void GeneticAlgorithm::resetParameters(int nPopulation, double scaleFactor, double crossingProbability){
_scaleFactor = scaleFactor;
_crossingProbability = crossingProbability;
_nPopulation = nPopulation;
delete [] _populationParametersOld;
delete [] _populationParametersNew;
_populationParametersOld = (Parameters::fitParameters *) mkl_malloc(sizeof(Parameters::fitParameters)*nPopulation,16);
_populationParametersNew = (Parameters::fitParameters *) mkl_malloc(sizeof(Parameters::fitParameters)*nPopulation,16);
for(int i = 0; i < _nPopulation; i++){
_populationParametersOld[i].c11 = (randomDouble(0.0,1.0))*pow(10,9);
_populationParametersOld[i].c22 = _populationParametersOld[i].c11;
_populationParametersOld[i].c33 = _populationParametersOld[i].c11;
_populationParametersOld[i].c44 = (randomDouble(0.0,1.0))*pow(10,9);
_populationParametersOld[i].c55 = _populationParametersOld[i].c44;
_populationParametersOld[i].c66 = _populationParametersOld[i].c44;
_populationParametersOld[i].c12 = (randomDouble(0.0,1.0))*pow(10,9);
_populationParametersOld[i].c13 = _populationParametersOld[i].c12;
_populationParametersOld[i].c23 = _populationParametersOld[i].c12;
_populationParametersOld[i].chiSq = 1;
_populationParametersOld[i].chiSq = calculateResidual(&_populationParametersOld[i],0);
}
//delete _integerDistribution;
delete [] ints1;
delete [] ints2;
delete [] ints3;
ints1 = new int[_nPopulation];
ints2 = new int[_nPopulation];
ints3 = new int[_nPopulation];
}
template<typename PointT> int
SmoothNormal<PointT>::applySegmentation(std::list<std::list<int> >& segments){
std::vector<float> pt_residuals;
pt_residuals.resize(this->input_->points.size(), -1);
std::vector<bool> used;
used.resize(this->input_->points.size(), false);
int pts_segmented =0;
int search_idx=0;
while(pts_segmented < this->input_->points.size()){
//iterate through the points and either find a
// point which is a really low residual or find the lowest
// residual left.
int idx =-1;
float res = std::numeric_limits<float>::max();
for(int i=search_idx+1; i<this->input_->points.size(); i++){
if(used[i]) continue;
if(pt_residuals[i] < 0){
pt_residuals[i] = calculateResidual(i);
}
if (pt_residuals[i] < rthresh_*2){ // if its 2 times smaller than rthresh
idx = i; // its small enough to use as a seed point
break;
}
else{
if (res > pt_residuals[i] ){
idx = i;
res = pt_residuals[i];
}
}
}
if (idx >=0){
std::list<int> nseg;
int s = smoothNormalCluster(idx,nseg,*this->input_, *this->normals_, *this->search_,
used,cluster_N_nbrs, this->smooth_thresh_,rthresh_);
std::cout << " Created segment " << idx << " with " << s << " pts \n";
if (s>2) segments.push_back(nseg);
pts_segmented += s;
search_idx = idx;
}
else{
std::cout << "There was no good seed\n";
break;
}
}
std::cout << "There were " << segments.size() << " segments \n";
return segments.size();
}
void GeneticAlgorithm::residualCalculatingThread(Parameters::arrayBounds * arrayBounds){
LARGE_INTEGER time1,time2;
for(int i = arrayBounds->start; i <= arrayBounds->end; i++){
QueryPerformanceCounter(&time1);
_populationParametersNew[i].chiSq = calculateResidual(&_populationParametersNew[i],arrayBounds->threadID);
QueryPerformanceCounter(&time2);
arrayBounds->time += 1000*(double)(time2.QuadPart-time1.QuadPart)/(freq.QuadPart);
if(_populationParametersNew[i].chiSq < _populationParametersOld[i].chiSq){
_populationParametersOld[i] = _populationParametersNew[i];
}
}
SetEvent(arrayBounds->handle);
return;
}
UINT FitThread::calculateMinT()
{
{
double F,dF1,dF2,phi,A,Td,T,m;
T = 4.2;
m = 1.7;
contents.myParams.m = m;
contents.myParams.T = T;
for(int i = 0; i <contents.iterations; i++)
{
F = randomDouble(450,500);
dF1 = randomDouble(30,50);
dF2 = randomDouble(-20,0);
phi = randomDouble(0,.5);
A = randomDouble(0,2);
Td = randomDouble(3,10);
contents.myOscFun->changeParams(F,dF1,dF2,phi);
contents.myEnvel->changeParams(A,T,Td,m);
double residual = calculateResidual();
if(residual < contents.myParams.chiSq)
{
contents.myParams.A = A;
contents.myParams.chiSq = residual;
contents.myParams.dF1 = dF1;
contents.myParams.dF2 = dF2;
contents.myParams.F = F;
contents.myParams.phi = phi;
contents.myParams.Td = Td;
}
}
}
SetEvent(contents.hEvent);
return 0;}
int main(int argc, char* argv[])
{
//--This first declaration is included for software engineering reasons: allow main method to control flow of data
//create function pointer for calculateDependentVariable
//We want to do this so that calculateJacobian can call calculateDependentVariables as it needs to,
//but we explicitly give it this authority from the main method
void (*yourCalculateDependentVariables)(const Teuchos::SerialDenseMatrix<int, std::complex<double> >&, const Teuchos::SerialDenseVector<int, std::complex<double> >&, Teuchos::SerialDenseVector<int, std::complex<double> >&);
yourCalculateDependentVariables = &calculateDependentVariables;
//Create an object to allow access to LAPACK using a library found in Trilinos
//
Teuchos::LAPACK<int, std::complex<double> > Teuchos_LAPACK_Object;
//The problem being solved is to find the intersection of three infinite paraboloids:
//(x-1)^2 + y^2 + z = 0
//x^2 + y^2 -(z+1) = 0
//x^2 + y^2 +(z-1) = 0
//
//They should intersect at the point (1, 0, 0)
Teuchos::SerialDenseMatrix<int, std::complex<double> > offsets(NUMDIMENSIONS, NUMDIMENSIONS);
offsets(0,0) = 1.0;
offsets(1,2) = 1.0;
offsets(2,2) = 1.0;
//We need to initialize the target vectors and provide an initial guess
Teuchos::SerialDenseVector<int, std::complex<double> > targetsDesired(NUMDIMENSIONS);
Teuchos::SerialDenseVector<int, std::complex<double> > targetsCalculated(NUMDIMENSIONS);
Teuchos::SerialDenseVector<int, std::complex<double> > unperturbedTargetsCalculated(NUMDIMENSIONS);
Teuchos::SerialDenseVector<int, std::complex<double> > currentGuess(NUMDIMENSIONS);
Teuchos::SerialDenseVector<int, std::complex<double> > guessAdjustment(NUMDIMENSIONS);
currentGuess[0] = 2.0;
currentGuess[1] = 1.0;
currentGuess[2] = 1.0;
//Place to store our tangent-stiffness matrix or Jacobian
//One element for every combination of dependent variable with independent variable
Teuchos::SerialDenseMatrix<int, std::complex<double> > jacobian(NUMDIMENSIONS, NUMDIMENSIONS);
int count = 0;
double error = 1.0E5;
std::cout << "Running Forward Difference Example" << std::endl;
while(count < MAXITERATIONS and error > ERRORTOLLERANCE)
{
//Calculate Jacobian tangent to currentGuess point
//at the same time, an unperturbed targetsCalculated is, well, calculated
calculateJacobian(offsets,
jacobian,
targetsCalculated,
unperturbedTargetsCalculated,
currentGuess,
yourCalculateDependentVariables);
//Compute a guessChange and immediately set the currentGuess equal to the guessChange
updateGuess(currentGuess,
targetsCalculated,
jacobian,
Teuchos_LAPACK_Object);
//Compute F(x) with the updated, currentGuess
calculateDependentVariables(offsets,
currentGuess,
targetsCalculated);
//Calculate the L2 norm of Ftarget - F(xCurrentGuess)
calculateResidual(targetsDesired,
targetsCalculated,
error);
count ++;
//If we have converged, or if we have exceeded our alloted number of iterations, discontinue the loop
std::cout << "Residual Error: " << error << std::endl;
}
std::cout << "******************************************" << std::endl;
std::cout << "Number of iterations: " << count << std::endl;
std::cout << "Final guess:\n x, y, z\n " << currentGuess[0] << ", " << currentGuess[1] << ", " << currentGuess[2] << std::endl;
std::cout << "Error tollerance: " << ERRORTOLLERANCE << std::endl;
std::cout << "Final error: " << error << std::endl;
std::cout << "--program complete--" << std::endl;
return 0;
}
int main(int argc, char* argv[])
{
//--This first declaration is included for software engineering reasons: allow main method to control flow of data
//create function pointer for calculateDependentVariable
//We want to do this so that calculateJacobian can call calculateDependentVariables as it needs to,
//but we explicitly give it this authority from the main method
void (*yourCalculateDependentVariables)(const arma::Mat<std::complex<double> >&, const arma::Col<std::complex<double> >&, arma::Col<std::complex<double> >&);
yourCalculateDependentVariables = &calculateDependentVariables;
//The problem being solved is to find the intersection of three infinite paraboloids:
//(x-1)^2 + y^2 + z = 0
//x^2 + y^2 -(z+1) = 0
//x^2 + y^2 +(z-1) = 0
//
//They should intersect at the point (1, 0, 0)
arma::Mat<std::complex<double> > offsets(NUMDIMENSIONS, NUMDIMENSIONS);
offsets.fill(0.0);
offsets.col(0)[0] = 1.0;
offsets.col(2)[1] = 1.0;
offsets.col(2)[2] = 1.0;
//We need to initialize the target vectors and provide an initial guess
arma::Col<std::complex<double> > targetsDesired(NUMDIMENSIONS);
targetsDesired.fill(0.0);
arma::Col<std::complex<double> > targetsCalculated(NUMDIMENSIONS);
targetsCalculated.fill(0.0);
arma::Col<double> realTargetsCalculated(NUMDIMENSIONS);
realTargetsCalculated.fill(0.0);
arma::Col<std::complex<double> > currentGuess(NUMDIMENSIONS);
currentGuess.fill(2.0);
//Place to store our tangent-stiffness matrix or Jacobian
//One element for every combination of dependent variable with independent variable
arma::Mat<double> jacobian(NUMDIMENSIONS, NUMDIMENSIONS);
jacobian.fill(0.0);
int count = 0;
double error = 1.0E5;
std::cout << "Running complex step example ................" << std::endl;
while(count < MAXITERATIONS and error > ERRORTOLLERANCE)
{
//Calculate Jacobian tangent to currentGuess point
//at the same time, an unperturbed targetsCalculated is, well, calculated
calculateJacobian(offsets,
jacobian,
targetsCalculated,
currentGuess,
yourCalculateDependentVariables);
//Compute a new currentGuess
updateGuess(currentGuess,
realTargetsCalculated,
targetsCalculated,
jacobian);
//Compute F(x) with the updated, currentGuess
calculateDependentVariables(offsets,
currentGuess,
targetsCalculated);
//Calculate the L2 norm of Ftarget - F(xCurrentGuess)
calculateResidual(targetsDesired,
targetsCalculated,
error);
count ++;
//If we have converged, or if we have exceeded our alloted number of iterations, discontinue the loop
std::cout << "Residual Error: " << error << std::endl;
}
std::cout << "******************************************" << std::endl;
std::cout << "Number of iterations: " << count << std::endl;
std::cout << "Final guess:\n x, y, z\n" << arma::real(currentGuess.t());
std::cout << "Error tollerance: " << ERRORTOLLERANCE << std::endl;
std::cout << "Final error: " << error << std::endl;
std::cout << "--program complete--" << std::endl;
return 0;
}
void GeneticAlgorithm::initializeParameters(double* dataSet, int dataSetLength, int nPopulation, double scaleFactor, double crossingProbability){
_scaleFactor = scaleFactor;
_crossingProbability = crossingProbability;
_dataSetLength = dataSetLength;
_dataSet = dataSet;
_residualArray = new double*[nThreads];
_paramArray = new double*[nThreads];
for(int i = 0; i < nThreads; i++){
_residualArray[i] = new double[_dataSetLength];
_paramArray[i] = new double[nParams];
}
_populationParametersOld = (Parameters::fitParameters *) mkl_malloc(sizeof(Parameters::fitParameters)*nPopulation,16);
_populationParametersNew = (Parameters::fitParameters *) mkl_malloc(sizeof(Parameters::fitParameters)*nPopulation,16);
for(int i = 0; i < _nPopulation; i++){
_populationParametersOld[i].missFreq = new long[_nMissing];
_populationParametersNew[i].missFreq = new long[_nMissing];
/* _populationParametersOld[i].c11 = (randomDouble(196,196.1))*pow(10,9);
_populationParametersOld[i].c22 = _populationParametersOld[i].c11;
_populationParametersOld[i].c33 = (randomDouble(187,187.1))*pow(10,9);
_populationParametersOld[i].c44 = (randomDouble(63.5,63.6))*pow(10,9);
_populationParametersOld[i].c55 = _populationParametersOld[i].c44;
_populationParametersOld[i].c66 = (randomDouble(55.7,55.8))*pow(10,9);
_populationParametersOld[i].c12 = (randomDouble(62.5,62.6))*pow(10,9);
_populationParametersOld[i].c13 = (randomDouble(69.8,69.9))*pow(10,9);
_populationParametersOld[i].c23 = _populationParametersOld[i].c13;
_populationParametersOld[i].chiSq = calculateResidual(&_populationParametersOld[i],0);/// ***Tetragonal PuCoGa5*/
/* _populationParametersOld[i].c11 = (randomDouble(260,300))*pow(10,9);
_populationParametersOld[i].c22 = _populationParametersOld[i].c11;
_populationParametersOld[i].c33 = (randomDouble(290,320))*pow(10,9);
_populationParametersOld[i].c44 = (randomDouble(90,110))*pow(10,9);
_populationParametersOld[i].c55 = _populationParametersOld[i].c44;
_populationParametersOld[i].c66 = (randomDouble(130,150))*pow(10,9);
_populationParametersOld[i].c12 = (randomDouble(140,165))*pow(10,9);
_populationParametersOld[i].c13 = (randomDouble(100,130))*pow(10,9);
_populationParametersOld[i].c23 = _populationParametersOld[i].c13;
_populationParametersOld[i].chiSq = calculateResidual(&_populationParametersOld[i],0);/// ***Tetragonal URu2Si2
*/
_populationParametersOld[i].c11 = (randomDouble(220,260))*pow(10,9);
_populationParametersOld[i].c22 = (randomDouble(210,250))*pow(10,9);
_populationParametersOld[i].c33 = (randomDouble(100,150))*pow(10,9);
_populationParametersOld[i].c44 = (randomDouble(32,38))*pow(10,9);
_populationParametersOld[i].c55 = (randomDouble(48,52))*pow(10,9);
_populationParametersOld[i].c66 = (randomDouble(94,98))*pow(10,9);
_populationParametersOld[i].c12 = (randomDouble(100,150))*pow(10,9);
_populationParametersOld[i].c13 = (randomDouble(25,60))*pow(10,9);
_populationParametersOld[i].c23 = (randomDouble(20,70))*pow(10,9);
_populationParametersOld[i].chiSq = calculateResidual(&_populationParametersOld[i],0);/// ***Orthorhombic YBCO67
//_populationParametersOld[i].c11 = (randomDouble(1,400))*pow(10,9);
//_populationParametersOld[i].c22 = _populationParametersOld[i].c11;
//_populationParametersOld[i].c33 = _populationParametersOld[i].c11;
//
//_populationParametersOld[i].c44 = (randomDouble(1,400))*pow(10,9);
//_populationParametersOld[i].c55 = _populationParametersOld[i].c44;
//_populationParametersOld[i].c66 = _populationParametersOld[i].c44;
//_populationParametersOld[i].c12 = (randomDouble(1,400))*pow(10,9);
//_populationParametersOld[i].c13 = _populationParametersOld[i].c12;
//_populationParametersOld[i].c23 = _populationParametersOld[i].c12;
//_populationParametersOld[i].chiSq = calculateResidual(&_populationParametersOld[i],0);/// ***Cubic Nb
//_populationParametersOld[i].c11 = (randomDouble(120,200))*pow(10,9);
//_populationParametersOld[i].c22 = _populationParametersOld[i].c11;
//
//_populationParametersOld[i].c33 = (randomDouble(120,200))*pow(10,9);
//_populationParametersOld[i].c44 = (randomDouble(10,100))*pow(10,9);
//_populationParametersOld[i].c55 = _populationParametersOld[i].c44;
//
//_populationParametersOld[i].c66 = (randomDouble(10,100))*pow(10,9);
//_populationParametersOld[i].c12 = (randomDouble(10,100))*pow(10,9);
//_populationParametersOld[i].c13 = (randomDouble(10,100))*pow(10,9);
//_populationParametersOld[i].c23 = _populationParametersOld[i].c13;
//_populationParametersOld[i].chiSq = calculateResidual(&_populationParametersOld[i],0);/// ***Tetragonal CeCoIn5
}
_minimumParameters.c11 = 1;
_minimumParameters.c22 = 1;
_minimumParameters.c33 = 1;
_minimumParameters.c44 = 1;
_minimumParameters.c55 = 1;
_minimumParameters.c66 = 1;
_minimumParameters.c12 = 1;
_minimumParameters.c13 = 1;
_minimumParameters.c23 = 1;
_minimumParameters.chiSq = std::numeric_limits<double>::infinity();
_minimumParameters.missFreq = new long[_nMissing];
}
