Java FastVector類代碼示例

import weka.core.FastVector; //導入依賴的package包/類
/**
 * Checks whether the scheme can handle zero training instances.
 *
 * @param attrTypes attribute types that can be estimated
 * @param classType the class type (NUMERIC, NOMINAL, etc.)
 * @return index 0 is true if the test was passed, index 1 is true if test 
 *         was acceptable
 */
protected boolean[] canHandleZeroTraining(AttrTypes attrTypes, int classType) {
  
  print("handle zero training instances");
  printAttributeSummary(attrTypes, classType);

  print("...");
  FastVector accepts = new FastVector();
  accepts.addElement("train");
  accepts.addElement("value");
  int numTrain = 0, numTest = getNumInstances(), numClasses = 2, 
  missingLevel = 0;
  boolean attributeMissing = false, classMissing = false;
  int numAtts = 1;
  int attrIndex = 0;
  return runBasicTest(
            attrTypes, numAtts, attrIndex,
            classType, 
            missingLevel, attributeMissing, classMissing,
            numTrain, numTest, numClasses, 
            accepts);
}
 

import weka.core.FastVector; //導入依賴的package包/類
/**
 * Defines the clusters if pattern is RANDOM
 *
 * @param random random number generator
 * @return the cluster definitions
 * @throws Exception if something goes wrong
 */
private FastVector defineClustersRANDOM(Random random)
  throws Exception {

  FastVector clusters = new FastVector(m_NumClusters);
  double diffInstNum = (double) (m_MaxInstNum - m_MinInstNum);
  double minInstNum = (double) m_MinInstNum;
  double diffRadius = m_MaxRadius - m_MinRadius;
  Cluster cluster;

  for (int i = 0; i < m_NumClusters; i++) {
    int instNum = (int) (random.nextDouble() * diffInstNum
                                 + minInstNum);
    double radius = (random.nextDouble() * diffRadius) + m_MinRadius;

    // center is defined in the constructor of cluster
    cluster = new Cluster(instNum, radius, random);
    clusters.addElement((Object) cluster);
  }
  return clusters;
}
 

import weka.core.FastVector; //導入依賴的package包/類
/** 
 * create sub tree
 * 
 * @param iNode index of the lowest node in the tree
 * @param nRecords set of records in instances to be considered
 * @param instances data set
        * @return ADNode representing an ADTree
 */
public static ADNode makeADTree(int iNode, FastVector nRecords, Instances instances) {
	ADNode _ADNode = new ADNode();
               _ADNode.m_nCount = nRecords.size();
               _ADNode.m_nStartNode = iNode;
               if (nRecords.size() < MIN_RECORD_SIZE) {
                 _ADNode.m_Instances = new Instance[nRecords.size()];
                 for (int iInstance = 0; iInstance < nRecords.size(); iInstance++) {
                   _ADNode.m_Instances[iInstance] = instances.instance(((Integer) nRecords.elementAt(iInstance)).intValue());
                 }
               } else {
                 _ADNode.m_VaryNodes = new VaryNode[instances.numAttributes() - iNode];
                 for (int iNode2 = iNode; iNode2 < instances.numAttributes(); iNode2++) {
                         _ADNode.m_VaryNodes[iNode2 - iNode] = makeVaryNode(iNode2, nRecords, instances);
                 }
               }
	return _ADNode;
}
 

import weka.core.FastVector; //導入依賴的package包/類
void layoutGraph() {
	if (m_BayesNet.getNrOfNodes() == 0) {
		return;
	}
	try {
		FastVector m_nodes = new FastVector();
		FastVector m_edges = new FastVector();
		BIFParser bp = new BIFParser(m_BayesNet.toXMLBIF03(), m_nodes, m_edges);
		bp.parse();
		updateStatus();
		m_layoutEngine = new HierarchicalBCEngine(m_nodes, m_edges, m_nPaddedNodeWidth, m_nNodeHeight);
		m_layoutEngine.addLayoutCompleteEventListener(this);
		m_layoutEngine.layoutGraph();
	} catch (Exception e) {
		e.printStackTrace();
	}
}
 

import weka.core.FastVector; //導入依賴的package包/類
/** 
  * Method that finds all association rules.
  *
  * @throws Exception if an attribute is numeric
  */
 private void findRulesQuickly() throws Exception {

   FastVector[] rules;
   // Build rules
   for (int j = 1; j < m_Ls.size(); j++) {
     FastVector currentItemSets = (FastVector)m_Ls.elementAt(j);
     Enumeration enumItemSets = currentItemSets.elements();
     while (enumItemSets.hasMoreElements()) {
AprioriItemSet currentItemSet = (AprioriItemSet)enumItemSets.nextElement();
       //AprioriItemSet currentItemSet = new AprioriItemSet((ItemSet)enumItemSets.nextElement());
rules = currentItemSet.generateRules(m_minMetric, m_hashtables, j + 1);
for (int k = 0; k < rules[0].size(); k++) {
  m_allTheRules[0].addElement(rules[0].elementAt(k));
  m_allTheRules[1].addElement(rules[1].elementAt(k));
  m_allTheRules[2].addElement(rules[2].elementAt(k));
  
  if (rules.length > 3) {
    m_allTheRules[3].addElement(rules[3].elementAt(k));
    m_allTheRules[4].addElement(rules[4].elementAt(k));
    m_allTheRules[5].addElement(rules[5].elementAt(k));
  }
}
     }
   }
 }
 

import weka.core.FastVector; //導入依賴的package包/類
public Instances timeInstances(SortedSet orders){
    FastVector attributes = new FastVector(1);
    attributes.addElement(new Attribute("time"));
    
    Instances cInstances = new Instances("wiki", attributes,orders.size());
    
    for(Iterator it = orders.iterator(); it.hasNext();){
        // System.out.println("class: " + data.instance(i).getClass());
        MyInstance instance = (MyInstance) it.next();
        double [] values = new double[1]; //instance.toDoubleArray();
        values[values.length  - 1] = instance.getTime();
        Instance newInstance = new Instance(1, values);
        newInstance = new MyInstance(newInstance, instance.getTime());
        cInstances.add(newInstance);
    }
    
    return cInstances;
}
 

import weka.core.FastVector; //導入依賴的package包/類
/** Called by JUnit after each test method */
protected void tearDown() {
  m_Classifier   = null;
  m_Tester       = null;
  m_OptionTester = null;
  m_GOETester    = null;

  m_updateableClassifier         = false;
  m_weightedInstancesHandler     = false;
  m_NominalPredictors            = new boolean[LAST_CLASSTYPE + 1];
  m_NumericPredictors            = new boolean[LAST_CLASSTYPE + 1];
  m_StringPredictors             = new boolean[LAST_CLASSTYPE + 1];
  m_DatePredictors               = new boolean[LAST_CLASSTYPE + 1];
  m_RelationalPredictors         = new boolean[LAST_CLASSTYPE + 1];
  m_handleMissingPredictors      = new boolean[LAST_CLASSTYPE + 1];
  m_handleMissingClass           = new boolean[LAST_CLASSTYPE + 1];
  m_handleClassAsFirstAttribute  = new boolean[LAST_CLASSTYPE + 1];
  m_handleClassAsSecondAttribute = new boolean[LAST_CLASSTYPE + 1];
  m_RegressionResults            = new FastVector[LAST_CLASSTYPE + 1];
  m_NClasses                     = 4;
}
 

import weka.core.FastVector; //導入依賴的package包/類
private Instances getInstances() {
	Attribute rleRateAttr = new Attribute(ARFFAttributes.DATA_COMPRESSION_RATE_BY_RLE_ATTRIBUTE);
	Attribute rleRateVwAttr = new Attribute(ARFFAttributes.DATA_COMPRESSION_RATE_OF_VERTICALLY_WINDING_TEXT_BY_RLE_ATTRIBUTE);
	Attribute linesAttr = new Attribute(ARFFAttributes.NUMBER_OF_LINES_ATTRIBUTE);
	Attribute sizeAttr = new Attribute(ARFFAttributes.TEXT_SIZE_ATTRIBUTE);

	// Create nominal attribute "class" 
	FastVector my_nominal_values = new FastVector(2); 
	my_nominal_values.addElement(ARFFAttributes.ASCIIART_CLASS_NAME); 
	my_nominal_values.addElement(ARFFAttributes.NON_ASCIIART_CLASS_NAME); 
	Attribute className = new Attribute(ARFFAttributes.CLASS_ATTRIBUTE, my_nominal_values);

	FastVector attributes = new FastVector(5);
	attributes.addElement(rleRateAttr);
	attributes.addElement(rleRateVwAttr);
	attributes.addElement(linesAttr);
	attributes.addElement(sizeAttr);
	attributes.addElement(className);

	// Create the empty dataset "textart" with above attributes
	Instances instances = new Instances(ARFFAttributes.ASCIIART_CLASS_NAME, attributes, 0);
	instances.setClassIndex(className.index());
	return instances;
}
 

import weka.core.FastVector; //導入依賴的package包/類
public TrialInstances buildVarInstances(InferenceRule ir, List allVars) {
    FastVector attributes = new FastVector();
    WekaInterface.addAllPairs(attributes, allVars);
    attributes.addElement(new weka.core.Attribute("score"));
    int capacity = 30;
    OrderTranslator filter = new FilterTranslator(allVars);
    TrialInstances data = new TrialInstances("Var Ordering Constraints", attributes, capacity);
    if (allVars.size() <= 1) return data;
    for (Iterator i = allTrials.iterator(); i.hasNext();) {
        EpisodeCollection tc2 = (EpisodeCollection) i.next();
        InferenceRule ir2 = tc2.getRule(solver);
        if (ir != ir2) continue;
        addToInstances(data, tc2, filter);
    }
    data.setClassIndex(data.numAttributes() - 1);
    return data;
}
 

import weka.core.FastVector; //導入依賴的package包/類
/**
 * Checks basic estimation of one attribute of the scheme, for simple non-troublesome
 * datasets.
 *
 * @param attrTypes the types the estimator can work with
 * @param classType the class type (NOMINAL, NUMERIC, etc.)
 * @return index 0 is true if the test was passed, index 1 is true if test 
 *         was acceptable
 */
protected boolean[] canEstimate(AttrTypes attrTypes, boolean supervised, int classType) {
  
// supervised is ignored, no supervised estimators used yet
  
  print("basic estimation");
  printAttributeSummary(attrTypes, classType);
  print("...");
  FastVector accepts = new FastVector();
  accepts.addElement("nominal");
  accepts.addElement("numeric");
  accepts.addElement("string");
  accepts.addElement("date");
  accepts.addElement("relational");
  accepts.addElement("not in classpath");
  int numTrain = getNumInstances(), numTest = getNumInstances(), 
  numClasses = 2, missingLevel = 0;
  boolean attributeMissing = false, classMissing = false;
  int numAtts = 1, attrIndex = 0;

  return runBasicTest(attrTypes, numAtts, attrIndex,
	classType, 
	missingLevel, attributeMissing, classMissing,
	numTrain, numTest, numClasses, 
	accepts);
}
 

import weka.core.FastVector; //導入依賴的package包/類
/**
 * generates the header
 * 
 * @return the header
 */
private Instances makeHeader() {

  FastVector fv = new FastVector();
  fv.addElement(new Attribute(TRUE_POS_NAME));
  fv.addElement(new Attribute(FALSE_NEG_NAME));
  fv.addElement(new Attribute(FALSE_POS_NAME));
  fv.addElement(new Attribute(TRUE_NEG_NAME));
  fv.addElement(new Attribute(FP_RATE_NAME));
  fv.addElement(new Attribute(TP_RATE_NAME));
  fv.addElement(new Attribute(PRECISION_NAME));
  fv.addElement(new Attribute(RECALL_NAME));
  fv.addElement(new Attribute(FALLOUT_NAME));
  fv.addElement(new Attribute(FMEASURE_NAME));
  fv.addElement(new Attribute(SAMPLE_SIZE_NAME));
  fv.addElement(new Attribute(LIFT_NAME));
  fv.addElement(new Attribute(THRESHOLD_NAME));      
  return new Instances(RELATION_NAME, fv, 100);
}
 

import weka.core.FastVector; //導入依賴的package包/類
private Instances getInstances() {
	Attribute rleRateAttr = new Attribute(ARFFAttributes.DATA_COMPRESSION_RATE_BY_RLE_ATTRIBUTE);
	Attribute linesAttr = new Attribute(ARFFAttributes.NUMBER_OF_LINES_ATTRIBUTE);
	Attribute sizeAttr = new Attribute(ARFFAttributes.TEXT_SIZE_ATTRIBUTE);
	Attribute ngramAttr = new Attribute(ARFFAttributes.NUMBER_OF_NGRAMS_ATTRIBUTE);

	// Create nominal attribute "class" 
	FastVector my_nominal_values = new FastVector(2); 
	my_nominal_values.addElement(ARFFAttributes.ASCIIART_CLASS_NAME); 
	my_nominal_values.addElement(ARFFAttributes.NON_ASCIIART_CLASS_NAME); 
	Attribute className = new Attribute(ARFFAttributes.CLASS_ATTRIBUTE, my_nominal_values);

	FastVector attributes = new FastVector(5);
	attributes.addElement(rleRateAttr);
	attributes.addElement(linesAttr);
	attributes.addElement(sizeAttr);
	attributes.addElement(ngramAttr);
	attributes.addElement(className);

	// Create the empty dataset "textart" with above attributes
	Instances instances = new Instances(ARFFAttributes.RELATION_NAME, attributes, 0);
	instances.setClassIndex(className.index());
	return instances;
}
 

import weka.core.FastVector; //導入依賴的package包/類
/**
 * Calculates the cumulative margin distribution for the set of
 * predictions, returning the result as a set of Instances. The
 * structure of these Instances is as follows:<p> <ul> 
 * <li> <b>Margin</b> contains the margin value (which should be plotted
 * as an x-coordinate) 
 * <li> <b>Current</b> contains the count of instances with the current 
 * margin (plot as y axis)
 * <li> <b>Cumulative</b> contains the count of instances with margin
 * less than or equal to the current margin (plot as y axis)
 * </ul> <p>
 *
 * @return datapoints as a set of instances, null if no predictions
 * have been made.  
 */
public Instances getCurve(FastVector predictions) {

  if (predictions.size() == 0) {
    return null;
  }

  Instances insts = makeHeader();
  double [] margins = getMargins(predictions);
  int [] sorted = Utils.sort(margins);
  int binMargin = 0;
  int totalMargin = 0;
  insts.add(makeInstance(-1, binMargin, totalMargin));
  for (int i = 0; i < sorted.length; i++) {
    double current = margins[sorted[i]];
    double weight = ((NominalPrediction)predictions.elementAt(sorted[i]))
      .weight();
    totalMargin += weight;
    binMargin += weight;
    if (true) {
      insts.add(makeInstance(current, binMargin, totalMargin));
      binMargin = 0;
    }
  }
  return insts;
}
 

import weka.core.FastVector; //導入依賴的package包/類
/**
 * This method is an implementation for LayoutCompleteEventListener class.
 * It sets the size appropriate for m_GraphPanel GraphPanel and and revalidates it's
 * container JScrollPane once a LayoutCompleteEvent is received from the
 * LayoutEngine. Also, it updates positions of the Bayesian network stored
 * in m_BayesNet.
 */
public void layoutCompleted(LayoutCompleteEvent le) {
	LayoutEngine layoutEngine  = m_layoutEngine; // (LayoutEngine) le.getSource();
	FastVector nPosX = new FastVector(m_BayesNet.getNrOfNodes());
	FastVector nPosY = new FastVector(m_BayesNet.getNrOfNodes());
	for (int iNode = 0; iNode < layoutEngine.getNodes().size(); iNode++) {
		GraphNode gNode = (GraphNode) layoutEngine.getNodes().elementAt(iNode);
		if (gNode.nodeType == GraphNode.NORMAL) {
			nPosX.addElement(gNode.x);
			nPosY.addElement(gNode.y);
		}
	}
	m_BayesNet.layoutGraph(nPosX, nPosY);
	m_jStatusBar.setText("Graph layed out");
	a_undo.setEnabled(true);
	a_redo.setEnabled(false);
	setAppropriateSize();
	m_GraphPanel.invalidate();
	m_jScrollPane.revalidate();
	m_GraphPanel.repaint();
}
 

import weka.core.FastVector; //導入依賴的package包/類
/**
  * Sets the format of the input instances.
  *
  * @param instanceInfo an Instances object containing the input instance
  * structure (any instances contained in the object are ignored - only the
  * structure is required).
  * @return true if the outputFormat may be collected immediately
  * @throws Exception if a problem occurs setting the input format
  */
 public boolean setInputFormat(Instances instanceInfo) throws Exception {
   super.setInputFormat(instanceInfo);
   
   FastVector attributes = new FastVector();
   int outputClass = -1;
   m_SelectedAttributes = determineIndices(instanceInfo.numAttributes());
   for (int i = 0; i < m_SelectedAttributes.length; i++) {
     int current = m_SelectedAttributes[i];
     if (instanceInfo.classIndex() == current) {
outputClass = attributes.size();
     }
     Attribute keep = (Attribute)instanceInfo.attribute(current).copy();
     attributes.addElement(keep);
   }
   
   initInputLocators(instanceInfo, m_SelectedAttributes);

   Instances outputFormat = new Instances(instanceInfo.relationName(),
				   attributes, 0); 
   outputFormat.setClassIndex(outputClass);
   setOutputFormat(outputFormat);
   
   return true;
 }