double Model::calculateTransRates(const EpiState& state,
vector<double>& transRates,
vector<double>& trueRates) const
{
size_t i = 0;
transRates[i] = transTypes[i]->applyRate(state,getPars(),trueRates[i]);
for (i = 1; i < transTypes.size(); ++i) {
if (simEvent[i]) {
transRates[i] = transRates[i-1] + transTypes[i]->applyRate(state,getPars(),trueRates[i]);
} else {
trueRates[i] = 0.0;
transRates[i] = transRates[i-1];
}
}
return transRates[i-1];
}
void CorrelatedGaussianParameters::ParseCGP(std::vector<ModelParameter>& ModPars,
std::ifstream& ifile,
boost::tokenizer<boost::char_separator<char> >::iterator & beg,
int rank)
{
name = *beg;
++beg;
int size = atoi((*beg).c_str());
int nlines = 0;
std::string line;
bool IsEOF;
boost::char_separator<char>sep(" \t");
for (int i = 0; i < size; i++) {
IsEOF = getline(ifile, line).eof();
if (line.empty() || line.at(0) == '#') {
if (rank == 0) std::cout << "ERROR: no comments or empty lines in CorrelatedGaussianParameters please!"
<< std::endl;
exit(EXIT_FAILURE);
}
lineNo++;
boost::tokenizer<boost::char_separator<char> > tok(line, sep);
beg = tok.begin();
std::string type = *beg;
++beg;
if (type.compare("ModelParameter") != 0)
if (rank == 0) throw std::runtime_error("ERROR: in line no." + boost::lexical_cast<std::string>(lineNo) + " of file " + filename + ", expecting a ModelParameter type here...\n");
ModelParameter tmpMP;
beg = tmpMP.ParseModelParameter(beg);
if (beg != tok.end())
if (rank == 0) std::cout << "WARNING: unread information in parameter " << tmpMP.getname() << std::endl;
tmpMP.setCgp_name(name);
AddPar(tmpMP);
nlines++;
}
if (nlines > 1) {
gslpp::matrix<double> myCorr(gslpp::matrix<double>::Id(nlines));
int ni = 0;
for (int i = 0; i < size; i++) {
IsEOF = getline(ifile, line).eof();
if (line.empty() || line.at(0) == '#') {
if (rank == 0) std::cout << "ERROR: no comments or empty lines in CorrelatedGaussianParameters please!"
<< std::endl;
exit(EXIT_FAILURE);
}
lineNo++;
boost::tokenizer<boost::char_separator<char> > mytok(line, sep);
beg = mytok.begin();
int nj = 0;
for (int j = 0; j < size; j++) {
if ((*beg).compare(0, 1, "0") == 0
|| (*beg).compare(0, 1, "1") == 0
|| (*beg).compare(0, 1, "-") == 0) {
if (std::distance(mytok.begin(), mytok.end()) < size && rank == 0) throw std::runtime_error(("ERROR: Correlation matrix is of wrong size in Correlated Gaussian Parameters: " + name).c_str());
myCorr(ni, nj) = atof((*beg).c_str());
nj++;
beg++;
} else {
if (rank == 0) std::cout << "ERROR: invalid correlation matrix for "
<< name << ". Check element (" << ni + 1 << "," << nj + 1 << ") in line number " + boost::lexical_cast<std::string>(lineNo) << std::endl;
exit(EXIT_FAILURE);
}
}
ni++;
}
DiagonalizePars(myCorr);
ModPars.insert(ModPars.end(), getDiagPars().begin(), getDiagPars().end());
} else {
if (rank == 0) std::cout << "\nWARNING: Correlated Gaussian Parameters " << name.c_str() << " defined with less than two correlated parameters. The set is being marked as normal Parameters." << std::endl;
if (getPars().size() == 1) ModPars.push_back(ModelParameter(getPar(0)));
for (int i = 0; i < size; i++) {
IsEOF = getline(ifile, line).eof();
lineNo++;
}
}
}
