本文整理了Java中weka.core.Instances.get()方法的一些代码示例，展示了Instances.get()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Instances.get()方法的具体详情如下：
包路径：weka.core.Instances
类名称：Instances
方法名：get

Instances.get介绍

[英]Returns the instance at the given position.
[中]返回给定位置的实例。

代码示例

代码示例来源：origin: stackoverflow.com

double[] prediction=model.distributionForInstance(test.get(s1));

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Sets the instance at index i of the supplied dataset to be the current
 * instance
 * 
 * @param i the index of the instance to be set
 * @throws UnsupportedOperationException if the full dataset has not been
 *           retained in memory
 */
public void setInstance(int i) {
 if (!dataRetained) {
  throw new UnsupportedOperationException(
   "Unable to set the instance based "
    + "on index because the dataset has not been retained in memory");
 }
 setInstance(dataset.get(i));
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Sets the instance at index i of the supplied dataset to be the current
 * instance
 * 
 * @param i the index of the instance to be set
 * @throws UnsupportedOperationException if the full dataset has not been
 *           retained in memory
 */
public void setInstance(int i) {
 if (!dataRetained) {
  throw new UnsupportedOperationException(
   "Unable to set the instance based "
    + "on index because the dataset has not been retained in memory");
 }
 setInstance(dataset.get(i));
}

代码示例来源：origin: sc.fiji/Trainable_Segmentation

/**
 * Merge two datasets of Weka instances in place
 * @param first first (and destination) dataset
 * @param second second dataset
 */
public void mergeDataInPlace(Instances first, Instances second)
{
  for(int i=0; i<second.numInstances(); i++)
    first.add(second.get(i));
}

代码示例来源：origin: fiji/Trainable_Segmentation

/**
 * Merge two datasets of Weka instances in place
 * @param first first (and destination) dataset
 * @param second second dataset
 */
public void mergeDataInPlace(Instances first, Instances second)
{
  for(int i=0; i<second.numInstances(); i++)
    first.add(second.get(i));
}

代码示例来源：origin: sc.fiji/Trainable_Segmentation

/**
 * Create leaf node based on the current split data
 *  
 * @param data pointer to original data
 * @param indices indices at this node
 */
public LeafNode(
    final Instances data, 
    ArrayList<Integer> indices)
{
  this.probability = new double[ data.numClasses() ];
  for(final Integer it : indices)
  {
    this.probability[ (int) data.get( it.intValue() ).classValue()] ++;
  }
  // Divide by the number of elements
  for(int i=0; i<data.numClasses(); i++)
    this.probability[i] /= (double) indices.size();
}

代码示例来源：origin: fiji/Trainable_Segmentation

/**
 * Create leaf node based on the current split data
 *  
 * @param data pointer to original data
 * @param indices indices at this node
 */
public LeafNode(
    final Instances data, 
    ArrayList<Integer> indices)
{
  this.probability = new double[ data.numClasses() ];
  for(final Integer it : indices)
  {
    this.probability[ (int) data.get( it.intValue() ).classValue()] ++;
  }
  // Divide by the number of elements
  for(int i=0; i<data.numClasses(); i++)
    this.probability[i] /= (double) indices.size();
}

代码示例来源：origin: dkpro/dkpro-similarity

@Override
  public double getSimilarity(JCas jcas1, JCas jcas2, Annotation coveringAnnotation1,
      Annotation coveringAnnotation2)
    throws SimilarityException
  {
    // The feature generation needs to have happened before!
    
    DocumentMetaData md = DocumentMetaData.get(jcas1);
    int id = Integer.parseInt(md.getDocumentId().substring(md.getDocumentId().indexOf("-") + 1));
    
    System.out.println(id);
    
    Instance testInst = test.get(id - 1);
    
    try {
      return filteredClassifier.classifyInstance(testInst);
    }
    catch (Exception e) {
      throw new SimilarityException(e);
    }
  }
}

代码示例来源：origin: org.dkpro.similarity/dkpro-similarity-algorithms-ml-gpl

@Override
  public double getSimilarity(JCas jcas1, JCas jcas2, Annotation coveringAnnotation1,
      Annotation coveringAnnotation2)
    throws SimilarityException
  {
    // The feature generation needs to have happened before!
    
    DocumentMetaData md = DocumentMetaData.get(jcas1);
    int id = Integer.parseInt(md.getDocumentId().substring(md.getDocumentId().indexOf("-") + 1));
    
    System.out.println(id);
    
    Instance testInst = test.get(id - 1);
    
    try {
      return filteredClassifier.classifyInstance(testInst);
    }
    catch (Exception e) {
      throw new SimilarityException(e);
    }
  }
}

代码示例来源：origin: de.tudarmstadt.ukp.similarity.algorithms/de.tudarmstadt.ukp.similarity.algorithms.ml-asl

@Override
  public double getSimilarity(JCas jcas1, JCas jcas2, Annotation coveringAnnotation1,
      Annotation coveringAnnotation2)
    throws SimilarityException
  {
    // The feature generation needs to have happened before!
    
    DocumentMetaData md = DocumentMetaData.get(jcas1);
    int id = Integer.parseInt(md.getDocumentId().substring(md.getDocumentId().indexOf("-") + 1));
    
    System.out.println(id);
    
    Instance testInst = test.get(id - 1);
    
    try {
      return filteredClassifier.classifyInstance(testInst);
    }
    catch (Exception e) {
      throw new SimilarityException(e);
    }
  }
}

代码示例来源：origin: dkpro/dkpro-similarity

@Override
  public double getSimilarity(JCas jcas1, JCas jcas2, Annotation coveringAnnotation1,
      Annotation coveringAnnotation2)
    throws SimilarityException
  {
    // The feature generation needs to have happened before!
    
    DocumentMetaData md = DocumentMetaData.get(jcas1);
    int id = Integer.parseInt(md.getDocumentId());
    
    System.out.println(id);
    
    Instance testInst = test.get(id - 1);
    
    try {
      return filteredClassifier.classifyInstance(testInst);
    }
    catch (Exception e) {
      throw new SimilarityException(e);
    }
  }
}

代码示例来源：origin: org.dkpro.similarity/dkpro-similarity-algorithms-ml-gpl

@Override
  public double getSimilarity(JCas jcas1, JCas jcas2, Annotation coveringAnnotation1,
      Annotation coveringAnnotation2)
    throws SimilarityException
  {
    // The feature generation needs to have happened before!
    
    DocumentMetaData md = DocumentMetaData.get(jcas1);
    int id = Integer.parseInt(md.getDocumentId());
    
    System.out.println(id);
    
    Instance testInst = test.get(id - 1);
    
    try {
      return filteredClassifier.classifyInstance(testInst);
    }
    catch (Exception e) {
      throw new SimilarityException(e);
    }
  }
}

代码示例来源：origin: de.tudarmstadt.ukp.similarity.algorithms/de.tudarmstadt.ukp.similarity.algorithms.ml-asl

@Override
  public double getSimilarity(JCas jcas1, JCas jcas2, Annotation coveringAnnotation1,
      Annotation coveringAnnotation2)
    throws SimilarityException
  {
    // The feature generation needs to have happened before!
    
    DocumentMetaData md = DocumentMetaData.get(jcas1);
    int id = Integer.parseInt(md.getDocumentId());
    
    System.out.println(id);
    
    Instance testInst = test.get(id - 1);
    
    try {
      return filteredClassifier.classifyInstance(testInst);
    }
    catch (Exception e) {
      throw new SimilarityException(e);
    }
  }
}

代码示例来源：origin: nz.ac.waikato.cms.moa/moa

public Clustering getClusteringResult() {
  Clustering clustering = null;
  weka.core.Instances wekaInstances= this.instanceConverter.wekaInstances(instances);
  try {
    
    clusterer.buildClusterer(wekaInstances);
    int numClusters = clusterer.numberOfClusters();
    Instances dataset = getDataset(instances.numAttributes(), numClusters);
    List<Instance> newInstances = new ArrayList<Instance>() ; //Instances(dataset);
    for (int i = 0; i < wekaInstances.numInstances(); i++) {
      weka.core.Instance inst = wekaInstances.get(i);
      int cnum = clusterer.clusterInstance(inst);
      Instance newInst = new DenseInstance(instances.instance(cnum));
      newInst.insertAttributeAt(inst.numAttributes());
      newInst.setDataset(dataset);
      newInst.setClassValue(cnum);
      newInstances.add(newInst);
    }
    clustering = new Clustering(newInstances);
  } catch (Exception e) {
    e.printStackTrace();
  }
  instances = null;
  return clustering;
}

代码示例来源：origin: Waikato/wekaDeeplearning4j

/** Test getDataSetIterator */
@Test
public void testGetIteratorNominalClass() throws Exception {
 final Instances data = DatasetLoader.loadAngerMetaClassification();
 final int batchSize = 1;
 final DataSetIterator it = this.cteii.getDataSetIterator(data, SEED, batchSize);
 Set<Integer> labels = new HashSet<>();
 for (int i = 0; i < data.size(); i++) {
  Instance inst = data.get(i);
  int label = Integer.parseInt(inst.stringValue(data.classIndex()));
  final DataSet next = it.next();
  int itLabel = next.getLabels().argMax().getInt(0);
  Assert.assertEquals(label, itLabel);
  labels.add(label);
 }
 final Set<Integer> collect =
   it.getLabels().stream().map(s -> Double.valueOf(s).intValue()).collect(Collectors.toSet());
 Assert.assertEquals(2, labels.size());
 Assert.assertTrue(labels.containsAll(collect));
 Assert.assertTrue(collect.containsAll(labels));
}

代码示例来源：origin: Waikato/wekaDeeplearning4j

protected void checkLayer(Dl4jMlpClassifier clf, Instances iris, String transformationLayerName,
   String clfPath) throws Exception {
  Instances activationsExpected = clf.getActivationsAtLayer(transformationLayerName, iris);
  Dl4jMlpFilter filter = new Dl4jMlpFilter();
  filter.setModelFile(new File(clfPath));
  filter.setTransformationLayerName(transformationLayerName);
  filter.setInputFormat(iris);

  Instances activationsActual = Filter.useFilter(iris, filter);

  for (int i = 0; i < activationsActual.size(); i++) {
   Instance expected = activationsExpected.get(i);
   Instance actual = activationsActual.get(i);
   for (int j = 0; j < expected.numAttributes(); j++) {
    assertEquals(expected.value(j), actual.value(j), 1e-6);
   }
  }
 }
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * tests the data whether the filter can actually handle it.
 * 
 * @param instanceInfo the data to test
 * @throws Exception if the test fails
 */
@Override
protected void testInputFormat(Instances instanceInfo) throws Exception {
 for (int i = 0; i < getRanges().length; i++) {
  Instances newi = new Instances(instanceInfo, 0);
  if (instanceInfo.size() > 0) {
   newi.add((Instance) instanceInfo.get(0).copy());
  }
  Range range = getRanges()[i];
  range.setUpper(instanceInfo.numAttributes() - 1);
  Instances subset = generateSubset(newi, range);
  getFilters()[i].setInputFormat(subset);
 }
}

代码示例来源：origin: Waikato/wekaDeeplearning4j

/** Test getDataSetIterator */
@Test
public void testGetIteratorNumericClass() throws Exception {
 final Instances data = DatasetLoader.loadAngerMeta();
 final int batchSize = 1;
 final DataSetIterator it = this.cteii.getDataSetIterator(data, SEED, batchSize);
 Set<Double> labels = new HashSet<>();
 for (int i = 0; i < data.size(); i++) {
  Instance inst = data.get(i);
  double label = inst.value(data.classIndex());
  final DataSet next = it.next();
  double itLabel = next.getLabels().getDouble(0);
  Assert.assertEquals(label, itLabel, 1e-5);
  labels.add(label);
 }
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * tests the data whether the filter can actually handle it.
 * 
 * @param instanceInfo the data to test
 * @throws Exception if the test fails
 */
@Override
protected void testInputFormat(Instances instanceInfo) throws Exception {
 for (int i = 0; i < getRanges().length; i++) {
  Instances newi = new Instances(instanceInfo, 0);
  if (instanceInfo.size() > 0) {
   newi.add((Instance) instanceInfo.get(0).copy());
  }
  Range range = getRanges()[i];
  range.setUpper(instanceInfo.numAttributes() - 1);
  Instances subset = generateSubset(newi, range);
  getFilters()[i].setInputFormat(subset);
 }
}

代码示例来源：origin: Waikato/wekaDeeplearning4j

/** Test getDataSetIterator */
@Test
public void testGetIteratorNumericClass() throws Exception {
 final Instances data = makeData();
 final int batchSize = 1;
 final DataSetIterator it = this.cteii.getDataSetIterator(data, SEED, batchSize);
 Set<Double> labels = new HashSet<>();
 for (int i = 0; i < data.size(); i++) {
  Instance inst = data.get(i);
  double label = inst.value(data.classIndex());
  final DataSet next = it.next();
  double itLabel = next.getLabels().getDouble(0);
  Assert.assertEquals(label, itLabel, 1e-5);
  labels.add(label);
 }
}