Java Instances.attribute方法代碼示例

import weka.core.Instances; //導入方法依賴的package包/類
@Override
public Instances collectPerfs(Instances samplePoints, String perfAttName) {
	Instances retVal = null;
	
	if(samplePoints.attribute(perfAttName) == null){
		Attribute performance = new Attribute(perfAttName);
		samplePoints.insertAttributeAt(performance, samplePoints.numAttributes());
	}
	
	File perfFolder = new File(perfsfilepath);
	int tot=0;
	if(perfFolder.exists()){
		//let's get all the name set for the sample points
		Iterator<Instance> itr = samplePoints.iterator();
		TreeSet<String> insNameSet = new TreeSet<String>();
		HashMap<String, Integer> mapping = new HashMap<String, Integer>();
		int pos=0;
		while(itr.hasNext()){
			String mdstr = getMD5(itr.next());
			insNameSet.add(mdstr);
			mapping.put(mdstr, new Integer(pos++));
		}
		
		//now we collect
		File[] perfFiles = perfFolder.listFiles(new PerfsFileFilter(insNameSet));
		tot = perfFiles.length;
		if(tot > 0) isInterrupt = true;
		for(int i=0;i<tot;i++){
			Instance ins = samplePoints.get(mapping.get(perfFiles[i].getName()));
			double[] results = getPerf(perfFiles[i].getAbsolutePath());
			if(results!=null){
				ins.setValue(samplePoints.numAttributes()-1, results[0]);
			}
		}
	}
	retVal = samplePoints;
	retVal.setClassIndex(retVal.numAttributes()-1);
	System.out.println("Total number of collected performances is : "+tot);
	return retVal;
}
 

import weka.core.Instances; //導入方法依賴的package包/類
protected Classifier generateDecisionTree(AbstractClusterer clusterer, MarkovAttributeSet aset, Instances data) throws Exception {
    // We need to create a new Attribute that has the ClusterId
    Instances newData = data; // new Instances(data);
    newData.insertAttributeAt(new Attribute("ClusterId"), newData.numAttributes());
    Attribute cluster_attr = newData.attribute(newData.numAttributes()-1);
    assert(cluster_attr != null);
    assert(cluster_attr.index() > 0);
    newData.setClass(cluster_attr);
    
    // We will then tell the Classifier to predict that ClusterId based on the MarkovAttributeSet
    ObjectHistogram<Integer> cluster_h = new ObjectHistogram<Integer>();
    for (int i = 0, cnt = newData.numInstances(); i < cnt; i++) {
        // Grab the Instance and throw it at the the clusterer to get the target cluster
        Instance inst = newData.instance(i);
        int c = (int)clusterer.clusterInstance(inst);
        inst.setClassValue(c);
        cluster_h.put(c);
    } // FOR
    System.err.println("Number of Elements: " + cluster_h.getValueCount());
    System.err.println(cluster_h);

    NumericToNominal filter = new NumericToNominal();
    filter.setInputFormat(newData);
    newData = Filter.useFilter(newData, filter);
    
    String output = this.catalog_proc.getName() + "-labeled.arff";
    FileUtil.writeStringToFile(output, newData.toString());
    LOG.info("Wrote labeled data set to " + output);
    
    // Decision Tree
    J48 j48 = new J48();
    String options[] = {
        "-S", Integer.toString(this.rand.nextInt()),
        
    };
    j48.setOptions(options);

    // Make sure we add the ClusterId attribute to a new MarkovAttributeSet so that
    // we can tell the Classifier to classify that!
    FilteredClassifier fc = new FilteredClassifier();
    MarkovAttributeSet classifier_aset = new MarkovAttributeSet(aset);
    classifier_aset.add(cluster_attr);
    fc.setFilter(classifier_aset.createFilter(newData));
    fc.setClassifier(j48);
    
    // Bombs away!
    fc.buildClassifier(newData);
    
    return (fc);
}
 

import weka.core.Instances; //導入方法依賴的package包/類
private static ArrayList<Attribute> scaleDownNeighbordists(Instances previousSet, Instance center){
	ArrayList<Attribute> localAtts = new ArrayList<Attribute>();
	int attNum = center.numAttributes();
	
	int pos = -1;
	if(previousSet.attribute(PerformanceAttName)!=null)
		pos = previousSet.attribute(PerformanceAttName).index();
	
	//traverse each dimension
	Enumeration<Instance> enu;
	double[] minDists = new double[2];
	double val;
	for(int i=0;i<attNum;i++){
		if(i==pos)
			continue;
		
		enu = previousSet.enumerateInstances();
		minDists[0] = 1-Double.MAX_VALUE;
		minDists[1] = Double.MAX_VALUE;
		
		while(enu.hasMoreElements()){
			Instance ins = enu.nextElement();
			if(!ins.equals(center)){
				val = ins.value(i)-center.value(i);
				if(val<0)
					minDists[0] = Math.max((double)((int)((ins.value(i)-center.value(i))*1000))/1000.0, minDists[0]);
				else
					minDists[1] = Math.min((double)((int)((ins.value(i)-center.value(i))*1000))/1000.0, minDists[1]);
			}
		}
		
		//now we set the range
		Properties p1 = new Properties();
		double upper = center.value(i)+minDists[1], lower=center.value(i)+minDists[0];
		
		TreeSet<Double> detourSet = new TreeSet<Double>();
		detourSet.add(upper);
		detourSet.add(lower);
		detourSet.add(previousSet.attribute(i).getUpperNumericBound());
		detourSet.add(previousSet.attribute(i).getLowerNumericBound());
		switch(detourSet.size()){
		case 1:
			upper=lower=detourSet.first();
			break;
		case 2:
			upper = detourSet.last();
			lower = detourSet.first();
			break;
		case 3:
			upper=lower=detourSet.higher(detourSet.first());
			break;
		default://case 4:
			upper=detourSet.lower(detourSet.last());
			lower=detourSet.higher(detourSet.first());
			break;
		}
		
		p1.setProperty("range", "["+String.valueOf(lower)+","+String.valueOf(upper)+"]");
		ProtectedProperties prop1 = new ProtectedProperties(p1);
		
		localAtts.add(new Attribute(previousSet.attribute(i).name(), prop1));
	}
	
	return localAtts;
}
 

import weka.core.Instances; //導入方法依賴的package包/類
public Instances runExp(Instances samplePoints, String perfAttName){
	Instances retVal = null;
	if(samplePoints.attribute(perfAttName) == null){
		Attribute performance = new Attribute(perfAttName);
		samplePoints.insertAttributeAt(performance, samplePoints.numAttributes());
	}
	int pos = samplePoints.numInstances();
	int count = 0;
	for (int i = 0; i < pos; i++) {
		Instance ins = samplePoints.get(i);
		HashMap hm = new HashMap();
		int tot = 0;
		for (int j = 0; j < ins.numAttributes(); j++) {
			hm.put(ins.attribute(j).name(), ins.value(ins.attribute(j)));
		}

		boolean testRet;
		if (Double.isNaN(ins.value(ins.attribute(ins.numAttributes() - 1)))) {
			testRet = this.startTest(hm, i, isInterrupt);
			double y = 0;
			if (!testRet) {// the setting does not work, we skip it
				y = -1;
				count++;
				if (count >= targetTestErrorNum) {
					System.out.println("There must be somthing wrong with the system. Please check and restart.....");
					System.exit(1);
				}
			} else {
				y = getPerformanceByType(performanceType);
				count = 0;
			}

			ins.setValue(samplePoints.numAttributes() - 1, y);
			writePerfstoFile(ins);
		} else {
			continue;
		}
	}
	retVal = samplePoints;
	retVal.setClassIndex(retVal.numAttributes()-1);
	
	return retVal;
}