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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.
*
*=========================================================================*/
#include "itkDecisionRule.h"
namespace itk
{
namespace Statistics
{
namespace DecisionRuleTest
{
class MyDecisionRule : public DecisionRule
{
public:
/** Standard class type alias. */
using Self = MyDecisionRule;
using Superclass = DecisionRule;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(MyDecisionRule);
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Types for discriminant values and vectors. */
using MembershipValueType = Superclass::MembershipValueType;
using MembershipVectorType = Superclass::MembershipVectorType;
/** Types for class identifiers. */
using ClassIdentifierType = Superclass::ClassIdentifierType;
/** Evaluate membership score */
ClassIdentifierType
Evaluate(const MembershipVectorType & scoreVector) const override
{
double max = scoreVector[0];
unsigned int maxIndex = 0;
unsigned int i;
for (i = 1; i < scoreVector.size(); ++i)
{
if (scoreVector[i] > max)
{
max = scoreVector[i];
maxIndex = i;
}
}
return maxIndex;
}
};
} // namespace DecisionRuleTest
} // namespace Statistics
} // namespace itk
int
itkDecisionRuleTest(int, char *[])
{
using DecisionRuleType = itk::Statistics::DecisionRuleTest::MyDecisionRule;
using MembershipVectorType = DecisionRuleType::MembershipVectorType;
auto decisionRule = DecisionRuleType::New();
std::cout << decisionRule->GetNameOfClass() << std::endl;
std::cout << decisionRule->DecisionRuleType::Superclass::GetNameOfClass() << std::endl;
decisionRule->Print(std::cout);
MembershipVectorType membershipScoreVector;
double membershipScore1;
membershipScore1 = 0.1;
membershipScoreVector.push_back(membershipScore1);
double membershipScore2;
membershipScore2 = 0.5;
membershipScoreVector.push_back(membershipScore2);
double membershipScore3;
membershipScore3 = 1.9;
membershipScoreVector.push_back(membershipScore3);
// the maximum score is the third component. The decision rule should
// return index ( 2)
if (decisionRule->Evaluate(membershipScoreVector) != 2)
{
std::cerr << "Decision rule computation is incorrect!" << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
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