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; }