void Reconstruction::ReconstructPlanarUnconstrIter(const mat& matchesInit, LaplacianMesh& resMesh, uvec& inlierMatchIdxs)
{
// Input check
if (matchesInit.n_rows == 0)
{
inlierMatchIdxs.resize(0);
return;
}
Timer timer;
double wr = this->wrInit; // Currently used regularization weight
double radius = this->radiusInit; // Currently used radius of the estimator
vec reprojErrors; // Reprojection errors
// First, we need to build the correspondent matrix with all given matches to avoid re-computation
this->buildCorrespondenceMatrix(matchesInit);
// Then compute MPinit. Function reconstructPlanarUnconstr() will use part of MPinit w.r.t currently used matches
this->MPinit = this->Minit * this->refMesh.GetBigParamMat();
uvec matchesInitIdxs = linspace<uvec>(0, matchesInit.n_rows-1, matchesInit.n_rows);
// Currently used matches represented by their indices. Initially, use all matches: [0,1,2..n-1]
inlierMatchIdxs = matchesInitIdxs;
for (int i = 0; i < nUncstrIters; i++)
{
this->reconstructPlanarUnconstr(inlierMatchIdxs, wr, resMesh);
// If it is the final iteration, break and don't update "inlierMatchIdxs" or "weights", "radius"
if (i == nUncstrIters - 1)
{
//cout << "Current radius: " << radius << endl;
//cout << "Current wr: " << wr << endl;
//Reconstruction::computeCurrentMatrices(currentMatchIdxs, 325); // For Ferns
break;
}
// Otherwise, remove outliers
int iterTO = nUncstrIters - 2;
if (i >= iterTO)
reprojErrors = this->computeReprojectionErrors(resMesh, matchesInit, matchesInitIdxs);
else
reprojErrors = this->computeReprojectionErrors(resMesh, matchesInit, inlierMatchIdxs);
uvec idxs = find( reprojErrors < radius );
if (idxs.n_elem == 0)
break;
if (i >= iterTO)
inlierMatchIdxs = matchesInitIdxs.elem(idxs);
else
inlierMatchIdxs = inlierMatchIdxs.elem(idxs);
// Update parameters
wr = wr / Reconstruction::ROBUST_SCALE;
radius = radius / Reconstruction::ROBUST_SCALE;
}
}
//-----------------------------------------------------
// Cross-validation across an array of lambda and pick up the best.
List CV_lam_grid_cpp(vec y_vect, mat x_mat, vec id_vect, mat hat_R_full, vec beta_ini, int fold, int n, vec m, int obs_n, int p, uvec start, uvec end, vec lam_vect, double eps_tozero, double eps_stop, int iter_try){
int lam_length = lam_vect.n_elem;
double lam_temp, cv_sum, flag_stop_sum, iter_n_sum, cv_min = math::inf(), lam_min = -1;
uvec cvgrps_seq = linspace<uvec>(0, (n-1), n);
uvec cvgrps_subsets = shuffle(cvgrps_seq);
uvec cvgrps_which = cvgrps_seq - floor( cvgrps_seq / fold) * fold;
uvec index_cv_train, index_cv_test;
vec cv_vect(lam_length), flag_stop_vect(lam_length), iter_n_vect(lam_length);
uvec idx_train, idx_test;
vec y_train, y_test, id_train, m_train, beta_train;
mat x_train, x_test;
List indGen_res, beta_shrink_res;
for(int lam_iter = 0; lam_iter < lam_length; lam_iter++)
{
lam_temp = lam_vect(lam_iter);
cv_sum = 0;
flag_stop_sum = 0;
iter_n_sum = 0;
for(int k = 0; k < fold; k++)
{
index_cv_train = cvgrps_subsets.elem(find(cvgrps_which != k));
index_cv_test = cvgrps_subsets.elem(find(cvgrps_which == k));
idx_train = unique(seqJoin_vec(start.elem(index_cv_train), end.elem(index_cv_train), m.elem(index_cv_train)));
idx_test = unique(seqJoin_vec(start.elem(index_cv_test), end.elem(index_cv_test), m.elem(index_cv_test)));
y_train = y_vect.elem(idx_train);
x_train = x_mat.rows(idx_train);
id_train = id_vect.elem(idx_train);
indGen_res = indGen_cpp(id_train);
y_test = y_vect.elem(idx_test);
x_test = x_mat.rows(idx_test);
beta_shrink_res = beta_shrink_normal_cpp(y_train, x_train, id_train, hat_R_full, beta_ini, as<int>(indGen_res[0]), as<vec>(indGen_res[1]), as<int>(indGen_res[2]), p, as<uvec>(indGen_res[3]), as<uvec>(indGen_res[4]), as<vec>(indGen_res[5]), as<uvec>(indGen_res[6]), lam_temp, eps_tozero, eps_stop, iter_try);
cv_sum += sqrt(mean(pow((y_test - x_test * as<vec>(beta_shrink_res[0])),2)));
flag_stop_sum += as<double>(beta_shrink_res[2]);
iter_n_sum += as<double>(beta_shrink_res[3]);
}
// Calculate average across k-fold validation
cv_vect(lam_iter) = cv_sum / fold;
flag_stop_vect(lam_iter) = flag_stop_sum / fold;
iter_n_vect(lam_iter) = iter_n_sum / fold;
if(cv_sum < cv_min)
{
lam_min = lam_temp;
cv_min = cv_sum;
}
}
return List::create(Named("lam.vect") = lam_vect,
Named("cv.vect") = cv_vect,
Named("flag_stop_vect") = flag_stop_vect,
Named("iter_n_vect") = iter_n_vect,
Named("lam.min") = lam_min,
Named("cv.min") = cv_min
);
}
