Java Code Examples for weka.classifiers.AbstractClassifier#makeCopy()

/**
 * the default constructor.
 */
public AbstractMultiSearch() {
	super();

	m_Factory           = newFactory();
	m_Metrics           = m_Factory.newMetrics();
	m_Evaluation        = m_Metrics.getDefaultMetric();
	m_Classifier        = defaultClassifier();
	m_DefaultParameters = defaultSearchParameters();
	m_Parameters        = defaultSearchParameters();
	m_Algorithm         = defaultAlgorithm();
	m_Trace             = new ArrayList<Entry<Integer, Performance>>();

	try {
		m_BestClassifier = new SearchResult();
		m_BestClassifier.classifier = AbstractClassifier.makeCopy(m_Classifier);
	}
	catch (Exception e) {
		System.err.println("Failed to create copy of default classifier!");
		e.printStackTrace();
	}
}
 

public AbstractClassifier cloneClassifierIfNeeded(AbstractClassifier classifier) throws Exception {
    if (cloneClassifierForEachParameterEval) {
        //for some reason, the (abstract classifiers)' copy method returns a (classifier interface) reference...
        return (AbstractClassifier)AbstractClassifier.makeCopy(classifier); 
    }
    else {
        //just reuse the same instance of the classifier, assume that no info 
        //that from the previous build/eval affects this one.
        //potentially saves a lot of memory/time etc.
        return classifier;
    }
}
 

public void buildClassifier(Instances data) throws Exception {
//Sometimes I just want to re-weight it, which must be done with findWeights(). 
//		rebuild stays true by default unless explicitly set by rebuildClassifier(boolean f)
// this is just a bit of a hack to speed up experiments,
		if(rebuild){	
			System.out.println("Build whole ...");
			init(data); //Assume its already standardised
			train.add(data);
			Instances t1=ps.process(data);
			Instances t2=acf.process(data);
			if(normaliseAtts){
				nPs=new NormalizeAttribute(t1);
				t1=nPs.process(t1);
				nAcf=new NormalizeAttribute(t2);
				t2=nAcf.process(t2);
			}
			pca.buildEvaluator(data);
			Instances t3=pca.transformedData(data);
			train.add(t1); //
			train.add(t2);
			train.add(t3);
			nosTransforms=train.size();
			findWeights();
			all= AbstractClassifier.makeCopies(base,train.size());
                        all[0]=AbstractClassifier.makeCopy(baseTime);
			for(int i=0;i<all.length;i++){
				all[i].buildClassifier(train.get(i));
			}
		}
	}
 

public static Classifier cloneClassifier(final Classifier c) throws Exception {
	Method cloneMethod = MethodUtils.getAccessibleMethod(c.getClass(), "clone");
	if (cloneMethod != null) {
		return (Classifier) cloneMethod.invoke(c);
	}
	return AbstractClassifier.makeCopy(c);
}
 

/**
 * Set the base learner.
 *
 * @param newClassifier 	the classifier to use.
 */
@Override
public void setClassifier(Classifier newClassifier) {
	super.setClassifier(newClassifier);
	try {
		m_BestClassifier.classifier = AbstractClassifier.makeCopy(m_Classifier);
	}
	catch (Exception e) {
		e.printStackTrace();
	}
}
 

/**
 * RebuildCC - rebuild a classifier chain 'h_old' to have a new sequence 's_new'.
 */
protected CC rebuildCC(CC h_old, int s_new[], Instances D) throws Exception {

	// make a deep copy
	CC h = (CC)AbstractClassifier.makeCopy(h_old);

	// rebuild this chain
	h.rebuildClassifier(s_new,new Instances(D));
	return h;
}
 

/**
 * Build - Create transformation for this node, and train classifier of type H upon it.
 * The dataset should have class as index 'j', and remove all indices less than L *not* in paY.
 */
public void build(Instances D, Classifier H) throws Exception {
	// transform data
	T = transform(D);
	// build SLC 'h'
	h = AbstractClassifier.makeCopy(H);
	h.buildClassifier(T);
	// save templates
	//t_ = new SparseInstance(T.numAttributes());
	//t_.setDataset(T);
	//t_.setClassMissing();								// [?,x,x,x]
	T.clear();
}
 

/** {@inheritDoc} */
public void buildEvaluator(Instances data) throws Exception {
  FastRandomForest forest = (FastRandomForest) AbstractClassifier.makeCopy(m_frfProto);
  forest.buildClassifier(data);
  m_Importances = forest.getFeatureImportances();
}