RcppExport SEXP GetAllPoints(SEXP x,SEXP n,SEXP c) {
try {
Rcpp::XPtr< flann::Index<flann::L2<float> > > index(x);
Rcpp::NumericVector npoints(n);
Rcpp::NumericVector cn(c);
int colNum = cn[0];
float *data = new float[colNum];
for(int i=0;i<colNum;i++) {
data[i] = 0;
i++;
}
flann::Matrix<float> dataset = flann::Matrix<float>(data,1,colNum);
delete [] data;
std::vector< std::vector<int> > indices;
std::vector< std::vector<float> > dists;
index->knnSearch(dataset,indices,dists,npoints[0],flann::SearchParams(-1));
std::sort (indices[0].begin(), indices[0].end());
Rcpp::NumericMatrix results(indices[0].size(), colNum);
Rcpp::IntegerVector rownames;
int num = indices[0].size();
//#pragma omp parallel for ordered schedule(dynamic)
for(int i=0;i<num;i++) {
float* indexPoint = index->getPoint(indices[0][i]);
for(int j=0;j<colNum;j++) {
results(i,j)=(*(indexPoint+j));
}
//#pragma omp ordered
rownames.push_back(indices[0][i]);
}
Rcpp::List dimnms = Rcpp::List::create(rownames, Rcpp::Range(1,colNum));
results.attr("dimnames") = dimnms;
return results;
} catch( std::exception &ex ) { // or use END_RCPP macro
forward_exception_to_r( ex );
} catch(...) {
::Rf_error( "c++ exception (unknown reason)" );
}
return R_NilValue; // -Wall
}
/**
* @return The mean and standard deviation of the current set of simulations
*/
SimulationWithErrors SimulationAggregator::average() const {
const size_t maxorder(results[0].maxorder),
ntimes(results[0].counts[0].size()), nruns(results.size());
SimulationWithErrors retval(maxorder, ntimes);
for (size_t i = 0; i < maxorder; ++i) {
auto &orderCounts = retval.sim.counts[i];
auto &orderErrors = retval.errors[i];
for (size_t j = 0; j < ntimes; ++j) {
double mean(0.0);
size_t npoints(0);
for (size_t k = 0; k < nruns; ++k) {
const double val = results[k].counts[i][j];
if (val > 0.0) {
mean += val;
npoints += 1;
}
}
if (npoints < 2) {
orderCounts[j] = 0.0;
orderErrors[j] = 0.0;
} else {
const double dblPts = static_cast<double>(npoints);
orderCounts[j] = mean / dblPts;
// error is std dev
double sumsq(0.0);
for (size_t k = 0; k < nruns; ++k) {
const double val = results[k].counts[i][j];
if (val > 0.0) {
const double diff = (val - mean);
sumsq += diff * diff;
}
}
orderErrors[j] = sqrt(sumsq / (dblPts * (dblPts - 1)));
}
}
}
return retval;
}
