Java MultiLabelInstances.getFeatureIndices方法代码示例

import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
 * Calculate metric value
 * 
 * @param mlData Multi-label dataset to which calculate the metric
 * @return Value of the metric
 */
public double calculate(MultiLabelInstances mlData){
	double mean = 0.0;
       
	Instances instances = mlData.getDataSet();
       
       int countNominal = 0;
       int [] featureIndices = mlData.getFeatureIndices();
       
       for(int fIndex : featureIndices){
           AttributeStats attStats = instances.attributeStats(fIndex);
           if(attStats.nominalCounts != null){
               countNominal++;
               mean += Utils.entropy(attStats.nominalCounts);
           }
       }
       
       mean = mean/countNominal;
	
	this.value = mean;
	return value;
}
 

import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
 * Calculate metric value
 * 
 * @param mlData Multi-label dataset to which calculate the metric
 * @return Value of the metric
 */
public double calculate(MultiLabelInstances mlData){
	double res = 0.0;
       
       try{
           ASEvaluation ase = new InfoGainAttributeEval();
       
           BinaryRelevanceAttributeEvaluator eval = new BinaryRelevanceAttributeEvaluator(ase, mlData, "avg", "none", "eval");

           int [] featureIndices = mlData.getFeatureIndices();

           for(int i : featureIndices){
               res += eval.evaluateAttribute(i);
           }

           res = res / featureIndices.length;
       }
       catch(Exception e){
           e.printStackTrace();
       	res = Double.NaN;
       }
	
	this.value = res;
	return value;
}
 

import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
 * Calculate metric value
 * 
 * @param mlData Multi-label dataset to which calculate the metric
 * @return Value of the metric
 */
public double calculate(MultiLabelInstances mlData){
	Statistics stat = new Statistics();
	stat.calculateStats(mlData);
	
	LabelsetsUpToNExamples upToN = new LabelsetsUpToNExamples(mlData.getFeatureIndices().length / 2);
	double n = upToN.calculate(mlData);
	
	this.value = n / stat.labelCombCount().values().size();
	return value;
}
 

import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
 * Save multi-view multi-label meka dataset
 * 
 * @param wr PrintWriter
 * @param dataset Dataset
 * @param relationName Name of the relation
 * @param views String with views intervals
 */
public static void saveMVMekaDataset(PrintWriter wr, MultiLabelInstances dataset, 
        String relationName, String views)
{
    int maxAttIndex;
    int minAttIndex;
    
    String c;
    c = "-C ";
    
    int [] attIndex = dataset.getFeatureIndices();
    
    maxAttIndex = getMax(attIndex);
    minAttIndex = getMin(attIndex);
    
    int [] labelIndices = dataset.getLabelIndices();
    
    boolean areLabelMaxIndices = true;
    boolean areLabelMinIndices = false;
    
    for(int i=0; i<labelIndices.length && areLabelMaxIndices; i++){
        if(labelIndices[i] < maxAttIndex){
            areLabelMaxIndices = false;
        }
    }
    
    if(!areLabelMaxIndices){
        areLabelMinIndices = true;
        for(int i=0; i<labelIndices.length && areLabelMinIndices; i++){
            if(labelIndices[i] > minAttIndex){
                areLabelMinIndices = false;
            }
        }
    }
    
    if((!areLabelMaxIndices) && (!areLabelMinIndices)){
        JOptionPane.showMessageDialog(null, "Cannot save as meka.", "alert", JOptionPane.ERROR_MESSAGE);
        return;
    }
    else if(areLabelMaxIndices){
        c = c + "-" + labelIndices.length;
    }
    else{
        c = c + labelIndices.length;
    }
    
    
    wr.write("@relation " + "\'" + relationName + ": " + c + " " + views + "\'");
    wr.write(System.getProperty("line.separator"));  

    Instances instances = dataset.getDataSet();
   
    Attribute att;
    for (int i=0; i< instances.numAttributes();i++)
    {
        att = instances.attribute(i);
        wr.write(att.toString());
        wr.write(System.getProperty("line.separator")); 
    }   

    String current;
    
    wr.write("@data");
    wr.write(System.getProperty("line.separator"));  
    for(int i=0; i<dataset.getNumInstances();i++)
    {
        current = dataset.getDataSet().get(i).toString();
        wr.write(current);
        wr.write(System.getProperty("line.separator"));  
    }
}
 

import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
 * Calculate metric value
 * 
 * @param mlData Multi-label dataset to which calculate the metric
 * @return Value of the metric
 */
public double calculate(MultiLabelInstances mlData){
	this.value = mlData.getFeatureIndices().length;
	return value;
}
 

import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
 * Calculate metric value
 * 
 * @param mlData Multi-label dataset to which calculate the metric
 * @return Value of the metric
 */
public double calculate(MultiLabelInstances mlData){
	this.value = ((double)mlData.getNumInstances()) / mlData.getFeatureIndices().length;
	return value;
}
 

import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
 * Calculate metric value
 * 
 * @param mlData Multi-label dataset to which calculate the metric
 * @return Value of the metric
 */
public double calculate(MultiLabelInstances mlData){
	this.value = mlData.getNumLabels() * mlData.getFeatureIndices().length * mlData.getNumInstances();
	return value;
}