Ponca basic line fit

This is an example of how to use Ponca to fit a Line on random 3D samples.

/*
 This Source Code Form is subject to the terms of the Mozilla Public
 License, v. 2.0. If a copy of the MPL was not distributed with this
 file, You can obtain one at http://mozilla.org/MPL/2.0/.
*/
 
#include <numeric> // transform_reduce
#include <algorithm>
#include <iostream>
#include <vector>
 
#include <Ponca/Fitting>
#include <Ponca/src/Common/pointTypes.h>
 
using namespace Eigen;
using namespace std;
using namespace Ponca;
 
 
using MyPoint    = PointPosition<double, 3>;
using Scalar     = MyPoint::Scalar;
using VectorType = MyPoint::VectorType;
using WeightFunc = DistWeightFunc<MyPoint, ConstantWeightKernel<Scalar>>;
using Fit        = Basket<MyPoint, WeightFunc, CovarianceLineFit>;
 
int main(int /*argc*/, char** /*argv*/)
{
    constexpr int n = 10000;
 
    // Generate a random set of n points along a line
    vector<MyPoint> points(n);
    VectorType dir = VectorType::Random().normalized();
    std::generate(points.begin(), points.end(),
                  [&dir]() { return (dir * Eigen::internal::random<Scalar>(0.1, 2)).eval(); });
    const VectorType& p = points.at(0).pos();
 
    std::cout << "====================\nLeastSquareLineFit:\n";
    cout << "Direction of the generated line: " << dir.transpose() << endl;
 
    // Fit line on data
    Fit _fit;
    _fit.setNeighborFilter({p, 1});
    _fit.compute(points.cbegin(), points.cend());
 
    // Check Fit output
    if (_fit.isStable())
    {
        cout << "Direction of the fitted 3D line: " << _fit.direction().transpose() << endl;
        cout << "Origin of the fitted line: " << _fit.origin().transpose() << endl;
        cout << "Projection of the basis center on the fitted line: " << _fit.project(p).transpose() << endl;
 
        Scalar dist =
            std::transform_reduce(points.cbegin(), points.cend(), Scalar(0), std::plus<>(),
                                  [&_fit](const MyPoint& q) { return (q.pos() - _fit.project(q.pos())).norm(); });
        cout << "Mean error between samples and fitted line: " << dist / Scalar(n) << endl;
        return EXIT_SUCCESS;
    }
    cerr << "Fit is not stable: " << _fit.getCurrentState() << endl;
    return EXIT_FAILURE;
}