Java Instance.numAttributes方法代码示例

import weka.core.Instance; //导入方法依赖的package包/类
public static DMatrix instanceToDenseDMatrix(Instance instance) throws XGBoostError {
    Attribute classAttribute = instance.classAttribute();
    int classAttrIndex = classAttribute.index();

    int colNum = instance.numAttributes()-1;
    int rowNum = 1;

    float[] data = new float[colNum*rowNum];

    Enumeration<Attribute> attributeEnumeration = instance.enumerateAttributes();
    int dataIndex = 0;
    while (attributeEnumeration.hasMoreElements()) {
        Attribute attribute = attributeEnumeration.nextElement();
        int attrIndex = attribute.index();
        if(attrIndex == classAttrIndex){
            continue;
        }
        data[dataIndex]= (float) instance.value(attribute);
        dataIndex++;
    }

    return new DMatrix(data, rowNum, colNum);
}
 

import weka.core.Instance; //导入方法依赖的package包/类
public static void getBestPerfFrom(String path){
	try {
		BestConf bestconf = new BestConf();
		Instances trainingSet = DataIOFile.loadDataFromArffFile(path);
		Instance best = trainingSet.firstInstance();
		//set the best configuration to the cluster
		Map<Attribute,Double> attsmap = new HashMap<Attribute,Double>();
		for(int i=0;i<best.numAttributes()-1;i++){
			attsmap.put(best.attribute(i), best.value(i));
		}

		double bestPerf = bestconf.setOptimal(attsmap, "getBestPerfFrom");
		System.out.println("=========================================");
		System.err.println("The actual performance for the best point is : "+bestPerf);
		System.out.println("=========================================");
	} catch (IOException e) {
		e.printStackTrace();
	}
}
 

import weka.core.Instance; //导入方法依赖的package包/类
private double scoreInstance(Instance instance) {
	// bias
	double score = 1 * this.weights[0];
	// ignore id and topic and class label
	for (int i = 2; i < instance.numAttributes() - 1; i++) {
		score += this.weights[i - 1] * instance.value(i);
	}
	return score;
}
 

import weka.core.Instance; //导入方法依赖的package包/类
@Override
public double classifyInstance(Instance sample) throws Exception {
	// transform instance to sequence
	MonoDoubleItemSet[] sequence = new MonoDoubleItemSet[sample.numAttributes() - 1];
	int shift = (sample.classIndex() == 0) ? 1 : 0;
	for (int t = 0; t < sequence.length; t++) {
		sequence[t] = new MonoDoubleItemSet(sample.value(t + shift));
	}
	SymbolicSequence seq = new SymbolicSequence(sequence);

	double minD = Double.MAX_VALUE;
	String classValue = null;
	seq.LB_KeoghFillUL(bestWarpingWindow, U, L);
	
	for (int i = 0; i < train.length; i++) {
		SymbolicSequence s = train[i];
		if (SymbolicSequence.LB_KeoghPreFilled(s, U, L) < minD) {
			double tmpD = seq.DTW(s,bestWarpingWindow, warpingMatrix);
			if (tmpD < minD) {
				minD = tmpD;
				classValue = classMap[i];
			}
		}
	}
	// System.out.println(prototypes.size());
	return sample.classAttribute().indexOfValue(classValue);
}
 

import weka.core.Instance; //导入方法依赖的package包/类
@Override
public double classifyInstance(Instance sample) throws Exception {
	// transform instance to sequence
	MonoDoubleItemSet[] sequence = new MonoDoubleItemSet[sample.numAttributes() - 1];
	int shift = (sample.classIndex() == 0) ? 1 : 0;
	for (int t = 0; t < sequence.length; t++) {
		sequence[t] = new MonoDoubleItemSet(sample.value(t + shift));
	}
	SymbolicSequence seq = new SymbolicSequence(sequence);

	double minD = Double.MAX_VALUE;
	String classValue = null;
	seq.LB_KeoghFillUL(bestWarpingWindow, U, L);
	
	for (int i = 0; i < train.length; i++) {
		SymbolicSequence s = train[i];
		if (SymbolicSequence.LB_KeoghPreFilled(s, U, L) < minD) {
			double tmpD = seq.DTW(s,bestWarpingWindow);
			if (tmpD < minD) {
				minD = tmpD;
				classValue = classMap[i];
			}
		}
	}
	// System.out.println(prototypes.size());
	return sample.classAttribute().indexOfValue(classValue);
}
 

import weka.core.Instance; //导入方法依赖的package包/类
public static String getMD5(Instance ins){
	StringBuffer name = new StringBuffer("");
	for(int i = 0; i < ins.numAttributes() - 2; i++){
		name.append(Math.round(ins.value(ins.attribute(i)))+",");
	}
	return getMD5(name.toString());
}
 

import weka.core.Instance; //导入方法依赖的package包/类
@Override
public void buildClassifier(Instances data) throws Exception {
   	// Initialise training dataset
	Attribute classAttribute = data.classAttribute();
	
	classedData = new HashMap<>();
	classedDataIndices = new HashMap<>();
	for (int c = 0; c < data.numClasses(); c++) {
		classedData.put(data.classAttribute().value(c), new ArrayList<SymbolicSequence>());
		classedDataIndices.put(data.classAttribute().value(c), new ArrayList<Integer>());
	}

	train = new SymbolicSequence[data.numInstances()];
	classMap = new String[train.length];
	maxLength = 0;
	for (int i = 0; i < train.length; i++) {
		Instance sample = data.instance(i);
		MonoDoubleItemSet[] sequence = new MonoDoubleItemSet[sample.numAttributes() - 1];
		maxLength = Math.max(maxLength, sequence.length);
		int shift = (sample.classIndex() == 0) ? 1 : 0;
		for (int t = 0; t < sequence.length; t++) {
			sequence[t] = new MonoDoubleItemSet(sample.value(t + shift));
		}
		train[i] = new SymbolicSequence(sequence);
		String clas = sample.stringValue(classAttribute);
		classMap[i] = clas;
		classedData.get(clas).add(train[i]);
		classedDataIndices.get(clas).add(i);
	}
	warpingMatrix = new double[maxLength][maxLength];	
	U = new double[maxLength];
	L = new double[maxLength];
	
	maxWindow = Math.round(1 * maxLength);
	searchResults = new String[maxWindow+1];
	nns = new int[maxWindow+1][train.length];
	dist = new double[train.length][train.length];
	
	// Start searching for the best window
	searchBestWarpingWindow();
	
	// Saving best windows found
	System.out.println("Windows found=" + bestWarpingWindow + " Best Acc=" + (1-bestScore));
}
 

import weka.core.Instance; //导入方法依赖的package包/类
@Override
public void buildClassifier(Instances data) throws Exception {
   	// Initialise training dataset
   	Attribute classAttribute = data.classAttribute();
	
	classedData = new HashMap<>();
	classedDataIndices = new HashMap<>();
	for (int c = 0; c < data.numClasses(); c++) {
		classedData.put(data.classAttribute().value(c), new ArrayList<SymbolicSequence>());
		classedDataIndices.put(data.classAttribute().value(c), new ArrayList<Integer>());
	}

	train = new SymbolicSequence[data.numInstances()];
	classMap = new String[train.length];
	maxLength = 0;
	for (int i = 0; i < train.length; i++) {
		Instance sample = data.instance(i);
		MonoDoubleItemSet[] sequence = new MonoDoubleItemSet[sample.numAttributes() - 1];
		maxLength = Math.max(maxLength, sequence.length);
		int shift = (sample.classIndex() == 0) ? 1 : 0;
		for (int t = 0; t < sequence.length; t++) {
			sequence[t] = new MonoDoubleItemSet(sample.value(t + shift));
		}
		train[i] = new SymbolicSequence(sequence);
		String clas = sample.stringValue(classAttribute);
		classMap[i] = clas;
		classedData.get(clas).add(train[i]);
		classedDataIndices.get(clas).add(i);
	}
			
	warpingMatrix = new double[maxLength][maxLength];
	U = new double[maxLength];
	L = new double[maxLength];
	U1 = new double[maxLength];
	L1 = new double[maxLength];
	
	maxWindow = Math.round(1 * maxLength);
	searchResults = new String[maxWindow+1];
	nns = new int[maxWindow+1][train.length];
	dist = new double[maxWindow+1][train.length];

	cache = new SequenceStatsCache(train, maxWindow);
	
	lazyUCR = new LazyAssessNNEarlyAbandon[train.length][train.length];
	
	for (int i = 0; i < train.length; i++) {
		for (int j  = 0; j < train.length; j++) {
			lazyUCR[i][j] = new LazyAssessNNEarlyAbandon(cache);
		}
	}
	
	// Start searching for the best window
	searchBestWarpingWindow();
	
	// Saving best windows found
	System.out.println("Windows found=" + bestWarpingWindow + " Best Acc=" + (1-bestScore));
}
 

import weka.core.Instance; //导入方法依赖的package包/类
@Override
public void buildClassifier(Instances data) throws Exception {
   	// Initialise training dataset
	Attribute classAttribute = data.classAttribute();
	
	classedData = new HashMap<>();
	classedDataIndices = new HashMap<>();
	for (int c = 0; c < data.numClasses(); c++) {
		classedData.put(data.classAttribute().value(c), new ArrayList<SymbolicSequence>());
		classedDataIndices.put(data.classAttribute().value(c), new ArrayList<Integer>());
	}

	train = new SymbolicSequence[data.numInstances()];
	classMap = new String[train.length];
	maxLength = 0;
	for (int i = 0; i < train.length; i++) {
		Instance sample = data.instance(i);
		MonoDoubleItemSet[] sequence = new MonoDoubleItemSet[sample.numAttributes() - 1];
		maxLength = Math.max(maxLength, sequence.length);
		int shift = (sample.classIndex() == 0) ? 1 : 0;
		for (int t = 0; t < sequence.length; t++) {
			sequence[t] = new MonoDoubleItemSet(sample.value(t + shift));
		}
		train[i] = new SymbolicSequence(sequence);
		String clas = sample.stringValue(classAttribute);
		classMap[i] = clas;
		classedData.get(clas).add(train[i]);
		classedDataIndices.get(clas).add(i);
	}
			
	warpingMatrix = new double[maxLength][maxLength];
	U = new double[maxLength];
	L = new double[maxLength];
	U1 = new double[maxLength];
	L1 = new double[maxLength];
	
	maxWindow = Math.round(1 * maxLength);
	searchResults = new String[maxWindow+1];
	nns = new int[maxWindow+1][train.length];
	dist = new double[train.length][train.length];

	cache = new SequenceStatsCache(train, maxWindow);
	
	lazyUCR = new LazyAssessNNEarlyAbandon[train.length][train.length];
	
	for (int i = 0; i < train.length; i++) {
		for (int j  = 0; j < train.length; j++) {
			lazyUCR[i][j] = new LazyAssessNNEarlyAbandon(cache);
		}
	}
	
	// Start searching for the best window
	searchBestWarpingWindow();

	// Saving best windows found
	System.out.println("Windows found=" + bestWarpingWindow + " Best Acc=" + (1-bestScore));
}
 

import weka.core.Instance; //导入方法依赖的package包/类
@Override
public void buildClassifier(Instances data) throws Exception {
   	// Initialise training dataset
	Attribute classAttribute = data.classAttribute();
	
	classedData = new HashMap<>();
	classedDataIndices = new HashMap<>();
	for (int c = 0; c < data.numClasses(); c++) {
		classedData.put(data.classAttribute().value(c), new ArrayList<SymbolicSequence>());
		classedDataIndices.put(data.classAttribute().value(c), new ArrayList<Integer>());
	}

	train = new SymbolicSequence[data.numInstances()];
	classMap = new String[train.length];
	maxLength = 0;
	for (int i = 0; i < train.length; i++) {
		Instance sample = data.instance(i);
		MonoDoubleItemSet[] sequence = new MonoDoubleItemSet[sample.numAttributes() - 1];
		maxLength = Math.max(maxLength, sequence.length);
		int shift = (sample.classIndex() == 0) ? 1 : 0;
		for (int t = 0; t < sequence.length; t++) {
			sequence[t] = new MonoDoubleItemSet(sample.value(t + shift));
		}
		train[i] = new SymbolicSequence(sequence);
		String clas = sample.stringValue(classAttribute);
		classMap[i] = clas;
		classedData.get(clas).add(train[i]);
		classedDataIndices.get(clas).add(i);
	}
	
	warpingMatrix = new double[maxLength][maxLength];
	U = new double[maxLength];
	L = new double[maxLength];
	
	maxWindow = Math.round(1 * maxLength);
	searchResults = new String[maxWindow+1];
	nns = new int[maxWindow+1][train.length];
	dist = new double[maxWindow+1][train.length];
	
	// Start searching for the best window
	searchBestWarpingWindow();
	
	// Saving best windows found
	System.out.println("Windows found=" + bestWarpingWindow + " Best Acc=" + (1-bestScore));
}
 

import weka.core.Instance; //导入方法依赖的package包/类
private static ArrayList<Attribute> scaleDownMindists(Instances previousSet, Instance center){
	ArrayList<Attribute> localAtts = new ArrayList<Attribute>();
	int attNum = center.numAttributes();
	
	int pos = previousSet.attribute(PerformanceAttName).index();
	
	//traverse each dimension
	Enumeration<Instance> enu;
	double minDis;
	for(int i=0;i<attNum;i++){
		if(i==pos)
			continue;
		
		enu = previousSet.enumerateInstances();
		minDis = Double.MAX_VALUE;
		
		while(enu.hasMoreElements()){
			Instance ins = enu.nextElement();
			if(!ins.equals(center))
				minDis = Math.min((double)((int)(Math.abs(ins.value(i)-center.value(i))*1000))/1000.0, minDis);
		}
		
		//now we set the range
		Properties p1 = new Properties();
		double upper = center.value(i)+minDis, lower=center.value(i)-minDis;
		
		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.Instance; //导入方法依赖的package包/类
public static ArrayList<Attribute> scaleDownDetour(Instances previousSet, Instance center){
	ArrayList<Attribute> localAtts = new ArrayList<Attribute>();
	int attNum = center.numAttributes();
	
	int pos = previousSet.attribute(PerformanceAttName).index();
	
	//traverse each dimension
	Enumeration<Instance> enu;
	double minDis;
	for(int i=0;i<attNum;i++){
		if(i==pos)
			continue;
		
		enu = previousSet.enumerateInstances();
		minDis = Double.MAX_VALUE;
		
		while(enu.hasMoreElements()){
			Instance ins = enu.nextElement();
			if(!ins.equals(center))
				minDis = Math.min((double)((int)(Math.abs(ins.value(i)-center.value(i))*100))/100.0, minDis);
		}
		
		//now we set the range
		Properties p1 = new Properties();
		double upper = center.value(i)+minDis, lower=center.value(i)-minDis;
		
		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.Instance; //导入方法依赖的package包/类
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;
}
 

import weka.core.Instance; //导入方法依赖的package包/类
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;
}