double ML_multi_DownhillSimplex::amotry(mat_ratep_type& p,
v_ratep_type& y,
v_ratep_type& psum,
ptr_eval_func funk,
const int ihi,
const double fac
)
{
size_t j;
int n_adjusted;
double fac1, fac2, ytry;
size_t ndim = p[0].size();
v_ratep_type ptry(ndim);
fac1 = (1.0 - fac) / ndim;
fac2 = fac1 - fac;
for (j = 0; j < ndim; ++j)
ptry[j] = psum[j]*fac1 - p[ihi][j]*fac2;
if (CONFIG_BOUNDS_ADJUST) {
bounds_adjust(ptry, n_adjusted);
}
if (DEBUG_BOUNDS_TRACE) bounds_trace(ptry, "amotry");
ytry = (this->*funk)(ptry);
if (ytry < y[ihi]) {
y[ihi] = ytry;
for (j = 0; j < ndim; ++j) {
psum[j] += ptry[j] - p[ihi][j];
p[ihi][j] = ptry[j];
}
if (CONFIG_BOUNDS_ADJUST) {
bounds_adjust(ptry, n_adjusted);
}
if (DEBUG_BOUNDS_TRACE) bounds_trace(p[ihi], "amotry");
}
if (DEBUG_AMOTRY) {
std::cout << "amotry(): ytry=" << ytry << std::endl;
if (DEBUG_PRINT_DATA_STRUCTURES)
print_data_structures(p, y);
}
return(ytry);
}
// Extrapolate by a factor of fac through the face of the simplex
// opposite the high point to a new point. If the new point is
// better, swap it with the high point.
double
NonGradient::amotry( std::vector<double>& simplex,
std::vector<double>& height,
double psum[], int ihi, double fac, PatchData &pd, MsqError &err)
{
int numRow = getDimension();
int numCol = numRow + 1;
std::vector<double> ptry(numRow); // does this make sense?
double fac1=(1.0-fac)/static_cast<double>(numRow);
double fac2=fac1-fac;
for (int row=0;row<numRow;row++)
{
ptry[row]=psum[row]*fac1-simplex[row+ihi*numRow]*fac2;
}
if( mNonGradDebug >= 3 )
{
std::cout << "Try ";
}
MSQ_PRINT(3)("Try");
double ytry = evaluate(&ptry[0], pd, err); // value at trial point
if( mNonGradDebug >= 3 )
{
std::cout << ytry << std::endl;
}
MSQ_PRINT(3)("yTry");
if (ytry < height[ihi]) // better than highest (worst)
{
height[ihi]=ytry; // swap ihi and ytry
for (int row=0;row<numRow;row++)
{
psum[row] += (ptry[row]-simplex[row+ihi*numRow]);
simplex[row+ihi*numRow]=ptry[row];
}
}
return ytry;
}