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package predictor;
import java.util.Random;
import org.apache.log4j.*;
import weka.core.*;
import weka.classifiers.*;
public class ClassifierAdapter implements PredictorInterface {
protected Logger logger = Logger.getLogger(ClassifierAdapter.class);
protected String name;
protected Classifier classifier;
protected Evaluation eval;
public ClassifierAdapter(Classifier classifier, String name) {
this.classifier = classifier;
this.name = name;
}
@Override
public String getName() {
return this.name;
}
@Override
public void crossValidate(Instances instances, int numFold, Random rand) throws Exception {
eval = new Evaluation(instances);
eval.crossValidateModel(this.classifier, instances, numFold, rand);
}
public String getEvaluationSummaryString() {
return this.eval.toSummaryString();
}
public String getEvaluationMatrixString() throws Exception {
return this.eval.toMatrixString();
}
public FastVector getEvaluationPredictions() {
return eval.predictions();
}
@Override
public void train(Instances instances) throws Exception {
this.classifier.buildClassifier(instances);
}
@Override
public int predict(Instance instance) throws Exception {
this.classifier.classifyInstance(instance);
return 0;
}
@Override
public int predict(Instances instances) throws Exception {
for (int i=0;i<instances.numInstances();i++) {
this.predict(instances.instance(i));
}
return 0;
}
@Override
public String getEvaluationResults() throws Exception {
String results = "\n-- ";
results += this.getName();
results += " --\n";
results += this.eval.toSummaryString();
results += this.eval.toMatrixString();
results += this.eval.toClassDetailsString();
results += "\n";
return results;
}
}
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