How to use

create

method

in

gov.sandia.cognition.learning.data.DefaultWeightedValueDiscriminant

@Override
public ValueDiscriminantPair<Boolean, Double> evaluateWithDiscriminant(
  final InputType input)
{
  // Evaluate the input as a double whose sign is the category.
  final double value = this.evaluateAsDouble(input);
  final boolean category = value >= 0.0;
  // The discriminant needs to be larger for a better match, even in
  // the case of the false category, which requires doing an absolute
  // value to the sign.
  final double discriminant = category ? value : -value;
  return DefaultWeightedValueDiscriminant.create(category, discriminant);
}

@Override
public ValueDiscriminantPair<Boolean, Double> evaluateWithDiscriminant(
  final InputType input)
{
  // Evaluate the input as a double whose sign is the category.
  final double value = this.evaluateAsDouble(input);
  final boolean category = value >= 0.0;
  // The discriminant needs to be larger for a better match, even in
  // the case of the false category, which requires doing an absolute
  // value to the sign.
  final double discriminant = category ? value : -value;
  return DefaultWeightedValueDiscriminant.create(category, discriminant);
}

@Override
public ValueDiscriminantPair<Boolean, Double> evaluateWithDiscriminant(
  final InputType input)
{
  // Evaluate the input as a double whose sign is the category.
  final double value = this.evaluateAsDouble(input);
  final boolean category = value >= 0.0;
  // The discriminant needs to be larger for a better match, even in
  // the case of the false category, which requires doing an absolute
  // value to the sign.
  final double discriminant = category ? value : -value;
  return DefaultWeightedValueDiscriminant.create(category, discriminant);
}

@Override
public DefaultWeightedValueDiscriminant<CategoryType> evaluateWithDiscriminant(
  final ObservationType input)
{
  CategoryType maxCategory = null;
  double maxPosterior = Double.NEGATIVE_INFINITY;
  for( CategoryType category : this.getCategories() )
  {
    double posterior = this.computePosterior(input, category);
    if( maxPosterior < posterior )
    {
      maxPosterior = posterior;
      maxCategory = category;
    }
  }
  return DefaultWeightedValueDiscriminant.create(maxCategory, maxPosterior);
}

@Override
public DefaultWeightedValueDiscriminant<CategoryType> evaluateWithDiscriminant(
  final ObservationType input)
{
  CategoryType maxCategory = null;
  double maxPosterior = Double.NEGATIVE_INFINITY;
  for( CategoryType category : this.getCategories() )
  {
    double posterior = this.computePosterior(input, category);
    if( maxPosterior < posterior )
    {
      maxPosterior = posterior;
      maxCategory = category;
    }
  }
  return DefaultWeightedValueDiscriminant.create(maxCategory, maxPosterior);
}

@Override
public DefaultWeightedValueDiscriminant<CategoryType> evaluateWithDiscriminant(
  final ObservationType input)
{
  CategoryType maxCategory = null;
  double maxPosterior = Double.NEGATIVE_INFINITY;
  for( CategoryType category : this.getCategories() )
  {
    double posterior = this.computePosterior(input, category);
    if( maxPosterior < posterior )
    {
      maxPosterior = posterior;
      maxCategory = category;
    }
  }
  return DefaultWeightedValueDiscriminant.create(maxCategory, maxPosterior);
}

@Override
public DefaultWeightedValueDiscriminant<CategoryType> evaluateWithDiscriminant(
  final Collection<InputType> input)
{
  // compute the product of the class conditionals
  double maxPosterior = -1.0;
  CategoryType maxCategory = null;
  for (CategoryType category : this.getCategories())
  {
    // Actually, this is only proportionate to the posterior
    // We would need to divide by the unconditional probability
    // of the inputs to compute the accordinng-to-Hoyle posterior.
    double posterior =
      this.computeConjuctiveProbability(input, category);
    if (maxPosterior < posterior)
    {
      maxPosterior = posterior;
      maxCategory = category;
    }
  }
  return DefaultWeightedValueDiscriminant.create(maxCategory, maxPosterior);
}

@Override
public DefaultWeightedValueDiscriminant<CategoryType> evaluateWithDiscriminant(
  final Collection<InputType> input)
{
  // compute the product of the class conditionals
  double maxPosterior = -1.0;
  CategoryType maxCategory = null;
  for (CategoryType category : this.getCategories())
  {
    // Actually, this is only proportionate to the posterior
    // We would need to divide by the unconditional probability
    // of the inputs to compute the accordinng-to-Hoyle posterior.
    double posterior =
      this.computeConjuctiveProbability(input, category);
    if (maxPosterior < posterior)
    {
      maxPosterior = posterior;
      maxCategory = category;
    }
  }
  return DefaultWeightedValueDiscriminant.create(maxCategory, maxPosterior);
}

@Override
public DefaultWeightedValueDiscriminant<CategoryType> evaluateWithDiscriminant(
  final Collection<InputType> input)
{
  // compute the product of the class conditionals
  double maxPosterior = -1.0;
  CategoryType maxCategory = null;
  for (CategoryType category : this.getCategories())
  {
    // Actually, this is only proportionate to the posterior
    // We would need to divide by the unconditional probability
    // of the inputs to compute the accordinng-to-Hoyle posterior.
    double posterior =
      this.computeConjuctiveProbability(input, category);
    if (maxPosterior < posterior)
    {
      maxPosterior = posterior;
      maxCategory = category;
    }
  }
  return DefaultWeightedValueDiscriminant.create(maxCategory, maxPosterior);
}

@Override
public DefaultWeightedValueDiscriminant<CategoryType> evaluateWithDiscriminant(
  final InputType input)
{
  // Get the vote distribution.
  final DefaultDataDistribution<CategoryType> votes =
    this.evaluateAsVotes(input);
  // Get the maximum value of the votes.
  final CategoryType bestCategory = votes.getMaxValueKey();
  final double bestFraction = votes.getFraction(bestCategory);
  return DefaultWeightedValueDiscriminant.create(
    bestCategory, bestFraction);
}

@Override
public DefaultWeightedValueDiscriminant<CategoryType> evaluateWithDiscriminant(
  final InputType input)
{
  // Get the vote distribution.
  final DefaultDataDistribution<CategoryType> votes =
    this.evaluateAsVotes(input);
  // Get the maximum value of the votes.
  final CategoryType bestCategory = votes.getMaxValueKey();
  final double bestFraction = votes.getFraction(bestCategory);
  return DefaultWeightedValueDiscriminant.create(
    bestCategory, bestFraction);
}

@Override
public DefaultWeightedValueDiscriminant<CategoryType> evaluateWithDiscriminant(
  final InputType input)
{
  // Get the vote distribution.
  final DefaultDataDistribution<CategoryType> votes =
    this.evaluateAsVotes(input);
  // Get the maximum value of the votes.
  final CategoryType bestCategory = votes.getMaxValueKey();
  final double bestFraction = votes.getFraction(bestCategory);
  return DefaultWeightedValueDiscriminant.create(
    bestCategory, bestFraction);
}

return DefaultWeightedValueDiscriminant.create(
  bestCategory, bestVotePercentage);

return DefaultWeightedValueDiscriminant.create(
  bestCategory, bestVotePercentage);

  return DefaultWeightedValueDiscriminant.create(
    CollectionUtil.getFirst(this.categories), 1.0);
return DefaultWeightedValueDiscriminant.create(
  bestCategory, bestFraction);

return DefaultWeightedValueDiscriminant.create(
  bestCategory, bestVotePercentage);

  return DefaultWeightedValueDiscriminant.create(
    CollectionUtil.getFirst(this.categories), 1.0);
return DefaultWeightedValueDiscriminant.create(
  bestCategory, bestFraction);

return DefaultWeightedValueDiscriminant.create(
  maxCategory, logMaximumLikelihood);

return DefaultWeightedValueDiscriminant.create(
  maxCategory, logMaximumLikelihood);

return DefaultWeightedValueDiscriminant.create(
  maxCategory, logMaximumLikelihood);