;
    Instances trainset = buildInstances();

    for (Pair<String, BytecodeData.MethodData> p : data.data()) {

        double[] instV = seqToFeats(p.getRight().getExtOpcodeSeq());
        instV[instV.length - 1] = (double) vocab.termToPos(p.getKey());

        Instance inst = new DenseInstance(1.0, instV);
        inst.setDataset(trainset);
        trainset.add(inst);
    }

    nb = new NaiveBayes();
    nb.buildClassifier(trainset);
}

项目：1stclass 文件：NaiveBayesSystem.java

public Map<String, Double> predict(BytecodeData.MethodData method) throws Exception {
    if (testHeader == null)
        testHeader = buildInstances();

    double[] instV = seqToFeats(method.getExtOpcodeSeq());
    instV[instV.length - 1] = 0.0;

    Instance inst = new DenseInstance(1.0, instV);
    inst.setDataset(testHeader);
    testHeader.add(inst);

    double[] probs = nb.distributionForInstance(inst);
    Map<String, Double> result = new HashMap<>();
    int i = 0;
    for (String term : vocab.terms()) {
        result.put(term, probs[i]);
        i++;
    }

    testHeader.clear();

    return result;
}

项目：1stclass 文件：RandomForestSystem.java

public void train(TrainData data, TermVocabulary vocab) throws Exception {
    this.vocab = vocab;
    Instances trainset = buildInstances();

    for (Pair<String, BytecodeData.MethodData> p : data.data()) {

        double[] instV = seqToFeats(p.getRight().getExtOpcodeSeq());
        instV[instV.length - 1] = (double) vocab.termToPos(p.getKey());

        Instance inst = new DenseInstance(1.0, instV);
        inst.setDataset(trainset);
        trainset.add(inst);
    }

    frf = new FastRandomForest();
    frf.setSeed(1993);
    // frf.setMaxDepth(20);
    // frf.setNumThreads(1);
    frf.setNumTrees(numTrees);
    frf.buildClassifier(trainset);
}

项目：1stclass 文件：RandomForestSystem.java

public Map<String, Double> predict(BytecodeData.MethodData method) throws Exception {
    if (testHeader == null)
        testHeader = buildInstances();

    double[] instV = seqToFeats(method.getExtOpcodeSeq());
    instV[instV.length - 1] = 0.0;

    Instance inst = new DenseInstance(1.0, instV);
    inst.setDataset(testHeader);
    testHeader.add(inst);

    double[] probs = frf.distributionForInstance(inst);
    Map<String, Double> result = new HashMap<>();
    int i = 0;
    for (String term : vocab.terms()) {
        result.put(term, probs[i]);
        i++;
    }

    testHeader.clear();

    return result;
}

项目：repo.kmeanspp.silhouette_score 文件：SimpleKMeans.java

/**
 * Launch the move centroids tasks
 * 
 * @param clusters the cluster centroids
 * @return the number of empty clusters
 */
protected int launchMoveCentroids(Instances[] clusters) {
  int emptyClusterCount = 0;
  List<Future<double[]>> results = new ArrayList<Future<double[]>>();

  for (int i = 0; i < m_NumClusters; i++) {
    if (clusters[i].numInstances() == 0) {
      emptyClusterCount++;
    } else {
      Future<double[]> futureCentroid =
        m_executorPool.submit(new KMeansComputeCentroidTask(i, clusters[i]));
      results.add(futureCentroid);
    }
  }

  try {
    for (Future<double[]> d : results) {
      m_ClusterCentroids.add(new DenseInstance(1.0, d.get()));
    }
  } catch (Exception ex) {
    ex.printStackTrace();
  }

  return emptyClusterCount;
}

项目：repo.kmeanspp.silhouette_score 文件：KMeansPlusPlusSC.java

/**
 * Launch the move centroids tasks
 * 
 * @param clusters the cluster centroids
 * @return the number of empty clusters
 */
protected int launchMoveCentroids(Instances[] clusters) {
  int emptyClusterCount = 0;
  List<Future<double[]>> results = new ArrayList<Future<double[]>>();

  for (int i = 0; i < m_NumClusters; i++) {
    if (clusters[i].numInstances() == 0) {
      emptyClusterCount++;
    } else {
      Future<double[]> futureCentroid =
        m_executorPool.submit(new KMeansComputeCentroidTask(i, clusters[i]));
      results.add(futureCentroid);
    }
  }

  try {
    for (Future<double[]> d : results) {
      m_ClusterCentroids.add(new DenseInstance(1.0, d.get()));
    }
  } catch (Exception ex) {
    ex.printStackTrace();
  }

  return emptyClusterCount;
}

项目：repo.kmeanspp.silhouette_score 文件：KMeansPlusPlus.java

/**
 * Launch the move centroids tasks
 * 
 * @param clusters the cluster centroids
 * @return the number of empty clusters
 */
protected int launchMoveCentroids(Instances[] clusters) {
  int emptyClusterCount = 0;
  List<Future<double[]>> results = new ArrayList<Future<double[]>>();

  for (int i = 0; i < m_NumClusters; i++) {
    if (clusters[i].numInstances() == 0) {
      emptyClusterCount++;
    } else {
      Future<double[]> futureCentroid =
        m_executorPool.submit(new KMeansComputeCentroidTask(i, clusters[i]));
      results.add(futureCentroid);
    }
  }

  try {
    for (Future<double[]> d : results) {
      m_ClusterCentroids.add(new DenseInstance(1.0, d.get()));
    }
  } catch (Exception ex) {
    ex.printStackTrace();
  }

  return emptyClusterCount;
}

项目：repo.kmeanspp.silhouette_score 文件：BoundaryPanel.java

/**
 * Adds a training instance to our dataset, based on the coordinates of the
 * mouse on the panel. This method sets the x and y attributes and the class
 * (as defined by classAttIndex), and sets all other values as Missing.
 * 
 * @param mouseX the x coordinate of the mouse, in pixels.
 * @param mouseY the y coordinate of the mouse, in pixels.
 * @param classAttIndex the index of the attribute that is currently selected
 *          as the class attribute.
 * @param classValue the value to set the class to in our new point.
 */
public void addTrainingInstanceFromMouseLocation(int mouseX, int mouseY,
  int classAttIndex, double classValue) {
  // convert to coordinates in the training instance space.
  double x = convertFromPanelX(mouseX);
  double y = convertFromPanelY(mouseY);

  // build the training instance
  Instance newInstance = new DenseInstance(m_trainingData.numAttributes());
  for (int i = 0; i < newInstance.numAttributes(); i++) {
    if (i == classAttIndex) {
      newInstance.setValue(i, classValue);
    } else if (i == m_xAttribute) {
      newInstance.setValue(i, x);
    } else if (i == m_yAttribute) {
      newInstance.setValue(i, y);
    } else {
      newInstance.setMissing(i);
    }
  }

  // add it to our data set.
  addTrainingInstance(newInstance);
}

项目：repo.kmeanspp.silhouette_score 文件：SubstringReplacerRules.java

/**
 * Make an output instance given an input one
 *
 * @param inputI the input instance to process
 * @return the output instance with substrings replaced
 */
public Instance makeOutputInstance(Instance inputI) {
  double[] vals = new double[m_outputStructure.numAttributes()];
  String[] stringVals = new String[m_outputStructure.numAttributes()];
  for (int i = 0; i < inputI.numAttributes(); i++) {
    if (inputI.attribute(i).isString() && !inputI.isMissing(i)) {
      stringVals[i] = inputI.stringValue(i);
    } else {
      vals[i] = inputI.value(i);
    }
  }

  for (SubstringReplacerMatchRule mr : m_matchRules) {
    mr.apply(stringVals);
  }

  for (int i = 0; i < m_outputStructure.numAttributes(); i++) {
    if (m_outputStructure.attribute(i).isString() && stringVals[i] != null) {
      m_outputStructure.attribute(i).setStringValue(stringVals[i]);
    }
  }

  Instance result = new DenseInstance(inputI.weight(), vals);
  result.setDataset(m_outputStructure);
  return result;
}

项目：NucleosomePatternClassifier 文件：WekaNGGFeatureVector.java

public Instance fillFeatureVector(NGGFeatureVector vSource, Instances data) {

    double[] values = new double[data.numAttributes()];

    values[0] = vSource.getContainmentSimilarityArrayAtIndex(0);
    values[1] = vSource.getSizeSimilarityArrayAtIndex(0);
    values[2] = vSource.getValueSimilarityArrayAtIndex(0);
    values[3] = vSource.getNVSArrayAtIndex(0);
    values[4] = vSource.getContainmentSimilarityArrayAtIndex(1);
    values[5] = vSource.getSizeSimilarityArrayAtIndex(1);
    values[6] = vSource.getValueSimilarityArrayAtIndex(1);
    values[7] = vSource.getNVSArrayAtIndex(1);
    values[8] = data.attribute(8).indexOfValue(vSource.getLabel());

    Instance inst = new DenseInstance(1.0, values);

    return inst;
}

项目：LibD3C-1.1 文件：KMeanz.java

public Instance decideCentroid(double[] vals,Instances members){
    Instance inst=new DenseInstance(vals.length);
    int q;
    for(q=0;q<vals.length;q++){
        inst.setValue(q, vals[q]);
    }

    double minDistance=Double.MAX_VALUE;
    double tempDistance;
    int instanceID=0;
    int i;
    for(i=0;i<members.numInstances();i++){
        tempDistance=myDistance(inst,members.instance(i));
        if(tempDistance<minDistance ){
            minDistance=tempDistance;
            instanceID=i;
        }
    }
    return members.instance(instanceID);
}

项目：autoweka 文件：BoundaryPanel.java

/** Adds a training instance to our dataset, based on the coordinates of the mouse on the panel.
     This method sets the x and y attributes and the class (as defined by classAttIndex), and sets
     all other values as Missing.
  *  @param mouseX the x coordinate of the mouse, in pixels.
  *  @param mouseY the y coordinate of the mouse, in pixels.
  *  @param classAttIndex the index of the attribute that is currently selected as the class attribute.
  *  @param classValue the value to set the class to in our new point.
  */
 public void addTrainingInstanceFromMouseLocation(int mouseX, int mouseY, int classAttIndex, double classValue) {
    //convert to coordinates in the training instance space.
double x = convertFromPanelX(mouseX);
double y = convertFromPanelY(mouseY);

//build the training instance
Instance newInstance = new DenseInstance(m_trainingData.numAttributes());
for (int i = 0; i < newInstance.numAttributes(); i++) {
    if (i == classAttIndex) {
        newInstance.setValue(i,classValue);
    }
    else if (i == m_xAttribute)
        newInstance.setValue(i,x);
    else if (i == m_yAttribute)
        newInstance.setValue(i,y);
    else newInstance.setMissing(i);
}

//add it to our data set.
addTrainingInstance(newInstance);
 }

项目：umple 文件：SimpleKMeans.java

/**
 * Launch the move centroids tasks
 * 
 * @param clusters the cluster centroids
 * @return the number of empty clusters
 */
protected int launchMoveCentroids(Instances[] clusters) {
  int emptyClusterCount = 0;
  List<Future<double[]>> results = new ArrayList<Future<double[]>>();

  for (int i = 0; i < m_NumClusters; i++) {
    if (clusters[i].numInstances() == 0) {
      emptyClusterCount++;
    } else {
      Future<double[]> futureCentroid =
        m_executorPool.submit(new KMeansComputeCentroidTask(i, clusters[i]));
      results.add(futureCentroid);
    }
  }

  try {
    for (Future<double[]> d : results) {
      m_ClusterCentroids.add(new DenseInstance(1.0, d.get()));
    }
  } catch (Exception ex) {
    ex.printStackTrace();
  }

  return emptyClusterCount;
}

项目：umple 文件：BoundaryPanel.java

/**
 * Adds a training instance to our dataset, based on the coordinates of the
 * mouse on the panel. This method sets the x and y attributes and the class
 * (as defined by classAttIndex), and sets all other values as Missing.
 * 
 * @param mouseX the x coordinate of the mouse, in pixels.
 * @param mouseY the y coordinate of the mouse, in pixels.
 * @param classAttIndex the index of the attribute that is currently selected
 *          as the class attribute.
 * @param classValue the value to set the class to in our new point.
 */
public void addTrainingInstanceFromMouseLocation(int mouseX, int mouseY,
  int classAttIndex, double classValue) {
  // convert to coordinates in the training instance space.
  double x = convertFromPanelX(mouseX);
  double y = convertFromPanelY(mouseY);

  // build the training instance
  Instance newInstance = new DenseInstance(m_trainingData.numAttributes());
  for (int i = 0; i < newInstance.numAttributes(); i++) {
    if (i == classAttIndex) {
      newInstance.setValue(i, classValue);
    } else if (i == m_xAttribute) {
      newInstance.setValue(i, x);
    } else if (i == m_yAttribute) {
      newInstance.setValue(i, y);
    } else {
      newInstance.setMissing(i);
    }
  }

  // add it to our data set.
  addTrainingInstance(newInstance);
}

项目：Infrastructure 文件：ReflectiveAdaptation.java

private Instances featuresToInstance(ArrayList<Double> features)
{
    // Create the instance
    FastVector attributes = new FastVector(features.size());
    for(int i = 0; i < features.size(); i++) attributes.addElement(new Attribute(""+i));
    Instances dataUnlabeled = new Instances("instances", attributes, 0);

    Instance instance = new DenseInstance(features.size());
    DecimalFormat df = new DecimalFormat("#.######");
    for(int i = 0; i < features.size(); i++) instance.setValue(i, Double.valueOf(df.format(features.get(i))));

    dataUnlabeled.add(instance);
    dataUnlabeled.setClassIndex(dataUnlabeled.numAttributes() - 1);        

    return dataUnlabeled;
}

项目：edits 文件：WekaEntailmentEngine.java

private Instance buildInstance(AnnotatedEntailmentPair pair) {
    Instance i = new DenseInstance(features.size() + 1);
    i.setDataset(instances);
    int pos = 0;
    for (Feature f : features) {
        if (f.isNumeric())
            i.setValue(pos, f.doubleValue(pair));
        else
            i.setValue(pos, f.stringValue(pair));
        pos++;
    }
    i.setValue(
            pos,
            pair.getEntailment() == null || pair.getEntailment().length() == 0
                    || !categories.contains(pair.getEntailment()) ? categories.get(0) : pair.getEntailment());
    return i;
}

项目：mHealthDroid 文件：Classification.java

/**
 * Method to get a Instances object from a featureVector
 * @param featureVector the featureVector to convert in Instances
 * @return an Instances object (in other words a set of Instance)
 */
public Instances featureVectorToInstances(ArrayList<Double> featureVector) {

    Instances instances = new Instances("Instances", attributes, 0);
    DenseInstance instance = new DenseInstance(attributes.size());

    for (int i = 0; i < featureVector.size(); i++)

        instance.setValue(i, featureVector.get(i));

    // instance.setValue(featureVector.size(), -1);
    instances.add(instance);
    // Set class attribute
    instances.setClassIndex(attributes.size() - 1);

    return instances;
}

项目：mHealthDroid 文件：Classification.java

/**
 * Method to get a Instances object from a featureMatrix
 * @param featureMatrix the featureMatrix to convert in Instances
 * @return an Instances object (in other words a set of Instance)
 */
public Instances featureMatrixToInstances(
        ArrayList<ArrayList<Double>> featureMatrix) {

    Instances instances = new Instances("Instances", attributes, 0);
    DenseInstance instance = new DenseInstance(attributes.size());

    for (int i = 0; i < featureMatrix.size(); i++) {

        for (int j = 0; j < featureMatrix.size(); j++) {

            instance.setValue(j, featureMatrix.get(i).get(j));
        }
        // instance.setValue(featureVector.size(), -1);
        instances.add(instance);
        // Set class attribute
        instances.setClassIndex(attributes.size() - 1);
    }

    return instances;
}

项目：weka-mnb-sentiment-analysis-template-project 文件：FiveWayMNBTrainer.java

public SentimentClass.FiveWayClazz classify(String sentence) throws Exception {
    double[] instanceValue = new double[dataRaw.numAttributes()];
    instanceValue[0] = dataRaw.attribute(0).addStringValue(sentence);

    Instance toClassify = new DenseInstance(1.0, instanceValue);
    dataRaw.setClassIndex(1);
    toClassify.setDataset(dataRaw);

    double prediction = this.classifier.classifyInstance(toClassify);

    double distribution[] = this.classifier.distributionForInstance(toClassify);

    return SentimentClass.FiveWayClazz.values()[(int)prediction];

    /*
    if (distribution[0] != distribution[1])
        return SentimentClass.FiveWayClazz.values()[(int)prediction];
    else

        return SentimentClass.FiveWayClazz.NEUTRAL;
*/
}

项目：weka-mnb-sentiment-analysis-template-project 文件：ThreeWayMNBTrainer.java

public SentimentClass.ThreeWayClazz classify(String sentence) throws Exception {
    double[] instanceValue = new double[dataRaw.numAttributes()];
    instanceValue[0] = dataRaw.attribute(0).addStringValue(sentence);

    Instance toClassify = new DenseInstance(1.0, instanceValue);
    dataRaw.setClassIndex(1);
    toClassify.setDataset(dataRaw);

    double prediction = this.classifier.classifyInstance(toClassify);

    double distribution[] = this.classifier.distributionForInstance(toClassify);

    if (distribution[0] != distribution[1])
        return SentimentClass.ThreeWayClazz.values()[(int)prediction];
    else
        return SentimentClass.ThreeWayClazz.NEUTRAL;
}

项目：justsemantics 文件：WordEmbedding.java

/**
 * Transform the vectors into Weka Instances. 
 * @return the Instances set. The attribute names are colX, where X is 0 to vector_size -1 and the values are "double" numbers.
 */
public Instances produceInstances(){
    ArrayList<Attribute> fvWekaAttributes = new ArrayList<Attribute>();
    for (int i = 0; i < vector_size ; i++){
        Attribute attr = new Attribute("col"+i);
        fvWekaAttributes.add(attr);
    }
    Instances instances = new Instances("kmeansInstances", fvWekaAttributes, vocab_size);
    for (String word:vectors.keySet()) {
        Instance iExample = new DenseInstance(fvWekaAttributes.size());
        for (int i = 0; i < vector_size; i++){
            iExample.setValue(fvWekaAttributes.get(i), vectors.get(word).get(i));
        }
        instances.add(iExample);
    }
    return instances;
}

项目：VoiceStressAnalysis 文件：VoiceStressInstance.java

/**
 * toWekaInstance
 * 
 * @param dataset
 * @return 
 */
public Instance toWekaInstance(Instances dataset) {
    // create instance
    DenseInstance instance = new DenseInstance(13);
    instance.setDataset(dataset);

    // set values
    instance.setValue(0, this.getMean());
    instance.setValue(1, this.getMedian());
    instance.setValue(2, this.getMin());
    instance.setValue(3, this.getMax());
    instance.setValue(4, this.getStd());
    instance.setValue(5, this.getLowQuantile());
    instance.setValue(6, this.getHighQuantile());
    instance.setValue(7, this.getIqr());
    instance.setValue(8, this.getKurtosis());
    instance.setValue(9, this.getRange());
    instance.setValue(10, this.getPower());
    instance.setValue(11, this.getTotalF0());
    instance.setValue(12, stressed ? "stressed" : "unstressed");

    // return instance
    return instance;
}

项目：Weka_AnalogicalModeling 文件：SubcontextTest.java

@BeforeClass
public static void setUpBeforeClass() throws Exception {
    ArrayList<Attribute> atts = new ArrayList<>();
    ArrayList<String> classes = new ArrayList<>();
    classes.add("e");
    classes.add("r");
    atts.add(new Attribute("a"));
    atts.add(new Attribute("class", classes));
    dataset = new Instances("TestInstances", atts, 0);
    dataset.setClassIndex(dataset.numAttributes() - 1);

    double[][] data = new double[][]{
        new double[]{1, 1}, new double[]{1, 0}
    };
    for (double[] datum : data) {
        Instance instance = new DenseInstance(2, datum);
        dataset.add(instance);
    }
}

项目：Weka_AnalogicalModeling 文件：MissingDataCompareTest.java

@BeforeClass
public static void setupBeforeClass() {
    ArrayList<Attribute> atts = new ArrayList<>();
    att = new Attribute("a");
    atts.add(att);

    Instances dataset = new Instances("TestInstances", atts, 0);
    dataset.setClassIndex(dataset.numAttributes() - 1);

    double[][] data = new double[][]{
        new double[]{Double.NaN}, new double[]{0}
    };
    instances = new ArrayList<>();
    for (double[] datum : data) {
        Instance instance = new DenseInstance(6, datum);
        instance.setDataset(dataset);
        instances.add(instance);
    }
}

项目：TrakEM2 文件：LineageClassifier.java

public static final boolean classify(final double[] vector) throws Exception {

        // Obtain or generate a Thread-local instance

        Operator op;
        synchronized (table) { // avoid clashes within weka
            final Thread t = Thread.currentThread();
            op = table.get(t);
            if (null == op) {
                op = new Operator();
                table.put(t, op);
            }
        }

        // Future weka versions will use new DenseInstance(1, vector) instead
        final Instance ins = new DenseInstance(1, vector);
        ins.setDataset(op.data);
        // Was trained to return true or false, represented in weka as 0 or 1
        return 1 == ((int) Math.round(op.c.classifyInstance(ins)));
    }

项目：Android-Context-Framework 文件：WekaManager.java

private void fillData(double[] featureValues, String className, Instances data) {

        double[] vals = new double[data.numAttributes()];

        if (vals.length != (featureValues.length + 1)) {
            if (FrameworkContext.WARN) Log.w(TAG, "Number of feature values and weka instance values differs.");
        }

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

        vals[vals.length - 1] = attClassVals.indexOf(className);

        DenseInstance instance = new DenseInstance(1.0, vals);

        if (isLogDirectlyToFile) {
            instance.setDataset(data);
            logArffData(instance.toString());
        } else {
            // add
            data.add(instance);
        }
    }

项目：Machine-Learning-End-to-Endguide-for-Java-developers 文件：BookDecisionTree.java

private Instance getTestInstance(
        String binding, String multicolor, String genre) {
    Instance instance = new DenseInstance(3);
    instance.setDataset(trainingData);
    instance.setValue(trainingData.attribute(0), binding);
    instance.setValue(trainingData.attribute(1), multicolor);
    instance.setValue(trainingData.attribute(2), genre);
    return instance;
}

项目：CS-436_580L_Introduction-to-Machine-Learning 文件：Utils.java

public static Instances convertToArff(List<Document> dataSet, List<String> vocabulary, String fileName) {
    int dataSetSize = dataSet.size();
    /* Create features */
    ArrayList<Attribute> attributes = new ArrayList<>();
    for (int i = 0; i < vocabulary.size(); i++) {
        attributes.add(new Attribute("word_" + i));
    }
    Attribute classAttribute = new Attribute("Class");
    attributes.add(classAttribute);

    /* Add examples */
    System.out.println("Building instances...");
    Instances trainingDataSet = new Instances(fileName, attributes, 0);
    for (int k = 0; k < dataSetSize; k++) {
        Document document = dataSet.get(k);
        Instance example = new DenseInstance(attributes.size());
        for (int i = 0; i < vocabulary.size(); i++) {
            String word = vocabulary.get(i);
            example.setValue(i, Collections.frequency(document.getTerms(), word));
        }
        example.setValue(classAttribute, document.getDocumentClass());
        trainingDataSet.add(example);
        int progress = (int) ((k * 100.0) / dataSetSize);
        System.out.printf("\rPercent completed: %3d%%", progress);
    }
    trainingDataSet.setClass(classAttribute);
    System.out.println();

    System.out.println("Writing to file ...");
    try {
        ArffSaver saver = new ArffSaver();
        saver.setInstances(trainingDataSet);
        saver.setFile(new File(fileName));
        saver.writeBatch();
    } catch (IOException e) {
        e.printStackTrace();
    }

    return trainingDataSet;
}

项目：Java-Data-Science-Made-Easy 文件：Main-SVG.java

public Main() {
    try {
        BufferedReader datafile;
        datafile = readDataFile("camping.txt");
        Instances data = new Instances(datafile);
        data.setClassIndex(data.numAttributes() - 1);

        Instances trainingData = new Instances(data, 0, 14);
        Instances testingData = new Instances(data, 14, 5);
        Evaluation evaluation = new Evaluation(trainingData);

        SMO smo = new SMO();
        smo.buildClassifier(data);

        evaluation.evaluateModel(smo, testingData);
        System.out.println(evaluation.toSummaryString());

        // Test instance 
        Instance instance = new DenseInstance(3);
        instance.setValue(data.attribute("age"), 78);
        instance.setValue(data.attribute("income"), 125700);
        instance.setValue(data.attribute("camps"), 1);            
        instance.setDataset(data);
        System.out.println("The instance: " + instance);
        System.out.println(smo.classifyInstance(instance));
    } catch (Exception ex) {
        ex.printStackTrace();
    }
}

项目：Java-Data-Science-Made-Easy 文件：BookDecisionTree.java

private Instance getTestInstance(
        String binding, String multicolor, String genre) {
    Instance instance = new DenseInstance(3);
    instance.setDataset(trainingData);
    instance.setValue(trainingData.attribute(0), binding);
    instance.setValue(trainingData.attribute(1), multicolor);
    instance.setValue(trainingData.attribute(2), genre);
    return instance;
}

项目：Java-for-Data-Science 文件：Main-SVG.java

public Main() {
    try {
        BufferedReader datafile;
        datafile = readDataFile("camping.txt");
        Instances data = new Instances(datafile);
        data.setClassIndex(data.numAttributes() - 1);

        Instances trainingData = new Instances(data, 0, 14);
        Instances testingData = new Instances(data, 14, 5);
        Evaluation evaluation = new Evaluation(trainingData);

        SMO smo = new SMO();
        smo.buildClassifier(data);

        evaluation.evaluateModel(smo, testingData);
        System.out.println(evaluation.toSummaryString());

        // Test instance 
        Instance instance = new DenseInstance(3);
        instance.setValue(data.attribute("age"), 78);
        instance.setValue(data.attribute("income"), 125700);
        instance.setValue(data.attribute("camps"), 1);            
        instance.setDataset(data);
        System.out.println("The instance: " + instance);
        System.out.println(smo.classifyInstance(instance));
    } catch (Exception ex) {
        ex.printStackTrace();
    }
}

项目：Java-for-Data-Science 文件：BookDecisionTree.java

private Instance getTestInstance(
        String binding, String multicolor, String genre) {
    Instance instance = new DenseInstance(3);
    instance.setDataset(trainingData);
    instance.setValue(trainingData.attribute(0), binding);
    instance.setValue(trainingData.attribute(1), multicolor);
    instance.setValue(trainingData.attribute(2), genre);
    return instance;
}

项目：twitter-user-gender-classification 文件：FeatureVectorsFactory.java

public static Instances constructInstances(List<Pair<String, Attribute>> attributesList, List<Observation> observations) {
    List<Pair<String, Attribute>> attributes = attributesList
            .stream()
            .filter(pair -> !pair.getValue().isString())
            .collect(Collectors.toList());

    Instances trainingSet = new Instances(
            "Standard set",
            (ArrayList<Attribute>) attributes
                    .stream()
                    .map(pair -> pair.getValue())
                    .collect(Collectors.toList()),
            observations.size());

    for(int i = 0; i < observations.size(); i ++) {
        Instance instance = new DenseInstance(attributes.size());

        for(Pair<String, Attribute> attributePair : attributes) {
            updateInstance(attributePair, instance, observations, i);
        }

        trainingSet.add(instance);
    }

    trainingSet.setClassIndex(attributes.stream().map(pair -> pair.getKey()).collect(Collectors.toList()).indexOf(Fields.GENDER.name()));

    return trainingSet;
}

项目：java-ml-projects 文件：Clustering.java

private void reloadSeries(Number xValue, Number yValue) {
    try {
        Instance instance = new DenseInstance(NUMBER_OF_CLASSES);
        instance.setDataset(data);
        instance.setValue(0, xValue.doubleValue());
        instance.setValue(1, yValue.doubleValue());
        double predictedClass = tree.classifyInstance(instance);
        instance.setValue(2, predictedClass);
        data.add(instance);
        reloadSeries();
    } catch (Exception e) {
        e.printStackTrace();
    }
}