// 构造一个tri-trainer分类器。
public Tritrainer(
String classifier, String trainingIns_File, String testIns_File, double precentage) {
try {
this.classifier1 = (Classifier) Class.forName(classifier).newInstance();
this.classifier2 = (Classifier) Class.forName(classifier).newInstance();
this.classifier3 = (Classifier) Class.forName(classifier).newInstance();
Instances trainingInstances = Util.getInstances(trainingIns_File);
// 将trainIns_File按照precentage和(1-precentage)的比例切割成labeledIns和unlabeledIns;
int length = trainingInstances.numInstances();
int i = new Double(length * precentage).intValue();
labeledIns = new Instances(trainingInstances, 0);
for (int j = 0; j < i; j++) {
labeledIns.add(trainingInstances.firstInstance());
trainingInstances.delete(0);
}
unlabeledIns = trainingInstances;
testIns = Util.getInstances(testIns_File);
Init();
} catch (Exception e) {
}
}
// 将样本集中裁剪提取成m个样本组成的集合；
public void SubSample(Instances inst, int m) {
inst.randomize(new Random());
while (inst.numInstances() != m) {
inst.delete(0);
}
// System.out.println("subsample:=" + inst.numInstances() + " m:=" + m );
}
/**
* Removes a specified number of instances from the given set of instances.
*
* @param data given instances
* @param numRemove number of instances to delete from the given instances
*/
protected void removeInstances(Instances data, int numRemove) {
int num = data.numInstances();
for (int i = num - 1; i > num - 1 - numRemove; i--) {
data.delete(i);
}
}