Java Instances.classAttribute方法代碼示例

本文整理匯總了Java中weka.core.Instances.classAttribute方法的典型用法代碼示例。如果您正苦於以下問題：Java Instances.classAttribute方法的具體用法？Java Instances.classAttribute怎麽用？Java Instances.classAttribute使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類weka.core.Instances的用法示例。

在下文中一共展示了Instances.classAttribute方法的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。

示例1: instancesToDenseDMatrix

import weka.core.Instances; //導入方法依賴的package包/類
public static DMatrix instancesToDenseDMatrix(Instances instances) throws XGBoostError {
    int colNum = instances.numAttributes()-1;
    int rowNum = instances.size();

    float[] data = new float[colNum*rowNum];
    float[] labels = new float[instances.size()];
    Attribute classAttribute = instances.classAttribute();
    int classAttrIndex = classAttribute.index();

    for(int i=0, dataIndex = 0; i<instances.size(); i++) {
        Instance instance = instances.get(i);

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


    DMatrix dMatrix = new DMatrix(data, rowNum, colNum);

    dMatrix.setLabel(labels);
    return dMatrix;

}

開發者ID:SigDelta，項目名稱:weka-xgboost，代碼行數:33，代碼來源:DMatrixLoader.java

示例2: getClasses

import weka.core.Instances; //導入方法依賴的package包/類
protected static String[] getClasses(Instances instances) {
	Attribute classAttribute = instances.classAttribute();
	String[] result = new String[classAttribute.numValues()];
	for (int i = 0; i < result.length; ++i)
		result[i] = classAttribute.value(i);
	return result;
}

開發者ID:Bibliome，項目名稱:alvisnlp，代碼行數:8，代碼來源:PredictionElementClassifier.java

示例3: buildClassifier

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));
}

開發者ID:ChangWeiTan，項目名稱:FastWWSearch，代碼行數:45，代碼來源:LbKeoghPrunedDTW.java

示例4: buildClassifier

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));
}

開發者ID:ChangWeiTan，項目名稱:FastWWSearch，代碼行數:58，代碼來源:UCRSuite.java

示例5: buildClassifier

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));
}

開發者ID:ChangWeiTan，項目名稱:FastWWSearch，代碼行數:58，代碼來源:UCRSuitePrunedDTW.java

示例6: buildClassifier

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));
}

開發者ID:ChangWeiTan，項目名稱:FastWWSearch，代碼行數:46，代碼來源:WindowSearcher.java

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