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/*=========================================================================
*
* Copyright Insight Software Consortium
*
* 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
*
* http://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.
*
*=========================================================================*/
// Software Guide : BeginLatex
//
// \index{itk::Statistics::Maximum\-Ratio\-Decision\-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 probabilites (likelihoods) and the class priors are
// actual a priori class probabilities, then this decision rule operates
// as Bayes rule, returning the class $i$ if
// \begin{equation}
// p(x|i) p(i) > p(x|j) p(j)
// \end{equation}
// for all class $j$. 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 $i$ if
// \begin{equation}
// \frac{p(x|i)}{p(x|j)} > \frac{p(j)}{p(i)}
// \end{equation}
// for all class $j$.
//
// We include the header files for the class as well as the header file for
// the \code{std::vector} class that will be the container for the
// discriminant scores.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkMaximumRatioDecisionRule.h"
#include <vector>
// Software Guide : EndCodeSnippet
int main(int, char*[])
{
// Software Guide : BeginLatex
//
// The instantiation of the function is done through the usual
// \code{New()} method and a smart pointer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::Statistics::MaximumRatioDecisionRule DecisionRuleType;
DecisionRuleType::Pointer decisionRule = DecisionRuleType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We create the discriminant score vector and fill it with three
// values. We also create a vector (\code{aPrioris}) for the \emph{a
// priori} values. The \code{Evaluate( discriminantScores )} will
// return 1.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
DecisionRuleType::MembershipVectorType discriminantScores;
discriminantScores.push_back( 0.1 );
discriminantScores.push_back( 0.3 );
discriminantScores.push_back( 0.6 );
DecisionRuleType::PriorProbabilityVectorType aPrioris;
aPrioris.push_back( 0.1 );
aPrioris.push_back( 0.8 );
aPrioris.push_back( 0.1 );
decisionRule->SetPriorProbabilities( aPrioris );
std::cout << "MaximumRatioDecisionRule: The index of the chosen = "
<< decisionRule->Evaluate( discriminantScores )
<< std::endl;
// Software Guide : EndCodeSnippet
return EXIT_SUCCESS;
}
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