* installing to library ‘/home/hornik/tmp/R.check/r-devel-gcc/Work/build/Packages’ * installing *source* package ‘ProjectionBasedClustering’ ... ** package ‘ProjectionBasedClustering’ successfully unpacked and MD5 sums checked ** using staged installation ** libs using C++ compiler: ‘g++-13 (Debian 13.2.0-23) 13.2.0’ using C++17 make[1]: Entering directory '/home/hornik/tmp/scratch/RtmpfGf9Jb/R.INSTALL25768b380fd4b9/ProjectionBasedClustering/src' g++-13 -std=gnu++17 -I"/home/hornik/tmp/R.check/r-devel-gcc/Work/build/include" -DNDEBUG -I'/home/hornik/tmp/R.check/r-devel-gcc/Work/build/Packages/Rcpp/include' -I/usr/local/include -D_FORTIFY_SOURCE=3 -fopenmp -fpic -g -O2 -Wall -pedantic -mtune=native -c DijkstraSSSP.cpp -o DijkstraSSSP.o DijkstraSSSP.cpp: In function ‘Rcpp::NumericVector DijkstraSSSP(Rcpp::NumericMatrix, Rcpp::NumericMatrix, int)’: DijkstraSSSP.cpp:38:3: warning: this ‘for’ clause does not guard... [-Wmisleading-indentation] 38 | for(int i=0;i::run(double*, int, int, double*, int, double, double, int, int, int, int, bool, int, double, double, double*, bool, double) [with treeT = SplitTree; double (* dist_fn)(const DataPoint&, const DataPoint&) = euclidean_distance]’, inlined from ‘Rcpp::NumericMatrix opt_multicore_tnse_cpp(Rcpp::NumericMatrix, int, double, int, int, double, int, double, double, int, double, int)’ at tsne.cpp:653:23: tsne.cpp:186:17: warning: ‘error_rc_prev’ may be used uninitialized [-Wmaybe-uninitialized] 186 | if (error_rc < error_rc_prev && iter > auto_iter_buffer_ee) { | ^~ tsne.cpp: In function ‘Rcpp::NumericMatrix opt_multicore_tnse_cpp(Rcpp::NumericMatrix, int, double, int, int, double, int, double, double, int, double, int)’: tsne.cpp:159:12: note: ‘error_rc_prev’ was declared here 159 | double error_rc_prev; // to store previous iteration's error rate of change in auto_iter mode | ^~~~~~~~~~~~~ In member function ‘double TSNE::computeGradient(int*, int*, double*, double*, int, int, double*, double, bool) [with treeT = SplitTree; double (* dist_fn)(const DataPoint&, const DataPoint&) = euclidean_distance]’, inlined from ‘void TSNE::run(double*, int, int, double*, int, double, double, int, int, int, int, bool, int, double, double, double*, bool, double) [with treeT = SplitTree; double (* dist_fn)(const DataPoint&, const DataPoint&) = euclidean_distance]’ at tsne.cpp:180:39, inlined from ‘Rcpp::NumericMatrix opt_multicore_tnse_cpp(Rcpp::NumericMatrix, int, double, int, int, double, int, double, double, int, double, int)’ at tsne.cpp:653:23: tsne.cpp:271:13: warning: ‘need_eval_error_algo’ may be used uninitialized [-Wmaybe-uninitialized] 271 | #pragma omp parallel for reduction(+:P_i_sum,C) | ^~~ tsne.cpp: In function ‘Rcpp::NumericMatrix opt_multicore_tnse_cpp(Rcpp::NumericMatrix, int, double, int, int, double, int, double, double, int, double, int)’: tsne.cpp:169:14: note: ‘need_eval_error_algo’ was declared here 169 | bool need_eval_error_algo, need_eval_error_verbose = false; | ^~~~~~~~~~~~~~~~~~~~ In member function ‘void TSNE::run(double*, int, int, double*, int, double, double, int, int, int, int, bool, int, double, double, double*, bool, double) [with treeT = SplitTree; double (* dist_fn)(const DataPoint&, const DataPoint&) = euclidean_distance_squared]’, inlined from ‘Rcpp::NumericMatrix opt_multicore_tnse_cpp(Rcpp::NumericMatrix, int, double, int, int, double, int, double, double, int, double, int)’ at tsne.cpp:649:23: tsne.cpp:186:17: warning: ‘error_rc_prev’ may be used uninitialized [-Wmaybe-uninitialized] 186 | if (error_rc < error_rc_prev && iter > auto_iter_buffer_ee) { | ^~ tsne.cpp: In function ‘Rcpp::NumericMatrix opt_multicore_tnse_cpp(Rcpp::NumericMatrix, int, double, int, int, double, int, double, double, int, double, int)’: tsne.cpp:159:12: note: ‘error_rc_prev’ was declared here 159 | double error_rc_prev; // to store previous iteration's error rate of change in auto_iter mode | ^~~~~~~~~~~~~ In member function ‘double TSNE::computeGradient(int*, int*, double*, double*, int, int, double*, double, bool) [with treeT = SplitTree; double (* dist_fn)(const DataPoint&, const DataPoint&) = euclidean_distance_squared]’, inlined from ‘void TSNE::run(double*, int, int, double*, int, double, double, int, int, int, int, bool, int, double, double, double*, bool, double) [with treeT = SplitTree; double (* dist_fn)(const DataPoint&, const DataPoint&) = euclidean_distance_squared]’ at tsne.cpp:180:39, inlined from ‘Rcpp::NumericMatrix opt_multicore_tnse_cpp(Rcpp::NumericMatrix, int, double, int, int, double, int, double, double, int, double, int)’ at tsne.cpp:649:23: tsne.cpp:271:13: warning: ‘need_eval_error_algo’ may be used uninitialized [-Wmaybe-uninitialized] 271 | #pragma omp parallel for reduction(+:P_i_sum,C) | ^~~ tsne.cpp: In function ‘Rcpp::NumericMatrix opt_multicore_tnse_cpp(Rcpp::NumericMatrix, int, double, int, int, double, int, double, double, int, double, int)’: tsne.cpp:169:14: note: ‘need_eval_error_algo’ was declared here 169 | bool need_eval_error_algo, need_eval_error_verbose = false; | ^~~~~~~~~~~~~~~~~~~~ g++-13 -std=gnu++17 -shared -L/home/hornik/tmp/R.check/r-devel-gcc/Work/build/lib -Wl,-O1 -o ProjectionBasedClustering.so DijkstraSSSP.o NeRV.o RcppExports.o c_measure.o calculatedistancematrix.o conjugategradientopt.o conttrust.o datamatrix.o dataset.o distancematrix.o dynamicdouble.o euclidean.o euclideansquared.o exception.o goldensectionsearch.o inputprobentropy.o klmeasure.o klrank.o nervcostfunction.o nervoptstrat.o nervprobability.o randomdatagenerator.o rankmatrix.o recorder.o splittree.o tsne.o -llapack -L/home/hornik/tmp/R.check/r-devel-gcc/Work/build/lib -lRblas -lgfortran -lm -lquadmath -fopenmp -L/home/hornik/tmp/R.check/r-devel-gcc/Work/build/lib -lR make[1]: Leaving directory '/home/hornik/tmp/scratch/RtmpfGf9Jb/R.INSTALL25768b380fd4b9/ProjectionBasedClustering/src' make[1]: Entering directory '/home/hornik/tmp/scratch/RtmpfGf9Jb/R.INSTALL25768b380fd4b9/ProjectionBasedClustering/src' make[1]: Leaving directory '/home/hornik/tmp/scratch/RtmpfGf9Jb/R.INSTALL25768b380fd4b9/ProjectionBasedClustering/src' installing to /home/hornik/tmp/R.check/r-devel-gcc/Work/build/Packages/00LOCK-ProjectionBasedClustering/00new/ProjectionBasedClustering/libs ** R ** data *** moving datasets to lazyload DB ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** testing if installed package can be loaded from temporary location ** checking absolute paths in shared objects and dynamic libraries ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * creating tarball packaged installation of ‘ProjectionBasedClustering’ as ‘ProjectionBasedClustering_1.2.1_R_x86_64-pc-linux-gnu.tar.gz’ * DONE (ProjectionBasedClustering)