本文整理汇总了Java中weka.core.Instances.enumerateInstances方法的典型用法代码示例。如果您正苦于以下问题:Java Instances.enumerateInstances方法的具体用法?Java Instances.enumerateInstances怎么用?Java Instances.enumerateInstances使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类weka.core.Instances
的用法示例。
在下文中一共展示了Instances.enumerateInstances方法的5个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: computeOmegaDelta
import weka.core.Instances; //导入方法依赖的package包/类
private static double computeOmegaDelta(M5P model, M5P modelPi, Instances omega) throws Exception{
double retval = 0., y;
Enumeration<Instance> enu = omega.enumerateInstances();
int idxClass = omega.classIndex();
Instance ins;
while(enu.hasMoreElements()){
ins = enu.nextElement();
y = ins.value(idxClass);
retval += Math.pow(y-model.classifyInstance(ins), 2)-Math.pow(y-modelPi.classifyInstance(ins), 2);
}
return retval;
}
示例2: testCOMT2
import weka.core.Instances; //导入方法依赖的package包/类
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);
}
示例3: scaleDownNeighbordists
import weka.core.Instances; //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;
}
示例4: scaleDownMindists
import weka.core.Instances; //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;
}
示例5: scaleDownDetour
import weka.core.Instances; //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;
}