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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 itkWeightedMeanSampleFilter_hxx
#define itkWeightedMeanSampleFilter_hxx
#include <vector>
#include "itkCompensatedSummation.h"
#include "itkMeasurementVectorTraits.h"
namespace itk
{
namespace Statistics
{
template <typename TSample>
WeightedMeanSampleFilter<TSample>::WeightedMeanSampleFilter()
{
this->ProcessObject::SetNthInput(1, nullptr);
}
template <typename TSample>
void
WeightedMeanSampleFilter<TSample>::GenerateData()
{
// if weighting function is specified, use it to compute the mean
const InputWeightingFunctionObjectType * functionObject = this->GetWeightingFunctionInput();
if (functionObject != nullptr)
{
this->ComputeMeanWithWeightingFunction();
return;
}
// if weight array is specified use it to compute the mean
const InputWeightArrayObjectType * weightArrayObject = this->GetWeightsInput();
if (weightArrayObject != nullptr)
{
this->ComputeMeanWithWeights();
return;
}
// Otherwise compute the regular mean ( without weight coefficients)
Superclass::GenerateData();
}
template <typename TSample>
void
WeightedMeanSampleFilter<TSample>::ComputeMeanWithWeights()
{
// set up input / output
const SampleType * input = this->GetInput();
const MeasurementVectorSizeType measurementVectorSize = input->GetMeasurementVectorSize();
auto * decoratedOutput =
itkDynamicCastInDebugMode<MeasurementVectorDecoratedType *>(this->ProcessObject::GetOutput(0));
MeasurementVectorRealType output = decoratedOutput->Get();
NumericTraits<MeasurementVectorRealType>::SetLength(output, this->GetMeasurementVectorSize());
// algorithm start
using MeasurementRealAccumulateType = CompensatedSummation<MeasurementRealType>;
std::vector<MeasurementRealAccumulateType> sum(measurementVectorSize);
const WeightArrayType & weightsArray = this->GetWeights();
WeightValueType totalWeight{};
typename SampleType::ConstIterator iter = input->Begin();
typename SampleType::ConstIterator end = input->End();
for (unsigned int sampleVectorIndex = 0; iter != end; ++iter, ++sampleVectorIndex)
{
const MeasurementVectorType & measurement = iter.GetMeasurementVector();
const typename SampleType::AbsoluteFrequencyType frequency = iter.GetFrequency();
const WeightValueType rawWeight = weightsArray[sampleVectorIndex];
const WeightValueType weight = (rawWeight * static_cast<WeightValueType>(frequency));
totalWeight += weight;
for (unsigned int dim = 0; dim < measurementVectorSize; ++dim)
{
const auto component = static_cast<MeasurementRealType>(measurement[dim]);
sum[dim] += (component * weight);
}
}
if (totalWeight > itk::Math::eps)
{
for (unsigned int dim = 0; dim < measurementVectorSize; ++dim)
{
output[dim] = (sum[dim].GetSum() / static_cast<MeasurementRealType>(totalWeight));
}
}
else
{
itkExceptionMacro("Total weight was too close to zero. Value = " << totalWeight);
}
decoratedOutput->Set(output);
}
template <typename TSample>
void
WeightedMeanSampleFilter<TSample>::ComputeMeanWithWeightingFunction()
{
// set up input / output
const SampleType * input = this->GetInput();
const MeasurementVectorSizeType measurementVectorSize = input->GetMeasurementVectorSize();
auto * decoratedOutput =
itkDynamicCastInDebugMode<MeasurementVectorDecoratedType *>(this->ProcessObject::GetOutput(0));
MeasurementVectorRealType output = decoratedOutput->Get();
NumericTraits<MeasurementVectorRealType>::SetLength(output, this->GetMeasurementVectorSize());
// algorithm start
using MeasurementRealAccumulateType = CompensatedSummation<MeasurementRealType>;
std::vector<MeasurementRealAccumulateType> sum(measurementVectorSize);
const WeightingFunctionType * const weightFunction = this->GetWeightingFunction();
WeightValueType totalWeight{};
typename SampleType::ConstIterator iter = input->Begin();
const typename SampleType::ConstIterator end = input->End();
for (; iter != end; ++iter)
{
const MeasurementVectorType & measurement = iter.GetMeasurementVector();
const typename SampleType::AbsoluteFrequencyType frequency = iter.GetFrequency();
const WeightValueType rawWeight = weightFunction->Evaluate(measurement);
const WeightValueType weight = (rawWeight * static_cast<WeightValueType>(frequency));
totalWeight += weight;
for (unsigned int dim = 0; dim < measurementVectorSize; ++dim)
{
const auto component = static_cast<MeasurementRealType>(measurement[dim]);
sum[dim] += (component * weight);
}
}
if (totalWeight > itk::Math::eps)
{
for (unsigned int dim = 0; dim < measurementVectorSize; ++dim)
{
output[dim] = (sum[dim].GetSum() / static_cast<MeasurementRealType>(totalWeight));
}
}
else
{
itkExceptionMacro("Total weight was too close to zero. Value = " << totalWeight);
}
decoratedOutput->Set(output);
}
} // end of namespace Statistics
} // end of namespace itk
#endif
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