// [[Rcpp::export(".lrSib")]]
double lrSib_Caller(IntegerVector ProfSib1, IntegerVector ProfSib2,
List listFreqs){
return lrSib(ProfSib1.begin(), ProfSib2.begin(), listFreqs);
}
// [[Rcpp::export(".IBS_Caller")]]
int IBS_Caller(IntegerVector Prof1, IntegerVector Prof2, int nLoci){
return IBS(Prof1.begin(), Prof2.begin(), nLoci);
}
// [[Rcpp::export(".lrPC")]]
double lrPC_Caller(IntegerVector ProfParent, IntegerVector ProfChild,
List listFreqs){
return lrPC(ProfParent.begin(), ProfChild.begin(), listFreqs);
}
// simulate genotypes given observed marker data
// [[Rcpp::export(".sim_geno")]]
IntegerVector sim_geno(const String& crosstype,
const IntegerMatrix& genotypes, // columns are individuals, rows are markers
const IntegerMatrix& founder_geno, // columns are markers, rows are founder lines
const bool is_X_chr,
const LogicalVector& is_female, // length n_ind
const IntegerMatrix& cross_info, // columns are individuals
const NumericVector& rec_frac, // length nrow(genotypes)-1
const IntegerVector& marker_index, // length nrow(genotypes)
const double error_prob,
const int n_draws) // number of imputations
{
const int n_ind = genotypes.cols();
const int n_pos = marker_index.size();
const int n_mar = genotypes.rows();
QTLCross* cross = QTLCross::Create(crosstype);
// check inputs
if(is_female.size() != n_ind)
throw std::range_error("length(is_female) != ncol(genotypes)");
if(cross_info.cols() != n_ind)
throw std::range_error("ncols(cross_info) != ncol(genotypes)");
if(rec_frac.size() != n_pos-1)
throw std::range_error("length(rec_frac) != length(marker_index)-1");
if(error_prob < 0.0 || error_prob > 1.0)
throw std::range_error("error_prob out of range");
for(int i=0; i<rec_frac.size(); i++) {
if(rec_frac[i] < 0 || rec_frac[i] > 0.5)
throw std::range_error("rec_frac must be >= 0 and <= 0.5");
}
if(!cross->check_founder_geno_size(founder_geno, n_mar))
throw std::range_error("founder_geno is not the right size");
// end of checks
const int mat_size = n_pos*n_draws;
IntegerVector draws(mat_size*n_ind); // output object
for(int ind=0; ind<n_ind; ind++) {
Rcpp::checkUserInterrupt(); // check for ^C from user
// possible genotypes for this individual
IntegerVector poss_gen = cross->possible_gen(is_X_chr, is_female[ind], cross_info(_,ind));
const int n_poss_gen = poss_gen.size();
NumericVector probs(n_poss_gen);
// backward equations
NumericMatrix beta = backwardEquations(cross, genotypes(_,ind), founder_geno, is_X_chr, is_female[ind],
cross_info(_,ind), rec_frac, marker_index, error_prob,
poss_gen);
// simulate genotypes
for(int draw=0; draw<n_draws; draw++) {
// first draw
// calculate first prob (on log scale)
probs[0] = cross->init(poss_gen[0], is_X_chr, is_female[ind], cross_info(_,ind)) + beta(0,0);
if(marker_index[0] >= 0)
probs[0] += cross->emit(genotypes(marker_index[0],ind), poss_gen[0], error_prob,
founder_geno(_, marker_index[0]), is_X_chr, is_female[ind], cross_info(_,ind));
double sumprobs = probs[0]; // to contain log(sum(probs))
// calculate rest of probs
for(int g=1; g<n_poss_gen; g++) {
probs[g] = cross->init(poss_gen[g], is_X_chr, is_female[ind], cross_info(_,ind)) + beta(g,0);
if(marker_index[0] >= 0)
probs[g] += cross->emit(genotypes(marker_index[0],ind), poss_gen[g], error_prob,
founder_geno(_, marker_index[0]), is_X_chr, is_female[ind], cross_info(_,ind));
sumprobs = addlog(sumprobs, probs[g]);
}
// re-scale probs
for(int g=0; g<n_poss_gen; g++)
probs[g] = exp(probs[g] - sumprobs);
// make draw, returns a value from 1, 2, ..., n_poss_gen
int curgeno = random_int(probs);
draws[draw*n_pos + ind*mat_size] = poss_gen[curgeno];
// move along chromosome
for(int pos=1; pos<n_pos; pos++) {
// calculate probs
for(int g=0; g<n_poss_gen; g++) {
probs[g] = cross->step(poss_gen[curgeno], poss_gen[g], rec_frac[pos-1],
is_X_chr, is_female[ind], cross_info(_,ind)) +
beta(g,pos) - beta(curgeno, pos-1);
if(marker_index[pos] >= 0)
probs[g] += cross->emit(genotypes(marker_index[pos],ind), poss_gen[g], error_prob,
founder_geno(_, marker_index[pos]), is_X_chr, is_female[ind], cross_info(_,ind));
probs[g] = exp(probs[g]);
}
// make draw
curgeno = random_int(probs);
draws[pos + draw*n_pos + ind*mat_size] = poss_gen[curgeno];
} // loop over positions
} // loop over draws
} // loop over individuals
draws.attr("dim") = Dimension(n_pos, n_draws, n_ind);
delete cross;
return draws;
}
// [[Rcpp::export(".locusLRmix")]]
double locusLRmix_Caller(IntegerVector ProfVic, IntegerVector ProfSus,
NumericVector Freq){
return locusLRmix(ProfVic.begin(), ProfSus.begin(), Freq);
}
List objectivex(const arma::mat& transition, NumericVector emissionArray,
const arma::vec& init, IntegerVector obsArray, const arma::imat& ANZ,
IntegerVector emissNZ, const arma::ivec& INZ, const arma::ivec& nSymbols,
const arma::mat& coef, const arma::mat& X, arma::ivec& numberOfStates,
int threads) {
IntegerVector eDims = emissionArray.attr("dim"); //m,p,r
IntegerVector oDims = obsArray.attr("dim"); //k,n,r
arma::cube emission(emissionArray.begin(), eDims[0], eDims[1], eDims[2], false, true);
arma::icube obs(obsArray.begin(), oDims[0], oDims[1], oDims[2], false, true);
arma::icube BNZ(emissNZ.begin(), emission.n_rows, emission.n_cols - 1, emission.n_slices, false, true);
unsigned int q = coef.n_rows;
arma::vec grad(
arma::accu(ANZ) + arma::accu(BNZ) + arma::accu(INZ) + (numberOfStates.n_elem- 1) * q,
arma::fill::zeros);
arma::mat weights = exp(X * coef).t();
if (!weights.is_finite()) {
grad.fill(-arma::math::inf());
return List::create(Named("objective") = arma::math::inf(), Named("gradient") = wrap(grad));
}
weights.each_row() /= sum(weights, 0);
arma::mat initk(emission.n_rows, obs.n_slices);
for (unsigned int k = 0; k < obs.n_slices; k++) {
initk.col(k) = init % reparma(weights.col(k), numberOfStates);
}
arma::cube alpha(emission.n_rows, obs.n_cols, obs.n_slices); //m,n,k
arma::cube beta(emission.n_rows, obs.n_cols, obs.n_slices); //m,n,k
arma::mat scales(obs.n_cols, obs.n_slices); //m,n,k
arma::sp_mat sp_trans(transition);
internalForwardx(sp_trans.t(), emission, initk, obs, alpha, scales, threads);
if (!scales.is_finite()) {
grad.fill(-arma::math::inf());
return List::create(Named("objective") = arma::math::inf(), Named("gradient") = wrap(grad));
}
internalBackwardx(sp_trans, emission, obs, beta, scales, threads);
if (!beta.is_finite()) {
grad.fill(-arma::math::inf());
return List::create(Named("objective") = arma::math::inf(), Named("gradient") = wrap(grad));
}
arma::ivec cumsumstate = arma::cumsum(numberOfStates);
arma::mat gradmat(
arma::accu(ANZ) + arma::accu(BNZ) + arma::accu(INZ) + (numberOfStates.n_elem- 1) * q,
obs.n_slices, arma::fill::zeros);
#pragma omp parallel for if(obs.n_slices >= threads) schedule(static) num_threads(threads) \
default(none) shared(q, alpha, beta, scales, gradmat, nSymbols, ANZ, BNZ, INZ, \
numberOfStates, cumsumstate, obs, init, initk, X, weights, transition, emission)
for (int k = 0; k < obs.n_slices; k++) {
int countgrad = 0;
// transitionMatrix
if (arma::accu(ANZ) > 0) {
for (int jj = 0; jj < numberOfStates.n_elem; jj++) {
arma::vec gradArow(numberOfStates(jj));
arma::mat gradA(numberOfStates(jj), numberOfStates(jj));
int ind_jj = cumsumstate(jj) - numberOfStates(jj);
for (int i = 0; i < numberOfStates(jj); i++) {
arma::uvec ind = arma::find(ANZ.row(ind_jj + i).subvec(ind_jj, cumsumstate(jj) - 1));
if (ind.n_elem > 0) {
gradArow.zeros();
gradA.eye();
gradA.each_row() -= transition.row(ind_jj + i).subvec(ind_jj, cumsumstate(jj) - 1);
gradA.each_col() %= transition.row(ind_jj + i).subvec(ind_jj, cumsumstate(jj) - 1).t();
for (int j = 0; j < numberOfStates(jj); j++) {
for (unsigned int t = 0; t < (obs.n_cols - 1); t++) {
double tmp = alpha(ind_jj + i, t, k);
for (unsigned int r = 0; r < obs.n_rows; r++) {
tmp *= emission(ind_jj + j, obs(r, t + 1, k), r);
}
gradArow(j) += tmp * beta(ind_jj + j, t + 1, k) / scales(t + 1, k);
}
}
gradArow = gradA * gradArow;
gradmat.col(k).subvec(countgrad, countgrad + ind.n_elem - 1) = gradArow.rows(ind);
countgrad += ind.n_elem;
}
}
}
}
if (arma::accu(BNZ) > 0) {
// emissionMatrix
for (unsigned int r = 0; r < obs.n_rows; r++) {
arma::vec gradBrow(nSymbols(r));
arma::mat gradB(nSymbols(r), nSymbols(r));
for (unsigned int i = 0; i < emission.n_rows; i++) {
arma::uvec ind = arma::find(BNZ.slice(r).row(i));
if (ind.n_elem > 0) {
gradBrow.zeros();
gradB.eye();
gradB.each_row() -= emission.slice(r).row(i).subvec(0, nSymbols(r) - 1);
gradB.each_col() %= emission.slice(r).row(i).subvec(0, nSymbols(r) - 1).t();
for (int j = 0; j < nSymbols(r); j++) {
if (obs(r, 0, k) == j) {
double tmp = initk(i, k);
for (unsigned int r2 = 0; r2 < obs.n_rows; r2++) {
if (r2 != r) {
tmp *= emission(i, obs(r2, 0, k), r2);
}
}
gradBrow(j) += tmp * beta(i, 0, k) / scales(0, k);
}
for (unsigned int t = 0; t < (obs.n_cols - 1); t++) {
if (obs(r, t + 1, k) == j) {
double tmp = beta(i, t + 1, k) / scales(t + 1, k);
for (unsigned int r2 = 0; r2 < obs.n_rows; r2++) {
if (r2 != r) {
tmp *= emission(i, obs(r2, t + 1, k), r2);
}
}
gradBrow(j) += arma::dot(alpha.slice(k).col(t), transition.col(i)) * tmp;
}
}
}
gradBrow = gradB * gradBrow;
gradmat.col(k).subvec(countgrad, countgrad + ind.n_elem - 1) = gradBrow.rows(ind);
countgrad += ind.n_elem;
}
}
}
}
if (arma::accu(INZ) > 0) {
for (int i = 0; i < numberOfStates.n_elem; i++) {
int ind_i = cumsumstate(i) - numberOfStates(i);
arma::uvec ind = arma::find(
INZ.subvec(ind_i, cumsumstate(i) - 1));
if (ind.n_elem > 0) {
arma::vec gradIrow(numberOfStates(i), arma::fill::zeros);
for (int j = 0; j < numberOfStates(i); j++) {
double tmp = weights(i, k);
for (unsigned int r = 0; r < obs.n_rows; r++) {
tmp *= emission(ind_i + j, obs(r, 0, k), r);
}
gradIrow(j) += tmp * beta(ind_i + j, 0, k) / scales(0, k);
}
arma::mat gradI(numberOfStates(i), numberOfStates(i), arma::fill::zeros);
gradI.eye();
gradI.each_row() -= init.subvec(ind_i, cumsumstate(i) - 1).t();
gradI.each_col() %= init.subvec(ind_i, cumsumstate(i) - 1);
gradIrow = gradI * gradIrow;
gradmat.col(k).subvec(countgrad, countgrad + ind.n_elem - 1) = gradIrow.rows(ind);
countgrad += ind.n_elem;
}
}
}
for (int jj = 1; jj < numberOfStates.n_elem; jj++) {
int ind_jj = (cumsumstate(jj) - numberOfStates(jj));
for (int j = 0; j < emission.n_rows; j++) {
double tmp = 1.0;
for (unsigned int r = 0; r < obs.n_rows; r++) {
tmp *= emission(j, obs(r, 0, k), r);
}
if ((j >= ind_jj) & (j < cumsumstate(jj))) {
gradmat.col(k).subvec(countgrad + q * (jj - 1), countgrad + q * jj - 1) += tmp
* beta(j, 0, k) / scales(0, k) * initk(j, k) * X.row(k).t() * (1.0 - weights(jj, k));
} else {
gradmat.col(k).subvec(countgrad + q * (jj - 1), countgrad + q * jj - 1) -= tmp
* beta(j, 0, k) / scales(0, k) * initk(j, k) * X.row(k).t() * weights(jj, k);
}
}
}
}
return List::create(Named("objective") = -arma::accu(log(scales)),
Named("gradient") = wrap(-sum(gradmat, 1)));
}
// [[Rcpp::export]]
List melt_dataframe(const DataFrame& data,
const IntegerVector& id_ind,
const IntegerVector& measure_ind,
String variable_name,
String value_name,
SEXP measure_attributes,
bool factorsAsStrings,
bool valueAsFactor) {
int nrow = data.nrows();
CharacterVector data_names = as<CharacterVector>(data.attr("names"));
int n_id = id_ind.size();
debug(Rprintf("n_id == %i\n", n_id));
int n_measure = measure_ind.size();
debug(Rprintf("n_measure == %i\n", n_measure));
// Don't melt if the value variables are non-atomic
for (int i = 0; i < n_measure; ++i) {
if (!Rf_isVectorAtomic(data[measure_ind[i]])) {
stop("Can't melt data.frames with non-atomic 'measure' columns");
}
}
// The output should be a data.frame with:
// number of columns == number of id vars + 'variable' + 'value',
// with number of rows == data.nrow() * number of value vars
List output = no_init(n_id + 2);
// First, allocate the ID variables
// we repeat each ID vector n_measure times
// A define to handle the different possible types
#define REP(OBJECT, RTYPE) \
case RTYPE: { \
output[i] = rep_(OBJECT, n_measure); \
Rf_copyMostAttrib(OBJECT, output[i]); \
break; \
}
for (int i = 0; i < n_id; ++i) {
SEXP object = data[id_ind[i]];
if (Rf_inherits(object, "POSIXlt")) {
std::string var = std::string(data_names[id_ind[i]]);
Rcpp::stop("'%s' is a POSIXlt. Please convert to POSIXct.", var);
}
switch (TYPEOF(object)) {
REP(object, LGLSXP);
REP(object, INTSXP);
REP(object, REALSXP);
REP(object, STRSXP);
REP(object, CPLXSXP);
REP(object, RAWSXP);
REP(object, VECSXP);
default: { stop("internal error: unnhandled vector type in REP"); }
}
}
// Now, we assign the 'variable' and 'value' columns
// 'variable' is made up of repeating the names of the 'measure' variables,
// each nrow times. We want this to be a factor as well.
CharacterVector id_names = no_init(n_measure);
for (int i = 0; i < n_measure; ++i) {
id_names[i] = data_names[measure_ind[i]];
}
output[n_id] = make_variable_column(id_names, nrow);
// 'value' is made by concatenating each of the 'value' variables
output[n_id + 1] = concatenate(data, measure_ind, factorsAsStrings);
if (!Rf_isNull(measure_attributes)) {
SET_ATTRIB(output[n_id + 1], measure_attributes);
// we also need to make sure the OBJECT bit is set for other 'object' types
// see: http://stackoverflow.com/questions/24059460/melt-data-frame-changes-behavior-how-posixct-columns-are-printed
// if we've entered this code block, the measure_attributes has been
// populated because all value variables have identical attributes
SET_OBJECT(output[n_id + 1], OBJECT(data[measure_ind[0]]));
}
// Make the List more data.frame like
// Set the row names
output.attr("row.names") =
IntegerVector::create(IntegerVector::get_na(), -(nrow * n_measure));
// Set the names
CharacterVector out_names = no_init(n_id + 2);
for (int i = 0; i < n_id; ++i) {
out_names[i] = data_names[id_ind[i]];
}
out_names[n_id] = variable_name;
out_names[n_id + 1] = value_name;
output.attr("names") = out_names;
// Set the class
output.attr("class") = "data.frame";
return output;
}
t extract(const t & in, const IntegerVector & index) {
int n = index.size();
t out (n);
for (int i = 0; i < n; i++) out[i] = in[index[i]];
return out;
}
// [[Rcpp::export]]
NumericMatrix CPP_col_dist_sparse(int nc1, IntegerVector xp, IntegerVector xrow, NumericVector x, int nc2, IntegerVector yp, IntegerVector yrow, NumericVector y, int metric_code, double param1, bool symmetric) {
check_metric(metric_code, param1);
NumericVector::iterator _x = x.begin();
NumericVector::iterator _y = y.begin();
IntegerVector::iterator _xrow = xrow.begin();
IntegerVector::iterator _yrow = yrow.begin();
NumericMatrix dist(nc1, nc2);
#ifdef _OPENMP
/* average number of entries scanned when comparing two columns, used to decide whether to try parallelization */
double avg_nr = (xp[nc1] - xp[0] + 0.0) / nc1 + (yp[nc2] - yp[0] + 0.0) / nc2;
#endif
#pragma omp parallel for \
if (openmp_threads > 1 && (nc1 + 0.0) * (nc2 + 0.0) * avg_nr > 40e6) \
num_threads(openmp_threads) \
shared(dist, nc1, xp, _xrow, _x, nc2, yp, _yrow, _y, metric_code, param1)
for (int col2 = 0; col2 < nc2; col2++) {
int col1_max = (symmetric) ? col2 + 1 : nc1;
int yi_max = yp[col2 + 1];
for (int col1 = 0; col1 < col1_max; col1++) {
int xi_max = xp[col1 + 1];
int xi = xp[col1];
int yi = yp[col2];
int xrow_curr = (xi < xi_max) ? _xrow[xi] : INT_MAX;
int yrow_curr = (yi < yi_max) ? _yrow[yi] : INT_MAX;
double accum = 0.0;
double x_curr, y_curr;
double d_xy, x_plus_y;
while (xi < xi_max || yi < yi_max) {
if (xrow_curr < yrow_curr) {
x_curr = _x[xi]; y_curr = 0.0;
xi++;
xrow_curr = (xi < xi_max) ? _xrow[xi] : INT_MAX;
}
else if (xrow_curr == yrow_curr) {
x_curr = _x[xi]; y_curr = _y[yi];
xi++; yi++;
xrow_curr = (xi < xi_max) ? _xrow[xi] : INT_MAX;
yrow_curr = (yi < yi_max) ? _yrow[yi] : INT_MAX;
}
else /* xrow_curr > yrow_curr */ {
x_curr = 0; y_curr = _y[yi];
yi++;
yrow_curr = (yi < yi_max) ? _yrow[yi] : INT_MAX;
}
switch (metric_code) {
case 0:
d_xy = x_curr - y_curr;
accum += d_xy * d_xy;
break;
case 1:
d_xy = fabs(x_curr - y_curr);
if (d_xy > accum) accum = d_xy;
break;
case 2:
d_xy = fabs(x_curr - y_curr);
accum += d_xy;
break;
case 3:
d_xy = fabs(x_curr - y_curr);
accum += pow(d_xy, param1);
break;
case 4:
x_plus_y = fabs(x_curr) + fabs(y_curr);
d_xy = fabs(x_curr - y_curr);
if (x_plus_y > 0) accum += d_xy / x_plus_y;
break;
}
} /* while (xi, yi) */
switch (metric_code) {
case 0:
dist(col1, col2) = sqrt(accum);
break;
case 1:
case 2:
case 4:
dist(col1, col2) = accum;
break;
case 3:
if (param1 > 1.0)
dist(col1, col2) = pow(accum, 1.0 / param1);
else
dist(col1, col2) = accum;
break;
}
} /* for (col1) */
} /* for (col2) */
if (symmetric) mk_symmetric_matrix(dist);
return dist;
}
//' @export
// [[Rcpp::export]]
List blockSizeCalibrate(NumericMatrix ret,
IntegerVector b_vec = IntegerVector::create(1, 3, 6, 10),
double alpha = 0.05,
int M = 199,
int K = 1000,
int b_av = 5,
int T_start = 50) {
int b_len = b_vec.length();
NumericVector emp_reject_probs = rep(0.0, b_len);
double Delta_hat = sharpeRatioDiff(ret);
NumericVector ret1 = ret(_,0);
NumericVector ret2 = ret(_,1);
int T = ret1.length();
NumericMatrix Var_data(T_start + T, 2);
Var_data(0,_) = ret(0,_);
IntegerVector range1 = seq(1, T-1);
IntegerVector range2 = seq(0, T-2);
NumericVector intercept = rep(1.0, ret1.length());
List fit1 = fastLm(ret1[range1], cbindCpp(intercept[range2], cbindCpp(ret1[range2],ret2[range2])));
List fit2 = fastLm(ret2[range1], cbindCpp(intercept[range2], cbindCpp(ret1[range2],ret2[range2])));
NumericVector coef1 = as<NumericVector>(wrap(fit1["coef"]));
NumericVector coef2 = as<NumericVector>(wrap(fit2["coef"]));
NumericMatrix resid_mat = cbindCpp(as<NumericVector>(fit1["resid"]),
as<NumericVector>(fit2["resid"]));
for(int k = 0; k < K; k++){
// create resid_mat_star
NumericMatrix resid_mat_star(T_start + T, resid_mat.cols());
// fill with NA by default
int xsize = resid_mat_star.nrow() * resid_mat_star.ncol();
for (int i = 0; i < xsize; i++) {
resid_mat_star[i] = NumericMatrix::get_na();
}
// fill first row with 0
for(int c = 0; c < resid_mat_star.ncol(); c++)
resid_mat_star(0,c) = 0.0;
IntegerVector index = sbSequenceCpp(T-1, b_av, T_start + T - 1) - 1;
for(int j = 0; j < index.length(); j++){
// handling NA values
if( IntegerVector::is_na(index(j)) ){
// do nothing
} else if( (j+1) > resid_mat_star.nrow() ){
// do nothing
} else if( index[j] > resid_mat.nrow() ){
// do nothing
} else {
resid_mat_star(j+1,_) = resid_mat(index[j],_);
}
}
for(int t = 1; t < (T_start + T); t++){
Var_data(t,0) = coef1[0] + coef1[1]*Var_data(t-1,0) +
coef1[2]*Var_data(t-1,1) + resid_mat_star(t,0);
Var_data(t,1) = coef2[0] + coef2[1]*Var_data(t-1,0) +
coef2[2]*Var_data(t-1,1) + resid_mat_star(t,1);
}
NumericMatrix Var_data_trunc = Var_data(Range(T_start,T_start+T-1), Range(0,Var_data.ncol()-1));
for(int j = 0; j < b_len; j++){
List bTI = bootTimeInference(Var_data_trunc, b_vec[j], M, Delta_hat);
double p_value = as<double>(wrap(bTI["p.Value"]));
if(p_value <= alpha)
emp_reject_probs[j] = emp_reject_probs[j] + 1;
}
}
emp_reject_probs = emp_reject_probs/(double)K;
Environment env = Environment::base_namespace();
Function order = env["order"];
IntegerVector b_order = order(abs(emp_reject_probs - alpha));
int b_opt = as<int>(wrap(b_vec(b_order[0]-1)));
NumericMatrix b_vec_with_probs = rbindCpp(as<NumericVector>(wrap(b_vec)), emp_reject_probs);
return(List::create(
_["Empirical.Rejection.Probs"] = b_vec_with_probs,
_["b.optimal"] = b_opt
));
}
void
madara::knowledge::containers::IntegerVector::exchange (
IntegerVector & other, bool refresh_keys, bool delete_keys)
{
if (context_ && other.context_)
{
ContextGuard context_guard (*context_);
ContextGuard other_context_guard (*other.context_);
MADARA_GUARD_TYPE guard (mutex_), guard2 (other.mutex_);
if (refresh_keys)
{
other.resize ();
this->resize ();
}
size_t other_size = other.vector_.size ();
size_t this_size = this->vector_.size ();
for (size_t i = 0; i < this_size; ++i)
{
// temp = this[i];
knowledge::KnowledgeRecord temp = context_->get (this->vector_[i], settings_);
if (i < other_size)
{
// this[i] = other[i];
context_->set (this->vector_[i],
context_->get (other.vector_[i], other.settings_),
settings_);
// other[i] = temp;
other.context_->set (other.vector_[i], temp, other.settings_);
}
else
{
if (delete_keys)
{
std::stringstream buffer;
buffer << this->name_;
buffer << delimiter_;
buffer << i;
this->context_->delete_variable (buffer.str (), other.settings_);
}
else
{
knowledge::KnowledgeRecord zero;
this->context_->set (this->vector_[i], zero, this->settings_);
}
{
std::stringstream buffer;
buffer << other.name_;
buffer << delimiter_;
buffer << i;
// other[i] = temp;
other.context_->set (buffer.str (), temp, other.settings_);
}
}
}
// copy the other vector's elements to this vector's location
for (size_t i = this_size; i < other_size; ++i)
{
std::stringstream buffer;
buffer << this->name_;
buffer << delimiter_;
buffer << i;
context_->set (buffer.str (),
other.context_->get (other.vector_[i], other.settings_), this->settings_);
}
// set the size appropriately
this->context_->set (this->size_,
knowledge::KnowledgeRecord::Integer (other_size), this->settings_);
other.context_->set (other.size_,
knowledge::KnowledgeRecord::Integer (this_size), other.settings_);
if (refresh_keys)
{
this->resize (-1, true);
other.resize (-1, true);
}
}
}
void wrapAssignments(int nBidders, int * assignments,
IntegerVector& outAssignments)
{
std::copy(assignments, assignments + nBidders, outAssignments.begin());
}
