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Commit bedfc48b authored by Roman Tolchenov's avatar Roman Tolchenov
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Re #11032. Put in the new changes.

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Code/Mantid/Framework/CurveFitting/inc/MantidCurveFitting/FABADAMinimizer.h
+ 37
− 38
View file @ bedfc48b
......@@ -40,53 +40,52 @@ public:
/// Constructor
FABADAMinimizer();
virtual ~FABADAMinimizer();
/// Lo ha puesto Roman y no se para que!! creo que es el destructor
/// Name of the minimizer.
std::string name() const { return "FABADA"; }
/// Initialize minimizer, i.e. pass a function to minimize.
virtual void initialize(API::ICostFunction_sptr function,
size_t maxIterations);
virtual void initialize(API::ICostFunction_sptr function,size_t maxIterations);
/// Do one iteration.
virtual bool iterate(size_t iter);
/// Return current value of the cost function
virtual double costFunctionVal();
private:
/// Pointer to the cost function. Must be the least squares.
/// Intentar encontrar una manera de sacar aqui el numero de parametros que
/// no sea necesaria la cost function
boost::shared_ptr<CostFuncLeastSquares> m_leastSquares;
/// The number of iterations done.
size_t m_counter;
///
size_t m_numberIterations;
/// The number of changes done in each parameter.
std::vector<double> m_changes;
/// The jump for each parameter
std::vector<double> m_jump;
/// Parameters.
GSLVector m_parameters;
/// Markov chain.
std::vector<std::vector<double>> m_chain;
/// The chi square result of previous iteration;
double m_chi2;
/// Boolean that indicates if converged
bool m_converged;
/// The point when convergence starts
size_t m_conv_point;
/// Convergence of each parameter
std::vector<bool> m_par_converged;
/// Lower bound for each parameter
std::vector<double> m_lower;
/// Upper bound for each parameter
std::vector<double> m_upper;
/// Bool that indicates if there is any boundary constraint
std::vector<bool> m_bound;
/// Convergence criteria for each parameter
std::vector<double> m_criteria;
};
private:
/// Pointer to the cost function. Must be the least squares.
/// Intentar encontrar una manera de sacar aqui el numero de parametros que no sea necesaria la cost function
boost::shared_ptr<CostFuncLeastSquares> m_leastSquares;
/// The number of iterations done.
size_t m_counter;
///
size_t m_numberIterations;
/// The number of changes done in each parameter.
std::vector<double> m_changes;
/// The jump for each parameter
std::vector<double> m_jump;
/// Parameters.
GSLVector m_parameters;
/// Markov chain.
std::vector<std::vector<double>> m_chain;
/// The chi square result of previous iteration;
double m_chi2;
/// Boolean that indicates if converged
bool m_converged;
/// The point when convergence starts
size_t m_conv_point;
/// Convergence of each parameter
std::vector<bool> m_par_converged;
///Lower bound for each parameter
std::vector<double> m_lower;
///Upper bound for each parameter
std::vector<double> m_upper;
/// Bool that indicates if there is any boundary constraint
std::vector<bool> m_bound;
/// Convergence criteria for each parameter
std::vector<double> m_criteria;
/// Maximum number of iterations
size_t m_max_iter;
};
} // namespace CurveFitting
} // namespace Mantid
......
Code/Mantid/Framework/CurveFitting/src/FABADAMinimizer.cpp
+ 367
− 298
View file @ bedfc48b
This diff is collapsed.
Code/Mantid/Framework/CurveFitting/test/FABADAMinimizerTest.h
+ 46
− 47
View file @ bedfc48b
......@@ -46,7 +46,7 @@ public:
fit.setProperty("WorkspaceIndex",0);
fit.setProperty("CreateOutput",true);
fit.setProperty("MaxIterations",100000);
fit.setProperty("Minimizer", "FABADA,ChainLength=5000,ConvergenceCriteria = 0.1, OutputWorkspaceConverged=conv");
fit.setProperty("Minimizer", "FABADA,ChainLength=5000,StepsBetweenValues=10,ConvergenceCriteria = 0.1");
TS_ASSERT_THROWS_NOTHING( fit.execute() );
......@@ -59,40 +59,40 @@ public:
size_t n = fun -> nParams();
TS_ASSERT( AnalysisDataService::Instance().doesExist("pdf") );
TS_ASSERT( AnalysisDataService::Instance().doesExist("PDF") );
MatrixWorkspace_sptr wsPDF = boost::dynamic_pointer_cast<MatrixWorkspace>(
API::AnalysisDataService::Instance().retrieve("pdf"));
API::AnalysisDataService::Instance().retrieve("PDF"));
TS_ASSERT(wsPDF);
TS_ASSERT_EQUALS(wsPDF->getNumberHistograms(),n);
const Mantid::MantidVec& X = wsPDF->dataX(0);
const Mantid::MantidVec& Y = wsPDF->dataY(0);
TS_ASSERT_EQUALS(X.size(), 51);
TS_ASSERT_EQUALS(Y.size(), 50);
TS_ASSERT( AnalysisDataService::Instance().doesExist("chi2") );
ITableWorkspace_sptr chi2table = boost::dynamic_pointer_cast<ITableWorkspace>(
API::AnalysisDataService::Instance().retrieve("chi2"));
TS_ASSERT(chi2table);
TS_ASSERT_EQUALS(chi2table->columnCount(), 4);
TS_ASSERT_EQUALS(chi2table->rowCount(), 1);
TS_ASSERT_EQUALS(chi2table->getColumn(0)->type(), "double");
TS_ASSERT_EQUALS(chi2table->getColumn(0)->name(), "Chi2min");
TS_ASSERT_EQUALS(chi2table->getColumn(1)->type(), "double");
TS_ASSERT_EQUALS(chi2table->getColumn(1)->name(), "Chi2MP");
TS_ASSERT_EQUALS(chi2table->getColumn(2)->type(), "double");
TS_ASSERT_EQUALS(chi2table->getColumn(2)->name(), "Chi2min_red");
TS_ASSERT_EQUALS(chi2table->getColumn(3)->type(), "double");
TS_ASSERT_EQUALS(chi2table->getColumn(3)->name(), "Chi2MP_red");
TS_ASSERT(chi2table->Double(0,0) <= chi2table->Double(0,1));
TS_ASSERT(chi2table->Double(0,2) <= chi2table->Double(0,3));
TS_ASSERT_DELTA(chi2table->Double(0,0), chi2table->Double(0,1), 1);
TS_ASSERT_DELTA(chi2table->Double(0,0), 0.0, 1.0);
TS_ASSERT( AnalysisDataService::Instance().doesExist("conv") );
TS_ASSERT_EQUALS(X.size(), 21);
TS_ASSERT_EQUALS(Y.size(), 20);
TS_ASSERT( AnalysisDataService::Instance().doesExist("CostFunction") );
ITableWorkspace_sptr CostFunctionTable = boost::dynamic_pointer_cast<ITableWorkspace>(
API::AnalysisDataService::Instance().retrieve("CostFunction"));
TS_ASSERT(CostFunctionTable);
TS_ASSERT_EQUALS(CostFunctionTable->columnCount(), 4);
TS_ASSERT_EQUALS(CostFunctionTable->rowCount(), 1);
TS_ASSERT_EQUALS(CostFunctionTable->getColumn(0)->type(), "double");
TS_ASSERT_EQUALS(CostFunctionTable->getColumn(0)->name(), "Chi2min");
TS_ASSERT_EQUALS(CostFunctionTable->getColumn(1)->type(), "double");
TS_ASSERT_EQUALS(CostFunctionTable->getColumn(1)->name(), "Chi2MP");
TS_ASSERT_EQUALS(CostFunctionTable->getColumn(2)->type(), "double");
TS_ASSERT_EQUALS(CostFunctionTable->getColumn(2)->name(), "Chi2min_red");
TS_ASSERT_EQUALS(CostFunctionTable->getColumn(3)->type(), "double");
TS_ASSERT_EQUALS(CostFunctionTable->getColumn(3)->name(), "Chi2MP_red");
TS_ASSERT(CostFunctionTable->Double(0,0) <= CostFunctionTable->Double(0,1));
TS_ASSERT(CostFunctionTable->Double(0,2) <= CostFunctionTable->Double(0,3));
TS_ASSERT_DELTA(CostFunctionTable->Double(0,0), CostFunctionTable->Double(0,1), 1);
TS_ASSERT_DELTA(CostFunctionTable->Double(0,0), 0.0, 1.0);
TS_ASSERT( AnalysisDataService::Instance().doesExist("ConvergedChain") );
MatrixWorkspace_sptr wsConv = boost::dynamic_pointer_cast<MatrixWorkspace>(
API::AnalysisDataService::Instance().retrieve("conv"));
API::AnalysisDataService::Instance().retrieve("ConvergedChain"));
TS_ASSERT(wsConv);
TS_ASSERT_EQUALS(wsConv->getNumberHistograms(),n+1);
......@@ -107,28 +107,27 @@ public:
TS_ASSERT_EQUALS(wsChain->getNumberHistograms(),n+1);
const Mantid::MantidVec& Xchain = wsChain->dataX(0);
TS_ASSERT_EQUALS(Xchain.size(), 6881);
TS_ASSERT_EQUALS(Xchain[5000], 5000);
TS_ASSERT(Xconv.size() < Xchain.size());
TS_ASSERT( AnalysisDataService::Instance().doesExist("pdfE") );
ITableWorkspace_sptr Etable = boost::dynamic_pointer_cast<ITableWorkspace>(
API::AnalysisDataService::Instance().retrieve("pdfE"));
TS_ASSERT(Etable);
TS_ASSERT_EQUALS(Etable->columnCount(), 4);
TS_ASSERT_EQUALS(Etable->rowCount(), n);
TS_ASSERT_EQUALS(Etable->getColumn(0)->type(), "str");
TS_ASSERT_EQUALS(Etable->getColumn(0)->name(), "Name");
TS_ASSERT_EQUALS(Etable->getColumn(1)->type(), "double");
TS_ASSERT_EQUALS(Etable->getColumn(1)->name(), "Value");
TS_ASSERT_EQUALS(Etable->getColumn(2)->type(), "double");
TS_ASSERT_EQUALS(Etable->getColumn(2)->name(), "Left's error");
TS_ASSERT_EQUALS(Etable->getColumn(3)->type(), "double");
TS_ASSERT_EQUALS(Etable->getColumn(3)->name(), "Rigth's error");
TS_ASSERT(Etable->Double(0,1) == fun->getParameter("Height"));
TS_ASSERT(Etable->Double(1,1) == fun->getParameter("Lifetime"));
TS_ASSERT( AnalysisDataService::Instance().doesExist("Parameters") );
ITableWorkspace_sptr Ptable = boost::dynamic_pointer_cast<ITableWorkspace>(
API::AnalysisDataService::Instance().retrieve("Parameters"));
TS_ASSERT(Ptable);
TS_ASSERT_EQUALS(Ptable->columnCount(), 4);
TS_ASSERT_EQUALS(Ptable->rowCount(), n);
TS_ASSERT_EQUALS(Ptable->getColumn(0)->type(), "str");
TS_ASSERT_EQUALS(Ptable->getColumn(0)->name(), "Name");
TS_ASSERT_EQUALS(Ptable->getColumn(1)->type(), "double");
TS_ASSERT_EQUALS(Ptable->getColumn(1)->name(), "Value");
TS_ASSERT_EQUALS(Ptable->getColumn(2)->type(), "double");
TS_ASSERT_EQUALS(Ptable->getColumn(2)->name(), "Left's error");
TS_ASSERT_EQUALS(Ptable->getColumn(3)->type(), "double");
TS_ASSERT_EQUALS(Ptable->getColumn(3)->name(), "Rigth's error");
TS_ASSERT(Ptable->Double(0,1) == fun->getParameter("Height"));
TS_ASSERT(Ptable->Double(1,1) == fun->getParameter("Lifetime"));
}
......@@ -150,7 +149,7 @@ public:
fit.setProperty("WorkspaceIndex",0);
fit.setProperty("CreateOutput",true);
fit.setProperty("MaxIterations",10);
fit.setProperty("Minimizer", "FABADA,ChainLength=5000,ConvergenceCriteria = 0.01, OutputWorkspaceConverged=conv");
fit.setProperty("Minimizer", "FABADA,ChainLength=5000,StepsBetweenValues=10,ConvergenceCriteria = 0.01");
TS_ASSERT_THROWS( fit.execute(), std::runtime_error );
......
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