static Rcpp::IntegerVector nz_vec(Rcpp::NumericMatrix alpha_new,
Rcpp::NumericMatrix eta_new,
Rcpp::NumericVector d_new,
double eps)
{
int K = alpha_new.ncol();
int p = alpha_new.nrow();
int L = eta_new.nrow();
Rcpp::IntegerVector result(p*K + L*K + L);
for (int i = 0; i < p*K; i++) {
result[i] = nz(alpha_new[i],eps);
}
for (int i = 0; i < L*K; i++) {
result[p*K + i] = nz(eta_new[i],eps);
}
for (int i = 0; i < L; i++) {
result[p*K + L*K + i] = nz(d_new[i],eps);
}
return result;
}
// [[Rcpp::export]]
SEXP FilterIndepOld (Rcpp::NumericVector y_rcpp,
Rcpp::NumericMatrix y_lagged_rcpp,
Rcpp::NumericMatrix z_dependent_rcpp,
Rcpp::NumericMatrix z_independent_rcpp,
Rcpp::NumericVector beta_rcpp,
Rcpp::NumericVector mu_rcpp,
Rcpp::NumericVector sigma_rcpp,
Rcpp::NumericMatrix gamma_dependent_rcpp,
Rcpp::NumericVector gamma_independent_rcpp,
Rcpp::NumericMatrix transition_probs_rcpp,
Rcpp::NumericVector initial_dist_rcpp
)
{
int n = y_rcpp.size();
int M = mu_rcpp.size();
arma::mat xi_k_t(M, n); // make a transpose first for easier column operations.
arma::colvec y(y_rcpp.begin(), y_rcpp.size(), false);
arma::mat y_lagged(y_lagged_rcpp.begin(),
y_lagged_rcpp.nrow(), y_lagged_rcpp.ncol(), false);
arma::mat z_dependent(z_dependent_rcpp.begin(),
z_dependent_rcpp.nrow(), z_dependent_rcpp.ncol(), false);
arma::mat z_independent(z_independent_rcpp.begin(),
z_independent_rcpp.nrow(), z_independent_rcpp.ncol(), false);
arma::colvec beta(beta_rcpp.begin(), beta_rcpp.size(), false);
arma::colvec mu(mu_rcpp.begin(), mu_rcpp.size(), false);
arma::colvec sigma(sigma_rcpp.begin(), sigma_rcpp.size(), false);
arma::mat gamma_dependent(gamma_dependent_rcpp.begin(),
gamma_dependent_rcpp.nrow(), gamma_dependent_rcpp.ncol(), false);
arma::colvec gamma_independent(gamma_independent_rcpp.begin(),
gamma_independent_rcpp.size(), false);
arma::mat transition_probs(transition_probs_rcpp.begin(),
transition_probs_rcpp.nrow(), transition_probs_rcpp.ncol(), false);
arma::colvec initial_dist(initial_dist_rcpp.begin(), initial_dist_rcpp.size(), false);
double likelihood = 0;
SEXP eta_rcpp = EtaIndep(y_rcpp, y_lagged_rcpp,
z_dependent_rcpp, z_independent_rcpp,
beta_rcpp, mu_rcpp, sigma_rcpp,
gamma_dependent_rcpp, gamma_independent_rcpp);
arma::mat eta_t = (Rcpp::as<arma::mat>(eta_rcpp)).t();
xi_k_t.col(0) = eta_t.col(0) % initial_dist;
double total = sum(xi_k_t.col(0));
xi_k_t.col(0) = xi_k_t.col(0) / total;
likelihood += log(total);
for (int k = 1; k < n; k++)
{
xi_k_t.col(k) = eta_t.col(k) % (transition_probs * xi_k_t.col(k-1));
total = sum(xi_k_t.col(k));
xi_k_t.col(k) = xi_k_t.col(k) / total;
likelihood += log(total);
}
return Rcpp::List::create(Named("xi.k") = wrap(xi_k_t.t()),
Named("likelihood") = wrap(likelihood));
}
// kernel Dist function on a Grid
// [[Rcpp::export]]
Rcpp::NumericVector
KdeDist(const Rcpp::NumericMatrix & X
, const Rcpp::NumericMatrix & Grid
, const double h
, const Rcpp::NumericVector & weight
, const bool printProgress
) {
const unsigned sampleNum = X.nrow();
const unsigned dimension = Grid.ncol();
const unsigned gridNum = Grid.nrow();
// first = sum K_h(X_i, X_j), second = K_h(x, x), third = sum K_h(x, X_i)
std::vector< double > firstValue;
const double second = 1.0;
std::vector< double > thirdValue;
double firstmean;
Rcpp::NumericVector kdeDistValue(gridNum);
int counter = 0, percentageFloor = 0;
int totalCount = sampleNum + gridNum;
if (printProgress) {
printProgressFrame(Rprintf);
}
firstValue = computeKernel< std::vector< double > >(
X, X, h, weight, printProgress, Rprintf, counter, totalCount,
percentageFloor);
if (dimension <= 1) {
thirdValue = computeKernel< std::vector< double > >(
X, Grid, h, weight, printProgress, Rprintf, counter, totalCount,
percentageFloor);
}
else {
thirdValue = computeGaussOuter< std::vector< double > >(
X, Grid, h, weight, printProgress, Rprintf, counter, totalCount,
percentageFloor);
}
if (weight.size() == 1) {
firstmean = std::accumulate(firstValue.begin(), firstValue.end(), 0.0) / sampleNum;
}
else {
firstmean = std::inner_product(
firstValue.begin(), firstValue.end(), weight.begin(), 0.0) /
std::accumulate(weight.begin(), weight.end(), 0.0);
}
for (unsigned gridIdx = 0; gridIdx < gridNum; ++gridIdx) {
kdeDistValue[gridIdx] = std::sqrt(firstmean + second - 2 * thirdValue[gridIdx]);
}
if (printProgress) {
Rprintf("\n");
}
return kdeDistValue;
}
// [[Rcpp::export]]
Rcpp::List SIMRE(int n1, Rcpp::NumericMatrix OMEGA, int n2, Rcpp::NumericMatrix SIGMA, int seed) {
arma::mat eta;
arma::mat eps;
if(OMEGA.nrow() > 0) eta = MVGAUSS(OMEGA,n1,-1);
if(SIGMA.nrow() > 0) eps = MVGAUSS(SIGMA,n2,-1);
Rcpp::List ans;
ans["eta"] = eta;
ans["eps"] = eps;
return(ans);
}
// [[Rcpp::export]]
Rcpp::NumericMatrix rcpp_sweep_(Rcpp::NumericMatrix x, Rcpp::NumericVector vec)
{
Rcpp::NumericMatrix ret(x.nrow(), x.ncol());
#pragma omp parallel for default(shared)
for (int j=0; j<x.ncol(); j++)
{
#pragma omp simd
for (int i=0; i<x.nrow(); i++)
ret(i, j) = x(i, j) - vec(i);
}
return ret;
}
std::vector<std::vector<float> > matrix_to_array_v(Rcpp::NumericMatrix& mat)
{
std::vector<std::vector<float> > v;
if(!mat.ncol() || !mat.nrow())
return(v);
v.resize(mat.nrow());
std::vector<float> v_row(mat.ncol()); // = new float[ mat.ncol() * mat.nrow() ];
for(unsigned int i=0; i < (unsigned int)mat.nrow(); ++i){
for(unsigned int j=0; j < (unsigned int)mat.ncol(); ++j){
v_row[j] = (float)mat(i, j);
}
v[i] = v_row;
}
return(v);
}
Rcpp::List RadiusSearch(Rcpp::NumericMatrix query_,
Rcpp::NumericMatrix ref_,
double radius,
int max_neighbour,
std::string build,
int cores,
int checks) {
const std::size_t n_dim = query_.ncol();
const std::size_t n_query = query_.nrow();
const std::size_t n_ref = ref_.nrow();
// Column major to row major
arma::mat query(n_dim, n_query);
{
arma::mat temp_q(query_.begin(), n_query, n_dim, false);
query = arma::trans(temp_q);
}
flann::Matrix<double> q_flann(query.memptr(), n_query, n_dim);
arma::mat ref(n_dim, n_ref);
{
arma::mat temp_r(ref_.begin(), n_ref, n_dim, false);
ref = arma::trans(temp_r);
}
flann::Matrix<double> ref_flann(ref.memptr(), n_ref, n_dim);
// Setting the flann index params
flann::IndexParams params;
if (build == "kdtree") {
params = flann::KDTreeSingleIndexParams(1);
} else if (build == "kmeans") {
params = flann::KMeansIndexParams(2, 10, flann::FLANN_CENTERS_RANDOM, 0.2);
} else if (build == "linear") {
params = flann::LinearIndexParams();
}
// Perform the radius search
flann::Index<flann::L2<double> > index(ref_flann, params);
index.buildIndex();
std::vector< std::vector<int> >
indices_flann(n_query, std::vector<int>(max_neighbour));
std::vector< std::vector<double> >
dists_flann(n_query, std::vector<double>(max_neighbour));
flann::SearchParams search_params;
search_params.cores = cores;
search_params.checks = checks;
search_params.max_neighbors = max_neighbour;
index.radiusSearch(q_flann, indices_flann, dists_flann, radius,
search_params);
return Rcpp::List::create(Rcpp::Named("indices") = indices_flann,
Rcpp::Named("distances") = dists_flann);
}
void ScoreGaussL0PenScatter::setData(Rcpp::List& data)
{
std::vector<int>::iterator vi;
//uint i;
// Cast preprocessed data from R list
dout.level(2) << "Casting preprocessed data...\n";
_dataCount = Rcpp::as<std::vector<int> >(data["data.count"]);
dout.level(3) << "# samples per vertex: " << _dataCount << "\n";
_totalDataCount = Rcpp::as<uint>(data["total.data.count"]);
dout.level(3) << "Total # samples: " << _totalDataCount << "\n";
Rcpp::List scatter = data["scatter"];
Rcpp::NumericMatrix scatterMat;
_disjointScatterMatrices.resize(scatter.size());
dout.level(3) << "# disjoint scatter matrices: " << scatter.size() << "\n";
for (R_len_t i = 0; i < scatter.size(); ++i) {
scatterMat = Rcpp::NumericMatrix((SEXP)(scatter[i]));
_disjointScatterMatrices[i] = arma::mat(scatterMat.begin(), scatterMat.nrow(), scatterMat.ncol(), false);
}
// Cast index of scatter matrices, adjust R indexing convention to C++
std::vector<int> scatterIndex = Rcpp::as<std::vector<int> >(data["scatter.index"]);
for (std::size_t i = 0; i < scatterIndex.size(); ++i)
_scatterMatrices[i] = &(_disjointScatterMatrices[scatterIndex[i] - 1]);
// Cast lambda: penalty constant
_lambda = Rcpp::as<double>(data["lambda"]);
dout.level(3) << "Penalty parameter lambda: " << _lambda << "\n";
// Check whether an intercept should be calculated
_allowIntercept = Rcpp::as<bool>(data["intercept"]);
dout.level(3) << "Include intercept: " << _allowIntercept << "\n";
}
static Rcpp::NumericMatrix x_tilde(Rcpp::NumericMatrix X,
Rcpp::IntegerVector nk,
Rcpp::IntegerMatrix groups,
Rcpp::NumericVector d_cur,
Rcpp::NumericMatrix eta_cur)
{
int K = nk.size();
int n_tot = X.nrow();
int p = X.ncol();
int L = groups.ncol();
Rcpp::NumericMatrix result(n_tot, p * K);
int idx = 0;
for (int k = 0; k < K; k++) {
int n = nk[k];
for (int j = 0; j < p; j++) {
//calculate sum for column j
double sum = 0.0;
for (int l = 0; l < L; l++) {
if (elem(groups, j, l)) {
sum += d_cur[l] * elem(eta_cur, l, k);
}
}
//multiply column j in submatrix k of X with sum
for (int i = 0; i < n; i++) {
elem(result, idx + i, p * k + j) = elem(X, idx + i, j) * sum;
}
}
idx += n;
}
return result;
}
//' Marginal correlation matrix
//'
//' Various workhorse functions to compute the marginal (or unconditional)
//' correlations (and cross-correlation) estimates efficiently.
//' They are (almost)
//' equivalent implementations of \code{\link[stats]{cor}} in Rcpp,
//' RcppArmadillo, and RcppEigen.
//'
//' @rdname corFamily
//' @aliases corFamily
//' corRcpp xcorRcpp corArma xcorArma corEigen xcorEigen
//' @param X A numeric matrix.
//' @param Y A numeric matrix of compatible dimension with the \code{X}, i.e.
//' \code{nrow(X)} equals \code{nrow(Y)}.
//' @return
//' The \code{corXX} family returns a numeric correlation matrix of size
//' \code{ncol(X)} times \code{ncol(X)}.
//'
//' The \code{xcorXX} family returns a numeric cross-correlation matrix
//' of size \code{ncol(X)} times \code{ncol(Y)}.
//' @details
//' Functions almost like \code{\link{cor}}.
//' For the \code{xcorXX} functions, the \code{i}'th and \code{j}'th
//' entry of the output matrix is the correlation between \code{X[i, ]} and
//' \code{X[j, ]}.
//' Likewise, for the \code{xcorXX} functions, the \code{i}'th and
//' \code{j}'th entry of the output is the correlation between \code{X[i, ]}
//' and \code{Y[j, ]}.
//' @note
//' \code{NA}s in \code{X} or \code{Y} will yield \code{NA}s in the correlation matrix.
//' This also includes the diagonal unlike the behavior of
//' \code{\link[stats]{cor}}.
//' @author Anders Ellern Bilgrau <anders.ellern.bilgrau (at) gmail.com>
//' @export
// [[Rcpp::export]]
Rcpp::NumericMatrix corRcpp(Rcpp::NumericMatrix & X) {
const int m = X.ncol();
const int n = X.nrow();
// Centering the matrix
X = centerNumericMatrix(X);
Rcpp::NumericMatrix cor(m, m);
// Degenerate case
if (n == 0) {
std::fill(cor.begin(), cor.end(), Rcpp::NumericVector::get_na());
return cor;
}
// Compute 1 over the sample standard deviation
Rcpp::NumericVector inv_sqrt_ss(m);
for (int i = 0; i < m; ++i) {
inv_sqrt_ss(i) = 1/sqrt(Rcpp::sum(X(Rcpp::_, i)*X(Rcpp::_, i)));
}
// Computing the correlation matrix
for (int i = 0; i < m; ++i) {
for (int j = 0; j <= i; ++j) {
cor(i, j) = Rcpp::sum(X(Rcpp::_,i)*X(Rcpp::_,j)) *
inv_sqrt_ss(i) * inv_sqrt_ss(j);
cor(j, i) = cor(i, j);
}
}
return cor;
}
static double bic_logistic(Rcpp::NumericMatrix X,
Rcpp::NumericVector y,
Rcpp::NumericMatrix beta_new,
double eps,
Rcpp::IntegerVector nk)
{
int n_tot = X.nrow();
int p = X.ncol();
int K = nk.size();
int idx = 0;
double ll = 0.0;
for (int k = 0; k < K; k++) {
int n = nk[k];
for (int i = 0; i < n; i++) {
double lp = 0.0;
for (int j = 0; j < p; j++) {
lp += elem(X, idx+i, j) * elem(beta_new, j, k);
}
ll += y[idx+i] * lp - log(1.0 + exp(lp));
}
idx += n;
}
double bic = -2.0 * ll + df(beta_new, eps) * log(n_tot);
return bic;
}
static Rcpp::NumericMatrix x_tilde_3(Rcpp::NumericMatrix X,
Rcpp::IntegerVector nk,
Rcpp::IntegerMatrix groups,
Rcpp::NumericMatrix alpha_new,
Rcpp::NumericVector d_new)
{
int K = nk.size();
int n_tot = X.nrow();
int p = X.ncol();
int L = groups.ncol();
Rcpp::NumericMatrix result(n_tot, L * K);
int idx = 0;
for (int k = 0; k < K; k++) {
int n = nk[k];
for (int l = 0; l < L; l++) {
for (int i = 0; i < n; i++) {
double sum = 0.0;
for (int j = 0; j < p; j++) {
if (elem(groups, j, l)) {
sum += elem(X, idx + i, j) * elem(alpha_new, j, k);
}
}
elem(result, idx + i, L * k + l) = d_new[l] * sum;
}
}
idx += n;
}
return result;
}
///********************************************************************
///** cdm_rcpp_irt_classify_individuals
// [[Rcpp::export]]
Rcpp::List cdm_rcpp_irt_classify_individuals( Rcpp::NumericMatrix like )
{
int N = like.nrow();
int TP = like.ncol();
Rcpp::IntegerVector class_index(N);
Rcpp::NumericVector class_maxval(N);
double val=0;
int ind=0;
for (int nn=0; nn<N; nn++){
val=0;
ind=0;
for (int tt=0; tt<TP; tt++){
if ( like(nn,tt) > val ){
val = like(nn,tt);
ind = tt;
}
}
class_index[nn] = ind + 1;
class_maxval[nn] = val;
}
//---- OUTPUT:
return Rcpp::List::create(
Rcpp::Named("class_index") = class_index,
Rcpp::Named("class_maxval") = class_maxval
);
}
// [[Rcpp::export]]
Rcpp::NumericMatrix update_sigma2_batch(Rcpp::S4 xmod){
Rcpp::RNGScope scope;
// get model
Rcpp::S4 model(xmod);
// get parameter estimates
Rcpp::NumericMatrix theta = model.slot("theta");
Rcpp::IntegerVector z = model.slot("z");
double nu_0 = model.slot("nu.0");
double sigma2_0 = model.slot("sigma2.0");
// get data and size attributes
Rcpp::NumericVector x = model.slot("data");
int n = x.size();
int K = theta.ncol();
int B = theta.nrow();
//IntegerVector nn = model.slot("zfreq");
// get batch info
Rcpp::NumericMatrix tabz = tableBatchZ(xmod);
Rcpp::IntegerVector batch = model.slot("batch");
Rcpp::IntegerVector ub = uniqueBatch(batch);
Rcpp::NumericMatrix ss(B, K);
for (int i = 0; i < n; ++i) {
for (int b = 0; b < B; ++b) {
if (batch[i] != ub[b]) {
continue;
}
for (int k = 0; k < K; ++k){
if (z[i] == k+1){
ss(b, k) += pow(x[i] - theta(b, k), 2);
}
}
}
}
//NumericMatrix sigma2_nh(B, K);
double shape;
double rate;
double sigma2_nh;
double nu_n;
Rcpp::NumericMatrix sigma2_tilde(B, K);
Rcpp::NumericMatrix sigma2_(B, K);
for (int b = 0; b < B; ++b) {
for (int k = 0; k < K; ++k) {
nu_n = nu_0 + tabz(b, k);
sigma2_nh = 1.0/nu_n*(nu_0*sigma2_0 + ss(b, k));
shape = 0.5 * nu_n;
rate = shape * sigma2_nh;
sigma2_tilde(b, k) = Rcpp::as<double>(rgamma(1, shape, 1.0/rate));
sigma2_(b, k) = 1.0 / sigma2_tilde(b, k);
}
}
return sigma2_;
}
// [[Rcpp::export]]
Rcpp::List europeanOptionArraysEngine(std::string type, Rcpp::NumericMatrix par) {
QuantLib::Option::Type optionType = getOptionType(type);
int n = par.nrow();
Rcpp::NumericVector value(n), delta(n), gamma(n), vega(n), theta(n), rho(n), divrho(n);
QuantLib::Date today = QuantLib::Date::todaysDate();
QuantLib::Settings::instance().evaluationDate() = today;
QuantLib::DayCounter dc = QuantLib::Actual360();
for (int i=0; i<n; i++) {
double underlying = par(i, 0); // first column
double strike = par(i, 1); // second column
QuantLib::Spread dividendYield = par(i, 2); // third column
QuantLib::Rate riskFreeRate = par(i, 3); // fourth column
QuantLib::Time maturity = par(i, 4); // fifth column
#ifdef QL_HIGH_RESOLUTION_DATE
// in minutes
boost::posix_time::time_duration length = boost::posix_time::minutes(boost::uint64_t(maturity * 360 * 24 * 60));
#else
int length = int(maturity*360 + 0.5); // FIXME: this could be better
#endif
double volatility = par(i, 5); // sixth column
boost::shared_ptr<QuantLib::SimpleQuote> spot(new QuantLib::SimpleQuote( underlying ));
boost::shared_ptr<QuantLib::SimpleQuote> vol(new QuantLib::SimpleQuote( volatility ));
boost::shared_ptr<QuantLib::BlackVolTermStructure> volTS = flatVol(today, vol, dc);
boost::shared_ptr<QuantLib::SimpleQuote> qRate(new QuantLib::SimpleQuote( dividendYield ));
boost::shared_ptr<QuantLib::YieldTermStructure> qTS = flatRate(today, qRate, dc);
boost::shared_ptr<QuantLib::SimpleQuote> rRate(new QuantLib::SimpleQuote( riskFreeRate ));
boost::shared_ptr<QuantLib::YieldTermStructure> rTS = flatRate(today, rRate, dc);
#ifdef QL_HIGH_RESOLUTION_DATE
QuantLib::Date exDate(today.dateTime() + length);
#else
QuantLib::Date exDate = today + length;
#endif
boost::shared_ptr<QuantLib::Exercise> exercise(new QuantLib::EuropeanExercise(exDate));
boost::shared_ptr<QuantLib::StrikedTypePayoff> payoff(new QuantLib::PlainVanillaPayoff(optionType, strike));
boost::shared_ptr<QuantLib::VanillaOption> option = makeOption(payoff, exercise, spot, qTS, rTS, volTS);
value[i] = option->NPV();
delta[i] = option->delta();
gamma[i] = option->gamma();
vega[i] = option->vega();
theta[i] = option->theta();
rho[i] = option->rho();
divrho[i] = option->dividendRho();
}
return Rcpp::List::create(Rcpp::Named("value") = value,
Rcpp::Named("delta") = delta,
Rcpp::Named("gamma") = gamma,
Rcpp::Named("vega") = vega,
Rcpp::Named("theta") = theta,
Rcpp::Named("rho") = rho,
Rcpp::Named("divRho") = divrho);
}
static double bic_linear(Rcpp::NumericMatrix X,
Rcpp::NumericVector y,
Rcpp::NumericMatrix beta_new,
double eps,
Rcpp::IntegerVector nk)
{
int n_tot = X.nrow();
int p = X.ncol();
int K = nk.size();
/*calculate SSe*/
double SSe = 0.0;
int idx = 0;
for (int k = 0; k < K; k++) {
int n = nk[k];
for (int i = 0; i < n; i++) {
double Xrow_betacol = 0.0;
for (int j = 0; j < p; j++) {
Xrow_betacol += elem(X, idx+i, j) * elem(beta_new, j, k);
}
SSe += pow(y[idx+i] - Xrow_betacol, 2);
}
idx += n;
}
double ll = -n_tot / 2.0 * (log(SSe) - log(n_tot) + log(2.0 * M_PI) + 1);
double bic = -2 * ll + df(beta_new, eps) * log(n_tot);
return bic;
}
//' Evaluate an h-function corresponding to a copula density estimate
//'
//' @param uev mx2 matrix of evaluation points
//' @param cond_var either 1 or 2; the variable to condition on.
//' @param vals matrix of density estimate evaluated on a kxk grid.
//' @param grid the grid points (1-dim) on which vals has been computed.
//'
//' @return H-function estimate evaluated at uev.
//'
//' @noRd
// [[Rcpp::export]]
Rcpp::NumericVector eval_hfunc_2d(const Rcpp::NumericMatrix& uev,
const int& cond_var,
const Rcpp::NumericMatrix& vals,
const Rcpp::NumericVector& grid)
{
int N = uev.nrow();
int m = grid.size();
NumericVector tmpvals(m), out(N), tmpa(4), tmpb(4);
NumericMatrix tmpgrid(m, 2);
double upr = 0.0;
double tmpint, int1;
tmpint = 0.0;
for (int n = 0; n < N; ++n) {
if (cond_var == 1) {
upr = uev(n, 1);
tmpgrid(_, 0) = rep(uev(n, 0), m);
tmpgrid(_, 1) = grid;
} else if (cond_var == 2) {
upr = uev(n, 0);
tmpgrid(_, 0) = grid;
tmpgrid(_, 1) = rep(uev(n, 1), m);
}
tmpvals = interp_2d(tmpgrid, vals, grid, tmpa, tmpb);
tmpint = int_on_grid(upr, tmpvals, grid);
int1 = int_on_grid(1.0, tmpvals, grid);
out[n] = tmpint/int1;
out[n] = fmax(out[n], 1e-10);
out[n] = fmin(out[n], 1-1e-10);
}
return out;
}
static Rcpp::NumericMatrix x_tilde_2(Rcpp::NumericMatrix X,
Rcpp::IntegerVector nk,
Rcpp::IntegerMatrix groups,
Rcpp::NumericMatrix alpha_new,
Rcpp::NumericMatrix eta_cur)
{
int K = nk.size();
int n_tot = X.nrow();
int p = X.ncol();
int L = groups.ncol();
Rcpp::NumericMatrix result(n_tot, L);
for (int l = 0; l < L; l++) {
int k = -1;
int n = 0;
for (int i = 0; i < n_tot; i++) {
if (i == n){
k +=1;
n += nk[k];
}
double sum = 0.0;
for (int j = 0; j < p; j++) {
if (elem(groups, j, l)) {
sum += elem(X, i, j) * elem(alpha_new, j, k);
}
}
elem(result, i, l) = elem(eta_cur, l, k) * sum;
}
}
return result;
}
///********************************************************************
///** scale2_NA_C
// [[Rcpp::export]]
Rcpp::NumericMatrix scale2_NA_C( Rcpp::NumericMatrix x )
{
int n = x.nrow();
int p = x.ncol();
Rcpp::NumericMatrix y(n,p);
double mvv=0;
double sdvv=0;
double nvv=0;
double eps_add = 1e-10;
for (int vv=0;vv<p;vv++){
mvv=0;
sdvv=0;
nvv=0;
for (int ii=0;ii<n;ii++){
if (! R_IsNA(x(ii,vv)) ) {
mvv += x(ii,vv);
sdvv += std::pow( x(ii,vv), 2.0 );
nvv ++;
}
}
mvv = mvv / nvv;
sdvv = std::sqrt( ( sdvv - nvv * mvv*mvv )/(nvv - 1.0 ) );
// define standardization
y(_,vv) = ( x(_,vv) - mvv ) / ( sdvv + eps_add );
}
//--- output
return y;
}
//' Check whether there are any non-finite values in a matrix
//'
//' The C++ functions will not work with NA values, and the calculation of the
//' summary profile will take a long time to run before crashing.
//'
//' @param matPtr matrix to check.
//'
//' @return
//' Throws an error if any \code{NA}, \code{NaN}, \code{Inf}, or \code{-Inf}
//' values are found, otherwise returns silently.
//'
// [[Rcpp::export]]
void CheckFinite(Rcpp::NumericMatrix matPtr) {
arma::mat mat = arma::mat(matPtr.begin(), matPtr.nrow(), matPtr.ncol(), false, true);
arma::uvec nonFiniteIdx = arma::find_nonfinite(mat);
if (nonFiniteIdx.n_elem > 0) {
throw Rcpp::exception("matrices cannot have non-finite or missing values");
}
}
void
run_mle(int argc, char* argv[])
{
// initialize R
RInside R(argc, argv);
// load BradleyTerry library
// load data to R object
// Run the MLE
// Do something with results
std::string str =
"cat('Requireing libraray\n');library('BradleyTerry2'); "
"cat('Loading data from file\n'); data <- read.table('data',sep=',') ; "
"cat('Running BTm()\n');fighterModel <- BTm(cbind(win1, win2), fighter1, fighter2, ~ fighter, id='fighter', data=data) ; "
"BTabilities(fighterModel)"; // returns a matrix of two colums: fighter; ability
Rcpp::NumericMatrix m = R.parseEval(str); // eval string, return value then as signed to num. vec
for (int i=0; i< m.nrow(); i++) {
cout << "Figher " << i << " has skill " << m(i,0) << endl;
}
cout << endl;
}
//' @title Calculating validation scores between two adjacency matrices
//'
//' @description
//' This function calculates the validation scores between two adjacency matrices.
//'
//' @param inf_mat matrix. It should be adjacency matrix of inferred network.
//' @param true_mat matrix. It should be adjacency matrix of true network.
// [[Rcpp::export]]
Rcpp::NumericVector rcpp_validate(Rcpp::NumericMatrix inf_mat, Rcpp::NumericMatrix true_mat) {
if(inf_mat.ncol() != true_mat.ncol()) {
throw std::invalid_argument( "Two input matrices should have the same number of columns." );
}
if(inf_mat.nrow() != true_mat.nrow()) {
throw std::invalid_argument( "Two input matrices should have the same number of rows." );
}
int tp=0;
int tn=0;
int fp=0;
int fn=0;
for(signed int i=0; i<inf_mat.nrow(); i++) {
//Convert R objects into C++ objects.
Rcpp::NumericVector xr = inf_mat.row(i);
Rcpp::NumericVector yr = true_mat.row(i);
std::vector<int> x = Rcpp::as<std::vector <int> >(xr);
std::vector<int> y = Rcpp::as<std::vector <int> >(yr);
std::vector<int> z;
//Calculate the frequency of numbers.
//tp=true positive [1,1], tn=true negative [0,0], fp=false positive [1,0], fn=false negative [0,1].
for(unsigned int k=0; k<x.size(); k++) {
z.push_back(x[k] + y[k]); //Calculate the summation of x and y between each element.
if(z[k] == 2) {
tp += 1;
} else if(z[k] == 0) {
tn += 1;
} else if(z[k] == 1) {
if(x[k] == 0) {
fp += 1;
} else {
fn += 1;
}
} else {
throw std::invalid_argument("Error in calculating the contigency table.");
}
}
}
//std::vector<int> output{tp, tn, fp, fn}; c++11 only
int tmp_arr[4] = {tp, tn, fp, fn};
std::vector<int> output(&tmp_arr[0], &tmp_arr[0]+4);
return Rcpp::wrap(output);
}
densePred::densePred(Rcpp::NumericMatrix x,
Rcpp::NumericVector coef0)
throw (std::runtime_error)
: predMod(coef0), d_X(x),
a_X(x.begin(), x.nrow(), x.ncol(), false, true) {
if (d_coef0.size() != d_X.ncol())
throw std::runtime_error("length(coef0) != ncol(X)");
}
//[[Rcpp::export]]
void decorr(Rcpp::NumericMatrix x) {
unsigned int i = 1, j=1, n=x.nrow();
if(n != x.ncol()) Rcpp::stop("matrix is not square");
for(i=0; i < n; i++) {
for(j=0; j < n; j++) {
if(j!=i) x(i,j) = x(i,j)*sqrt(x(i,i)*x(j,j));
}
}
}
// distance to measure function on a Grid, with weight
// [[Rcpp::export]]
Rcpp::NumericVector
DtmWeight(const Rcpp::NumericMatrix & knnDistance
, const double weightBound
, const double r
, const Rcpp::NumericMatrix & knnIndex
, const Rcpp::NumericVector & weight
) {
const unsigned gridNum = knnDistance.nrow();
unsigned gridIdx, kIdx;
double distanceTemp = 0.0;
Rcpp::NumericVector dtmValue(gridNum, 0.0);
double weightTemp, weightSumTemp;
if (r == 2.0) {
for (gridIdx = 0; gridIdx < gridNum; ++gridIdx) {
for (kIdx = 0, weightSumTemp = 0.0; weightSumTemp < weightBound;
++kIdx) {
distanceTemp = knnDistance[gridIdx + kIdx * gridNum];
weightTemp = weight[knnIndex[gridIdx + kIdx * gridNum] - 1];
dtmValue[gridIdx] += distanceTemp * distanceTemp * weightTemp;
weightSumTemp += weightTemp;
}
dtmValue[gridIdx] += distanceTemp * distanceTemp *
(weightBound - weightSumTemp);
dtmValue[gridIdx] = std::sqrt(dtmValue[gridIdx] / weightBound);
}
}
else if (r == 1.0) {
for (gridIdx = 0; gridIdx < gridNum; ++gridIdx) {
for (kIdx = 0, weightSumTemp = 0.0; weightSumTemp < weightBound;
++kIdx) {
distanceTemp = knnDistance[gridIdx + kIdx * gridNum];
weightTemp = weight[knnIndex[gridIdx + kIdx * gridNum] - 1];
dtmValue[gridIdx] += distanceTemp * weightTemp;
weightSumTemp += weightTemp;
}
dtmValue[gridIdx] += distanceTemp * (weightBound - weightSumTemp);
dtmValue[gridIdx] /= weightBound;
}
}
else {
for (gridIdx = 0; gridIdx < gridNum; ++gridIdx) {
for (kIdx = 0, weightSumTemp = 0.0; weightSumTemp < weightBound;
++kIdx) {
distanceTemp = knnDistance[gridIdx + kIdx * gridNum];
weightTemp = weight[knnIndex[gridIdx + kIdx * gridNum] - 1];
dtmValue[gridIdx] += std::pow(distanceTemp, r) * weightTemp;
weightSumTemp += weightTemp;
}
dtmValue[gridIdx] += std::pow(distanceTemp, r) *
(weightBound - weightSumTemp);
dtmValue[gridIdx] = std::pow(dtmValue[gridIdx] / weightBound, 1 / r);
}
}
return (dtmValue);
}
static int df(Rcpp::NumericMatrix beta_new, double eps)
{
int result = 0;
int n = beta_new.nrow() * beta_new.ncol();
for(int i=0; i < n; i++){
result += nz(beta_new[i],eps);
}
return result;
}
static void print(Rcpp::NumericMatrix A)
{
for (int i = 0; i < A.nrow(); i++) {
for (int j = 0; j < A.ncol(); j++) {
printf("%9f ", elem(A, i, j));
}
putchar('\n');
}
}
//[[Rcpp::export]]
Rcpp::NumericMatrix ZERO(Rcpp::NumericMatrix x) {
int i=0, j=0;
for(i=0; i < x.ncol(); i++) {
for(j=0; j < x.nrow(); j++) {
x(i,j) = 0;
}
}
return(x);
}
// [[Rcpp::export]]
Rcpp::NumericMatrix testColPost(Rcpp::NumericMatrix post, Rcpp::List m2u, int nthreads){
Rcpp::IntegerVector values = Rcpp::as<Rcpp::IntegerVector>(m2u["values"]);
Rcpp::IntegerVector map = Rcpp::as<Rcpp::IntegerVector>(m2u["map"]);
if (post.ncol() != map.length()) Rcpp::stop("posteriors doesn't match with m2u");
Rcpp::NumericMatrix smallerPost(post.nrow(), values.length());
Vec<double> foo; NMPreproc preproc(asVec(values), asVec(map), foo);
collapsePosteriors_core(asMat(smallerPost), asMat(post), preproc);
return smallerPost;
}
static Rcpp::IntegerMatrix nz(Rcpp::NumericMatrix m, double eps)
{
int nr = m.nrow();
int nc = m.ncol();
Rcpp::IntegerMatrix result(nr, nc);
for(int i=0; i < nr*nc; i++){
result[i] = nz(m[i],eps);
}
return result;
}
