/* Constructor */
modeler_t (DataFrame& A,
const RandomAccessDoubleContainer& y,
RandomAccessDoubleContainer& x,
RandomAccessKPIContainter& selected,
SetContainer& prohibit,
SetContainer& forced,
const RandomAccessDoubleContainer& kpi_weights,
double& variance,
double& intercept,
const int& target,
const StopPredicate& stop,
const IntervalMapper& interval_mapper,
const Factorizer& factorizer,
const double lambda,
const int M,
const int N,
const int MAX_ITERS,
const int debug
#if USE_PFUNC
,taskmgr* global_taskmgr
#endif
) :
A(A),y(y),x(x),selected(selected),
prohibit(prohibit),forced(forced),kpi_weights(kpi_weights),
variance(variance),intercept(intercept),target(target),
stop(stop),interval_mapper(interval_mapper),factorizer(factorizer),
lambda(lambda), M(M), N(N), MAX_ITERS(MAX_ITERS), debug(debug),
filter (prohibit, forced, selected),
my_space (A.begin(), A.end()),
test_A (M*N*MAX_ITERS),
r (M),
y_inv (M)
#if USE_PFUNC
,global_taskmgr (global_taskmgr)
#endif
{
/* Set up the transmission channel */
MPI_Type_contiguous (2, MPI_DOUBLE, &MPI_Kpi_pair);
MPI_Type_commit (&MPI_Kpi_pair);
MPI_Op_create (kpi_pair_max,true,&MPI_KPI_MAX_OP);
MPI_Op_create (kpi_pair_min,true,&MPI_KPI_MIN_OP);
/* Compute the inverse of y */
ConfidenceFunctor::pseudo_inverse(y, y_inv, M);
}
