/**
* Update the cost function, derivatives and hessian by adding values calculated
* on a domain.
* @param function :: Function to use to calculate the value and the derivatives
* @param domain :: The domain.
* @param values :: The fit function values
* @param evalFunction :: Flag to evaluate the function
* @param evalDeriv :: Flag to evaluate the derivatives
* @param evalHessian :: Flag to evaluate the Hessian
*/
void CostFuncLeastSquares::addValDerivHessian(
API::IFunction_sptr function,
API::FunctionDomain_sptr domain,
API::FunctionValues_sptr values,
bool evalFunction , bool evalDeriv, bool evalHessian) const
{
UNUSED_ARG(evalDeriv);
size_t np = function->nParams(); // number of parameters
size_t ny = domain->size(); // number of data points
Jacobian jacobian(ny,np);
if (evalFunction)
{
function->function(*domain,*values);
}
function->functionDeriv(*domain,jacobian);
size_t iActiveP = 0;
double fVal = 0.0;
if (debug)
{
std::cerr << "Jacobian:\n";
for(size_t i = 0; i < ny; ++i)
{
for(size_t ip = 0; ip < np; ++ip)
{
if ( !m_function->isActive(ip) ) continue;
std::cerr << jacobian.get(i,ip) << ' ';
}
std::cerr << std::endl;
}
}
for(size_t ip = 0; ip < np; ++ip)
{
if ( !function->isActive(ip) ) continue;
double d = 0.0;
for(size_t i = 0; i < ny; ++i)
{
double calc = values->getCalculated(i);
double obs = values->getFitData(i);
double w = values->getFitWeight(i);
double y = ( calc - obs ) * w;
d += y * jacobian.get(i,ip) * w;
if (iActiveP == 0 && evalFunction)
{
fVal += y * y;
}
}
PARALLEL_CRITICAL(der_set)
{
double der = m_der.get(iActiveP);
m_der.set(iActiveP, der + d);
}
//std::cerr << "der " << ip << ' ' << der[iActiveP] << std::endl;
++iActiveP;
}
if (evalFunction)
{
PARALLEL_ATOMIC
m_value += 0.5 * fVal;
}
if (!evalHessian) return;
//size_t na = m_der.size(); // number of active parameters
size_t i1 = 0; // active parameter index
for(size_t i = 0; i < np; ++i) // over parameters
{
if ( !function->isActive(i) ) continue;
size_t i2 = 0; // active parameter index
for(size_t j = 0; j <= i; ++j) // over ~ half of parameters
{
if ( !function->isActive(j) ) continue;
double d = 0.0;
for(size_t k = 0; k < ny; ++k) // over fitting data
{
double w = values->getFitWeight(k);
d += jacobian.get(k,i) * jacobian.get(k,j) * w * w;
}
PARALLEL_CRITICAL(hessian_set)
{
double h = m_hessian.get(i1,i2);
m_hessian.set(i1,i2, h + d);
//std::cerr << "hess " << i1 << ' ' << i2 << std::endl;
if (i1 != i2)
{
m_hessian.set(i2,i1,h + d);
}
}
++i2;
}
++i1;
}
}
/**
* Update the cost function, derivatives and hessian by adding values calculated
* on a domain.
* @param function :: Function to use to calculate the value and the derivatives
* @param domain :: The domain.
* @param values :: The fit function values
* @param evalDeriv :: Flag to evaluate the derivatives
* @param evalHessian :: Flag to evaluate the Hessian
*/
void CostFuncLeastSquares::addValDerivHessian(API::IFunction_sptr function,
API::FunctionDomain_sptr domain,
API::FunctionValues_sptr values,
bool evalDeriv,
bool evalHessian) const {
UNUSED_ARG(evalDeriv);
function->function(*domain, *values);
size_t np = function->nParams(); // number of parameters
size_t ny = values->size(); // number of data points
Jacobian jacobian(ny, np);
function->functionDeriv(*domain, jacobian);
size_t iActiveP = 0;
double fVal = 0.0;
std::vector<double> weights = getFitWeights(values);
for (size_t ip = 0; ip < np; ++ip) {
if (!function->isActive(ip))
continue;
double d = 0.0;
for (size_t i = 0; i < ny; ++i) {
double calc = values->getCalculated(i);
double obs = values->getFitData(i);
double w = weights[i];
double y = (calc - obs) * w;
d += y * jacobian.get(i, ip) * w;
if (iActiveP == 0) {
fVal += y * y;
}
}
PARALLEL_CRITICAL(der_set) {
double der = m_der.get(iActiveP);
m_der.set(iActiveP, der + d);
}
++iActiveP;
}
PARALLEL_ATOMIC
m_value += 0.5 * fVal;
if (!evalHessian)
return;
size_t i1 = 0; // active parameter index
for (size_t i = 0; i < np; ++i) // over parameters
{
if (!function->isActive(i))
continue;
size_t i2 = 0; // active parameter index
for (size_t j = 0; j <= i; ++j) // over ~ half of parameters
{
if (!function->isActive(j))
continue;
double d = 0.0;
for (size_t k = 0; k < ny; ++k) // over fitting data
{
double w = weights[k];
d += jacobian.get(k, i) * jacobian.get(k, j) * w * w;
}
PARALLEL_CRITICAL(hessian_set) {
double h = m_hessian.get(i1, i2);
m_hessian.set(i1, i2, h + d);
if (i1 != i2) {
m_hessian.set(i2, i1, h + d);
}
}
++i2;
}
++i1;
}
}
