/// Create a submatrix. A submatrix is a view into the parent matrix.
/// Lifetime of a submatrix cannot exceed the lifetime of the parent.
/// @param M :: The parent matrix.
/// @param row :: The first row in the submatrix.
/// @param col :: The first column in the submatrix.
/// @param nRows :: The number of rows in the submatrix.
/// @param nCols :: The number of columns in the submatrix.
GSLMatrix::GSLMatrix(const GSLMatrix &M, size_t row, size_t col, size_t nRows,
size_t nCols) {
if (row + nRows > M.size1() || col + nCols > M.size2()) {
throw std::runtime_error("Submatrix exceeds matrix size.");
}
auto view = gsl_matrix_const_submatrix(M.gsl(), row, col, nRows, nCols);
m_matrix = gsl_matrix_alloc(nRows, nCols);
gsl_matrix_memcpy(m_matrix, &view.matrix);
}
/// Calculate the eigensystem of a symmetric matrix
/// @param eigenValues :: Output variable that receives the eigenvalues of this
/// matrix.
/// @param eigenVectors :: Output variable that receives the eigenvectors of
/// this matrix.
void GSLMatrix::eigenSystem(GSLVector &eigenValues, GSLMatrix &eigenVectors) {
size_t n = size1();
if (n != size2()) {
throw std::runtime_error("Matrix eigenSystem: the matrix must be square.");
}
eigenValues.resize(n);
eigenVectors.resize(n, n);
auto workspace = gsl_eigen_symmv_alloc(n);
gsl_eigen_symmv(gsl(), eigenValues.gsl(), eigenVectors.gsl(), workspace);
gsl_eigen_symmv_free(workspace);
}
/**
* Calculates covariance matrix for fitting function's active parameters.
*/
void CostFuncFitting::calActiveCovarianceMatrix(GSLMatrix &covar,
double epsrel) {
// construct the jacobian
GSLJacobian J(m_function, m_values->size());
size_t na = this->nParams(); // number of active parameters
assert(J.getJ()->size2 == na);
covar.resize(na, na);
// calculate the derivatives
m_function->functionDeriv(*m_domain, J);
// let the GSL to compute the covariance matrix
gsl_multifit_covar(J.getJ(), epsrel, covar.gsl());
}
/** 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);
}
/// Copy constructor
/// @param M :: The other matrix.
GSLMatrix::GSLMatrix(const GSLMatrix &M) {
m_matrix = gsl_matrix_alloc(M.size1(), M.size2());
gsl_matrix_memcpy(m_matrix, M.gsl());
}