weka.core.Instances.<init>()方法的使用及代码示例

x33g5p2x  于2022-01-21 转载在 其他  
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本文整理了Java中weka.core.Instances.<init>()方法的一些代码示例，展示了Instances.<init>()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.<init>()方法的具体详情如下：
包路径：weka.core.Instances
类名称：Instances
方法名：<init>

Instances.<init>介绍

[英]Reads an ARFF file from a reader, and assigns a weight of one to each instance. Lets the index of the class attribute be undefined (negative).
[中]从读取器读取ARFF文件，并为每个实例分配一个权重。允许未定义类属性的索引（负数）。

代码示例

代码示例来源：origin: net.sf.meka/meka

/**
 * Stack two Instances together row-wise.
 */
public static final Instances combineInstances(Instances D1, Instances D2) {
  Instances D = new Instances(D1);
  for(int i = 0; i < D2.numInstances(); i++) {
    D.add(D2.instance(i));
  }
  return D;
}

代码示例来源：origin: stackoverflow.com

JFrame1 form = new JFrame1();
form.setVisible(true);
form.addPropertyChangeListener(new PropertyChangeListener() {

  @Override
  public void propertyChange(PropertyChangeEvent pce) {
    // Handle the change here

    String pth = (String) pce.getNewValue();
    BufferedReader datafile = readDataFile(pth);

    Instances data = new Instances(datafile);
    data.setClassIndex(data.numAttributes() - 1);

    (...)
  }

});

代码示例来源：origin: stackoverflow.com

public static void LoadAndTest(String filename_test, String filename_model) throws Exception {
  BufferedReader datafile_test = readDataFile(filename_test);
  Instances      data_test     = new Instances(datafile_test);
  data_test.setClassIndex(data_test.numAttributes() - 1);

  Classifier cls = (Classifier) weka.core.SerializationHelper.read(filename_model);
  int act = 0;
  for (int i = 0; i < data_test.numInstances(); i++) {
   double pred = cls.classifyInstance(data_test.instance(i));
   double real = data_test.instance(i).classValue();
   if (pred==real) {
    act = act + 1;
   }
  }  
  double pct = (double) act / (double) data_test.numInstances();
  System.out.println("Accuracy = " + pct);
}

代码示例来源：origin: net.sf.meka/meka

/**
 * Transform - transform dataset D for this node.
 * this.j defines the current node index, e.g., 3
 * this.paY[] defines parents,            e.g., [1,4]
 * we should remove the rest,             e.g., [0,2,5,...,L-1]
 * @return dataset we should remove all variables from D EXCEPT current node, and parents.
 */
public Instances transform(Instances D) throws Exception {
  int L = D.classIndex();
  d = D.numAttributes() - L;
  int keep[] = A.append(this.paY,j);		// keep all parents and self!
  Arrays.sort(keep);
  int remv[] = A.invert(keep,L); 	// i.e., remove the rest < L
  Arrays.sort(remv);
  map = new int[L];
  for(int j = 0; j < L; j++) {
    map[j] = Arrays.binarySearch(keep,j);
  }
  Instances D_ = F.remove(new Instances(D),remv, false); 
  D_.setClassIndex(map[this.j]);
  return D_;
}

代码示例来源：origin: net.sf.meka/meka

@Override
public Instance transformInstance(Instance x) throws Exception{

Instances tmpInst = new Instances(x.dataset());
tmpInst.delete();
tmpInst.add(x);

Instances features = this.extractPart(tmpInst, false);
Instances pseudoLabels = new Instances(this.compressedTemplateInst);
Instance tmpin = pseudoLabels.instance(0);
pseudoLabels.delete();

pseudoLabels.add(tmpin);
for ( int i = 0; i< pseudoLabels.classIndex(); i++) {
  pseudoLabels.instance(0).setMissing(i);
}
Instances newDataSet = Instances.mergeInstances(pseudoLabels, features);
newDataSet.setClassIndex(pseudoLabels.numAttributes());

return newDataSet.instance(0);
}

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

/**
 * bag class for getting the result of the loaded classifier
 */
private static class LoadedClassifier {
  private AbstractClassifier newClassifier = null;
  private Instances newHeader = null;
}

代码示例来源：origin: net.sf.meka/meka

protected Instances convert(Instances D, int j, int k) {
  int L = D.classIndex();
  D = new Instances(D);
  D.insertAttributeAt(classAttribute,0);
  D.setClassIndex(0);
  for(int i = 0; i < D.numInstances(); i++) {
    String c = (String)((int)Math.round(D.instance(i).value(j+1))+""+(int)Math.round(D.instance(i).value(k+1)));
    D.instance(i).setClassValue(c);
  }
  for (int i = 0; i < L; i++)
    D.deleteAttributeAt(1);
  m_InstancesTemplate = new Instances(D,0);
  return D;
}

代码示例来源：origin: Stratio/wikipedia-parser

public GroupFeature(List<FeatureExtractor> features) {
  this.features = ImmutableList.copyOf(features);
  ImmutableList.Builder<Attribute> result = ImmutableList.builder();
  for (FeatureExtractor fe: this.features) {
    for (Attribute att: fe.attributes()) {
      result.add((Attribute)att.copy());
    }
  }
  _attributes = result.build();
  _instances = new Instances("FOO", newArrayList(_attributes), 0);
  result = ImmutableList.builder();
  for (int i = 0; i < _instances.numAttributes(); i++) {
    result.add(_instances.attribute(i));
  }
  _attributes = result.build();
}

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

/**
 * performs a typical test
 */
public void testTypical() {
 Instances icopy = new Instances(m_Instances);
 
 m_Filter = getFilter();
 Instances result = useFilter();
 assertEquals(result.numAttributes(), icopy.numInstances() + 1);
}

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

public void testPruneMinFreq() throws Exception {
 Instances data1 = getData1();
 Instances structure = new Instances(data1, 0);
 DictionaryBuilder builder = new DictionaryBuilder();
 builder.setMinTermFreq(1);
 builder.setup(structure);
 for (int i = 0; i < data1.numInstances(); i++) {
  builder.processInstance(data1.instance(i));
 }
 assertEquals(15, builder.getDictionaries(false)[0].size());
 Map<String, int[]> consolidated = builder.finalizeDictionary();
 // min freq of 1 should keep all terms
 assertEquals(15, consolidated.size());
}

代码示例来源：origin: net.sf.meka/meka

public static double[][] LEAD(Instances D, Classifier h, Random r, String MDType)  throws Exception {
  Instances D_r = new Instances(D);
  D_r.randomize(r);
  Instances D_train = new Instances(D_r,0,D_r.numInstances()*60/100);
  Instances D_test = new Instances(D_r,D_train.numInstances(),D_r.numInstances()-D_train.numInstances());
  BR br = new BR();
  br.setClassifier(h);
  Result result = Evaluation.evaluateModel((MultiLabelClassifier)br,D_train,D_test,"PCut1","1");
  return LEAD(D_test, result, MDType);
}

代码示例来源：origin: Waikato/wekaDeeplearning4j

@Override
public double[] distributionForInstance(Instance instance) throws Exception {
 Instances data = new Instances(instance.dataset());
 data.add(instance);
 return distributionsForInstances(data)[0];
}

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

/**
 * performs the application with no options set
 */
public void testDefault() {
 Instances icopy = new Instances(m_Instances);
 
 m_Filter = getFilter();
 Instances result = useFilter();
 assertEquals(result.numAttributes(), icopy.numAttributes());
}

代码示例来源：origin: sc.fiji/T2-NIT

Operator() {
    ArrayList<Attribute> a = new ArrayList<Attribute>();
    for (int i=0; i<attrs.length-1; i++) {
      a.add(new Attribute(attrs[i])); // numeric
    }
    ArrayList<String> d = new ArrayList<String>();
    d.add("false");
    d.add("true");
    a.add(new Attribute(attrs[attrs.length-1], d)); // nominal attribute
    data = new Instances("Buh", a, 0);
    data.setClassIndex(attrs.length-1); // the CLASS
  }
}

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

private Instances parseTransactionsMustContain(Instances data) {
 String[] split = m_transactionsMustContain.trim().split(",");
 boolean[] transactionsMustContainIndexes = new boolean[data.numAttributes()];
 int numInTransactionsMustContainList = split.length;
 for (String element : split) {
  String attName = element.trim();
  Attribute att = data.attribute(attName);
  if (att == null) {
   System.err.println("[FPGrowth] : WARNING - can't find attribute "
    + attName + " in the data.");
   numInTransactionsMustContainList--;
  } else {
   transactionsMustContainIndexes[att.index()] = true;
  }
 }
 if (numInTransactionsMustContainList == 0) {
  return data;
 } else {
  Instances newInsts = new Instances(data, 0);
  for (int i = 0; i < data.numInstances(); i++) {
   if (passesMustContain(data.instance(i), transactionsMustContainIndexes,
    numInTransactionsMustContainList)) {
    newInsts.add(data.instance(i));
   }
  }
  newInsts.compactify();
  return newInsts;
 }
}

代码示例来源：origin: Waikato/meka

/**
 * Transform - transform dataset D for this node.
 * this.j defines the current node index, e.g., 3
 * this.paY[] defines parents,            e.g., [1,4]
 * we should remove the rest,             e.g., [0,2,5,...,L-1]
 * @return dataset we should remove all variables from D EXCEPT current node, and parents.
 */
public Instances transform(Instances D) throws Exception {
  int L = D.classIndex();
  d = D.numAttributes() - L;
  int keep[] = A.append(this.paY,j);		// keep all parents and self!
  Arrays.sort(keep);
  int remv[] = A.invert(keep,L); 	// i.e., remove the rest < L
  Arrays.sort(remv);
  map = new int[L];
  for(int j = 0; j < L; j++) {
    map[j] = Arrays.binarySearch(keep,j);
  }
  Instances D_ = F.remove(new Instances(D),remv, false); 
  D_.setClassIndex(map[this.j]);
  return D_;
}

代码示例来源：origin: Waikato/meka

/**
 * Stack two Instances together row-wise.
 */
public static final Instances combineInstances(Instances D1, Instances D2) {
  Instances D = new Instances(D1);
  for(int i = 0; i < D2.numInstances(); i++) {
    D.add(D2.instance(i));
  }
  return D;
}

代码示例来源：origin: Waikato/meka

@Override
public Instance transformInstance(Instance x) throws Exception{

Instances tmpInst = new Instances(x.dataset());
tmpInst.delete();
tmpInst.add(x);

Instances features = this.extractPart(tmpInst, false);
Instances pseudoLabels = new Instances(this.compressedTemplateInst);
Instance tmpin = pseudoLabels.instance(0);
pseudoLabels.delete();

pseudoLabels.add(tmpin);
for ( int i = 0; i< pseudoLabels.classIndex(); i++) {
  pseudoLabels.instance(0).setMissing(i);
}
Instances newDataSet = Instances.mergeInstances(pseudoLabels, features);
newDataSet.setClassIndex(pseudoLabels.numAttributes());

return newDataSet.instance(0);
}

代码示例来源：origin: fiji/Trainable_Segmentation

/**
 * bag class for getting the result of the loaded classifier
 */
private static class LoadedClassifier {
  private AbstractClassifier newClassifier = null;
  private Instances newHeader = null;
}

代码示例来源：origin: Waikato/wekaDeeplearning4j

/**
 * Load the diabetes arff file
 *
 * @return Diabetes data as Instances
 * @throws Exception IO error.
 */
public static Instances loadDiabetes() throws Exception {
 Instances data =
   new Instances(new FileReader("src/test/resources/numeric/diabetes_numeric.arff"));
 data.setClassIndex(data.numAttributes() - 1);
 return data;
}

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