// ////////////////////////////////////////////////////////////////////
bool NewQFF::
forecast (stdair::SegmentCabin& ioSegmentCabin,
const stdair::Date_T& iCurrentDate,
const stdair::DTD_T& iCurrentDTD,
const stdair::UnconstrainingMethod& iUnconstrainingMethod,
const stdair::NbOfSegments_T& iNbOfDepartedSegments) {
// Retrieve the snapshot table.
const stdair::SegmentSnapshotTable& lSegmentSnapshotTable =
ioSegmentCabin.getSegmentSnapshotTable();
// Browse the list of fare families and execute "Q-forecasting" within
// each fare family.
const stdair::FareFamilyList_T& lFFList =
stdair::BomManager::getList<stdair::FareFamily>(ioSegmentCabin);
for (stdair::FareFamilyList_T::const_iterator itFF = lFFList.begin();
itFF != lFFList.end(); ++itFF) {
stdair::FareFamily* lFF_ptr = *itFF;
assert (lFF_ptr != NULL);
forecast (*lFF_ptr,
iCurrentDate,
iCurrentDTD,
iUnconstrainingMethod,
iNbOfDepartedSegments,
lSegmentSnapshotTable);
}
// Dispatch the demand forecast to the policies.
dispatchDemandForecastToPolicies (ioSegmentCabin);
return true;
}
void etsforecast(double *x, int *m, int *trend, int *season, double *phi, int *h, double *f)
{
int j;
double l, b, s[24];
if((*m > 24) & (*season > NONE))
return;
else if(*m < 1)
*m = 1;
// Copy initial state components
l = x[0];
b = 0.0;
if(*trend > NONE)
b = x[1];
if(*season > NONE)
{
for(j=0; j<(*m); j++)
s[j] = x[(*trend>NONE)+j+1];
}
// Compute forecasts
forecast(l, b, s, *m, *trend, *season, *phi, f, *h);
}
void etssimulate(double *x, int *m, int *error, int *trend, int *season,
double *alpha, double *beta, double *gamma, double *phi, int *h, double *y, double *e)
{
int i, j, nstates;
double oldl, l, oldb, b, olds[24], s[24], f[10];
if((*m > 24) & (*season > NONE))
return;
else if(*m < 1)
*m = 1;
nstates = (*m)*(*season>NONE) + 1 + (*trend>NONE);
// Copy initial state components
l = x[0];
if(*trend > NONE)
b = x[1];
if(*season > NONE)
{
for(j=0; j<(*m); j++)
s[j] = x[(*trend>NONE)+j+1];
}
for (i=0; i<(*h); i++)
{
// COPY PREVIOUS STATE
oldl = l;
if(*trend > NONE)
oldb = b;
if(*season > NONE)
{
for(j=0; j<(*m); j++)
olds[j] = s[j];
}
// ONE STEP FORECAST
forecast(oldl, oldb, olds, *m, *trend, *season, *phi, f, 1);
if(fabs(f[0]-NA) < TOL)
{
y[0]=NA;
return;
}
if(*error == ADD)
y[i] = f[0] + e[i];
else
y[i] = f[0]*(1.0+e[i]);
// UPDATE STATE
update(&oldl, &l, &oldb, &b, olds, s, *m, *trend, *season, *alpha, *beta, *gamma, *phi, y[i]);
}
}
int main() {
kmeans("./dataset", "./result");
Forecast forecast("./result");
HMM hmm = HMM("./dataset", "./dictionnary.txt", forecast);
hmm.print();
{
cout << "Save" << endl;
std::ofstream ofs("hmm_save");
boost::archive::text_oarchive oa(ofs);
oa << hmm;
}
HMM hmm2 = HMM();
{
cout << "Load" << endl;
std::ifstream ifs("hmm_save");
boost::archive::text_iarchive ia(ifs);
ia >> hmm2;
}
hmm2.print();
return (EXIT_SUCCESS);
}
void LNLP::run()
{
prepare_forecast(); // check parameters
forecast(); // forecast code is in forecast_machine
return;
}
void Xmap::run()
{
prepare_forecast(); // check parameters
// setup data structures and compute maximum lib size
predicted_stats.clear();
predicted_lib_sizes.clear();
std::vector<size_t> full_lib = which_lib;
size_t max_lib_size = full_lib.size();
std::mt19937 rng(seed); // init mersenne twister with seed
// need to update with true random seed
std::uniform_int_distribution<uint32_t> lib_sampler(0, max_lib_size-1);
std::uniform_real_distribution<double> unif_01(0, 1);
std::vector<int> idx;
size_t m;
size_t t;
for(auto lib_size: lib_sizes)
{
if(lib_size >= max_lib_size && (!random_libs || !replace))
// no possible lib variation if using all vectors and
// [no random libs OR (random_libs and sampling without replacement)]
{
which_lib = full_lib; // use all lib vectors
forecast();
predicted_stats.push_back(make_stats());
predicted_lib_sizes.push_back(lib_size);
break;
}
else if(random_libs)
{
which_lib.resize(lib_size, 0);
for(size_t k = 0; k < num_samples; ++k)
{
if(replace)
{
for(auto& lib: which_lib)
{
lib = full_lib[lib_sampler(rng)];
}
}
else
{
// sample without replacement (algorithm from Knuth)
m = 0;
t = 0;
while(m < lib_size)
{
if(double(max_lib_size - t) * unif_01(rng) >= double(lib_size - m))
{
++t;
}
else
{
which_lib[m] = full_lib[t];
++t; ++m;
}
}
}
forecast();
predicted_stats.push_back(make_stats());
predicted_lib_sizes.push_back(lib_size);
}
}
else
// no random libs and using contiguous segments
{
for(size_t k = 0; k < max_lib_size; ++k)
{
if((k + lib_size) > max_lib_size) // need to loop around
{
which_lib.assign(full_lib.begin()+k, full_lib.end()); // k to end
which_lib.insert(which_lib.begin(),
full_lib.begin(),
full_lib.begin() + lib_size - (max_lib_size-k));
}
else
{
which_lib.assign(full_lib.begin()+k, full_lib.begin()+k+lib_size);
}
forecast();
predicted_stats.push_back(make_stats());
predicted_lib_sizes.push_back(lib_size);
}
}
}
which_lib = full_lib;
return;
}
void etscalc(double *y, int *n, double *x, int *m, int *error, int *trend, int *season,
double *alpha, double *beta, double *gamma, double *phi, double *e, double *lik, double *amse)
{
int i, j, nstates;
double oldl, l, oldb, b, olds[24], s[24], f[10], lik2, tmp;
if((*m > 24) & (*season > NONE))
return;
else if(*m < 1)
*m = 1;
nstates = (*m)*(*season>NONE) + 1 + (*trend>NONE);
// Copy initial state components
l = x[0];
if(*trend > NONE)
b = x[1];
if(*season > NONE)
{
for(j=0; j<(*m); j++)
s[j] = x[(*trend>NONE)+j+1];
}
*lik = 0.0;
lik2 = 0.0;
for(j=0; j<10; j++)
amse[j] = 0.0;
for (i=0; i<(*n); i++)
{
// COPY PREVIOUS STATE
oldl = l;
if(*trend > NONE)
oldb = b;
if(*season > NONE)
{
for(j=0; j<(*m); j++)
olds[j] = s[j];
}
// ONE STEP FORECAST
forecast(oldl, oldb, olds, *m, *trend, *season, *phi, f, 10);
if(fabs(f[0]-NA) < TOL)
{
*lik = NA;
return;
}
if(*error == ADD)
e[i] = y[i] - f[0];
else
e[i] = (y[i] - f[0])/f[0];
for(j=0; j<10; j++)
{
if(i+j<(*n))
{
tmp = y[i+j]-f[j];
amse[j] += (tmp*tmp)/(*n);
}
}
// UPDATE STATE
update(&oldl, &l, &oldb, &b, olds, s, *m, *trend, *season, *alpha, *beta, *gamma, *phi, y[i]);
// STORE NEW STATE
x[nstates*(i+1)] = l;
if(*trend > NONE)
x[nstates*(i+1)+1] = b;
if(*season > NONE)
{
for(j=0; j<(*m); j++)
x[(*trend>NONE)+nstates*(i+1)+j+1] = s[j];
}
*lik = *lik + e[i]*e[i];
lik2 += log(fabs(f[0]));
}
*lik = (*n) * log(*lik);
if(*error == MULT)
*lik += 2*lik2;
}
void double_es::future_forecast(const int& c) {
for(int i = 1; i<=c; i++) {
_newf.push_back(forecast(i+now(), now()));
}
}
