// [[Rcpp::export]]
NumericMatrix CPP_similarity_to_distance(NumericMatrix M, int opcode, double tol, bool duplicate = true) {
if (!R_FINITE(opcode) || opcode < 0 || opcode > 0)
stop("internal error -- invalid transformation method code");
unsigned int n_items = M.length();
NumericMatrix res = M;
if (duplicate)
res = clone(M);
NumericMatrix::iterator _res = res.begin();
unsigned int n_clamped = 0;
for (unsigned int i = 0; i < n_items; i++) {
double x = _res[i];
switch (opcode) {
case 0:
if (x < -(1-tol)) {
if (x < -(1+tol)) n_clamped++;
x = -1;
}
else if (x > (1-tol)) {
if (x > (1+tol)) n_clamped++;
x = 1;
}
x = acos(x) * 180 / M_PI;
break;
}
_res[i] = x;
}
if (n_clamped > 0)
Rf_warning("angular distance may be inaccurate (some cosine values out of range)");
return res;
}
// [[Rcpp::export]]
NumericMatrix parallelMatrixSqrt(NumericMatrix x) {
// allocate the output matrix
NumericMatrix output(x.nrow(), x.ncol());
// SquareRoot instance that takes a pointer to the input & output data
SquareRoot squareRoot(x.begin(), output.begin());
// call parallelFor to do the work
parallelFor(0, x.length(), squareRoot);
// return the output matrix
return output;
}
// [[Rcpp::export]]
NumericMatrix samplehouseholds_format2(NumericMatrix phi, NumericMatrix w, NumericVector pi,
NumericVector d, List lambda,
int currrentbatch, int nHouseholds, int householdsize) {
int K = w.nrow();
int L = w.ncol();
int p = d.length();
int n_lambdas = lambda.length();
int *lambda_columns = new int[n_lambdas];
double **lambdas = new double*[n_lambdas];
int maxDDtp = phi.nrow();
int maxdd = maxDDtp / p;
//int ncol = householdsize * DIM + 1 + householdsize;
//output data: zero-based
//column 0: household unique index
//column 1: member index within the household (pernum:person number?)
//column 2 to 2+p-1: individual data
//column 2+p: 2+p+n_lambdas-2: household level data
//column 2+p+n_lambdas-1: household group indicator
//column last hh_size: individual group indicator
int DIM = 2 + p + n_lambdas - 1; //not output the household size
int ncol = DIM * householdsize + householdsize + 1;
NumericMatrix data(nHouseholds, ncol);
//copy data from list of matrices to C++
for (int i = 0; i < n_lambdas; i++) {
NumericMatrix l = lambda[i];
lambda_columns[i] = l.ncol();
lambdas[i] = new double[l.length()];
std::copy(l.begin(), l.end(), lambdas[i]);
}
//printf("in samplehouseholds\n");
NumericVector rand = runif(nHouseholds * ncol); //at most this many
sampleHouseholds_imp_format2(data.begin(), rand.begin(), lambdas, lambda_columns, w.begin(),
phi.begin(), pi.begin(),d.begin(),
nHouseholds, householdsize, K, L,maxdd,p, currrentbatch,n_lambdas);
//clean up
delete [] lambda_columns;
for (int i = 0; i < n_lambdas; i++) {
delete [] lambdas[i];
}
delete [] lambdas;
//printf("done samplehouseholds\n");
return data;
}
