/// 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);
}
/**
* 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();
}
}
/// Copy constructor
/// @param M :: The other matrix.
GSLMatrix::GSLMatrix(const GSLMatrix &M)
: m_data(M.m_data),
m_view(gsl_matrix_view_array(m_data.data(), M.size1(), M.size2())) {}
/// Execute algorithm.
void Schrodinger1D::exec()
{
double startX = get("StartX");
double endX = get("EndX");
if (endX <= startX)
{
throw std::invalid_argument("StartX must be <= EndX");
}
IFunction_sptr VPot = getClass("VPot");
chebfun vpot( 0, startX, endX );
vpot.bestFit( *VPot );
size_t nBasis = vpot.n() + 1;
std::cerr << "n=" << nBasis << std::endl;
//if (n < 3)
{
nBasis = 200;
vpot.resize( nBasis );
}
const double beta = get("Beta");
auto kinet = new ChebCompositeOperator;
kinet->addRight( new ChebTimes(-beta) );
kinet->addRight( new ChebDiff2 );
auto hamiltonian = new ChebPlus;
hamiltonian->add('+', kinet );
hamiltonian->add('+', new ChebTimes(VPot) );
GSLMatrix L;
hamiltonian->createMatrix( vpot.getBase(), L );
GSLVector d;
GSLMatrix v;
L.diag( d, v );
std::vector<double> norms = vpot.baseNorm();
assert(norms.size() == L.size1());
assert(norms.size() == L.size2());
for(size_t i = 0; i < norms.size(); ++i)
{
double factor = 1.0 / norms[i];
for(size_t j = i; j < norms.size(); ++j)
{
v.multiplyBy(i,j,factor);
}
}
// eigenvectors orthogonality check
// GSLMatrix v1 = v;
// GSLMatrix tst;
// tst = Tr(v1) * v;
// std::cerr << tst << std::endl;
std::vector<size_t> indx(L.size1());
getSortedIndex( d, indx );
auto eigenvalues = API::TableWorkspace_ptr(dynamic_cast<API::TableWorkspace*>(
API::WorkspaceFactory::instance().create("TableWorkspace"))
);
eigenvalues->setRowCount(nBasis);
setProperty("Eigenvalues", eigenvalues);
eigenvalues->addColumn("double","N");
auto nColumn = static_cast<API::TableColumn<double>*>(eigenvalues->getColumn("N").get());
nColumn->asNumeric()->setPlotRole(API::NumericColumn::X);
auto& nc = nColumn->data();
eigenvalues->addDoubleColumn("Energy");
auto eColumn = static_cast<API::TableColumn<double>*>(eigenvalues->getColumn("Energy").get());
eColumn->asNumeric()->setPlotRole(API::NumericColumn::Y);
auto& ec = eigenvalues->getDoubleData("Energy");
boost::scoped_ptr<ChebfunVector> eigenvectors(new ChebfunVector);
chebfun fun0(nBasis,startX,endX);
ChebFunction_sptr theSum(new ChebFunction(fun0));
// collect indices of spurious eigenvalues to move them to the back
std::vector<size_t> spurious;
// index for good eigenvalues
size_t n = 0;
for(size_t j = 0; j < nBasis; ++j)
{
size_t i = indx[j];
chebfun fun(fun0);
fun.setP(v,i);
// check eigenvalues for spurious ones
chebfun dfun(fun);
dfun.square();
double norm = dfun.integr();
// I am not sure that it's a solid condition
if ( norm < 0.999999 )
{
// bad eigenvalue
spurious.push_back(j);
}
else
{
nc[n] = double(n);
ec[n] = d[i];
eigenvectors->add(ChebFunction_sptr(new ChebFunction(fun)));
// test sum of functions squares
*theSum += dfun;
// chebfun dfun(fun);
// hamiltonian->apply(fun,dfun);
// dfun *= fun;
// std::cerr << "ener["<<n<<"]=" << ec[n] << ' ' << norm << ' ' << dfun.integr() << std::endl;
++n;
}
}
GSLVector eigv;
ChebfunVector *eigf = NULL;
improve(hamiltonian, eigenvectors.get(), eigv, &eigf);
eigenvalues->setRowCount( eigv.size() );
for(size_t i = 0; i < eigv.size(); ++i)
{
nc[i] = double(i);
ec[i] = eigv[i];
}
eigf->add(theSum);
setProperty("Eigenvectors",ChebfunVector_sptr(eigf));
//makeQuadrature(eigf);
}
/// 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());
}
