weka.filters.unsupervised.attribute.Add类的使用及代码示例

x33g5p2x  于2022-01-17 转载在 其他  
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本文整理了Java中weka.filters.unsupervised.attribute.Add类的一些代码示例，展示了Add类的具体用法。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。Add类的具体详情如下：
包路径：weka.filters.unsupervised.attribute.Add
类名称：Add

Add介绍

[英]An instance filter that adds a new attribute to the dataset. The new attribute will contain all missing values.

Valid options are:

-T <NUM|NOM|STR|DAT> 
The type of attribute to create: 
NUM = Numeric attribute 
NOM = Nominal attribute 
STR = String attribute 
DAT = Date attribute 
(default: NUM)
-C <index> 
Specify where to insert the column. First and last 
are valid indexes.(default: last)
-N <name> 
Name of the new attribute. 
(default: 'Unnamed')
-L <label1,label2,...> 
Create nominal attribute with given labels 
(default: numeric attribute)
-F <format> 
The format of the date values (see ISO-8601) 
(default: yyyy-MM-dd'T'HH:mm:ss)
-W <double> 
The weight for the new attribute (default: 1.0)

[中]向数据集添加新属性的实例筛选器。新属性将包含所有缺少的值。
有效选项包括：

-T <NUM|NOM|STR|DAT> 
The type of attribute to create: 
NUM = Numeric attribute 
NOM = Nominal attribute 
STR = String attribute 
DAT = Date attribute 
(default: NUM)
-C <index> 
Specify where to insert the column. First and last 
are valid indexes.(default: last)
-N <name> 
Name of the new attribute. 
(default: 'Unnamed')
-L <label1,label2,...> 
Create nominal attribute with given labels 
(default: numeric attribute)
-F <format> 
The format of the date values (see ISO-8601) 
(default: yyyy-MM-dd'T'HH:mm:ss)
-W <double> 
The weight for the new attribute (default: 1.0)

代码示例

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

private Instances makeClusterDataSetProbabilities(Instances format,
 weka.clusterers.Clusterer clusterer, String relationNameModifier)
 throws Exception {
 Instances newInstances = new Instances(format);
 for (int i = 0; i < clusterer.numberOfClusters(); i++) {
  weka.filters.unsupervised.attribute.Add addF =
   new weka.filters.unsupervised.attribute.Add();
  addF.setAttributeIndex("last");
  addF.setAttributeName("prob_cluster" + i);
  addF.setInputFormat(newInstances);
  newInstances = weka.filters.Filter.useFilter(newInstances, addF);
 }
 newInstances.setRelationName(format.relationName() + relationNameModifier);
 return newInstances;
}

代码示例来源：origin: stackoverflow.com

Add add = new Add();
if ( addWindow == false ) { //Thanks to @TofuBeer's comment. I didn't notice this at all.         
  add.setVisible(true);
}
else
  add.setVisible(true);

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

/**
  * Main method for testing this class.
  * 
  * @param argv should contain arguments to the filter: use -h for help
  */
 public static void main(String[] argv) {
  runFilter(new Add(), argv);
 }
}

代码示例来源：origin: stackoverflow.com

Add obj1 = new Add();
 c = obj1.addfn(obj1.a,obj1.b);
 return (c-d);

代码示例来源：origin: org.dkpro.tc/dkpro-tc-ml-weka

int numTrainLabels = trainData.classIndex();
int numTestLabels = testData.classIndex();
Add filter = new Add();
for (int i = 0; i < numTrainLabels; i++) {
  filter.setAttributeIndex(Integer.toString(numTestLabels + i + 1));
  filter.setNominalLabels("0,1");
  filter.setAttributeName(trainData.attribute(i).name() + COMPATIBLE_OUTCOME_CLASS);
  filter.setInputFormat(testData);
  testData = Filter.useFilter(testData, filter);

代码示例来源：origin: org.dkpro.tc/dkpro-tc-ml-weka

Enumeration trainOutcomeValues = trainData.classAttribute().enumerateValues();
Enumeration testOutcomeValues = testData.classAttribute().enumerateValues();
List trainLabels = Collections.list(trainOutcomeValues);
List testLabels = Collections.list(testOutcomeValues);
Add addFilter = new Add();
addFilter.setNominalLabels(StringUtils.join(trainLabels, ','));
addFilter.setAttributeName(Constants.CLASS_ATTRIBUTE_NAME + COMPATIBLE_OUTCOME_CLASS);
addFilter.setInputFormat(testData);
testData = Filter.useFilter(testData, addFilter);
compTestData = new Instances(testData, testData.numInstances());
for (int i = 0; i < testData.numInstances(); i++) {
  weka.core.Instance instance = testData.instance(i);

代码示例来源：origin: org.dkpro.tc/dkpro-tc-ml-weka

if (oldData.attribute(Constants.ID_FEATURE_NAME) != null) {
  int instanceIdOffset = oldData.attribute(Constants.ID_FEATURE_NAME).index();
  Add add = new Add();
  add.setAttributeName(Constants.ID_FEATURE_NAME);
    add.setAttributeIndex("last");
    add.setAttributeIndex("first");
  add.setAttributeType(new SelectedTag(Attribute.STRING, Add.TAGS_TYPE));
  add.setInputFormat(newData);
  filteredData = Filter.useFilter(newData, add);
  int j = isMultilabel ? filteredData.numAttributes() - 1 : 0;
  for (int i = 0; i < filteredData.numInstances(); i++) {
    String outcomeId = oldData.instance(i).stringValue(instanceIdOffset);

代码示例来源：origin: net.sf.meka.thirdparty/mulan

Add add = new Add();
add.setAttributeIndex("first");
add.setAttributeName("instanceID");
add.setInputFormat(transformed);
transformed = Filter.useFilter(transformed, add);
for (int i=0; i<transformed.numInstances(); i++) {
  transformed.instance(i).setValue(0, i);
transformed.setClassIndex(transformed.numAttributes()-1);

代码示例来源：origin: de.tudarmstadt.ukp.dkpro.tc/de.tudarmstadt.ukp.dkpro.tc.weka-gpl

Instances randData = new Instances(data);
randData.randomize(new Random(new Date().getTime()));
randData.stratify(FOLDS);
    Add filter = new Add();
    for (int i = 0; i < numLabels; i++) {
      filter.setAttributeIndex(new Integer(numLabels + i + 1).toString());
      filter.setNominalLabels("0,1");
      filter.setAttributeName(test.attribute(i).name() + "_classification");
      filter.setInputFormat(test);
      test = Filter.useFilter(test, filter);
    weka.core.SerializationHelper.write(evalOutput.getAbsolutePath(), eval);
    Add filter = new Add();
    filter.setAttributeIndex(new Integer(test.classIndex() + 1).toString());
    filter.setAttributeName("goldlabel");
    filter.setInputFormat(test);
    test = Filter.useFilter(test, filter);

代码示例来源：origin: org.dkpro.tc/dkpro-tc-ml-weka

private Instances getPredictionInstancesMultiLabel(Instances testData, Classifier cl,
    double[] thresholdArray)
  throws Exception
{
  int numLabels = testData.classIndex();
  // get predictions
  List<double[]> labelPredictionList = new ArrayList<double[]>();
  for (int i = 0; i < testData.numInstances(); i++) {
    labelPredictionList.add(cl.distributionForInstance(testData.instance(i)));
  }
  // add attributes to store predictions in test data
  Add filter = new Add();
  for (int i = 0; i < numLabels; i++) {
    filter.setAttributeIndex(Integer.toString(numLabels + i + 1));
    filter.setNominalLabels("0,1");
    filter.setAttributeName(
        testData.attribute(i).name() + "_" + WekaTestTask.PREDICTION_CLASS_LABEL_NAME);
    filter.setInputFormat(testData);
    testData = Filter.useFilter(testData, filter);
  }
  // fill predicted values for each instance
  for (int i = 0; i < labelPredictionList.size(); i++) {
    for (int j = 0; j < labelPredictionList.get(i).length; j++) {
      testData.instance(i).setValue(j + numLabels,
          labelPredictionList.get(i)[j] >= thresholdArray[j] ? 1. : 0.);
    }
  }
  return testData;
}

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

/**
 * Adds an instance number attribute to the plottable instances,
 */
public void addInstanceNumberAttribute() {
 String originalRelationName = m_plotInstances.relationName();
 int originalClassIndex = m_plotInstances.classIndex();
 try {
  Add addF = new Add();
  addF.setAttributeName("Instance_number");
  addF.setAttributeIndex("first");
  addF.setInputFormat(m_plotInstances);
  m_plotInstances = Filter.useFilter(m_plotInstances, addF);
  m_plotInstances.setClassIndex(originalClassIndex + 1);
  for (int i = 0; i < m_plotInstances.numInstances(); i++) {
   m_plotInstances.instance(i).setValue(0, i);
  }
  m_plotInstances.setRelationName(originalRelationName);
 } catch (Exception ex) {
  ex.printStackTrace();
 }
}

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

return m_clusterInstances.toString();
tempNode.m_clusterInstances = new Instances(m_clusterInstances, 1);
for (int i = 0; i < m_children.size(); i++) {
 tempNode.addChildNode(m_children.get(i));
tempNode = null;
Add af = new Add();
af.setAttributeName("Cluster");
String labels = "";
for (int i = 0; i < m_children.size(); i++) {
af.setNominalLabels(labels);
af.setInputFormat(tempInst);
tempInst = Filter.useFilter(tempInst, af);
tempInst.setRelationName("Cluster " + m_clusterNum);

代码示例来源：origin: dkpro/dkpro-tc

public Instances getPredictionInstancesSingleLabel(Instances testData, Classifier cl)
  throws Exception
{
  StringBuffer classVals = new StringBuffer();
  for (int i = 0; i < testData.classAttribute().numValues(); i++) {
    if (classVals.length() > 0) {
      classVals.append(",");
    }
    classVals.append(testData.classAttribute().value(i));
  }
  // get predictions
  List<Double> labelPredictionList = new ArrayList<Double>();
  for (int i = 0; i < testData.size(); i++) {
    labelPredictionList.add(cl.classifyInstance(testData.instance(i)));
  }
  // add an attribute with the predicted values at the end off the attributes
  Add filter = new Add();
  filter.setAttributeName(WekaTestTask.PREDICTION_CLASS_LABEL_NAME);
  if (classVals.length() > 0) {
    filter.setAttributeType(new SelectedTag(Attribute.NOMINAL, Add.TAGS_TYPE));
    filter.setNominalLabels(classVals.toString());
  }
  filter.setInputFormat(testData);
  testData = Filter.useFilter(testData, filter);
  // fill predicted values for each instance
  for (int i = 0; i < labelPredictionList.size(); i++) {
    testData.instance(i).setValue(testData.classIndex() + 1, labelPredictionList.get(i));
  }
  return testData;
}

代码示例来源：origin: net.sf.meka.thirdparty/mulan

/**
 * Constructor
 *
 * @param data a multi-label dataset
 */
public BinaryRelevanceTransformation(MultiLabelInstances data) {
  try {
    this.data = data;
    remove = new Remove();
    int[] labelIndices = data.getLabelIndices();
    int[] indices = new int[labelIndices.length];
    System.arraycopy(labelIndices, 0, indices, 0, labelIndices.length);
    remove.setAttributeIndicesArray(indices);
    remove.setInvertSelection(false);
    remove.setInputFormat(data.getDataSet());
    shell = Filter.useFilter(data.getDataSet(), remove);
    add = new Add();
    add.setAttributeIndex("last");
    add.setNominalLabels("0,1");
    add.setAttributeName("BinaryRelevanceLabel");
    add.setInputFormat(shell);
    shell = Filter.useFilter(shell, add);
    shell.setClassIndex(shell.numAttributes() - 1);
  } catch (Exception ex) {
    Logger.getLogger(BinaryRelevanceTransformation.class.getName()).log(Level.SEVERE, null, ex);
  }
}

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

setAttributeType(new SelectedTag(tmpStr, TAGS_TYPE));
} else {
 setAttributeType(new SelectedTag(Attribute.NUMERIC, TAGS_TYPE));
 tmpStr = "last";
setAttributeIndex(tmpStr);
setAttributeName(Utils.unbackQuoteChars(Utils.getOption('N', options)));
  setNominalLabels(tmpStr);
  setDateFormat(tmpStr);
 setWeight(1.0);
} else {
 setWeight(Double.parseDouble(tmpStr));
if (getInputFormat() != null) {
 setInputFormat(getInputFormat());

代码示例来源：origin: stackoverflow.com

function Add(x) {
  this.counter = x;

  this.addOne = function() {
    return this.counter += 1;
  }
}

var add = new Add(0);

function myFunction() {
  document.getElementById("demo").innerHTML = add.addOne();
}

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

/** Creates a specialized Add */
public Filter getFilter(int pos) {
 Add af = new Add();
 af.setAttributeIndex("" + (pos + 1));
 return af;
}

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

tempNode = null;
Add af = new Add();
af.setAttributeName("Cluster");
String labels = "";
for (int i = 0; i < m_children.size(); i++) {
af.setNominalLabels(labels);

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

/**
 * Input an instance for filtering. Ordinarily the instance is processed and
 * made available for output immediately. Some filters require all instances
 * be read before producing output.
 * 
 * @param instance the input instance
 * @return true if the filtered instance may now be collected with output().
 * @throws IllegalStateException if no input format has been defined.
 */
@Override
public boolean input(Instance instance) {
 if (getInputFormat() == null) {
  throw new IllegalStateException("No input instance format defined");
 }
 if (m_NewBatch) {
  resetQueue();
  m_NewBatch = false;
 }
 Instance inst = (Instance) instance.copy();
 // First copy string values from input to output
 copyValues(inst, true, inst.dataset(), outputFormatPeek());
 // Insert the new attribute and reassign to output
 inst.setDataset(null);
 inst.insertAttributeAt(m_Insert.getIndex());
 push(inst); // No need to copy instance
 return true;
}

代码示例来源：origin: stackoverflow.com

Add a = new Add();
mOut.print(a.toString(argumentOne, argumentTwo));

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