// [[Rcpp::export]]
NumericVector computeAllLogLkhd(
NumericVector observedCases, NumericVector expectedCases,
List nearestNeighborsList, int nZones, String logLkhdType) {
NumericVector allLogLkhd(nZones);
int nAreas = expectedCases.size(), C = sum(observedCases),
N = sum(expectedCases), index = 0;
int nNeighbors = 0;
double cz = 0.0, nz = 0.0;
for (int i = 0; i < nAreas; ++i) {
cz = 0;
nz = 0;
Rcpp::NumericVector nearestNeighbors = nearestNeighborsList[i];
nNeighbors = nearestNeighbors.size();
// For each area's nearest neighbors
for(int j = 0; j < nNeighbors; ++j) {
// Watch off by 1 vector indexing in C as opposed to R
cz += observedCases[nearestNeighbors[j]-1];
nz += expectedCases[nearestNeighbors[j]-1];
if (logLkhdType=="poisson") {
allLogLkhd[index] = poissonLogLkhd(cz, nz, N, C);
} else if (logLkhdType == "binomial" ) {
allLogLkhd[index] = binomialLogLkhd(cz, nz, N, C);
}
index++;
}
}
return allLogLkhd;
}
// [[Rcpp::export]]
Rcpp::NumericVector LearnJ(Rcpp::NumericVector x,
Rcpp::NumericVector y,
Rcpp::NumericVector extent,
Rcpp::NumericVector image_labels){
assert(x.size() == y.size());
assert(y.size() == image_labels.size());
const int size_x = extent[1] - extent[0] + 1;
const int size_y = extent[3] - extent[2] + 1;
int x0 = extent[0];
int y0 = extent[2];
std::cout << "Size of grid: "<< size_x << " " << size_y << std::endl;
// Construct the grid of image_labels from the 1D data
std::vector<std::vector<int> > grid(size_y, std::vector<int>(size_x,0));
for (int i = 0 ; i < x.size() ; i++){
int posy = y[i] - y0;
int posx = x[i] - x0;
//std::cout << "i: " << i << " x, y: ";
//std::cout << posy << ", ";
//std::cout << posx << std::endl;
grid[posy][posx] = image_labels[i];
}
return 0;
}
void eigs_sym_shift_c(
mat_op op, int n, int k, double sigma,
const spectra_opts *opts, void *data,
int *nconv, int *niter, int *nops,
double *evals, double *evecs, int *info
)
{
BEGIN_RCPP
CRealShift cmat_op(op, n, data);
Rcpp::List res;
try {
res = run_eigs_shift_sym((RealShift*) &cmat_op, n, k, opts->ncv, opts->rule,
sigma, opts->maxitr, opts->tol, opts->retvec != 0);
*info = 0;
} catch(...) {
*info = 1; // indicates error
}
*nconv = Rcpp::as<int>(res["nconv"]);
*niter = Rcpp::as<int>(res["niter"]);
*nops = Rcpp::as<int>(res["nops"]);
Rcpp::NumericVector val = res["values"];
std::copy(val.begin(), val.end(), evals);
if(opts->retvec != 0)
{
Rcpp::NumericMatrix vec = res["vectors"];
std::copy(vec.begin(), vec.end(), evecs);
}
VOID_END_RCPP
}
SEXP rawSymmetricMatrixToDist(SEXP object)
{
BEGIN_RCPP
Rcpp::S4 rawSymmetric = object;
Rcpp::NumericVector levels = Rcpp::as<Rcpp::NumericVector>(rawSymmetric.slot("levels"));
Rcpp::CharacterVector markers = Rcpp::as<Rcpp::CharacterVector>(rawSymmetric.slot("markers"));
Rcpp::RawVector data = Rcpp::as<Rcpp::RawVector>(rawSymmetric.slot("data"));
R_xlen_t size = markers.size(), levelsSize = levels.size();
Rcpp::NumericVector result(size*(size - 1)/2, 0);
int counter = 0;
for(R_xlen_t row = 0; row < size; row++)
{
for(R_xlen_t column = row+1; column < size; column++)
{
int byte = data(column * (column + (R_xlen_t)1)/(R_xlen_t)2 + row);
if(byte == 255) result(counter) = std::numeric_limits<double>::quiet_NaN();
else result(counter) = levels(byte);
counter++;
}
}
result.attr("Size") = (int)size;
result.attr("Labels") = markers;
result.attr("Diag") = false;
result.attr("Upper") = false;
result.attr("class") = "dist";
return result;
END_RCPP
}
//[[Rcpp::export]]
extern "C" SEXP notbayesEstimation(SEXP Xs, SEXP Ys, SEXP Zs, SEXP Vs) {
Rcpp::NumericVector Xr(Xs);
Rcpp::NumericMatrix Yr(Ys);
Rcpp::NumericMatrix Zr(Zs);
Rcpp::NumericMatrix Vr(Vs);
int n = Yr.nrow(), k = Yr.ncol();
int l = Vr.nrow(), m = Vr.ncol();
arma::mat x(Xr.begin(),n,k,false); //fit2$sigma
arma::mat y(Yr.begin(),n,k,false); //fit2b$stdev.unscaled
arma::mat z(Zr.begin(),1,1,false); //min.variance.factor
arma::mat v(Vr.begin(),l,m,false); //fit2$df.residual
arma::mat sda(n,1);
arma::vec dof(n);
arma::vec sd(n);
sd = sqrt( (y%x)%(y%x) + as_scalar(z));
sda = sd/(x%y);
dof = v;
Rcpp::NumericVector Sd = Rcpp::wrap(sd);
Rcpp::NumericVector Sda = Rcpp::wrap(sda);
Rcpp::NumericVector DOF = Rcpp::wrap(dof);
Sd.names() = Rcpp::List(Yr.attr("dimnames"))[0];
Sda.names() = Rcpp::List(Yr.attr("dimnames"))[0];
return Rcpp::List::create( Rcpp::Named("SD") = Sd,
Rcpp::Named("DOF") = DOF,
Rcpp::Named("sd.alpha") = Sda);
}
// [[Rcpp::export]]
Rcpp::NumericVector randomSample(Rcpp::NumericVector a, int n) {
// clone a into b to leave a alone
Rcpp::NumericVector b(n);
__gnu_cxx::random_sample(a.begin(), a.end(),
b.begin(), b.end(), randWrapper);
return b;
}
void MapTematikQuantile::createMapTematikQuantile()
{
Rcpp::NumericVector quantile ;
QString command;
try {
command = QString("q <- quantile(dframe[[\"%1\"]], prob = seq(0, 1, length = (%2 + 1)), type = 7);").arg(var).arg(typeMap);
rconn.parseEvalQ(command.toStdString());
quantile = rconn["q"];
} catch (...) {
}
QList<int> temp[quantile.size()-1];
for(int i=0; i<numvar.size(); i++){
if(numvar[i] <= quantile[1]){
temp[0].append(table->verticalHeaderItem(i)->text().toInt());
}else{
for(int j=2; j<quantile.size(); j++){
if(numvar[i] > quantile[j-1] && numvar[i] <= quantile[j]){
temp[j-1].append(table->verticalHeaderItem(i)->text().toInt());
}
}
}
}
QList<QList<int> > temp2;
for(int i=0; i<quantile.size()-1; i++){
temp2.append(temp[i]);
}
MapTematikConfig* configWidget = new MapTematikConfig(mviewResult,vv,rconn,temp2,var,typeMap.toInt());
setupResultViewVariableTypeChooser("Quantile Map",var, temp2,quantile,configWidget);
}
//' rcpp_neutral_hotspots_ntests
//'
//' Performs repeated neutral tests to yield average distributions of both
//' hotspot values and spatial autocorrelation statistics.
//'
//' @param nbs An \code{spdep} \code{nb} object listing all neighbours of each
//' point.
//' @param wts Weighting factors for each neighbour; must have same length as
//' nbs.
//' @param nbsi List of matrices as returned from \code{get_nbsi}. each element
//' of which contains the i-th nearest neighbour to each point.
//' @param alpha Strength of spatial autocorrelation
//' @param sd0 Standard deviation of truncated normal distribution used to model
//' environmental variation (with mean of 1)
//' @param nt Number of successive layers of temporal and spatial autocorrelation
//' used to generate final modelled values
//' @param ntests Number of tests used to obtain average values
//' @param ac_type Character string specifying type of aucorrelation
//' (\code{moran}, \code{geary}, or code{getis-ord}).
//'
//' @return A matrix of dimension (size, 2), with first column containing
//' sorted and re-scaled hotspot values, and second column containing sorted and
//' re-scaled spatial autocorrelation statistics.
//'
// [[Rcpp::export]]
Rcpp::NumericMatrix rcpp_neutral_hotspots_ntests (Rcpp::List nbs,
Rcpp::List wts, Rcpp::List nbsi, double alpha, double sd0, int niters,
std::string ac_type, bool log_scale, int ntests)
{
const int size = nbs.size ();
Rcpp::NumericMatrix hs1;
Rcpp::NumericVector z (size), z1 (size), ac (size), ac1 (size);
std::fill (ac.begin (), ac.end (), 0.0);
std::fill (z.begin (), z.end (), 0.0);
for (int n=0; n<ntests; n++)
{
hs1 = rcpp_neutral_hotspots (nbs, wts, nbsi, alpha, sd0, log_scale,
niters, ac_type);
z += hs1 (Rcpp::_, 0);
ac += hs1 (Rcpp::_, 1);
}
Rcpp::NumericMatrix result (size, 2);
result (Rcpp::_, 0) = z / (double) ntests;
result (Rcpp::_, 1) = ac / (double) ntests;
Rcpp::colnames (result) = Rcpp::CharacterVector::create ("z", "ac");
return result;
}
lmResp::lmResp(Rcpp::NumericVector y)
throw (std::runtime_error)
: d_y(y), d_weights(y.size(), 1.0), d_offset(y.size()),
d_mu(y.size()), d_sqrtrwt(y.size()),
d_sqrtXwt(y.size(), 1.0) {
init();
}
// [[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_;
}
//' Update eigen values, vectors, and inverse matrices for irms
//'
//' @param tpm_prods array of transition probability matrix products
//' @param tpms array of transition probability matrices
//' @param pop_mat population level bookkeeping matrix
//' @param obstimes vector of observation times
//'
//' @return Updated eigenvalues, eigenvectors, and inverse matrices
// [[Rcpp::export]]
void tpmProdSeqs(Rcpp::NumericVector& tpm_prods, Rcpp::NumericVector& tpms, const Rcpp::IntegerVector obs_time_inds) {
// Get indices
int n_obs = obs_time_inds.size();
Rcpp::IntegerVector tpmDims = tpms.attr("dim");
// Ensure obs_time_inds starts at 0
Rcpp::IntegerVector obs_inds = obs_time_inds - 1;
// Set array pointers
arma::cube prod_arr(tpm_prods.begin(), tpmDims[0], tpmDims[1], tpmDims[2], false);
arma::cube tpm_arr(tpms.begin(), tpmDims[0], tpmDims[1], tpmDims[2], false);
// Generate tpm products and store them in the appropriate spots in the tpm product array
for(int j = 0; j < (n_obs - 1); ++j) {
prod_arr.slice(obs_inds[j+1] - 1) = tpm_arr.slice(obs_inds[j+1] - 1);
for(int k = (obs_inds[j+1] - 2); k > (obs_inds[j] - 1); --k) {
prod_arr.slice(k) = tpm_arr.slice(k) * prod_arr.slice(k + 1);
}
}
}
// Bellman recursion using row rearrangement
//[[Rcpp::export]]
Rcpp::List Bellman(const arma::mat& grid,
Rcpp::NumericVector reward_,
const arma::cube& scrap,
Rcpp::NumericVector control_,
const arma::cube& disturb,
const arma::vec& weight) {
// Passing R objects to C++
const std::size_t n_grid = grid.n_rows;
const std::size_t n_dim = grid.n_cols;
const arma::ivec r_dims = reward_.attr("dim");
const std::size_t n_pos = r_dims(3);
const std::size_t n_action = r_dims(2);
const std::size_t n_dec = r_dims(4) + 1;
const arma::cube
reward(reward_.begin(), n_grid, n_dim * n_action * n_pos, n_dec - 1, false);
const arma::ivec c_dims = control_.attr("dim");
arma::cube control2;
arma::imat control;
bool full_control;
if (c_dims.n_elem == 3) {
full_control = false;
arma::cube temp_control2(control_.begin(), n_pos, n_action, n_pos, false);
control2 = temp_control2;
} else {
full_control = true;
arma::mat temp_control(control_.begin(), n_pos, n_action, false);
control = arma::conv_to<arma::imat>::from(temp_control);
}
const std::size_t n_disturb = disturb.n_slices;
// Bellman recursion
arma::cube value(n_grid, n_dim * n_pos, n_dec);
arma::cube cont(n_grid, n_dim * n_pos, n_dec - 1, arma::fill::zeros);
arma::mat d_value(n_grid, n_dim);
Rcpp::Rcout << "At dec: " << n_dec - 1 << "...";
for (std::size_t pp = 0; pp < n_pos; pp++) {
value.slice(n_dec - 1).cols(n_dim * pp, n_dim * (pp + 1) - 1) =
scrap.slice(pp);
}
for (int tt = (n_dec - 2); tt >= 0; tt--) {
Rcpp::Rcout << tt;
// Approximating the continuation value
for (std::size_t pp = 0; pp < n_pos; pp++) {
cont.slice(tt).cols(n_dim * pp, n_dim * (pp + 1) - 1) =
Expected(grid,
value.slice(tt + 1).cols(pp * n_dim, n_dim * (pp + 1) - 1),
disturb, weight);
}
Rcpp::Rcout << "..";
// Optimise value function
if (full_control) {
BellmanOptimal(grid, control, value, reward, cont, tt);
} else {
BellmanOptimal2(grid, control2, value, reward, cont, tt);
}
Rcpp::Rcout << ".";
}
return Rcpp::List::create(Rcpp::Named("value") = value,
Rcpp::Named("expected") = cont);
}
// 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;
}
// redis "set a vector" -- without R serialization, without attributes, ...
// this is somewhat experimental
std::string setVector(std::string key, Rcpp::NumericVector x) {
redisReply *reply =
static_cast<redisReply*>(redisCommand(prc_, "SET %s %b",
key.c_str(), x.begin(), x.size()*szdb));
std::string res(reply->str);
freeReplyObject(reply);
return(res);
}
double lmerResp::updateMu(const Rcpp::NumericVector& gamma) {
#ifdef USE_RCPP_SUGAR
d_mu = d_offset + gamma;
#else
std::transform(gamma.begin(), gamma.end(), d_offset.begin(),
d_mu.begin(), std::plus<double>());
#endif
return updateWrss();
}
// [[Rcpp::export]]
arma::mat rmvnorm_arma(int n,
const arma::vec& mu,
const arma::mat& Sigma) {
unsigned int p = Sigma.n_cols;
Rcpp::NumericVector draw = Rcpp::rnorm(n*p);
arma::mat Z = arma::mat(draw.begin(), n, p, false, true);
arma::mat Y = arma::repmat(mu, 1, n).t()+Z * arma::chol(Sigma);
return Y;
}
glmResp::glmResp(Rcpp::List fam, Rcpp::NumericVector y,
Rcpp::NumericVector weights,
Rcpp::NumericVector offset,
Rcpp::NumericVector n)
throw (std::runtime_error)
: lmResp(y, weights, offset), d_fam(fam), d_n(n),
d_eta(y.size()) {
if (n.size() != y.size())
throw std::runtime_error("lengths of y and n must agree");
}
// Calculate lk, k=1, ..., M with m0=0;
// where h=(h0, h1, ..., hM) with h0=0 and d=(d0, d1, ..., dM) with d0=0, dM=R_PosInf
void Getlk(Rcpp::NumericVector& lk, const Rcpp::IntegerVector& Mt, int M1, Rcpp::NumericVector d,
const Rcpp::NumericVector& t, const Rcpp::NumericVector& Xbeta){
int n = Mt.size();
std::fill(lk.begin(), lk.end(), 0);
for (int k=1; k<M1; ++k){
for (int i=0; i<n; ++i){
if(Mt[i]>=k) lk[k] += (std::min(d[k],t[i])-d[k-1])*std::exp(Xbeta[i]);
}
}
}
// [[Rcpp::export]]
NumericVector freqsempiricalC(NumericVector x_) {
Rcpp::NumericVector x = Rcpp::clone(x_);
int n=x.length();
double sum=0;
for (int i=0;i<n;i++) {
sum+=x[i];
}
x=x/sum;
return x;
}
// gradient-descent ------------------------------------------------------------
//' @title Regular gradient descent for latent factor model with covariates
//' @param X The ratings matrix. Unobserved entries must be marked NA. Users
//' must be along rows, and tracks must be along columns.
//' @param Z The covariates associated with each pair. This must be shaped as an
//' array with users along rows, tracks along columns, and features along
//' slices.
//' @param k_factors The number of latent factors for the problem.
//' @param lambdas The regularization parameters for P, Q, and beta, respectively.
//' @param n_iter The number of gradient descent iterations to run.
//' @param batch_samples The proportion of the observed entries to sample in the
//' gradient for each factor in the gradient descent.
//' @param batch_factors The proportion of latent factors to update at each
//' iteration.
//' @param gamma The step-size in the gradient descent.
//' @param verbose Print the objective at each iteration of the descent?
//' @return A list with the following elements, \cr
//' $P The learned user latent factors. \cr
//' $Q The learned track latent factors. \cr
//' $beta The learned regression coefficients.
//' @export
// [[Rcpp::export]]
Rcpp::List svd_cov(Rcpp::NumericMatrix X, Rcpp::NumericVector Z_vec,
int k_factors, Rcpp::NumericVector lambdas, int n_iter,
double batch_samples, double batch_factors,
double gamma_pq, double gamma_beta, bool verbose) {
// convert to arma
Rcpp::IntegerVector Z_dim = Z_vec.attr("dim");
arma::cube Z(Z_vec.begin(), Z_dim[0], Z_dim[1], Z_dim[2], false);
// initialize results
int m = X.nrow();
int n = X.ncol();
int l = Z_dim[2];
arma::mat P = 1.0 / sqrt(m) * arma::randn(m, k_factors);
arma::mat Q = 1.0 / sqrt(n) * arma::randn(n, k_factors);
arma::vec beta = 1.0 / sqrt(l) * arma::randn(l);
arma::vec objs = arma::zeros(n_iter);
// perform gradient descent steps
for(int cur_iter = 0; cur_iter < n_iter; cur_iter++) {
// update user factors
arma::uvec cur_factors = get_batch_ix(m, batch_factors);
for(int i = 0; i < cur_factors.n_elem; i++) {
int cur_ix = cur_factors(i);
P.row(cur_ix) = update_factor(X.row(cur_ix),
Z(arma::span(cur_ix), arma::span(), arma::span()),
arma::conv_to<arma::vec>::from(P.row(cur_ix)), Q, beta,
batch_samples, lambdas[0], gamma_pq).t();
}
// update track factors
cur_factors = get_batch_ix(n, batch_factors);
for(int j = 0; j < cur_factors.n_elem; j++) {
int cur_ix = cur_factors(j);
Q.row(cur_ix) = update_factor(X.column(cur_ix),
Z(arma::span(), arma::span(cur_ix), arma::span()),
arma::conv_to<arma::vec>::from(Q.row(cur_ix)), P, beta,
batch_samples, lambdas[1], gamma_pq).t();
}
// update regression coefficients
beta = update_beta(Rcpp::as<arma::mat>(X), Z, P, Q, beta, lambdas[2], gamma_beta);
objs[cur_iter] = objective_fun(Rcpp::as<arma::mat>(X), Z, P, Q, beta, lambdas);
if(verbose) {
printf("iteration %d \n", cur_iter);
printf("Objective: %g \n", objs[cur_iter]);
}
}
return Rcpp::List::create(Rcpp::Named("P") = P,
Rcpp::Named("Q") = Q,
Rcpp::Named("beta") = beta,
Rcpp::Named("objs") = objs);
}
// redis "prepend to list" -- without R serialization
// as above: pure vector, no attributes, ...
std::string listLPush(std::string key, Rcpp::NumericVector x) {
// uses binary protocol, see hiredis doc at github
redisReply *reply =
static_cast<redisReply*>(redisCommand(prc_, "LPUSH %s %b",
key.c_str(), x.begin(), x.size()*szdb));
//std::string res(reply->str);
std::string res = "";
freeReplyObject(reply);
return(res);
}
// [[Rcpp::export(.cpp_convolve)]]
Rcpp::NumericVector cpp_convolve(Rcpp::NumericVector xa, Rcpp::NumericVector xb) {
int n_xa = xa.size(), n_xb = xb.size();
Rcpp::NumericVector xab(n_xa + n_xb - 1);
typedef Rcpp::NumericVector::iterator vec_iterator;
vec_iterator ia = xa.begin(), ib = xb.begin();
vec_iterator iab = xab.begin();
for (int i = 0; i < n_xa; i++)
for (int j = 0; j < n_xb; j++)
iab[i + j] += ia[i] * ib[j];
return xab;
}
glmResp::glmResp(Rcpp::List fam, Rcpp::NumericVector y,
Rcpp::NumericVector weights,
Rcpp::NumericVector offset,
Rcpp::NumericVector n, Rcpp::NumericVector eta)
throw (std::runtime_error)
: lmResp(y, weights, offset), d_fam(fam), d_n(n),
d_eta(eta) {
int nn = y.size();
if (n.size() != nn || eta.size() != nn )
throw std::runtime_error(
"lengths of y, n and eta must agree");
}
//Returns the value of any((object@data >= length(object@levels)) & object@data != as.raw(255))
SEXP checkRawSymmetricMatrix(SEXP rawSymmetric_)
{
BEGIN_RCPP
Rcpp::S4 rawSymmetric = rawSymmetric_;
Rcpp::NumericVector levels = Rcpp::as<Rcpp::NumericVector>(rawSymmetric.slot("levels"));
Rcpp::RawVector data = Rcpp::as<Rcpp::RawVector>(rawSymmetric.slot("data"));
R_xlen_t size = data.size(), levelsSize = levels.size();
for(R_xlen_t i = 0; i < size; i++)
{
if(data[i] >= levelsSize && data[i] != 0xff) return Rcpp::wrap(true);
}
return Rcpp::wrap(false);
END_RCPP
}
// [[Rcpp::export]]
NumericVector freqsshrinkC(NumericVector y_,double lambda) {
Rcpp::NumericVector y = Rcpp::clone(y_);
int m=y.length();
double n=0;
for (int i=0;i<m;i++) {
n+=y(i);
}
y=y/n;
NumericVector add(m,lambda/m);
y=y*(1-lambda);
y+=add;
return y;
}
// Returns the linear predictor from the full step
Rcpp::NumericVector
densePred::gamma(const Rcpp::NumericVector& xwts,
const Rcpp::NumericVector& wtres)
throw (std::runtime_error) {
int n = d_X.nrow();
if (xwts.size() != n || wtres.size() != n)
throw
std::runtime_error("length(xwts) or length(wtres) != nrow(X)");
arma::vec a_xwts = arma::vec(xwts.begin(), n, false, true),
a_wtres = arma::vec(wtres.begin(), n, false, true);
arma::vec tmp = solve(diagmat(a_xwts) * a_X, a_wtres);
std::copy(tmp.begin(), tmp.end(), a_delta.begin());
d_coef = d_coef0 + d_delta;
return NumericVector(wrap(a_X * a_coef));
}
// [[Rcpp::export]]
Rcpp::List worstcase_l1(Rcpp::NumericVector z, Rcpp::NumericVector q, double t){
// resulting probability
craam::numvec p;
// resulting objective value
double objective;
craam::numvec vz(z.begin(), z.end()), vq(q.begin(), q.end());
std::tie(p,objective) = craam::worstcase_l1(vz,vq,t);
Rcpp::List result;
result["p"] = Rcpp::NumericVector(p.cbegin(), p.cend());
result["obj"] = objective;
return result;
}
//' 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;
}
//' Construct a copula using uniform sampling from the unit simplex
//'
//' Given two families of parallel hyperplanes intersecting the canonical simplex, this function uniformly samples from the canonical simplex and construct an approximation of the bivariate probability distribution, called copula.
//'
//' @param h1 A \eqn{d}-dimensional vector that describes the direction of the first family of parallel hyperplanes.
//' @param h2 A \eqn{d}-dimensional vector that describes the direction of the second family of parallel hyperplanes.
//' @param numSlices The number of the slices for the copula. Default value is 100.
//' @param N The number of points to sample. Default value is \eqn{4\cdot 10^6}.
//'
//' @references \cite{L. Cales, A. Chalkis, I.Z. Emiris, V. Fisikopoulos,
//' \dQuote{Practical volume computation of structured convex bodies, and an application to modeling portfolio dependencies and financial crises,} \emph{Proc. of Symposium on Computational Geometry, Budapest, Hungary,} 2018.}
//'
//' @return A \eqn{numSlices\times numSlices} numerical matrix that corresponds to a copula.
//' @examples
//' # compute a copula for two random families of parallel hyperplanes
//' h1 = runif(n = 10, min = 1, max = 1000)
//' h1 = h1 / 1000
//' h2=runif(n = 10, min = 1, max = 1000)
//' h2 = h2 / 1000
//' cop = copula1(h1=h1, h2=h2, numSlices = 10, N = 100000)
//' @export
// [[Rcpp::export]]
Rcpp::NumericMatrix copula1 (Rcpp::NumericVector h1, Rcpp::NumericVector h2,
Rcpp::Nullable<unsigned int> numSlices, Rcpp::Nullable<unsigned int> N){
typedef double NT;
typedef Cartesian<NT> Kernel;
typedef typename Kernel::Point Point;
typedef boost::mt19937 RNGType;
unsigned int num_slices = 100, numpoints = 4000000;
if (numSlices.isNotNull()) {
num_slices = Rcpp::as<unsigned int>(numSlices);
}
if (N.isNotNull()) {
numpoints = Rcpp::as<unsigned int>(N);
}
Rcpp::NumericMatrix copula(num_slices, num_slices);
std::vector<std::vector<NT> > StdCopula;
unsigned int dim = h1.size(), i, j;
std::vector<NT> hyp1 = Rcpp::as<std::vector<NT> >(h1);
std::vector<NT> hyp2 = Rcpp::as<std::vector<NT> >(h2);
StdCopula = twoParHypFam<Point, RNGType >(dim, numpoints, num_slices, hyp1, hyp2);
for(i=0; i<num_slices; i++) {
for(j=0; j<num_slices; j++){
copula(i,j) = StdCopula[i][j];
}
}
return copula;
}
glmResp::glmResp(Rcpp::List fam, Rcpp::NumericVector y,
Rcpp::NumericVector weights,
Rcpp::NumericVector offset)
throw (std::runtime_error)
: lmResp(y, weights, offset), d_fam(fam),
d_eta(y.size()) {
}
