本文整理汇总了C++中eigen::VectorXf::mean方法的典型用法代码示例。如果您正苦于以下问题：C++ VectorXf::mean方法的具体用法？C++ VectorXf::mean怎么用？C++ VectorXf::mean使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类eigen::VectorXf的用法示例。
在下文中一共展示了VectorXf::mean方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: get_variance
float math_util::get_variance(std::vector<float> distribution_)
{
// Initialize an Eigen vector representing the distribution
// In the following the formula for variance is implemented:
// Var(x) = 1/N * SUM((x-mean(x))^2)
Eigen::VectorXf distribution;
distribution = Eigen::Map<Eigen::VectorXf>(&distribution_[0],distribution_.size(),1);
// Retrieve mean value, mean(x)
float distribution_mean = distribution.mean();
// Get deviation vector. (x-mean(x))^2
distribution = (distribution.array() - distribution_mean) * (distribution.array() - distribution_mean);
// Get variance from deviation vector. Var(x)
return distribution.mean();
}示例2: sqrt
void
mutualproximity::set_normfacts(
musly_trackid trackid,
Eigen::VectorXf& sim)
{
double mu = sim.mean();
Eigen::VectorXd sim_mu = sim.cast<double>().array() - mu;
double std = (sim_mu.transpose() * sim_mu);
std /= (static_cast<double>(sim.size()) - 1.0);
// allocate space
if (trackid >= (int)norm_facts.size()) {
norm_facts.resize(trackid+1);
norm_facts[trackid].mu = mu;
norm_facts[trackid].std = sqrt(std);
}
}示例3: main
//.........这里部分代码省略.........
const InputImageType * inputy = readery->GetOutput();
const InputImageType * inputz = readerz->GetOutput();
//create the output image
OutputImageType::Pointer image = OutputImageType::New();
image->SetRegions(inputx->GetLargestPossibleRegion());
image->SetSpacing(inputx->GetSpacing());
image->SetOrigin(inputx->GetOrigin());
image->Allocate();
image->FillBuffer(0);
InputImageType::SizeType radius;
radius[0] = neighborhood_radius;
radius[1] = neighborhood_radius;
radius[2] = neighborhood_radius;
itk::ConstNeighborhoodIterator<InputImageType>
iteratorx(radius, inputx, image->GetRequestedRegion());
itk::ConstNeighborhoodIterator<InputImageType>
iteratory(radius, inputy, image->GetRequestedRegion());
itk::ConstNeighborhoodIterator<InputImageType>
iteratorz(radius, inputz, image->GetRequestedRegion());
itk::ImageRegionIterator<OutputImageType> it( image, image->GetRequestedRegion() );
iteratorx.GoToBegin();
iteratory.GoToBegin();
iteratorz.GoToBegin();
it.GoToBegin();
const unsigned int nneighbors = (neighborhood_radius*2+1)*(neighborhood_radius*2+1)*(neighborhood_radius*2+1);
Eigen::MatrixXf neighbors( nneighbors-1, 3 ); //to store the vectors
Eigen::Vector3f central_vector;
int c_id = (int) (nneighbors / 2); // get offset of center pixel
//check image size for the progress indicator
OutputImageType::RegionType region = image->GetLargestPossibleRegion();
OutputImageType::SizeType size = region.GetSize();
const unsigned long int npixels = size[0]*size[1]*size[2];
unsigned long int k = 0;
while ( ! iteratorx.IsAtEnd() )
{
if(k%10000 == 0)
{
std::cout<<"Processed "<<k<<"/"<<npixels<<"\r"<<std::flush;
}
k++;
unsigned int j = 0;
for (unsigned int i = 0; i < nneighbors; ++i)
{
if(i!=c_id) //exclude the center
{
neighbors(j, 0) = iteratorx.GetPixel(i);
neighbors(j, 1) = iteratory.GetPixel(i);
neighbors(j, 2) = iteratorz.GetPixel(i);
j++;
}
else
{
central_vector(0) = iteratorx.GetPixel(i);
central_vector(1) = iteratory.GetPixel(i);
central_vector(2) = iteratorz.GetPixel(i);
}
}
// calculate the angles
Eigen::VectorXf dp = neighbors*central_vector;
const float variance = ((dp.array()-dp.mean()).pow(2)).mean();
it.Set(variance);
++iteratorx;
++iteratory;
++iteratorz;
++it;
}
std::cout<<std::endl;
// The output of the resampling filter is connected to a writer and the
// execution of the pipeline is triggered by a writer update.
try {
writer->SetInput(image);
writer->Update();
} catch (itk::ExceptionObject & excep) {
std::cerr << "Exception caught !" << std::endl;
std::cerr << excep << std::endl;
}
return EXIT_SUCCESS;
}