/**
* Calculate the fitting errors and assign them to the fitting function.
* @param covar :: A covariance matrix to use for error calculations.
* It can be calculated with calCovarianceMatrix().
* @param chi2 :: The final chi-squared of the fit.
*/
void CostFuncFitting::calFittingErrors(const GSLMatrix &covar, double chi2) {
size_t np = m_function->nParams();
auto covarMatrix = boost::shared_ptr<Kernel::Matrix<double>>(
new Kernel::Matrix<double>(np, np));
size_t ia = 0;
for (size_t i = 0; i < np; ++i) {
if (m_function->isFixed(i)) {
m_function->setError(i, 0);
} else {
size_t ja = 0;
for (size_t j = 0; j < np; ++j) {
if (!m_function->isFixed(j)) {
(*covarMatrix)[i][j] = covar.get(ia, ja);
++ja;
}
}
double err = sqrt(covar.get(ia, ia));
m_function->setError(i, err);
++ia;
}
}
m_function->setCovarianceMatrix(covarMatrix);
m_function->setChiSquared(chi2);
}
/**
* Calculate the fitting errors and assign them to the fitting function.
* @param covar :: A covariance matrix to use for error calculations.
* It can be calculated with calCovarianceMatrix().
*/
void CostFuncFitting::calFittingErrors(const GSLMatrix& covar)
{
size_t np = m_function->nParams();
size_t ia = 0;
for(size_t i = 0; i < np; ++i)
{
if (m_function->isFixed(i))
{
m_function->setError(i,0);
}
else
{
double err = sqrt(covar.get(ia,ia));
m_function->setError(i,err);
++ia;
}
}
}
/**
* Calculates covariance matrix for fitting function's active parameters.
* @param covar :: Output cavariance matrix.
* @param epsrel :: Tolerance.
*/
void CostFuncLeastSquares::calActiveCovarianceMatrix(GSLMatrix &covar,
double epsrel) {
UNUSED_ARG(epsrel);
if (m_hessian.isEmpty()) {
valDerivHessian();
}
if (g_log.is(Kernel::Logger::Priority::PRIO_DEBUG)) {
std::ostringstream osHess;
osHess << "== Hessian (H) ==\n";
osHess << std::left << std::fixed;
for (size_t i = 0; i < m_hessian.size1(); ++i) {
for (size_t j = 0; j < m_hessian.size2(); ++j) {
osHess << std::setw(10);
osHess << m_hessian.get(i, j) << " ";
}
osHess << "\n";
}
g_log.debug() << osHess.str();
}
covar = m_hessian;
covar.invert();
if (g_log.is(Kernel::Logger::Priority::PRIO_DEBUG)) {
std::ostringstream osCovar;
osCovar << "== Covariance matrix (H^-1) ==\n";
osCovar << std::left << std::fixed;
for (size_t i = 0; i < covar.size1(); ++i) {
for (size_t j = 0; j < covar.size2(); ++j) {
osCovar << std::setw(10);
osCovar << covar.get(i, j) << " ";
}
osCovar << "\n";
}
g_log.debug() << osCovar.str();
}
}
/** Executes the algorithm
*
* @throw runtime_error Thrown if algorithm cannot execute
*/
void Fit::execConcrete() {
std::string ties = getPropertyValue("Ties");
if (!ties.empty()) {
m_function->addTies(ties);
}
std::string contstraints = getPropertyValue("Constraints");
if (!contstraints.empty()) {
m_function->addConstraints(contstraints);
}
// prepare the function for a fit
m_function->setUpForFit();
API::FunctionDomain_sptr domain;
API::FunctionValues_sptr values;
m_domainCreator->createDomain(domain, values);
// do something with the function which may depend on workspace
m_domainCreator->initFunction(m_function);
// get the minimizer
std::string minimizerName = getPropertyValue("Minimizer");
API::IFuncMinimizer_sptr minimizer =
API::FuncMinimizerFactory::Instance().createMinimizer(minimizerName);
// Try to retrieve optional properties
int intMaxIterations = getProperty("MaxIterations");
const size_t maxIterations = static_cast<size_t>(intMaxIterations);
// get the cost function which must be a CostFuncFitting
boost::shared_ptr<CostFuncFitting> costFunc =
boost::dynamic_pointer_cast<CostFuncFitting>(
API::CostFunctionFactory::Instance().create(
getPropertyValue("CostFunction")));
costFunc->setFittingFunction(m_function, domain, values);
minimizer->initialize(costFunc, maxIterations);
const int64_t nsteps = maxIterations * m_function->estimateNoProgressCalls();
API::Progress prog(this, 0.0, 1.0, nsteps);
m_function->setProgressReporter(&prog);
// do the fitting until success or iteration limit is reached
size_t iter = 0;
bool success = false;
std::string errorString;
g_log.debug("Starting minimizer iteration\n");
while (iter < maxIterations) {
g_log.debug() << "Starting iteration " << iter << "\n";
m_function->iterationStarting();
if (!minimizer->iterate(iter)) {
errorString = minimizer->getError();
g_log.debug() << "Iteration stopped. Minimizer status string="
<< errorString << "\n";
success = errorString.empty() || errorString == "success";
if (success) {
errorString = "success";
}
break;
}
prog.report();
m_function->iterationFinished();
++iter;
}
g_log.debug() << "Number of minimizer iterations=" << iter << "\n";
minimizer->finalize();
if (iter >= maxIterations) {
if (!errorString.empty()) {
errorString += '\n';
}
errorString += "Failed to converge after " +
boost::lexical_cast<std::string>(maxIterations) +
" iterations.";
}
// return the status flag
setPropertyValue("OutputStatus", errorString);
// degrees of freedom
size_t dof = domain->size() - costFunc->nParams();
if (dof == 0)
dof = 1;
double rawcostfuncval = minimizer->costFunctionVal();
double finalCostFuncVal = rawcostfuncval / double(dof);
setProperty("OutputChi2overDoF", finalCostFuncVal);
// fit ended, creating output
// get the workspace
API::Workspace_const_sptr ws = getProperty("InputWorkspace");
bool doCreateOutput = getProperty("CreateOutput");
std::string baseName = getPropertyValue("Output");
if (!baseName.empty()) {
doCreateOutput = true;
}
bool doCalcErrors = getProperty("CalcErrors");
if (doCreateOutput) {
doCalcErrors = true;
}
if (costFunc->nParams() == 0) {
doCalcErrors = false;
}
GSLMatrix covar;
if (doCalcErrors) {
// Calculate the covariance matrix and the errors.
costFunc->calCovarianceMatrix(covar);
costFunc->calFittingErrors(covar, rawcostfuncval);
}
if (doCreateOutput) {
copyMinimizerOutput(*minimizer);
if (baseName.empty()) {
baseName = ws->name();
if (baseName.empty()) {
baseName = "Output";
}
}
baseName += "_";
declareProperty(
new API::WorkspaceProperty<API::ITableWorkspace>(
"OutputNormalisedCovarianceMatrix", "", Kernel::Direction::Output),
"The name of the TableWorkspace in which to store the final covariance "
"matrix");
setPropertyValue("OutputNormalisedCovarianceMatrix",
baseName + "NormalisedCovarianceMatrix");
Mantid::API::ITableWorkspace_sptr covariance =
Mantid::API::WorkspaceFactory::Instance().createTable("TableWorkspace");
covariance->addColumn("str", "Name");
// set plot type to Label = 6
covariance->getColumn(covariance->columnCount() - 1)->setPlotType(6);
// std::vector<std::string> paramThatAreFitted; // used for populating 1st
// "name" column
for (size_t i = 0; i < m_function->nParams(); i++) {
if (m_function->isActive(i)) {
covariance->addColumn("double", m_function->parameterName(i));
// paramThatAreFitted.push_back(m_function->parameterName(i));
}
}
size_t np = m_function->nParams();
size_t ia = 0;
for (size_t i = 0; i < np; i++) {
if (m_function->isFixed(i))
continue;
Mantid::API::TableRow row = covariance->appendRow();
row << m_function->parameterName(i);
size_t ja = 0;
for (size_t j = 0; j < np; j++) {
if (m_function->isFixed(j))
continue;
if (j == i)
row << 100.0;
else {
if (!covar.gsl()) {
throw std::runtime_error(
"There was an error while allocating the (GSL) covariance "
"matrix "
"which is needed to produce fitting error results.");
}
row << 100.0 * covar.get(ia, ja) /
sqrt(covar.get(ia, ia) * covar.get(ja, ja));
}
++ja;
}
++ia;
}
setProperty("OutputNormalisedCovarianceMatrix", covariance);
// create output parameter table workspace to store final fit parameters
// including error estimates if derivative of fitting function defined
declareProperty(new API::WorkspaceProperty<API::ITableWorkspace>(
"OutputParameters", "", Kernel::Direction::Output),
"The name of the TableWorkspace in which to store the "
"final fit parameters");
setPropertyValue("OutputParameters", baseName + "Parameters");
Mantid::API::ITableWorkspace_sptr result =
Mantid::API::WorkspaceFactory::Instance().createTable("TableWorkspace");
result->addColumn("str", "Name");
// set plot type to Label = 6
result->getColumn(result->columnCount() - 1)->setPlotType(6);
result->addColumn("double", "Value");
result->addColumn("double", "Error");
// yErr = 5
result->getColumn(result->columnCount() - 1)->setPlotType(5);
for (size_t i = 0; i < m_function->nParams(); i++) {
Mantid::API::TableRow row = result->appendRow();
row << m_function->parameterName(i) << m_function->getParameter(i)
<< m_function->getError(i);
}
// Add chi-squared value at the end of parameter table
Mantid::API::TableRow row = result->appendRow();
#if 1
std::string costfuncname = getPropertyValue("CostFunction");
if (costfuncname == "Rwp")
row << "Cost function value" << rawcostfuncval;
else
row << "Cost function value" << finalCostFuncVal;
setProperty("OutputParameters", result);
#else
row << "Cost function value" << finalCostFuncVal;
Mantid::API::TableRow row2 = result->appendRow();
std::string name(getPropertyValue("CostFunction"));
name += " value";
row2 << name << rawcostfuncval;
#endif
setProperty("OutputParameters", result);
bool outputParametersOnly = getProperty("OutputParametersOnly");
if (!outputParametersOnly) {
const bool unrollComposites = getProperty("OutputCompositeMembers");
bool convolveMembers = existsProperty("ConvolveMembers");
if (convolveMembers) {
convolveMembers = getProperty("ConvolveMembers");
}
m_domainCreator->separateCompositeMembersInOutput(unrollComposites,
convolveMembers);
m_domainCreator->createOutputWorkspace(baseName, m_function, domain,
values);
}
}
progress(1.0);
}