int main() {
std::vector<int> vi;
std::istream_iterator<int> inIter(std::cin), eof;
std::ostream_iterator<int> outIter(std::cout, " ");
while (inIter != eof)
vi.push_back(*inIter++);
sort(vi.begin(), vi.end());
copy(vi.begin(), vi.end(), outIter);
std::cout << std::endl;
return 0;
}
Iterator MultiLayerNet::getInLayer() const{
Iterator inIter(inNeurons);
return inIter;
}
void mitk::MRNormLinearStatisticBasedFilter::InternalComputeMask(itk::Image<TPixel, VImageDimension>* itkImage)
{
// Define all necessary Types
typedef itk::Image<TPixel, VImageDimension> ImageType;
typedef itk::Image<int, VImageDimension> MaskType;
typedef itk::LabelStatisticsImageFilter<ImageType, MaskType> FilterType;
typedef itk::MinimumMaximumImageCalculator<ImageType> MinMaxComputerType;
typename MaskType::Pointer itkMask0 = MaskType::New();
mitk::CastToItkImage(this->GetMask(), itkMask0);
typename ImageType::Pointer outImage = ImageType::New();
mitk::CastToItkImage(this->GetOutput(0), outImage);
typename MinMaxComputerType::Pointer minMaxComputer = MinMaxComputerType::New();
minMaxComputer->SetImage(itkImage);
minMaxComputer->Compute();
typename FilterType::Pointer labelStatisticsImageFilter = FilterType::New();
labelStatisticsImageFilter->SetUseHistograms(true);
labelStatisticsImageFilter->SetHistogramParameters(256, minMaxComputer->GetMinimum(),minMaxComputer->GetMaximum());
labelStatisticsImageFilter->SetInput( itkImage );
labelStatisticsImageFilter->SetLabelInput(itkMask0);
labelStatisticsImageFilter->Update();
double median0 = labelStatisticsImageFilter->GetMedian(1);
double mean0 = labelStatisticsImageFilter->GetMean(1);
double stddev = labelStatisticsImageFilter->GetSigma(1);
double modulo0=0;
{
auto histo = labelStatisticsImageFilter->GetHistogram(1);
double maxFrequency=0;
for (auto hIter=histo->Begin();hIter!=histo->End();++hIter)
{
if (maxFrequency < hIter.GetFrequency())
{
maxFrequency = hIter.GetFrequency();
modulo0 = (histo->GetBinMin(0,hIter.GetInstanceIdentifier()) + histo->GetBinMax(0,hIter.GetInstanceIdentifier())) / 2.0;
}
}
}
double value0=0;
switch (m_CenterMode)
{
case MRNormLinearStatisticBasedFilter::MEAN:
value0=mean0; break;
case MRNormLinearStatisticBasedFilter::MEDIAN:
value0=median0; break;
case MRNormLinearStatisticBasedFilter::MODE:
value0=modulo0; break;
}
double offset = value0;
double scaling = stddev;
if (scaling < 0.0001)
return;
itk::ImageRegionIterator<ImageType> inIter(itkImage, itkImage->GetLargestPossibleRegion());
itk::ImageRegionIterator<ImageType> outIter(outImage, outImage->GetLargestPossibleRegion());
while (! inIter.IsAtEnd())
{
TPixel value = inIter.Value();
outIter.Set((value - offset) / scaling);
++inIter;
++outIter;
}
}
