void ba81NormalQuad::layer::detectTwoTier(Eigen::ArrayBase<T1> ¶m,
Eigen::MatrixBase<T2> &mean, Eigen::MatrixBase<T3> &cov)
{
if (mean.rows() < 3) return;
std::vector<int> orthogonal;
Eigen::Matrix<Eigen::DenseIndex, Eigen::Dynamic, 1>
numCov((cov.array() != 0.0).matrix().colwise().count());
std::vector<int> candidate;
for (int fx=0; fx < numCov.rows(); ++fx) {
if (numCov(fx) == 1) candidate.push_back(fx);
}
if (candidate.size() > 1) {
std::vector<bool> mask(numItems());
for (int cx=candidate.size() - 1; cx >= 0; --cx) {
std::vector<bool> loading(numItems());
for (int ix=0; ix < numItems(); ++ix) {
loading[ix] = param(candidate[cx], itemsMap[ix]) != 0;
}
std::vector<bool> overlap(loading.size());
std::transform(loading.begin(), loading.end(),
mask.begin(), overlap.begin(),
std::logical_and<bool>());
if (std::find(overlap.begin(), overlap.end(), true) == overlap.end()) {
std::transform(loading.begin(), loading.end(),
mask.begin(), mask.begin(),
std::logical_or<bool>());
orthogonal.push_back(candidate[cx]);
}
}
}
std::reverse(orthogonal.begin(), orthogonal.end());
if (orthogonal.size() == 1) orthogonal.clear();
if (orthogonal.size() && orthogonal[0] != mean.rows() - int(orthogonal.size())) {
mxThrow("Independent specific factors must be given after general dense factors");
}
numSpecific = orthogonal.size();
if (numSpecific) {
Sgroup.assign(numItems(), 0);
for (int ix=0; ix < numItems(); ix++) {
for (int dx=orthogonal[0]; dx < mean.rows(); ++dx) {
if (param(dx, itemsMap[ix]) != 0) {
Sgroup[ix] = dx - orthogonal[0];
continue;
}
}
}
//Eigen::Map< Eigen::ArrayXi > foo(Sgroup.data(), param.cols());
//mxPrintMat("sgroup", foo);
}
}
void ifaGroup::detectTwoTier()
{
int mlen = maxAbilities;
if (!twotier || mlen < 3) return;
std::vector<int> orthogonal;
if (mlen >= 3) {
Eigen::Map<Eigen::MatrixXd> Ecov(cov, mlen, mlen);
Eigen::Matrix<Eigen::DenseIndex, Eigen::Dynamic, 1> numCov((Ecov.array() != 0.0).matrix().colwise().count());
std::vector<int> candidate;
for (int fx=0; fx < numCov.rows(); ++fx) {
if (numCov(fx) == 1) candidate.push_back(fx);
}
if (candidate.size() > 1) {
std::vector<bool> mask(numItems());
for (int cx=candidate.size() - 1; cx >= 0; --cx) {
std::vector<bool> loading(numItems());
for (int ix=0; ix < numItems(); ++ix) {
loading[ix] = param[ix * paramRows + candidate[cx]] != 0;
}
std::vector<bool> overlap(loading.size());
std::transform(loading.begin(), loading.end(),
mask.begin(), overlap.begin(),
std::logical_and<bool>());
if (std::find(overlap.begin(), overlap.end(), true) == overlap.end()) {
std::transform(loading.begin(), loading.end(),
mask.begin(), mask.begin(),
std::logical_or<bool>());
orthogonal.push_back(candidate[cx]);
}
}
}
std::reverse(orthogonal.begin(), orthogonal.end());
}
if (orthogonal.size() == 1) orthogonal.clear();
if (orthogonal.size() && orthogonal[0] != mlen - int(orthogonal.size())) {
Rf_error("Independent factors must be given after dense factors");
}
numSpecific = orthogonal.size();
if (numSpecific) {
Sgroup.assign(numItems(), 0);
for (int ix=0; ix < numItems(); ix++) {
for (int dx=orthogonal[0]; dx < maxAbilities; ++dx) {
if (param[ix * paramRows + dx] != 0) {
Sgroup[ix] = dx - orthogonal[0];
continue;
}
}
}
}
}
