List EM(const arma::mat& transition_, const arma::cube& emission_, const arma::vec& init_,
const arma::ucube& obs, const arma::uvec& nSymbols, int itermax, double tol,
int trace, unsigned int threads) {
// Make sure we don't alter the original vec/mat/cube
// needed for cube, in future maybe in other cases as well
arma::cube emission(emission_);
arma::mat transition(transition_);
arma::vec init(init_);
// EM-algorithm begins
double change = tol + 1.0;
int iter = -1; //for backward compatibility
double sumlogLik_new = 0;
double sumlogLik = -1e150; //sum(ll);
while ((change > tol) & (iter < itermax)) {
iter++;
arma::mat ksii(emission.n_rows, emission.n_rows, arma::fill::zeros);
arma::cube gamma(emission.n_rows, emission.n_cols, emission.n_slices, arma::fill::zeros);
arma::vec delta(emission.n_rows, arma::fill::zeros);
sumlogLik_new = 0;
double max_sf = 1;
unsigned int error_code = 0;
#pragma omp parallel for if(obs.n_slices>=threads) schedule(static) reduction(+:sumlogLik_new) num_threads(threads) \
default(none) shared(init, transition, obs, emission, delta, ksii, gamma, nSymbols, error_code, max_sf)
for (unsigned int k = 0; k < obs.n_slices; k++) {
arma::mat alpha(emission.n_rows, obs.n_cols); //m,n,k
arma::vec scales(obs.n_cols);
arma::sp_mat sp_trans(transition);
uvForward(sp_trans.t(), emission, init, obs.slice(k), alpha, scales);
arma::mat beta(emission.n_rows, obs.n_cols); //m,n,k
uvBackward(sp_trans, emission, obs.slice(k), beta, scales);
sumlogLik_new -= arma::sum(log(scales));
arma::mat ksii_k(emission.n_rows, emission.n_rows, arma::fill::zeros);
arma::cube gamma_k(emission.n_rows, emission.n_cols, emission.n_slices, arma::fill::zeros);
arma::vec delta_k(emission.n_rows);
delta_k = alpha.col(0) % beta.col(0);
if (obs.n_cols > 1) {
for (unsigned int j = 0; j < emission.n_rows; j++) {
for (unsigned int i = 0; i < emission.n_rows; i++) {
if (transition(i, j) > 0.0) {
for (unsigned int t = 0; t < (obs.n_cols - 1); t++) {
double tmp = alpha(i, t) * transition(i, j) * beta(j, t + 1)
* scales(t + 1);
for (unsigned int r = 0; r < obs.n_rows; r++) {
tmp *= emission(j, obs(r, t + 1, k), r);
}
ksii_k(i, j) += tmp;
}
}
}
}
}
for (unsigned int r = 0; r < emission.n_slices; r++) {
for (unsigned int l = 0; l < nSymbols(r); l++) {
for (unsigned int i = 0; i < emission.n_rows; i++) {
if (emission(i, l, r) > 0.0) {
for (unsigned int t = 0; t < obs.n_cols; t++) {
if (l == (obs(r, t, k))) {
gamma_k(i, l, r) += alpha(i, t) * beta(i, t);
}
}
}
}
}
}
#pragma omp critical
{
if(!scales.is_finite()) {
error_code = 1;
}
if(!beta.is_finite()) {
error_code = 2;
}
max_sf = std::min(max_sf, scales.max());
delta += delta_k;
ksii += ksii_k;
gamma += gamma_k;
}
}
if(error_code == 1) {
return List::create(Named("error") = 1);
}
if(error_code == 2) {
return List::create(Named("error") = 2);
}
if (max_sf > 1e150) {
Rcpp::warning("Largest scaling factor was %e, results can be numerically unstable.", max_sf);
}
change = (sumlogLik_new - sumlogLik) / (std::abs(sumlogLik) + 0.1);
sumlogLik = sumlogLik_new;
if (trace > 0) {
if(iter == 1) {
Rcout << "Log-likelihood of initial model: " << sumlogLik << std::endl;
} else {
if (trace > 1) {
Rcout << "iter: " << iter;
Rcout << " logLik: " << sumlogLik;
Rcout << " relative change: " << change << std::endl;
}
}
}
if (change > tol) {
if (obs.n_cols > 1) {
ksii.each_col() /= sum(ksii, 1);
transition = ksii;
}
for (unsigned int r = 0; r < emission.n_slices; r++) {
gamma.slice(r).cols(0, nSymbols(r) - 1).each_col() /= sum(
gamma.slice(r).cols(0, nSymbols(r) - 1), 1);
emission.slice(r).cols(0, nSymbols(r) - 1) = gamma.slice(r).cols(0, nSymbols(r) - 1);
}
delta /= arma::as_scalar(arma::accu(delta));
init = delta;
}
// internalForward(transition, emission, init, obs, alpha, scales, threads);
// if(!scales.is_finite()) {
// return List::create(Named("error") = 1);
// }
// internalBackward(transition, emission, obs, beta, scales, threads);
// if(!beta.is_finite()) {
// return List::create(Named("error") = 2);
// }
// double min_sf = scales.min();
// if (min_sf < 1e-150) {
// Rcpp::warning("Smallest scaling factor was %e, results can be numerically unstable.", min_sf);
// }
//
// ll = sum(log(scales));
//double tmp = sum(ll);
}
if (trace > 0) {
if (iter == itermax) {
Rcpp::Rcout << "EM algorithm stopped after reaching the maximum number of " << iter
<< " iterations." << std::endl;
} else {
Rcpp::Rcout << "EM algorithm stopped after reaching the relative change of " << change;
Rcpp::Rcout << " after " << iter << " iterations." << std::endl;
}
Rcpp::Rcout << "Final log-likelihood: " << sumlogLik << std::endl;
}
return List::create(Named("initialProbs") = wrap(init),
Named("transitionMatrix") = wrap(transition), Named("emissionArray") = wrap(emission),
Named("logLik") = sumlogLik, Named("iterations") = iter, Named("change") = change, Named("error") = 0);
}
List EM(NumericVector transitionMatrix, NumericVector emissionArray, NumericVector initialProbs,
IntegerVector obsArray, const arma::ivec& nSymbols, int itermax, double tol, int trace, 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], true);
arma::icube obs(obsArray.begin(), oDims[0], oDims[1], oDims[2], false, true);
arma::vec init(initialProbs.begin(), emission.n_rows, true);
arma::mat transition(transitionMatrix.begin(), emission.n_rows, emission.n_rows, true);
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);
internalForward(transition, emission, init, obs, alpha, scales, threads);
if(!scales.is_finite()) {
return List::create(Named("error") = 1);
}
double min_sf = scales.min();
if (min_sf < 1e-150) {
Rcpp::warning("Smallest scaling factor was %e, results can be numerically unstable.", min_sf);
}
internalBackward(transition, emission, obs, beta, scales, threads);
if(!beta.is_finite()) {
return List::create(Named("error") = 2);
}
arma::rowvec ll = arma::sum(log(scales));
double sumlogLik = sum(ll);
if (trace > 0) {
Rcout << "Log-likelihood of initial model: " << sumlogLik << std::endl;
}
//
// //EM-algorithm begins
//
double change = tol + 1.0;
int iter = 0;
while ((change > tol) & (iter < itermax)) {
iter++;
arma::mat ksii(emission.n_rows, emission.n_rows, arma::fill::zeros);
arma::cube gamma(emission.n_rows, emission.n_cols, emission.n_slices, arma::fill::zeros);
arma::vec delta(emission.n_rows, arma::fill::zeros);
for (unsigned int k = 0; k < obs.n_slices; k++) {
delta += alpha.slice(k).col(0) % beta.slice(k).col(0);
}
#pragma omp parallel for if(obs.n_slices>=threads) schedule(static) num_threads(threads) \
default(none) shared(transition, obs, alpha, beta, scales, \
emission, ksii, gamma, nSymbols)
for (int k = 0; k < obs.n_slices; k++) {
if (obs.n_cols > 1) {
for (unsigned int j = 0; j < emission.n_rows; j++) {
for (unsigned int i = 0; i < emission.n_rows; i++) {
if (transition(i, j) > 0.0) {
for (unsigned int t = 0; t < (obs.n_cols - 1); t++) {
double tmp = alpha(i, t, k) * transition(i, j) * beta(j, t + 1, k)
/ scales(t + 1, k);
for (unsigned int r = 0; r < obs.n_rows; r++) {
tmp *= emission(j, obs(r, t + 1, k), r);
}
#pragma omp atomic
ksii(i, j) += tmp;
}
}
}
}
}
for (unsigned int r = 0; r < emission.n_slices; r++) {
for (int l = 0; l < nSymbols(r); l++) {
for (unsigned int i = 0; i < emission.n_rows; i++) {
if (emission(i, l, r) > 0.0) {
for (unsigned int t = 0; t < obs.n_cols; t++) {
if (l == (obs(r, t, k))) {
#pragma omp atomic
gamma(i, l, r) += alpha(i, t, k) * beta(i, t, k);
}
}
}
}
}
}
}
if (obs.n_cols > 1) {
ksii.each_col() /= sum(ksii, 1);
transition = ksii;
}
for (unsigned int r = 0; r < emission.n_slices; r++) {
gamma.slice(r).cols(0, nSymbols(r) - 1).each_col() /= sum(
gamma.slice(r).cols(0, nSymbols(r) - 1), 1);
emission.slice(r).cols(0, nSymbols(r) - 1) = gamma.slice(r).cols(0, nSymbols(r) - 1);
}
delta /= arma::as_scalar(arma::accu(delta));
init = delta;
internalForward(transition, emission, init, obs, alpha, scales, threads);
if(!scales.is_finite()) {
return List::create(Named("error") = 1);
}
internalBackward(transition, emission, obs, beta, scales, threads);
if(!beta.is_finite()) {
return List::create(Named("error") = 2);
}
double min_sf = scales.min();
if (min_sf < 1e-150) {
Rcpp::warning("Smallest scaling factor was %e, results can be numerically unstable.", min_sf);
}
ll = sum(log(scales));
double tmp = sum(ll);
change = (tmp - sumlogLik) / (std::abs(sumlogLik) + 0.1);
sumlogLik = tmp;
if (trace > 1) {
Rcout << "iter: " << iter;
Rcout << " logLik: " << sumlogLik;
Rcout << " relative change: " << change << std::endl;
}
}
if (trace > 0) {
if (iter == itermax) {
Rcpp::Rcout << "EM algorithm stopped after reaching the maximum number of " << iter
<< " iterations." << std::endl;
} else {
Rcpp::Rcout << "EM algorithm stopped after reaching the relative change of " << change;
Rcpp::Rcout << " after " << iter << " iterations." << std::endl;
}
Rcpp::Rcout << "Final log-likelihood: " << sumlogLik << std::endl;
}
return List::create(Named("initialProbs") = wrap(init),
Named("transitionMatrix") = wrap(transition), Named("emissionArray") = wrap(emission),
Named("logLik") = sumlogLik, Named("iterations") = iter, Named("change") = change, Named("error") = 0);
}
List log_EMx(const arma::mat& transition_, const arma::cube& emission_,
const arma::vec& init_, const arma::ucube& obs, const arma::uvec& nSymbols,
const arma::mat& coef_, const arma::mat& X, const arma::uvec& numberOfStates,
int itermax, double tol, int trace, unsigned int threads) {
// Make sure we don't alter the original vec/mat/cube
// needed for cube, in future maybe in other cases as well
arma::cube emission = log(emission_);
arma::mat transition = log(transition_);
arma::vec init = log(init_);
arma::mat coef(coef_);
coef.col(0).zeros();
arma::mat weights = exp(X * coef).t();
if (!weights.is_finite()) {
return List::create(Named("error") = 3);
}
weights.each_row() /= sum(weights, 0);
weights = log(weights);
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
log_internalForwardx(transition, emission, initk, obs, alpha, threads);
log_internalBackward(transition, emission, obs, beta, threads);
arma::vec ll(obs.n_slices);
#pragma omp parallel for if(obs.n_slices >= threads) schedule(static) num_threads(threads) \
default(none) shared(obs, alpha, ll)
for (unsigned int k = 0; k < obs.n_slices; k++) {
ll(k) = logSumExp(alpha.slice(k).col(obs.n_cols - 1));
}
double sumlogLik = sum(ll);
if (trace > 0) {
Rcout << "Log-likelihood of initial model: " << sumlogLik << std::endl;
}
//
// //EM-algorithm begins
//
double change = tol + 1.0;
int iter = 0;
arma::uvec cumsumstate = cumsum(numberOfStates);
while ((change > tol) & (iter < itermax)) {
iter++;
arma::mat ksii(emission.n_rows, emission.n_rows, arma::fill::zeros);
arma::cube gamma(emission.n_rows, emission.n_cols, emission.n_slices, arma::fill::zeros);
arma::vec delta(emission.n_rows, arma::fill::zeros);
for (unsigned int k = 0; k < obs.n_slices; k++) {
delta += exp(alpha.slice(k).col(0) + beta.slice(k).col(0) - ll(k));
}
#pragma omp parallel for if(obs.n_slices>=threads) schedule(static) num_threads(threads) \
default(none) shared(transition, obs, ll, alpha, beta, emission, ksii, gamma, nSymbols)
for (unsigned int k = 0; k < obs.n_slices; k++) {
if (obs.n_cols > 1) {
for (unsigned int j = 0; j < emission.n_rows; j++) {
for (unsigned int i = 0; i < emission.n_rows; i++) {
if (transition(i, j) > -arma::datum::inf) {
arma::vec tmpnm1(obs.n_cols - 1);
for (unsigned int t = 0; t < (obs.n_cols - 1); t++) {
tmpnm1(t) = alpha(i, t, k) + transition(i, j) + beta(j, t + 1, k);
for (unsigned int r = 0; r < obs.n_rows; r++) {
tmpnm1(t) += emission(j, obs(r, t + 1, k), r);
}
}
#pragma omp atomic
ksii(i, j) += exp(logSumExp(tmpnm1) - ll(k));
}
}
}
}
for (unsigned int r = 0; r < emission.n_slices; r++) {
for (unsigned int l = 0; l < nSymbols[r]; l++) {
for (unsigned int i = 0; i < emission.n_rows; i++) {
if (emission(i, l, r) > -arma::datum::inf) {
arma::vec tmpn(obs.n_cols);
for (unsigned int t = 0; t < obs.n_cols; t++) {
if (l == (obs(r, t, k))) {
tmpn(t) = alpha(i, t, k) + beta(i, t, k);
} else
tmpn(t) = -arma::datum::inf;
}
#pragma omp atomic
gamma(i, l, r) += exp(logSumExp(tmpn) - ll(k));
}
}
}
}
}
unsigned int error = log_optCoef(weights, obs, emission, initk, beta, ll, coef, X, cumsumstate,
numberOfStates, trace);
if (error != 0) {
return List::create(Named("error") = error);
}
if (obs.n_cols > 1) {
ksii.each_col() /= sum(ksii, 1);
transition = log(ksii);
}
for (unsigned int r = 0; r < emission.n_slices; r++) {
gamma.slice(r).cols(0, nSymbols(r) - 1).each_col() /= sum(
gamma.slice(r).cols(0, nSymbols(r) - 1), 1);
emission.slice(r).cols(0, nSymbols(r) - 1) = log(gamma.slice(r).cols(0, nSymbols(r) - 1));
}
for (unsigned int i = 0; i < numberOfStates.n_elem; i++) {
delta.subvec(cumsumstate(i) - numberOfStates(i), cumsumstate(i) - 1) /= arma::as_scalar(
arma::accu(delta.subvec(cumsumstate(i) - numberOfStates(i), cumsumstate(i) - 1)));
}
init = log(delta);
for (unsigned int k = 0; k < obs.n_slices; k++) {
initk.col(k) = init + reparma(weights.col(k), numberOfStates);
}
log_internalForwardx(transition, emission, initk, obs, alpha, threads);
log_internalBackward(transition, emission, obs, beta, threads);
for (unsigned int k = 0; k < obs.n_slices; k++) {
ll(k) = logSumExp(alpha.slice(k).col(obs.n_cols - 1));
}
double tmp = sum(ll);
change = (tmp - sumlogLik) / (std::abs(sumlogLik) + 0.1);
sumlogLik = tmp;
if (!arma::is_finite(sumlogLik)) {
return List::create(Named("error") = 6);
}
if (trace > 1) {
Rcout << "iter: " << iter;
Rcout << " logLik: " << sumlogLik;
Rcout << " relative change: " << change << std::endl;
}
}
if (trace > 0) {
if (iter == itermax) {
Rcpp::Rcout << "EM algorithm stopped after reaching the maximum number of " << iter
<< " iterations." << std::endl;
} else {
Rcpp::Rcout << "EM algorithm stopped after reaching the relative change of " << change;
Rcpp::Rcout << " after " << iter << " iterations." << std::endl;
}
Rcpp::Rcout << "Final log-likelihood: " << sumlogLik << std::endl;
}
return List::create(Named("coefficients") = wrap(coef), Named("initialProbs") = wrap(exp(init)),
Named("transitionMatrix") = wrap(exp(transition)),
Named("emissionArray") = wrap(exp(emission)), Named("logLik") = sumlogLik,
Named("iterations") = iter, Named("change") = change, Named("error") = 0);
}
// [[Rcpp::export]]
Rcpp::List EMx(const arma::mat& transition_, const arma::cube& emission_, const arma::vec& init_,
const arma::ucube& obs, const arma::uvec& nSymbols, const arma::mat& coef_, const arma::mat& X,
const arma::uvec& numberOfStates, int itermax, double tol, int trace, unsigned int threads) {
// Make sure we don't alter the original vec/mat/cube
// needed for cube, in future maybe in other cases as well
arma::cube emission(emission_);
arma::mat transition(transition_);
arma::vec init(init_);
arma::mat coef(coef_);
coef.col(0).zeros();
arma::mat weights = exp(X * coef).t();
if (!weights.is_finite()) {
return Rcpp::List::create(Rcpp::Named("error") = 3);
}
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);
}
//
// //EM-algorithm begins
//
double change = tol + 1.0;
int iter = 0;
arma::uvec cumsumstate = arma::cumsum(numberOfStates);
double sumlogLik_new = 0;
double sumlogLik = -1e150;
while ((change > tol) & (iter < itermax)) {
iter++;
arma::mat ksii(emission.n_rows, emission.n_rows, arma::fill::zeros);
arma::cube gamma(emission.n_rows, emission.n_cols, emission.n_slices, arma::fill::zeros);
arma::vec delta(emission.n_rows, arma::fill::zeros);
arma::mat bsi(emission.n_rows, obs.n_slices);
sumlogLik_new = 0;
double max_sf = 1;
unsigned int error_code = 0;
#pragma omp parallel for if(obs.n_slices >= threads) schedule(static) reduction(+:sumlogLik_new) num_threads(threads) \
default(shared) //shared(bsi, initk, transition, obs, emission, delta, ksii, gamma, nSymbols, error_code, max_sf, arma::fill::zeros)
for (unsigned int k = 0; k < obs.n_slices; k++) {
if (error_code == 0) {
arma::mat alpha(emission.n_rows, obs.n_cols); //m,n,k
arma::vec scales(obs.n_cols);
arma::sp_mat sp_trans(transition);
uvForward(sp_trans.t(), emission, initk.col(k), obs.slice(k), alpha, scales);
arma::mat beta(emission.n_rows, obs.n_cols); //m,n,k
uvBackward(sp_trans, emission, obs.slice(k), beta, scales);
sumlogLik_new -= arma::sum(log(scales));
arma::mat ksii_k(emission.n_rows, emission.n_rows, arma::fill::zeros);
arma::cube gamma_k(emission.n_rows, emission.n_cols, emission.n_slices, arma::fill::zeros);
arma::vec delta_k(emission.n_rows);
delta_k = alpha.col(0) % beta.col(0) / scales(0);
for (unsigned int i = 0; i < emission.n_rows; i++) {
for (unsigned int j = 0; j < emission.n_rows; j++) {
if (transition(i, j) > 0.0) {
for (unsigned int t = 0; t < (obs.n_cols - 1); t++) {
double tmp = alpha(i, t) * transition(i, j) * beta(j, t + 1);
for (unsigned int r = 0; r < obs.n_rows; r++) {
tmp *= emission(j, obs(r, t + 1, k), r);
}
ksii_k(i, j) += tmp;
}
}
}
}
for (unsigned int r = 0; r < emission.n_slices; r++) {
for (unsigned int l = 0; l < nSymbols(r); l++) {
for (unsigned int i = 0; i < emission.n_rows; i++) {
if (emission(i, l, r) > 0.0) {
for (unsigned int t = 0; t < obs.n_cols; t++) {
if (l == (obs(r, t, k))) {
double tmp = alpha(i, t) * beta(i, t) / scales(t);
gamma_k(i, l, r) += tmp;
}
}
}
}
}
}
for (unsigned int j = 0; j < emission.n_rows; j++) {
bsi(j, k) = beta(j, 0) * initk(j, k);
}
#pragma omp critical
{
if(!scales.is_finite()) {
error_code = 1;
}
if(!beta.is_finite()) {
error_code = 2;
}
max_sf = std::min(max_sf, scales.max());
delta += delta_k;
ksii += ksii_k;
gamma += gamma_k;
}
}
}
if(error_code == 1) {
return Rcpp::List::create(Rcpp::Named("error") = 1);
}
if(error_code == 2) {
return Rcpp::List::create(Rcpp::Named("error") = 2);
}
if (max_sf > 1e150) {
Rcpp::warning("Largest scaling factor was %e, results can be numerically unstable.", max_sf);
}
change = (sumlogLik_new - sumlogLik) / (std::abs(sumlogLik) + 0.1);
sumlogLik = sumlogLik_new;
if (trace > 0) {
if(iter == 0) {
Rcpp::Rcout << "Log-likelihood of initial model: " << sumlogLik << std::endl;
} else {
if (trace > 1) {
Rcpp::Rcout << "iter: " << iter;
Rcpp::Rcout << " logLik: " << sumlogLik;
Rcpp::Rcout << " relative change: " << change << std::endl;
}
}
}
if (change > tol) {
unsigned int error = optCoef(weights, obs, emission, bsi, coef, X, cumsumstate,
numberOfStates, trace);
if (error != 0) {
return Rcpp::List::create(Rcpp::Named("error") = error);
}
if (obs.n_cols > 1) {
ksii.each_col() /= sum(ksii, 1);
transition = ksii;
}
for (unsigned int r = 0; r < emission.n_slices; r++) {
gamma.slice(r).cols(0, nSymbols(r) - 1).each_col() /= sum(
gamma.slice(r).cols(0, nSymbols(r) - 1), 1);
emission.slice(r).cols(0, nSymbols(r) - 1) = gamma.slice(r).cols(0, nSymbols(r) - 1);
}
for (unsigned int i = 0; i < numberOfStates.n_elem; i++) {
delta.subvec(cumsumstate(i) - numberOfStates(i), cumsumstate(i) - 1) /= arma::as_scalar(
arma::accu(delta.subvec(cumsumstate(i) - numberOfStates(i), cumsumstate(i) - 1)));
}
init = delta;
for (unsigned int k = 0; k < obs.n_slices; k++) {
initk.col(k) = init % reparma(weights.col(k), numberOfStates);
}
}
}
if (trace > 0) {
if (iter == itermax) {
Rcpp::Rcout << "EM algorithm stopped after reaching the maximum number of " << iter
<< " iterations." << std::endl;
} else {
Rcpp::Rcout << "EM algorithm stopped after reaching the relative change of " << change;
Rcpp::Rcout << " after " << iter << " iterations." << std::endl;
}
Rcpp::Rcout << "Final log-likelihood: " << sumlogLik << std::endl;
}
return Rcpp::List::create(Rcpp::Named("coefficients") = Rcpp::wrap(coef), Rcpp::Named("initialProbs") = Rcpp::wrap(init),
Rcpp::Named("transitionMatrix") = Rcpp::wrap(transition), Rcpp::Named("emissionArray") = Rcpp::wrap(emission),
Rcpp::Named("logLik") = sumlogLik, Rcpp::Named("iterations") = iter, Rcpp::Named("change") = change,
Rcpp::Named("error") = 0);
}
