Ponca basic plane fit

This is an example of how to use Ponca to compute surface variation on random data with plane fitting.

/*
 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 <cmath>
#include <algorithm>
#include <iostream>
#include <vector>
 
#include <Ponca/Ponca>
 
using namespace std;
using namespace Ponca;
 
using MyPoint    = PointPositionNormal<double, 3>;
using Scalar     = MyPoint::Scalar;
using VectorType = MyPoint::VectorType;
 
// Define related structure
using WeightFunc  = DistWeightFilter<MyPoint, SmoothWeightKernel<Scalar>>;
using CovPlaneFit = Basket<MyPoint, WeightFunc, CovariancePlaneFit>;
 
template <typename Fit>
void test_fit(Fit& _fit, vector<MyPoint>& _vecs, const VectorType& _p)
{
    Scalar tmax = 100.0;
 
    // Set a weighting function instance
    _fit.setNeighborFilter({_p, tmax});
 
    // Fit plane (method compute handles multipass fitting
    _fit.compute(_vecs.begin(), _vecs.end());
 
    if (_fit.isStable())
    {
        cout << "Value of the scalar field at the initial point: " << _p.transpose() << " is equal to "
             << _fit.potential(_p) << endl;
 
        cout << "It's gradient at this place is equal to: " << _fit.primitiveGradient(_p).transpose() << endl;
 
        cout << "The initial point " << _p.transpose() << endl
             << "Is projected at   " << _fit.project(_p).transpose() << endl;
 
        cout << "Value of the surface variation: " << _fit.surfaceVariation() << endl;
    }
}
 
int main()
{
    // init random point cloud
    int n = 10000;
    vector<MyPoint> vecs(n);
    std::generate(vecs.begin(), vecs.end(), getRandomPoint<MyPoint>);
 
    std::cout << "====================\nCovariancePlaneFit:\n";
    CovPlaneFit fit;
    test_fit(fit, vecs, vecs.at(0).pos());
}