Java Code Examples for weka.core.Instances#delete()

/**
    * 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);
}
   }
 

/**
  * Builds a new LinearRegression without the 'bad' data
  * found by buildWeight
  *
  * @throws Exception if building fails
  */
 private void buildRLSRegression()throws Exception{

   buildWeight();
   m_RLSData = new Instances(m_Data);
   int x = 0;
   int y = 0;
   int n = m_RLSData.numInstances();
   while(y < n){
     if (m_weight[x] == 0){
m_RLSData.delete(y);
n = m_RLSData.numInstances();
y--;
     }
     x++;
     y++;
   }
   if ( m_RLSData.numInstances() == 0){
     System.err.println("rls regression unbuilt");
     m_ls = m_currentRegression;
   }else{
     m_ls = new LinearRegression();
     m_ls.setOptions(new String[]{"-S", "1"});
     m_ls.buildClassifier(m_RLSData);
     m_currentRegression = m_ls;
   }

 }
 

/**
 * Resets the class values of all instances using voting.
 * For each instance the class value that satisfies the most rules
 * is choosen as new class value.
 *
 * @param dataset the dataset to work on
 * @return the changed instances
 * @throws Exception if something goes wrong
 */
private Instances voteDataset(Instances dataset) throws Exception {
  for (int i = 0; i < dataset.numInstances(); i++) {
    Instance inst = dataset.firstInstance();
    inst = votedReclassifyExample(inst); 
    dataset.add(inst);
    dataset.delete(0);
  }  

  return dataset;
}
 

@Override
protected Instances process(Instances instances) throws Exception {
	
	Instances output = new Instances(instances);
	
	double[] dif = m_errors.clone();
	Arrays.sort(dif);
	double quantil = dif[(int)(dif.length*(1-1.0/m_ratio))];
	int after = (int)(dif.length*(1-1.0/m_ratio));
	int middle = (int)(dif.length*(1-1.0/m_ratio));
	int before = (int)(dif.length*(1-1.0/m_ratio));
	while (after < dif.length && dif[after] == quantil){after++;}
	while (before >= 0 && dif[before] == quantil){before--;}
	Random r = new Random();
	if(instances.numInstances() <= m_ratio){return output;}

	for(int i=output.numInstances()-1;i>=0;i--){
		if(output.numInstances() <= m_ratio){break;}
		if(m_errors.length > i && m_errors[i] < quantil){output.delete(i);}
		if(m_errors.length > i && m_errors[i]==quantil && r.nextInt(after-before)>middle-before){
			output.delete(i);
		}
		
		

	}		
	
	return output;
}
 

@Override
protected Instances process(Instances instances) throws Exception {
	if (m_select == null || m_select.length != instances.numInstances()){
		return instances;
	}
	else{
		Instances output = new Instances(instances);
		for(int i=m_select.length-1;i>=0;i--){
			if(!m_select[i]){output.delete(i);}
		}
		return output;
	}
}
 

@Override
   public Instance transformInstance(Instance x) throws Exception{
Instances tmpInst = new Instances(x.dataset());

tmpInst.delete();
tmpInst.add(x);

Instances features = this.extractPart(tmpInst, false);

Instances pseudoLabels = new Instances(this.compressedMatrix);
Instance tmpin = pseudoLabels.instance(0);
pseudoLabels.delete();

pseudoLabels.add(tmpin);

for ( int i = 0; i< pseudoLabels.classIndex(); i++) {
    pseudoLabels.instance(0).setMissing(i);
}

Instances newDataSet = Instances.mergeInstances(pseudoLabels, features);
newDataSet.setClassIndex(this.size);


return newDataSet.instance(0);
   }
 

@Override
   public Instance transformInstance(Instance x) throws Exception{

Instances tmpInst = new Instances(x.dataset());

tmpInst.delete();
tmpInst.add(x);

Instances features = this.extractPart(tmpInst, false);

Instances pseudoLabels = new Instances(this.compressedTemplateInst);
Instance tmpin = pseudoLabels.instance(0);
pseudoLabels.delete();

pseudoLabels.add(tmpin);

for ( int i = 0; i< pseudoLabels.classIndex(); i++) {
    pseudoLabels.instance(0).setMissing(i);
}

Instances newDataSet = Instances.mergeInstances(pseudoLabels, features);
newDataSet.setClassIndex(pseudoLabels.numAttributes());

return newDataSet.instance(0);
   }
 

/**
    * Transforms the instance in the prediction process before given to the internal multi-label
    * or multi-target classifier. The instance is passed having the original set of labels, these
    * must be replaced with the transformed labels (attributes) so that the internla classifier
    * can predict them.
    *
    * @param x The instance to transform. Consists of features and labels.
    * @return The transformed instance. Consists of features and transformed labels.
    */
   @Override
   public Instance transformInstance(Instance x) throws Exception{
Instances tmpInst = new Instances(x.dataset());

tmpInst.delete();
tmpInst.add(x);

Instances features = this.extractPart(tmpInst, false);

Instances labels = new Instances(this.m_PatternInstances);

labels.add(new DenseInstance(labels.numAttributes()));

Instances result = Instances.mergeInstances(labels, features);

result.setClassIndex(labels.numAttributes());

return result.instance(0);
   }
 

@Override
protected Instances process(Instances instances) throws Exception {

	instances.sort(m_index);
	
	Instances output = new Instances(instances);
	
	if(instances.numInstances() <= m_ratio){return output;}
	
	for(int i=output.numInstances()-1;i>=0;i--){
		if((i+1) % m_ratio != 0){output.delete(i);}
	}
	//output.compactify();
	
	
	return output;
}
 

@Override
protected Instances process(Instances instances) throws Exception {
	
	Instances output = new Instances(instances);

	if(instances.numInstances() <= 2*m_ratio){return output;}
	
	Random r = new Random();
	
	for(int i=output.numInstances()-1;i>=0;i--){
		if(output.numInstances()>2 && r.nextInt(m_ratio) != 0){output.delete(i);}
	}		
	
	return output;
}