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/*=========================================================================
*
* Copyright NumFOCUS
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#ifndef itkMaximumRatioDecisionRule_h
#define itkMaximumRatioDecisionRule_h
#include <vector>
#include "vnl/vnl_matrix.h"
#include "itkNumericTraits.h"
#include "itkDecisionRule.h"
#include "ITKStatisticsExport.h"
namespace itk
{
namespace Statistics
{
/**
* \class MaximumRatioDecisionRule
* \brief A decision rule that operates as a frequentest's
* approximation to Bayes rule.
*
* MaximumRatioDecisionRule returns the class label using a Bayesian
* style decision rule. The discriminant scores are evaluated in the
* context of class priors. If the discriminant scores are actual
* conditional probabilities (likelihoods) and the class priors are
* actual a priori class probabilities, then this decision rule operates
* as Bayes rule, returning the class \f$i\f$ if
* \f$p(x|i) p(i) > p(x|j) p(j)\f$ for all class \f$j\f$. The
* discriminant scores and priors are not required to be true
* probabilities.
*
* This class is named the MaximumRatioDecisionRule as it can be
* implemented as returning the class \f$i\f$ if
* \f$\frac{p(x|i)}{p(x|j)} > \frac{p(j)}{p(i)}\f$ for all class
* \f$j\f$.
*
* A priori values need to be set before calling the Evaluate
* method. If they are not set, a uniform prior is assumed.
*
* \sa MaximumDecisionRule, MinimumDecisionRule
* \ingroup ITKStatistics
*/
class ITKStatistics_EXPORT MaximumRatioDecisionRule : public DecisionRule
{
public:
ITK_DISALLOW_COPY_AND_MOVE(MaximumRatioDecisionRule);
/** Standard class type aliases */
using Self = MaximumRatioDecisionRule;
using Superclass = DecisionRule;
using Pointer = SmartPointer<Self>;
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(MaximumRatioDecisionRule);
/** Standard New() method support */
itkNewMacro(Self);
/** Types for discriminant values and vectors. */
using MembershipValueType = Superclass::MembershipValueType;
using MembershipVectorType = Superclass::MembershipVectorType;
/** Types for class identifiers. */
using ClassIdentifierType = Superclass::ClassIdentifierType;
/** Types for priors and values */
using PriorProbabilityValueType = MembershipValueType;
using PriorProbabilityVectorType = std::vector<PriorProbabilityValueType>;
using PriorProbabilityVectorSizeType = PriorProbabilityVectorType::size_type;
/**
* Evaluate the decision rule \f$p(x|i) p(i) > p(x|j) p(j)\f$. Prior
* probabilities need to be set before calling Evaluate() using the
* SetPriorProbabilities() method (otherwise a uniform prior is
* assumed). Parameter to Evaluate() is the discriminant score in
* the form of a likelihood \f$p(x|i)\f$.
*/
ClassIdentifierType
Evaluate(const MembershipVectorType & discriminantScores) const override;
/** Set the prior probabilities used in evaluating
* \f$p(x|i) p(i) > p(x|j) p(j)\f$. The likelihoods are set using
* the Evaluate() method. SetPriorProbabilities needs to be called before
* Evaluate(). If not set, assumes a uniform prior. */
void
SetPriorProbabilities(const PriorProbabilityVectorType & p);
/** Get the prior probabilities. */
itkGetConstReferenceMacro(PriorProbabilities, PriorProbabilityVectorType);
protected:
MaximumRatioDecisionRule();
~MaximumRatioDecisionRule() override = default;
void
PrintSelf(std::ostream & os, Indent indent) const override;
private:
PriorProbabilityVectorType m_PriorProbabilities{};
}; // end of class
} // namespace Statistics
} // namespace itk
#endif
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