Java Instances.numClasses方法代碼示例

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
/** 
 * 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
@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.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];
	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.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;
}