Java Instances.numInstances方法代碼示例

import weka.core.Instances; //導入方法依賴的package包/類
/**
 * 
 * @param data
 * @return
 */
protected Instances[] splitWorkload(Instances data) {
    int offset = 0;
    int all_cnt = data.numInstances();
    for (SplitType stype : SplitType.values()) {
        int idx = stype.ordinal();
        this.split_counts[idx] = (int)Math.round(all_cnt * stype.percentage);
        
        try {
            this.splits[idx] = new Instances(data, offset, this.split_counts[idx]);
        
            // Apply NumericToNominal filter!
            NumericToNominal filter = new NumericToNominal();
            filter.setInputFormat(this.splits[idx]);
            this.splits[idx] = Filter.useFilter(this.splits[idx], filter);
            
        } catch (Exception ex) {
            throw new RuntimeException("Failed to split " + stype + " workload", ex);
        }
        
        offset += this.split_counts[idx];
        if (debug.val) LOG.debug(String.format("%-12s%d", stype.toString()+":", this.split_counts[idx]));
    } // FOR
    return (this.splits);
}
 

import weka.core.Instances; //導入方法依賴的package包/類
/**
 * <p>To get the distribution of inTrace and outTrace instance in given dataset in <b>path</b>.</p>
 * @param ins Instances of each project
 * @throws Exception 
 */
public static void getDist(String path) throws Exception{
	
	Instances ins = DataSource.read(path);
	int numAttr = ins.numAttributes();
	ins.setClassIndex(numAttr-1);
	
	int numIns = ins.numInstances();
	int intrace = 0;
	int outtrace = 0;
	for(int i=0; i<numIns; i++){
		if(ins.get(i).stringValue(ins.attribute(ins.classIndex())).equals("InTrace")){
			intrace++;
		}else{	
			outtrace++;
		}
	}
	
	System.out.printf("[ %-30s ] inTrace:%4d, outTrace:%4d.\n", path, intrace, outtrace);
}
 

import weka.core.Instances; //導入方法依賴的package包/類
protected double[][] predictDataDistribution(Instances unlabeled) throws Exception {
        // set class attribute
        unlabeled.setClassIndex(unlabeled.numAttributes() - 1);

        // distribution for instance
        double[][] dist = new double[unlabeled.numInstances()][unlabeled.numClasses()];

        // label instances
        for (int i = 0; i < unlabeled.numInstances(); i++) {
//            System.out.println("debug: "+this.getClass().getName()+": classifier: "+m_Classifier.toString());
            LibSVM libsvm = (LibSVM) m_Classifier;
            libsvm.setProbabilityEstimates(true);
            double[] instanceDist = libsvm.distributionForInstance(unlabeled.instance(i));
            dist[i] = instanceDist;
        }

        return dist;
    }
 

import weka.core.Instances; //導入方法依賴的package包/類
public static Instance findBestPerf(Instances data){
	int idx = data.numAttributes()-1;
	double bestPerf = data.attributeStats(idx).numericStats.max;
	for(int i=0;i<data.numInstances();i++)
		if(data.get(i).value(idx)==bestPerf)
			return data.get(i);
	return null;//should never return NULL
}
 

import weka.core.Instances; //導入方法依賴的package包/類
public static void testCOMT2() throws Exception{
	BestConf bestconf = new BestConf();
	Instances trainingSet = DataIOFile.loadDataFromArffFile("data/trainingBestConf0.arff");
	trainingSet.setClassIndex(trainingSet.numAttributes()-1);
	
	Instances samplePoints = LHSInitializer.getMultiDimContinuous(bestconf.getAttributes(), InitialSampleSetSize, false);
	samplePoints.insertAttributeAt(trainingSet.classAttribute(), samplePoints.numAttributes());
	samplePoints.setClassIndex(samplePoints.numAttributes()-1);
	
	COMT2 comt = new COMT2(samplePoints, COMT2Iteration);
	
	comt.buildClassifier(trainingSet);
	
	Evaluation eval = new Evaluation(trainingSet);
	eval.evaluateModel(comt, trainingSet);
	System.err.println(eval.toSummaryString());
	
	Instance best = comt.getInstanceWithPossibleMaxY(samplePoints.firstInstance());
	Instances bestInstances = new Instances(trainingSet,2);
	bestInstances.add(best);
	DataIOFile.saveDataToXrffFile("data/trainingBestConf_COMT2.arff", bestInstances);
	
	//now we output the training set with the class value updated as the predicted value
	Instances output = new Instances(trainingSet, trainingSet.numInstances());
	Enumeration<Instance> enu = trainingSet.enumerateInstances();
	while(enu.hasMoreElements()){
		Instance ins = enu.nextElement();
		double[] values = ins.toDoubleArray();
		values[values.length-1] = comt.classifyInstance(ins);
		output.add(ins.copy(values));
	}
	DataIOFile.saveDataToXrffFile("data/trainingBestConf0_predict.xrff", output);
}
 

import weka.core.Instances; //導入方法依賴的package包/類
@Override
public double[][] distributionsForInstances(Instances batch) {

	double[][] dists = new double[batch.numInstances()][2];
	for (int i = 0; i < batch.numInstances(); i++) {
		Instance ins = batch.instance(i);
		dists[i] = new double[2];
		dists[i][1] = this.scoreInstance(ins);
	}

	return dists;
}
 

import weka.core.Instances; //導入方法依賴的package包/類
public static void main(String[] args){
    GRules GRulesLearner = new GRules();
    
    try {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource("data/car.arff");
    Instances originalDataset = source.getDataSet();
    
    // split data 80/20
    int trainSize = (int) Math.round(originalDataset.numInstances() * 0.8
        / 100);
    int testSize = originalDataset.numInstances() - trainSize;
    Instances train = new Instances(originalDataset, 0, trainSize);
    Instances test = new Instances(originalDataset, trainSize, testSize);
    
    train.randomize(new java.util.Random(0));
    
    // train the rules learner
    List<List<Term>> rightSideRules = GRulesLearner.learnRightSide(train);
    List<Rule> completedRules = GRulesLearner.induceCompleteRules(rightSideRules, originalDataset);
    
    // try to predict an instance
    System.out.println("A testing instance: ");
    System.out.println(test.get(10));
    System.out.println("A rule covered the instance: ");
    System.out.println(GRulesLearner.predict(test.get(10), completedRules).nicePrint());
    
    } catch (Exception ex) {
        System.err.println(ex.toString());
    }
}
 

import weka.core.Instances; //導入方法依賴的package包/類
public static int findBestPerfIndex(Instances data){
	int idx = data.numAttributes()-1;
	double bestPerf = data.attributeStats(idx).numericStats.max;
	for(int i=0;i<data.numInstances();i++)
		if(data.get(i).value(idx)==bestPerf)
			return i;
	return -1;//should never return -1
}
 

import weka.core.Instances; //導入方法依賴的package包/類
/** 
 * Run LBKeogh-PrunedDTW method for a given size
 * @param data
 * @param sampleSize
 * @return
 */
public static double keoghPrunedDTW(Instances data, int sampleSize) {
	double share = 1, searchTime = 0;
	long start, stop;
	LbKeoghPrunedDTW classifier = new LbKeoghPrunedDTW(datasetName);
	classifier.setResDir(resDir);
	classifier.setType(method);
	try{
		Instances newTrain = Sampling.sample(data, sampleSize);

		System.out.println("Size: " + sampleSize + ", Launching SDM16");
		if (sampleSize < estimate+1) {
			start = System.nanoTime();
			classifier.buildClassifier(newTrain);
			stop = System.nanoTime();
		} 
		else {
			start = System.nanoTime();
			classifier.buildClassifierEstimate(newTrain, estimate);
			stop = System.nanoTime();
			share = 1.0 * (estimate+1) /newTrain.numInstances();
		}
		searchTime = 1.0 * ((stop - start)/1e9);
		searchTime = searchTime/share;
		saveSearchTime(sampleSize, searchTime);
		System.out.println("Size: " + sampleSize + ", " + searchTime + " s");
	} catch (Exception e) {
		e.printStackTrace();
	}
	
	return searchTime;
}
 

import weka.core.Instances; //導入方法依賴的package包/類
/** 
 * Reorder the dataset by its largest class
 * @param data
 * @return
 */
public static Instances orderByLargestClass(Instances data) {
	Instances newData = new Instances(data, data.numInstances());
	
	// get the number of class in the data
	int nbClass = data.numClasses();
	int[] instancePerClass = new int[nbClass];
	int[] labels = new int[nbClass];
	int[] classIndex = new int[nbClass];
	
	// sort the data base on its class
	data.sort(data.classAttribute());
	
	// get the number of instances per class in the data
	for (int i = 0; i < nbClass; i++) {
		instancePerClass[i] = data.attributeStats(data.classIndex()).nominalCounts[i];
		labels[i] = i;
		if (i > 0)
			classIndex[i] = classIndex[i-1] + instancePerClass[i-1];
	}
	QuickSort.sort(instancePerClass, labels);
	
	for (int i = nbClass-1; i >=0 ; i--) {
		for (int j = 0; j < instancePerClass[i]; j++) {
			newData.add(data.instance(classIndex[labels[i]] + j));
		}
	}
	
	return newData;
}
 

import weka.core.Instances; //導入方法依賴的package包/類
/**<p>Generate Random sample according to random seed on Desktop, each sample has the same distribution of InTrace/OutTrace
 *  and have <b>SIZE</b> instances.
 * </p>
 * @param path original arff file to be sampled in path
 * @param rand random seed
 * @param num the number of selection
 * */
public static void generateARFF(String path, int rand, int num) throws Exception{
	/*** original dataset reading */
	Instances data = DataSource.read(path);
	data.setClassIndex(data.numAttributes()-1);
	
	/*** randomize the dataset */
	data.randomize(new Random(rand));
	
	/*** dataIn to save instances of InTrace class */
	Instances dataIn = new Instances("dataIn", InsMerge.getStandAttrs(), 1);
	dataIn.setClassIndex(dataIn.numAttributes() - 1);

	/*** dataOut to save instances of OutTrace class */
	Instances dataOut = new Instances("dataOut", InsMerge.getStandAttrs(), 1);
	dataIn.setClassIndex(dataIn.numAttributes() - 1);
	
	/*** add OutTrace instances into dataOut */
	for(int i=0; i<data.numInstances(); i++){
		if(data.get(i).stringValue(data.get(i).classAttribute()).equals("OutTrace")){
			dataOut.add(data.get(i));
		}
	}
	
	/** add InTrace instances into dataIn */
	for(int i=0; i<data.numInstances(); i++){
		if(data.get(i).stringValue(data.get(i).classAttribute()).equals("InTrace")){
			dataIn.add(data.get(i));
		}
	}
	
	/*** get the In/Out ratio in original dataset */
	int inTrace = dataIn.numInstances();
	int outTrace = dataOut.numInstances();
	double ratioI = inTrace*1.0/(outTrace + inTrace);
	
	/*** expected number to select from original dataset*/
	int intrace = (int) (num * ratioI);
	int outtrace = num - intrace;
	
	/** create new generated dataset train*/
	Instances train = new Instances("dataIn", InsMerge.getStandAttrs(), 1);
	train.setClassIndex(train.numAttributes() - 1);

	/** train get X instances from dataIn*/
	for(int i=0; i<intrace; i++){
		train.add(dataIn.get(i));
	}
	
	/** train get Y instances from dataOut*/
	for(int j=0; j<outtrace; j++){
		train.add(dataOut.get(j));
	}
	
	/** save the dataset in path, we save the arff into D:/Users/LEE/Desktop/New_Data/XXX.arff */
	String filename = "files/generated/" + filterName(path) + rand + ".arff";
	DataSink.write(filename, train);

}
 

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;
}
 

import weka.core.Instances; //導入方法依賴的package包/類
@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.Instances; //導入方法依賴的package包/類
@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.Instances; //導入方法依賴的package包/類
/** 
 * Reorder the data by compactness of each class using Euclidean distance
 * @param data
 * @return
 */
public static Instances orderByCompactClass(Instances data) {
	Instances newData = new Instances(data, data.numInstances());
	
	// get the number of class in the data
	int nbClass = data.numClasses();
	int[] instancePerClass = new int[nbClass];
	int[] labels = new int[nbClass];
	int[] classIndex = new int[nbClass];
	double[] compactness = new double[nbClass];
	
	// sort the data base on its class
	data.sort(data.classAttribute());
	
	int start = 0;
	// get the number of instances per class in the data
	for (int i = 0; i < nbClass; i++) {
		instancePerClass[i] = data.attributeStats(data.classIndex()).nominalCounts[i];
		labels[i] = i;
		if (i > 0) 
			classIndex[i] = classIndex[i-1] + instancePerClass[i-1];
		int end = start + instancePerClass[i];
		int counter = 0;
		double[][] dataPerClass = new double[instancePerClass[i]][data.numAttributes()-1];
		for (int j = start; j < end; j++) {
			dataPerClass[counter++] = data.instance(j).toDoubleArray();
		}
		double[] mean = arithmeticMean(dataPerClass);
		double d = 0;
		for (int j = 0; j < instancePerClass[i]; j++) {
			double temp = euclideanDistance(mean, dataPerClass[j]);
			temp *= temp;
			temp -= (mean[0] - dataPerClass[j][0]) * (mean[0] - dataPerClass[j][0]);
			d += temp;
		}
		compactness[i] = d / instancePerClass[i];
		start = end;
	}
	
	QuickSort.sort(compactness, labels);
	
	for (int i = nbClass-1; i >=0 ; i--) {
		for (int j = 0; j < instancePerClass[labels[i]]; j++) {
			newData.add(data.instance(classIndex[labels[i]] + j));
		}
	}
	
	return newData;
}