ITK  5.4.0 Insight Toolkit
Examples/Statistics/MaximumRatioDecisionRule.cxx
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// 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 probabilities (likelihoods) and the class priors are
// actual a priori class probabilities, then this decision rule operates
// as Bayes rule, returning the class $i$ if
//
// p(x|i) p(i) > p(x|j) p(j)
//
// 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
//
// \frac{p(x|i)}{p(x|j)} > \frac{p(j)}{p(i)}
//
// 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 <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
auto 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;
}
itk::Statistics::MaximumRatioDecisionRule
A decision rule that operates as a frequentest's approximation to Bayes rule.
Definition: itkMaximumRatioDecisionRule.h:59
itkMaximumRatioDecisionRule.h
New
static Pointer New()