int main()
{
fillArray(test_array, 4, 128);
cache = cache_new(CACHE_SIZE_IN_BLOCKS, block_size, 1, CACHE_REPLACEMENTPOLICY_LRU);
print_stats(sumA(test_array, 4, 128));
cache = cache_new(CACHE_SIZE_IN_BLOCKS, block_size, 1, CACHE_REPLACEMENTPOLICY_LRU);
print_stats(sumB(test_array, 4, 128));
cache = cache_new(CACHE_SIZE_IN_BLOCKS, block_size, 1, CACHE_REPLACEMENTPOLICY_LRU);
print_stats(sumC(test_array, 4, 128));
}
// for obtaining a fast empirical distribution of mean differences between two sets of columns for randomly sampled 'clusters'
SEXP emp_diffs(SEXP matrix_, SEXP nrow_, SEXP const colsA_, SEXP const colsB_, SEXP nsample_, SEXP niter_){
SEXP diffs = NULL;
try{
srand(time(NULL));
PROTECT(nrow_ = AS_INTEGER(nrow_));
int const nrow = *INTEGER_POINTER(nrow_);
UNPROTECT(1);
PROTECT(colsA_);
int * const colsA = INTEGER_POINTER(colsA_);
int const ncolA = LENGTH(colsA_);
PROTECT(colsB_);
int * const colsB = INTEGER_POINTER(colsB_);
int const ncolB = LENGTH(colsB_);
PROTECT(nsample_ = AS_INTEGER(nsample_));
int const nsample = *INTEGER_POINTER(nsample_);
UNPROTECT(1);
PROTECT(niter_ = AS_INTEGER(niter_));
int const niter = *INTEGER_POINTER(niter_);
UNPROTECT(1);
PROTECT(matrix_ = AS_NUMERIC(matrix_));
const double * const matrix = NUMERIC_POINTER(matrix_);
PROTECT(diffs = NEW_NUMERIC(niter));
double * const diffsp = NUMERIC_POINTER(diffs);
t_float val(0), diff(0), sumA(0), sumB(0);
int row(0), i(0), j(0);
for(int iter(0); iter<niter; ++iter){
// compute mean over nsample rows for column indices colsA
// R matrices are filled BY COLUMN
diff=0;
for(i=0; i<nsample; ++i){
row = rand() % nrow;
sumA=0; sumB=0;
for(j=0; j<ncolA; ++j){
// R is 1-indexed
val = matrix[row+nrow*(colsA[j]-1)];
if(ISNA(val)) continue;
sumA += val;
}
for(j=0; j<ncolB; ++j){
val = matrix[row+nrow*(colsB[j]-1)];
if(ISNA(val)) continue;
sumB += val;
}
diff += sumB/ncolB - sumA/ncolA;
}
diffsp[iter] = diff/nsample;
}
UNPROTECT(1); // matrix_
UNPROTECT(1); // colsA_
UNPROTECT(1); // colsB_
UNPROTECT(1); // diffs
}
catch (const std::bad_alloc&) {
Rf_error( "Memory overflow.");
}
catch(const std::exception& e){
Rf_error( e.what() );
}
catch(const nan_error&){
Rf_error("NaN dissimilarity value.");
}
catch(...){
Rf_error( "C++ exception (unknown reason)." );
}
return diffs;
}
