How to use

getOutput

method

in

gov.sandia.cognition.learning.data.InputOutputPair

/**
 * Gets the target value of the given training example index, as a double.
 *
 * @param   i
 *      The training example index. Must be between 0 and dataSize - 1.
 * @return
 *      The target value for that index represented as a double, which is
 *      either +1.0 or -1.0.
 */
private double getTarget(
  final int i)
{
  return this.dataList.get(i).getOutput() ? +1.0 : -1.0;
}

/**
 * Gets the target value of the given training example index, as a double.
 *
 * @param   i
 *      The training example index. Must be between 0 and dataSize - 1.
 * @return
 *      The target value for that index represented as a double, which is
 *      either +1.0 or -1.0.
 */
private double getTarget(
  final int i)
{
  return this.dataList.get(i).getOutput() ? +1.0 : -1.0;
}

public NamedValue<Double> getPerformance()
{
  return new DefaultNamedValue<Double>(
    "Function Value", this.getResult().getOutput() );
}

@Override
public void update(
  final ResultType target,
  final InputOutputPair<? extends InputType, OutputType> data)
{
  // Unpack the pair.
  this.update(target, data.getInput(), data.getOutput());
}

@Override
public void update(
  final ResultType target,
  final InputOutputPair<? extends InputType, OutputType> data)
{
  // Unpack the pair.
  this.update(target, data.getInput(), data.getOutput());
}

@Override
public void update(
  final ResultType target,
  final InputOutputPair<? extends InputType, OutputType> data)
{
  // Unpack the pair.
  this.update(target, data.getInput(), data.getOutput());
}

public NamedValue<Double> getPerformance()
{
  return new DefaultNamedValue<Double>(
    "Function Value", this.getResult().getOutput() );
}

public NamedValue<Double> getPerformance()
{
  return new DefaultNamedValue<Double>(
    "Function Value", this.getResult().getOutput() );
}

@Override
public <InputType> void update(
  final DefaultKernelBinaryCategorizer<InputType> target,
  final InputType input,
  final Boolean output)
{
  this.update(target, input, (boolean) output);
}

@Override
public DiscreteNaiveBayesCategorizer<InputType, CategoryType> learn(
  final Collection<? extends InputOutputPair<? extends Collection<InputType>, CategoryType>> data)
{
  DiscreteNaiveBayesCategorizer<InputType,CategoryType> nbc =
    new DiscreteNaiveBayesCategorizer<InputType, CategoryType>();
  for( InputOutputPair<? extends Collection<InputType>,CategoryType> sample : data )
  {
    nbc.update(sample.getInput(), sample.getOutput());
  }
  return nbc;
}

@Override
public DiscreteNaiveBayesCategorizer<InputType, CategoryType> learn(
  final Collection<? extends InputOutputPair<? extends Collection<InputType>, CategoryType>> data)
{
  DiscreteNaiveBayesCategorizer<InputType,CategoryType> nbc =
    new DiscreteNaiveBayesCategorizer<InputType, CategoryType>();
  for( InputOutputPair<? extends Collection<InputType>,CategoryType> sample : data )
  {
    nbc.update(sample.getInput(), sample.getOutput());
  }
  return nbc;
}

@Override
public DiscreteNaiveBayesCategorizer<InputType, CategoryType> learn(
  final Collection<? extends InputOutputPair<? extends Collection<InputType>, CategoryType>> data)
{
  DiscreteNaiveBayesCategorizer<InputType,CategoryType> nbc =
    new DiscreteNaiveBayesCategorizer<InputType, CategoryType>();
  for( InputOutputPair<? extends Collection<InputType>,CategoryType> sample : data )
  {
    nbc.update(sample.getInput(), sample.getOutput());
  }
  return nbc;
}

@Override
public <InputType> void update(
  final DefaultKernelBinaryCategorizer<InputType> target,
  final InputType input,
  final Boolean output)
{
  this.update(target, input, (boolean) output);
}

@Override
public <InputType> void update(
  final DefaultKernelBinaryCategorizer<InputType> target,
  final InputType input,
  final Boolean output)
{
  this.update(target, input, (boolean) output);
}

public Double evaluate(
  Evaluator<? super InputType, ? extends TargetType> evaluator )
{
  ArrayList<WeightedTargetEstimatePair<TargetType, TargetType>> targetEstimatePairs =
    new ArrayList<WeightedTargetEstimatePair<TargetType, TargetType>>( this.getCostParameters().size() );
  for (InputOutputPair<? extends InputType, ? extends TargetType> io
    : this.getCostParameters())
  {
    TargetType target = io.getOutput();
    TargetType estimate = evaluator.evaluate(io.getInput());
    targetEstimatePairs.add(DefaultWeightedTargetEstimatePair.create(
      target, estimate, DatasetUtil.getWeight(io)));
  }
  return this.evaluatePerformance( targetEstimatePairs );
}

public OutputType evaluate(
  InputType input)
{
  Collection<InputOutputPair<? extends InputType, OutputType>> neighbors =
    this.getData().findNearest(input, 1, this.getDivergenceFunction());
  InputOutputPair<?,OutputType> pair = CollectionUtil.getFirst(neighbors);
  if( pair != null )
  {
    return pair.getOutput();
  }
  else
  {
    return null;
  }
  
}

public OutputType evaluate(
  InputType input)
{
  Collection<InputOutputPair<? extends InputType, OutputType>> neighbors =
    this.getData().findNearest(input, 1, this.getDivergenceFunction());
  InputOutputPair<?,OutputType> pair = CollectionUtil.getFirst(neighbors);
  if( pair != null )
  {
    return pair.getOutput();
  }
  else
  {
    return null;
  }
  
}

public NamedValue<? extends Number> getPerformance()
{
  double cost = (this.getAlgorithm().getResult() == null) ? 0.0 : this.getAlgorithm().getResult().getOutput();
  return new DefaultNamedValue<Double>( "Cost", cost );
}

public NamedValue<? extends Number> getPerformance()
{
  double cost = (this.getAlgorithm().getResult() == null) ? 0.0 : this.getAlgorithm().getResult().getOutput();
  return new DefaultNamedValue<Double>( "Cost", cost );
}

public NamedValue<? extends Number> getPerformance()
{
  double cost = (this.getAlgorithm().getResult() == null) ? 0.0 : this.getAlgorithm().getResult().getOutput();
  return new DefaultNamedValue<Double>( "Cost", cost );
}