QDate PollenOnsetDateFromCurve::calculate() {
typedef boost::uniform_01<double> Distribution;
typedef boost::variate_generator<UniSim::Random::Generator&, Distribution> Variate;
Distribution distribution;
Variate variate(*randomGenerator(), distribution);
double fractile = variate();
Q_ASSERT(fractile >= 0. && fractile < 1.);
Model *calendar = seekOne<Model*>("calendar");
Model *weather = seekOne<Model*>("weather");
calendar->deepReset();
weather->deepCleanup();
weather->deepReset();
model->deepReset();
const double *curveTotal = model->pullValuePtr<double>("total");
int day = 0;
while (*curveTotal < fractile && day < 365) {
calendar->update();
weather->update();
model->update();
++day;
}
Q_ASSERT(day < 365);
calendar->deepReset();
weather->deepCleanup();
weather->deepReset();
model->deepReset();
QDate firstDate = calendar->pullValue<QDate>("initialDate");
return firstDate.addDays(day);
}
typename graph_traits<Graph>::edge_descriptor
random_out_edge(Graph& g, typename graph_traits<Graph>::vertex_descriptor src, RandomNumGen& gen) {
typedef typename graph_traits<Graph>::degree_size_type degree_size_type;
typedef boost::uniform_int<degree_size_type> ui_type;
ui_type ui(0, out_degree(src, g) - 1);
boost::variate_generator<RandomNumGen&, ui_type>
variate(gen, ui);
typename graph_traits<Graph>::out_edge_iterator it = out_edges(src, g).first;
std::advance(it, variate());
return *it;
}
void variate(unsigned i)
{ unsigned j;
if (i>=k) { print(); return; }
for (j=0; j<n; j++)
{ mp[i]=j;
variate(i+1);
}
}
void
RandomProbabilities::assign_random_value(RandomNumberEngine& rne)
{
/// @todo Creating the variate every time from scratch is slow.
typedef uniform_real<float> Distribution;
Distribution distribution(0.0f, 1.0f);
variate_generator<RandomNumberEngine&, Distribution> variate(rne,
distribution);
float sum = 0.0;
for (iterator i = pt_.begin(); i != pt_.end(); ++i)
{
i->second = variate();
sum += i->second;
}
for (iterator i = pt_.begin(); i != pt_.end(); ++i)
i->second /= sum;
}
/* рекурсия */
void variate(unsigned i)
{ unsigned j;
/* без if (i>=k) и return; тук (а само print(i); ако искаме всички
* генерирания с дължина 1,2, …, k, а не само вариациите с дължина k */
if (i >= k) { print(i); return; }
for (j = 0; j < n; j++) {
/* if (allowed(k)) { */
taken[i] = j;
variate(i + 1);
}
}
QDate PollenOnsetDateFromFile::calculate() {
Model *calendar = seekOne<Model*>("calendar");
QDate date = calendar->pullValue<QDate>("date");
int year = date.year();
if (date.month() == 12)
++year;
int ixMin = yearsFirstLine.value(year);
int ixMax = (year == lastYear) ? (data.rowNumber() - 1) : (yearsFirstLine.value(year+1) - 1);
Q_ASSERT(ixMin < ixMax);
typedef boost::random::uniform_int_distribution<> Distribution;
typedef boost::variate_generator<UniSim::Random::Generator&, Distribution> Variate;
Distribution distribution(ixMin, ixMax);
Variate variate(*randomGenerator(), distribution);
int index = variate();
Q_ASSERT(index>=0 && index<data.rowNumber());
QDate onset = stringToValue<QDate>(data.row(index).at(1));
Q_ASSERT(onset.year() == year);
return onset;
}
int main()
{ variate(0); return 0; }
return *it;
}
template <typename Graph, typename WeightMap, typename RandomNumGen>
typename graph_traits<Graph>::edge_descriptor
weighted_random_out_edge(Graph& g, typename graph_traits<Graph>::vertex_descriptor src, WeightMap weight, RandomNumGen& gen) {
typedef graph_traits<Graph> gt;
typedef typename gt::vertex_descriptor vertex_descriptor;
typedef typename property_traits<WeightMap>::value_type weight_type;
weight_type weight_sum(0);
BGL_FORALL_OUTEDGES_T(src, e, g, Graph) {weight_sum += get(weight, e);}
typedef boost::uniform_real<> ur_type;
ur_type ur(0, weight_sum);
boost::variate_generator<RandomNumGen&, ur_type>
variate(gen, ur);
weight_type chosen_weight = variate();
BGL_FORALL_OUTEDGES_T(src, e, g, Graph) {
weight_type w = get(weight, e);
if (chosen_weight < w) {
return e;
} else {
chosen_weight -= w;
}
}
assert (false); // Should not get here
}
namespace detail {
class dummy_property_copier {
public:
template<class V1, class V2>
int state2()
/* Do what needs to be done in state 2.
pre: The global variable 'mu' contains the number of indiviuals
you need to read using 'read_sel()'.
The global variable 'lambda' contains the number of individuals
you need to create by variation of the individuals specified the
'sel' file.
post: Optionally call read_arc() in order to delete old uncessary
individuals from the global population.
read_sel() called
'lambda' children generated from the 'mu' parents
Children added to the global population using add_individual().
Information about children written to the 'var' file using
write_var().
Return value == 0 if successful,
== 1 if unspecified errors happened,
== 2 if file reading failed.
*/
{
int *parent_identities, *offspring_identities; /* array of identities */
int result; /* stores return values of called functions */
parent_identities = (int *) malloc(mu * sizeof(int));
if (parent_identities == NULL)
{
log_to_file(log_file, __FILE__, __LINE__, "variator out of memory");
return (1);
}
offspring_identities = (int *) malloc(lambda * sizeof(int));
if (offspring_identities == NULL)
{
log_to_file(log_file, __FILE__, __LINE__, "variator out of memory");
return (1);
}
result = read_sel(parent_identities);
if (result != 0) /* if some file reading error occurs, return 2 */
return (2);
result = read_arc();
if (result != 0) /* if some file reading error occurs, return 2 */
return (2);
/**********| added for DTLZ |**************/
result = variate(parent_identities, offspring_identities);
if (result != 0)
return (1);
gen++;
/**********| addition for DTLZ end |*******/
result = write_var(offspring_identities);
if (result != 0)
{
log_to_file(log_file, __FILE__, __LINE__,
"couldn't write var");
free(offspring_identities);
free(parent_identities);
return (1);
}
free(offspring_identities);
free(parent_identities);
return (0);
}
int main(void) {
variate(0);
return 0;
}
