/**
* Calculates and stores the gradient values given a set of parameters.
*/
void SoftmaxRegressionFunction::Gradient(const arma::mat& parameters,
arma::mat& gradient) const
{
// Calculate the class probabilities for each training example. The
// probabilities for each of the classes are given by:
// p_j = exp(theta_j' * x_i) / sum(exp(theta_k' * x_i))
// The sum is calculated over all the classes.
// x_i is the input vector for a particular training example.
// theta_j is the parameter vector associated with a particular class.
arma::mat probabilities;
GetProbabilitiesMatrix(parameters, probabilities);
// Calculate the parameter gradients.
gradient.set_size(parameters.n_rows, parameters.n_cols);
if (fitIntercept)
{
// Treating the intercept term parameters.col(0) seperately to avoid
// the cost of building matrix [1; data].
arma::mat inner = probabilities - groundTruth;
gradient.col(0) =
inner * arma::ones<arma::mat>(data.n_cols, 1) / data.n_cols +
lambda * parameters.col(0);
gradient.cols(1, parameters.n_cols - 1) =
inner * data.t() / data.n_cols +
lambda * parameters.cols(1, parameters.n_cols - 1);
}
else
{
gradient = (probabilities - groundTruth) * data.t() / data.n_cols +
lambda * parameters;
}
}
void subspaceIdMoor::buildNMatrix(arma::uword k, arma::mat const &M, arma::mat const &L1, arma::mat const &L2, arma::mat const &X,
arma::uword i, arma::uword n, arma::uword ny, arma::mat &N){
mat Upper, Lower;
Upper = join_horiz(M.cols((k - 1)*ny, ny*i - 1) - L1.cols((k-1)*ny, ny*i - 1), zeros(n, (k-1)*ny));
Lower = join_horiz(-L2.cols((k - 1) * ny, ny*i - 1), zeros(ny, (k - 1)*ny));
N = join_vert(Upper, Lower);
if (k == 1)
N.submat(n, 0, n + ny - 1, ny - 1) = eye(ny, ny) + N.submat(n, 0, n + ny - 1, ny - 1);
N = N * X;
}
/**
* Evaluate the probabilities matrix. If fitIntercept flag is true,
* it should consider the parameters.cols(0) intercept term.
*/
void SoftmaxRegressionFunction::GetProbabilitiesMatrix(
const arma::mat& parameters,
arma::mat& probabilities) const
{
arma::mat hypothesis;
if (fitIntercept)
{
// In order to add the intercept term, we should compute following matrix:
// [1; data] = arma::join_cols(ones(1, data.n_cols), data)
// hypothesis = arma::exp(parameters * [1; data]).
//
// Since the cost of join maybe high due to the copy of original data,
// split the hypothesis computation to two components.
hypothesis = arma::exp(arma::repmat(parameters.col(0), 1, data.n_cols) +
parameters.cols(1, parameters.n_cols - 1) * data);
}
else
{
hypothesis = arma::exp(parameters * data);
}
probabilities = hypothesis / arma::repmat(arma::sum(hypothesis, 0),
numClasses, 1);
}
void
HMM::computeXiCached() {
arma::mat temp = B_.rows(1,T_-1) % beta_.cols(1,T_-1).t();
for(unsigned int i = 0; i < N_; ++i) {
xi_.slice(i) = temp %
(alpha_(i,arma::span(0, T_-2)).t() * A_.row(i));
}
}
void anderson::update(arma::vec & v, const arma::vec & f) {
using namespace arma;
if (updates == 0) {
v_old = v;
f_old = f;
v += f * beta;
} else {
// update the history matrices
K = join_horiz(K, v - v_old);
D = join_horiz(D, f - f_old);
// cut the matrices to desired maximum length
if (D.n_cols > N) {
K = K.cols(1 , K.n_cols - 1);
D = D.cols(1 , D.n_cols - 1);
}
// temporary variable
mat I = D.t() * D;
// check if I's condition number is to large, i.e. I^-1 close to singular
while ((cond(I) > 1e16) && (D.n_cols > 1)) {
// in that case, cut older values until it works again
K = K.cols(1, K.n_cols - 1);
D = D.cols(1, D.n_cols - 1);
I = D.t() * D;
}
v_old = v;
f_old = f;
// substract the anderson-mixing term
v += f * beta - (K + D * beta) * arma::solve(I, D.t()) * f;
}
++updates;
}
arma::mat subspaceIdMoor::blkhank(arma::mat const &y, uword i, uword j){
assert(y.n_rows < y.n_cols);
uword ny = y.n_rows;
uword N = y.n_cols;
if (j > N - i + 1)
cerr << ("blkHank: j too big") << endl;
mat H(ny*i, j);
for (uword k = 0; k < i; k++)
H.rows(k*ny, (k + 1)*ny - 1) = y.cols((k), k + j - 1);
//H.save("H.dat", raw_ascii);
return H;
}
std::vector<GMM>
operator() (const arma::mat & data, const arma::urowvec & labels) {
const unsigned int numLabels = (unsigned int) arma::as_scalar(arma::max(labels)) + 1;
std::vector<GMM> BModels;
for (unsigned int i = 0; i < numLabels; ++i){
arma::uvec indices = arma::find(labels == i);
if (indices.n_elem > 0) {
BModels.push_back(GMM(data.cols(indices), kmin_, kmax_));
}
}
return BModels;
}
// [[Rcpp::export]]
arma::mat reFitUnivariate(arma::vec y,
arma::mat design_mat,
arma::mat beta,
int nlam, int J, int n) {
arma::uvec solo(1);
// Initialize ans matrix to store re-fitted values.
arma::mat ans_beta(J, nlam, fill::zeros);
for(uword i = 0; i < nlam; i++) {
arma::vec temp = beta.col(i);
arma::uvec inds = find(temp);
if(inds.n_elem > 0){
arma::vec temp_beta = solve(design_mat.cols(inds), y);
solo(0) = i;
ans_beta(inds, solo) = temp_beta;
}
}
return ans_beta;
}
//' @title Sample FSV loads
//' @description Given the data, the factors and the idiosyncratic variances,
//' this function sample the loadings matrix with PLT prior constraints.
//' @param y data matrix which follow the FSV model.
//' @param factors matrix of factors \eqn(T \times k).
//' @param psi idiosyncratic variances.
//' @param m0 prior mean of the loads.
//' @param C0 prior variance of the loads;
//' @return A matrix with the loads.
// [[Rcpp::export]]
arma::mat SampleFsvLoads(arma::mat y, arma::mat factors, arma::vec psi,
double m0, double C0){
int k = factors.n_cols;
int q = psi.n_rows;
arma::mat Fi, Ci, invCi, rootInvCi, rootCi;
arma::vec mi;
// simulacao da matriz com restricoes
arma::mat Lambda_1(k, k);
Lambda_1.eye();
arma::mat FtF = factors.t() * factors;
for (int ik = 1; ik < k; ik++){
Fi = factors.cols(0, ik-1);
invCi = (1/psi(ik, 0)) * FtF.submat(0, 0, ik-1, ik-1) + (1/C0);
rootInvCi = arma::chol(arma::symmatu(invCi));
rootCi = arma::trans(arma::inv(arma::trimatu(rootInvCi)));
mi = rootCi.t() * rootCi * ((1/psi(ik, 0)) * Fi.t() * y.col(ik) + (m0/C0));
Lambda_1.submat(ik, 0, ik, ik-1) = mi.t() + randn(1, ik)*rootCi;
}
// simulacao da matriz sem restricoes
arma::mat Lambda_2(q-k, k);
Lambda_2.zeros();
for (int ik = k; ik < q; ik++){
invCi = (1/psi(ik, 0)) * FtF + (1/C0);
rootInvCi = arma::chol(arma::symmatu(invCi));
rootCi = arma::trans(arma::inv(arma::trimatu(rootInvCi)));
mi = rootCi.t() * rootCi *
((1/psi(ik, 0)) * factors.t() * y.col(ik) + (m0/C0));
Lambda_2.row(ik-k) = mi.t() + randn(1, k)*rootCi;
}
arma::mat Lambda = join_cols(Lambda_1, Lambda_2);
return Lambda;
}
// [[Rcpp::export]]
List real_cont(
arma::mat coeffs_cont, arma::mat X, int n_exog, int n_endog,
int n_cont, arma::rowvec rho, arma::rowvec sig_eps, int N,
arma::rowvec upper, arma::rowvec lower, bool cheby, int seed=222 ){
// Computes a matrix of realized next-period controls from a simulation
int n_pts = X.n_rows ;
// The number of points at which the error is assessed
mat exog = zeros<rowvec>( n_exog ) ;
mat endog = zeros<rowvec>( n_endog ) ;
mat cont_sim = zeros( n_pts, n_cont ) ;
// Temporary containers used in the loop. Make cont bigger than size 0
// here - just passing a useless empty container
arma_rng::set_seed(seed) ;
// Set the seed
mat exog_sim = ( ones( n_pts ) * rho ) % X.cols( 0, n_exog - 1 ) +
( ones( n_pts ) * sig_eps ) % randn<mat>( n_pts, n_exog ) ;
// The random draws
/** Now compute the model errors **/
for( int i = 0 ; i < n_pts ; i++ ){
// Loop over the evaluation points
exog = exog_sim.row(i) ;
// The updated exogenous variable in the next period
endog = X.row(i).subvec( n_exog, n_exog + n_endog - 1 ) ;
// Select the current-period endogenous states
cont_sim.row(i) = endog_update( exog, endog, coeffs_cont, n_exog, n_endog, N,
upper, lower, cheby ) ;
// The integral over realizations of the shock
}
List out ;
out["r.exog"] = exog_sim ;
out["r.cont"] = cont_sim ;
// Create the output list
return out ;
}
///
/// \brief Vespucci::Math::DimensionReduction::VCA
/// Vertex Component Analysis
/// \param R The dataset
/// \param endmembers Number of endmembers to compute
/// \param indices Row indices of pure components.
/// \param endmember_spectra Spectra of pure components (note that these are in
/// columns, not rows as in spectra_)
/// \param projected_data Projected data
/// \param fractional_abundances Purity of a given spectrum relative to endmember
/// \return Convergeance (no actual test implemented...)
///
bool Vespucci::Math::DimensionReduction::VCA(const arma::mat &R, arma::uword p,
arma::uvec &indices, arma::mat &endmember_spectra,
arma::mat &projected_data, arma::mat &fractional_abundances)
{
//Initializations
arma::uword L = R.n_rows;
arma::uword N = R.n_cols;
if (L == 0 || N == 0){
std::cerr << "No data!" << std::endl;
return false;
}
if (p > L){
std::cerr << "wrong number of endmembers (" << p << ")!"<< std::endl;
std::cerr << "set to 5 or one less than number of spectra" << std::endl;
p = (L < 5? 5: L-1);
}
//mat of SNR
arma::mat r_m = mean(R, 1);
arma::mat R_m = arma::repmat(r_m, 1, N); //the mean of each spectral band
arma::mat R_o = R - R_m; //mean-center the data
arma::mat Ud;
arma::vec Sd;
arma::mat Vd;
//arma::svds(Ud, Sd, Vd, arma::sp_mat(R_o * R_o.t()/N), p);
Vespucci::Math::DimensionReduction::svds(R_o*R_o.t()/N, p, Ud, Sd, Vd);
arma::mat x_p;
try{
x_p = Ud.t() * R_o;
}catch(std::exception e){
std::cout << "Ud.t() * R_o" << std::endl;
}
double SNR = Vespucci::Math::DimensionReduction::estimate_snr(R, r_m, x_p);
double SNR_th = 15 + 10*log10(p);
//Choose projective projection or projection to p-1 subspace
arma::mat y;
if (SNR < SNR_th){
arma::uword d = p - 1;
Ud = Ud.cols(0, d-1);
projected_data = Ud * x_p.rows(0, d-1) + R_m; //in dimension L
arma::mat x = x_p.rows(0, d-1);//x_p = trans(Ud)*R_o, p-dimensional subspace
//following three lines are one in arma::matlab...
arma::mat sum_squares = sum(pow(x, 2));
double c = sum_squares.max();
c = std::sqrt(c);
y = arma::join_vert(x, c*arma::ones(1, N));
}
else{
arma::uword d = p;
Vespucci::Math::DimensionReduction::svds(R*R.t()/N, p, Ud, Sd, Vd);
arma::svds(Ud, Sd, Vd, arma::sp_mat(R*R.t()/N), d);//R_o is a mean centered version...
x_p = Ud.t() * R;
projected_data = Ud * x_p.rows(0, d-1);
arma::mat x = Ud.t() * R;
arma::mat u = arma::mean(x, 1);
y = x / arma::repmat(sum(x % arma::repmat(u, 1, N)), d, 1);
}
// The VCA algorithm
arma::vec w;
w.set_size(p);
arma::vec f;
arma::rowvec v;
indices.set_size(p);
//there are no fill functions for arma::uvecs
for (arma::uword i = 0; i < p; ++i)
indices(i) = 0;
arma::mat A = arma::zeros(p, p);
double v_max;
double sum_squares;
arma::uvec q1;
A(p-1, 0) = 1;
for (arma::uword i = 0; i < p; ++i){
w.randu();
f = w - A*arma::pinv(A)*w;
sum_squares = sqrt(sum(square(f)));
f /= sum_squares;
v = f.t() * y;
v_max = arma::max(abs(v));
q1 = arma::find(abs(v) == v_max, 1);
indices(i) = q1(0);
A.col(i) = y.col(indices(i)); //same as x.col(indices(i));
}
endmember_spectra = projected_data.cols(indices);
fractional_abundances = arma::trans(pinv(endmember_spectra) * projected_data);
return true;
}
void build_density(arma::mat& P, arma::mat& C, size_t NOcc) {
P = C.cols(0, NOcc-1) * C.cols(0, NOcc-1).t();
}
// [[Rcpp::export]]
List glm_forward_c( arma::mat x, // inputs (features)
Function pseudo_obs, // R-function returning the pseudo-data based on the quadratic approximation
double lambda, // regularization parameter (multiplier for L2-penalty)
bool intercept, // whether to use intercept
arma::vec penalty, // relative penalties for the variables
double thresh, // threshold for stopping the iterative reweighted least squares
int qa_updates_max, // max number or quadratic approximation updates
int pmax, // maximum number of variables up to which the search is continued
arma::vec w0, // initial guess for the weights of the pseudo-gaussian observations (needed for Student-t model)
int ls_iter_max=50 ) // max number of line search iterations
{
mat xp; // x for the current active set
mat xp_temp; // x for the current active set + the variable to be added
size_t D = x.n_cols; // total number of inputs
size_t pmaxu = (size_t) pmax; // converting pmax to unsigned int (avoids some compiler warnings)
uvec chosen(D); chosen.zeros(); // keeps track of added variables
uvec varorder(pmaxu); varorder.zeros(); // stores the order in which the variables are added to the model
mat beta_all(D,pmaxu); beta_all.zeros(); // collects beta from all steps
rowvec beta0_all(pmaxu); beta0_all.zeros(); // collects beta0 from all steps
mat w_all(x.n_rows,pmaxu); // collects weights of the gaussian pseudo-observations from all steps
// declare a few variables that are needed during the iteration
vec w = w0;
int qau;
size_t j,k,jopt=0;
uvec varind;
uvec step(1);
for (k=0; k<pmaxu; ++k) {
varind = find(chosen);
vec beta(varind.size() + 1 + intercept);
vec betaopt;
double loss_min = std::numeric_limits<double>::infinity();
double loss;
// loop through all the candidate variables that could be added next
for (j=0; j<D; ++j) {
if (chosen(j))
continue;
chosen(j) = 1;
beta.zeros();
glm_ridge(beta, loss, w, qau, x.cols(find(chosen)), pseudo_obs, lambda, intercept,
penalty.elem(find(chosen)), thresh, qa_updates_max, ls_iter_max);
chosen(j) = 0;
if (loss < loss_min) {
loss_min = loss;
jopt = j;
betaopt = beta;
}
}
varorder(k) = jopt;
chosen(jopt) = 1;
step(0) = k;
if (intercept) {
beta0_all(k) = betaopt(0);
beta_all.submat(find(chosen), step) = betaopt.tail(k+1);
} else {
beta0_all(k) = 0;
beta_all.submat(find(chosen), step) = betaopt;
}
w_all.col(k) = w;
}
return List::create(beta_all, beta0_all, varorder, w_all);
}
ss subspaceIdMoor::subidKnownOrder(arma::uword ny, arma::uword nu, arma::mat const &R, arma::mat const &Usvd, arma::vec const &singval,
arma::uword i, arma::uword n){
ss ssout;
mat U1 = Usvd.cols(0, n - 1);
/* STEP 4 in Subspace Identification*/
/*Determine gam and gamm*/
mat gam = U1 * diagmat(sqrt(singval.subvec(0, n - 1)));
mat gamm = gam.rows(0, ny*(i - 1) - 1);
mat gam_inv = pinv(gam); /*pseudo inverse*/
mat gamm_inv = pinv(gamm); /*pseudo inverse*/
/* STEP 5*/
mat Rhs, Lhs;
buildRhsLhsMatrix(gam_inv, gamm_inv, R, i, n, ny, nu, Rhs, Lhs);
/* Solve least square*/
mat solls;
solls = solve(Rhs.t(), Lhs.t()).t();
/* Extract system matrix:*/
mat A, C;
A = solls.submat(0, 0, n - 1, n - 1);
C = solls.submat(n, 0, n + ny - 1, n - 1);
mat res = Lhs - solls*Rhs;
/* Recompute gamma from A and C:*/
gam.zeros();
gam.rows(0, ny - 1) = C;
for (uword k = 2; k <= i; k++){
gam.rows((k - 1)*ny, k*ny - 1) = gam.rows((k-2)*ny, (k-1)*ny - 1) * A;
}
gamm = gam.rows(0, ny*(i - 1) - 1);
gam_inv = pinv(gam);
gamm_inv = pinv(gamm);
/* Recompute the states with the new gamma:*/
buildRhsLhsMatrix(gam_inv, gamm_inv, R, i, n, ny, nu, Rhs, Lhs);
/* STEP 6:*/
/* Computing system Matrix B and D*/
/*ref pag 125 for P and Q*/
mat P = Lhs - join_vert(A, C) * Rhs.rows(0, n - 1);
P = P.cols(0, 2*nu*i - 1);
mat Q = R.submat(nu*i, 0, 2 * nu*i - 1, 2 * nu*i - 1); /*Future inputs*/
/* Matrix L1, L2 and M as on page 119*/
mat L1 = A * gam_inv;
mat L2 = C * gam_inv;
mat M = join_horiz(zeros(n, ny), gamm_inv);
mat X = join_vert(join_horiz(eye(ny, ny), zeros(ny, n)), join_horiz(zeros(ny*(i-1), ny), gamm));
/* Calculate N and the Kronecker products (page 126)*/
mat N;
uword kk = 1;
buildNMatrix(kk, M, L1, L2, X, i, n, ny, N);
mat totm = kron(Q.rows((kk-1)*nu, kk*nu - 1).t(), N);
for (kk = 2; kk <= i; kk++){
buildNMatrix(kk, M, L1, L2, X, i, n, ny, N);
totm = totm + kron(Q.rows((kk - 1)*nu, kk*nu - 1).t(), N);
}
/* Solve Least Squares: */
mat Pvec = vectorise(P);
mat sollsq2 = solve(totm, Pvec);
/*Mount B and D*/
sollsq2.reshape(n + ny, nu);
mat D = sollsq2.rows(0, ny - 1);
mat B = sollsq2.rows(ny, ny+n - 1);
/* STEP 7: Compute sys Matrix G, L0*/
mat covv, Qs, Ss, Rs, sig, G, L0, K, Ro;
if (norm(res) > 1e-10){ /*Determine QSR from the residuals*/
covv = res*res.t();
Qs = covv.submat(0, 0, n - 1, n - 1);
Ss = covv.submat(0, n, n - 1, n + ny - 1);
Rs = covv.submat(n, n, n+ny - 1, n+ny - 1);
simple_dlyap(A, Qs, sig); /*solves discrete lyapunov matrix equation*/
G = A*sig*C.t() + Ss;
L0 = C*sig*C.t() + Rs;
/* Determine K and Ro*/
g12kr(A, G, C, L0, K, Ro);
}
/* Set to ss structure:*/
ssout.A = A;
ssout.B = B;
ssout.C = C;
ssout.D = D;
//ssout.A = A; parei aqui -> add later the ones related with stochastic to ss.
return ssout;
}
std::vector<int> HighOrderMeshGenerator::
insertNewNodes(const unique_element_ptr& el,
const arma::mat& newelnodes,
const arma::mat& parametric_coords,
int order){
const int type = el->getElementType();
const NodeIndexer* ni_old =
index_factory->getNodeIndexer(type,el->getOrder());
const NodeIndexer* ni_new =
index_factory->getNodeIndexer(type,order);
std::vector<int> newnode_indices(ni_new->Ndof(),-1);
const std::vector<indtype>& corner_new = ni_new->getCornerNodes();
const gind* old_nodes = el->getNodes();
const std::vector<indtype>& corner_old = ni_old->getCornerNodes();
for(int i = 0; i < el->numCornerNodes(); i++){
//std::cout << "corner node: " << corner_new[i] << " " <<
// corner_old[i] << std::endl;
newnode_indices[corner_new[i]] = old_nodes[corner_old[i]];
}
//std::cout << el->getDim() << std::endl;
for(int ch = 0; ch < el->NumChildren(); ch++){
const MEl* child_old = el->getChild(ch);
if(el->getDim() > 1) assert(child_old);
if(child_old){
//int child_dim = child_old->getDim();
auto child_it = elmap[el->getDim()-1].find(child_old);
assert(child_it != elmap[el->getDim()-1].end());
const MEl* child_new = child_it->second;
const gind* child_nodes = child_new->getNodes();
int orient = el->getChildOrientation(ch);
const std::vector<indtype>& child_node_indices =
ni_new->getChildNodes(ch);
const NodeIndexer* ni_child =
index_factory->getNodeIndexer(child_new->getElementType(),
child_new->getOrder());
const std::vector<indtype>& orient_indices =
ni_child->getOrientedNodes(orient);
assert(child_node_indices.size() == child_new->NumNodes());
for(int nd = 0; nd < child_new->NumNodes(); nd++){
newnode_indices[child_node_indices[nd]] =
child_nodes[orient_indices[nd]];
}
}
} // end child loop
// delete the old interior nodes from map
const std::vector<indtype>& old_interior_indices = ni_old->getInteriorNodes();
for(int i = 0; i < old_interior_indices.size(); i++){
meshcurr->getNodesNC().erase(old_nodes[old_interior_indices[i]]);
}
// insert new nodes into node map
//std::cout << "ni_new type: " << ni_new->getType() << std::endl;
//arma::uvec new_interior_indices;
//std::cout << "el type: " << el->getElementType() << std::endl;
arma::uvec new_interior_indices(ni_new->getInteriorNodes());
//std::cout << "h3.1" << std::endl;
//std::cout << new_interior_indices << std::endl;
//std::cout << newelnodes << std::endl;
arma::mat new_interior_nodes = newelnodes.cols(new_interior_indices);
//std::cout << "h3.2" << std::endl;
//std::cout << new_interior_indices << std::endl;
//std::cout << parametric_coords << std::endl;
arma::mat new_interior_parametric_coords;
if(!parametric_coords.is_empty()){
new_interior_parametric_coords =
parametric_coords.cols(new_interior_indices);
}
//std::cout << "h4" << std::endl;
nodeFactory* node_factory = nodeFactory::Instance();
int node_type = 3;
if(el->hasGeoEntity()) node_type = el->getGeoType()+1;
//std::cout << new_interior_nodes << std::endl;
if(el->getElementType() == 1){
//std::cout << el->getGeoType() << std::endl;
//if(!el->hasGeoEntity()) std::cout << "Line does not have a geo entity!" << std::endl;
}
for(int i = 0; i < new_interior_indices.size(); i++){
double uv[2];
for(int j = 0; j < parametric_coords.n_rows; j++){
uv[j] = new_interior_parametric_coords.at(j,i);
}
int nd_count = node_factory->GetNodeCount();
newnode_indices[new_interior_indices[i]] = nd_count;
auto newnode =
node_factory->CreateNode(node_type,
new_interior_nodes.colptr(i),
el->getGeoEntity(),
uv[0],uv[1]);
meshcurr->getNodesNC()[nd_count] = std::move(newnode);
}
//std::cout << "h5" << std::endl;
return newnode_indices;
}
tuple<double, double, int, int, double, double> simulate(const arma::Col<double> &Y, const vector<int> X,
double sigma, bool varianceKnown,
arma::mat &Z, mt19937_64 &rng,
bool interceptTerm) {
bernoulli_distribution bernoulli(0.5);
int N = X.size();
Z.fill(0);
// bestColumns[k] keeps track of the k + 1 or k + 2 columns that produce the smallest p-value depending on interceptTerm
vector<arma::uvec> bestColumns; bestColumns.reserve(N - 1);
if (interceptTerm) { // make intercept term last column of Z
fill(Z.begin_col(N - 1), Z.end_col(N - 1), 1);
copy(X.begin(), X.end(), Z.begin_col(0));
bestColumns.push_back(arma::uvec{0, (unsigned long long) N - 1ULL});
} else {
copy(X.begin(), X.end(), Z.begin_col(0));
bestColumns.push_back(arma::uvec{0});
}
// bestPValues[k] corresponds to p-value if the columns bestColumns[k] are used
vector<pair<double, double>> bestPValues; bestPValues.reserve(N - 1);
bestPValues.push_back(calculateBetaPValue(Z.cols(bestColumns.front()), Y, sigma, varianceKnown));
if (bestPValues.front().first <= 0.05) {
return make_tuple(bestPValues.front().first, bestPValues.front().second, 0, 0, -1, bestPValues.front().first);
} else { // need more covariates
bool done = false;
int smallestSubsetSize = INT_MAX;
/* add covariates one-by-one, we always include the treatment
* if we're using the intercept two covariates are included by default
*/
for (int j = 1; j < N - 2 || (j == N - 2 && !interceptTerm); ++j) {
for (int k = 0; k < N; ++k) Z(k, j) = bernoulli(rng);
if (!interceptTerm) {
while (arma::rank(Z) <= j) {
for (int k = 0; k < N; ++k) Z(k, j) = bernoulli(rng);
}
} else { // offset rank by 1 for intercept term
while (arma::rank(Z) <= j + 1) {
for (int k = 0; k < N; ++k) Z(k, j) = bernoulli(rng);
}
}
for (int k = j; k >= 1; --k) { // loop through subset sizes, k is the number of additional covariates
pair<double, double> newPValue;
if (k == j) { // use all available covariates
bestColumns.emplace_back(bestColumns.back().n_rows + 1); // add one more to biggest subset
for (int l = 0; l < bestColumns.back().n_rows - 1; ++l) {
bestColumns.back()(l) = bestColumns[j - 1](l); // copy over from original subset
}
bestColumns.back()(bestColumns.back().n_rows - 1) = j; // add new covariate
newPValue = calculateBetaPValue(Z.cols(bestColumns.back()), Y, sigma, varianceKnown);
bestPValues.push_back(newPValue);
} else { // make a new subset of same size with new covariate
arma::uvec columnSubset(bestColumns[k].n_rows);
for (int l = 0; l < columnSubset.n_rows - 1; ++l)
columnSubset(l) = bestColumns[k - 1](l); // copy over from smaller subset
columnSubset(columnSubset.n_rows - 1) = j; // add new covariate
newPValue = calculateBetaPValue(Z.cols(columnSubset), Y, sigma, varianceKnown);
if (bestPValues[k].first > newPValue.first) { // if better subset replace
bestPValues[k] = newPValue;
bestColumns[k] = columnSubset;
}
}
if (newPValue.first <= 0.05) { // stop when we reach significance
done = true;
smallestSubsetSize = k;
}
}
if (done) {
// compute balance p value in special case that only 1 covariate was needed
double balancePValue = -1;
if (smallestSubsetSize == 1 && !interceptTerm) {
balancePValue = testBalance(Z.col(bestColumns[1](1)), Z.col(0));
} else if (smallestSubsetSize == 1 && interceptTerm) {
balancePValue = testBalance(Z.col(bestColumns[1](2)), Z.col(0));
}
return make_tuple(bestPValues.front().first, bestPValues[smallestSubsetSize].second,
j, smallestSubsetSize, balancePValue, bestPValues[smallestSubsetSize].first);
}
}
}
return make_tuple(bestPValues.front().first, bestPValues.front().second, -1, -1, -1, bestPValues.front().first);
}
