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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.
*
*=========================================================================*/
#include <iostream>
#include "itkMaximumRatioDecisionRule.h"
int itkMaximumRatioDecisionRuleTest(int,char *[] )
{
std::cout << "==================================" << std::endl;
std::cout << "Testing MaximumRatioDecionRule " << std::endl << std::endl;
typedef itk::Statistics::MaximumRatioDecisionRule DecisionRuleType;
DecisionRuleType::Pointer decisionRule = DecisionRuleType::New();
DecisionRuleType::MembershipVectorType discriminantScores;
discriminantScores.resize( 3 );
discriminantScores[0] = 0.3;
discriminantScores[1] = 0.5;
discriminantScores[2] = 0.2;
DecisionRuleType::PriorProbabilityVectorType aPrioris;
aPrioris.resize(3);
aPrioris[0] = 0.2;
aPrioris[1] = 0.1;
aPrioris[2] = 0.6;
decisionRule->SetPriorProbabilities( aPrioris );
if ( decisionRule->Evaluate( discriminantScores ) != 2 )
{
std::cout << "[FAILED]" << std::endl;
return EXIT_FAILURE;
}
// run with uniform prior
aPrioris.clear();
decisionRule->SetPriorProbabilities( aPrioris );
if ( decisionRule->Evaluate( discriminantScores ) != 1 )
{
std::cout << "[FAILED]" << std::endl;
return EXIT_FAILURE;
}
std::cout << "[SUCCEEDED]" << std::endl;
return EXIT_SUCCESS;
}
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