//---------------------------------------------------------------------------
double TEstimation::getArea(ColumnVector densities, ColumnVector theta){
double area=0;
for(int i=1; i<densities.size();++i){
area+=(theta(i+1)-theta(i))*(densities(i+1)+densities(i))/2;
}
return area;
}
void constant_vector(
T1 x, const ColumnVector<T2> & y,
const DateLUTImpl & timezone_x, const DateLUTImpl & timezone_y,
ColumnInt64::Container & result)
{
const auto & y_data = y.getData();
for (size_t i = 0, size = y.size(); i < size; ++i)
result[i] = calculate<Transform>(x, y_data[i], timezone_x, timezone_y);
}
void vector_constant(
const ColumnVector<T1> & x, T2 y,
const DateLUTImpl & timezone_x, const DateLUTImpl & timezone_y,
ColumnInt64::Container & result)
{
const auto & x_data = x.getData();
for (size_t i = 0, size = x.size(); i < size; ++i)
result[i] = calculate<Transform>(x_data[i], y, timezone_x, timezone_y);
}
void ExplicitSystemManager::save_column_vector(const ColumnVector &vec, std::string filename) {
std::ofstream out_file(filename);
if (out_file.is_open()) {
for (int i = 0; i < vec.size(); ++i) {
out_file << std::setprecision(15) << vec[i] << "\n";
}
out_file.close();
}
else {
throw std::runtime_error((boost::format("Unable to open %s.") % filename).str());
}
}
void LoadDNSSCD::loadHuber(ITableWorkspace_sptr tws) {
ColumnVector<double> huber = tws->getVector("Huber(degrees)");
// set huber[0] for each run in m_data
for (auto &ds : m_data) {
ds.huber = huber[0];
}
// dublicate runs for each huber in the table
std::vector<ExpData> old(m_data);
for (size_t i = 1; i < huber.size(); ++i) {
for (auto &ds : old) {
ds.huber = huber[i];
m_data.push_back(ds);
}
}
}
//constraint, gradient approximated with central difference
//opt++ has bug in approximating gradient with finite difference, so I compute it myself
void optppConstraint(int mode, int n, const ColumnVector &x, ColumnVector &c_x, Matrix &c_grad_x, int &result)
{
//constraint: A^T*A=I,B^T*B=I ==> A^T*A-I=0,B^T*B-I=0
//Note: A^T*A and B^T*B are both symmetric matrices, inactive constraints need to be removed
// otherwise it leads to singular jacobian of optimization system
int col=CoupledQuasiHarmonics::static_col;
int new_col=n/(2*col);
if(CoupledQuasiHarmonics::static_objective_type==CoupledQuasiHarmonics::ONLY_A)
new_col=n/col;
//function evaluation
if(mode&NLPFunction)
{
int idx_1d=1;
for(int i=1;i<=new_col;++i)
for(int j=1;j<=i;++j)
{
c_x(idx_1d)=0;
for(int k=1;k<=col;++k)
c_x(idx_1d)+=x((i-1)*col+k)*x((j-1)*col+k);
if(i==j)
c_x(idx_1d)-=1.0;
++idx_1d;
}
if(CoupledQuasiHarmonics::static_objective_type==CoupledQuasiHarmonics::A_AND_B)
{
int bias=new_col*col;
for(int i=1;i<=new_col;++i)
for(int j=1;j<=i;++j)
{
c_x(idx_1d)=0;
for(int k=1;k<=col;++k)
c_x(idx_1d)+=x((i-1)*col+k+bias)*x((j-1)*col+k+bias);
if(i==j)
c_x(idx_1d)-=1.0;
++idx_1d;
}
}
result=NLPFunction;
}
//gradient evaluation
if(mode&NLPGradient)
{
ColumnVector temp_x=x;
ColumnVector fplus(c_x.size()),fminus(c_x.size());
Matrix c_grad_x_t(c_x.size(),n);
Matrix temp_grad(n,c_x.size());
int temp_result;
double perturb=1.0e-8;
for(int i=1;i<=n;++i)
{
temp_x(i)+=perturb;
optppConstraint(NLPFunction,n,temp_x,fplus,temp_grad,temp_result);
temp_x(i)-=2*perturb;
optppConstraint(NLPFunction,n,temp_x,fminus,temp_grad,temp_result);
c_grad_x_t.Column(i)=(fplus-fminus)/(2*perturb);
temp_x(i)=x(i);
}
c_grad_x=c_grad_x_t.t();
result=NLPGradient;
}
}
