void CompositeOFTest::test_composite_clone( ObjectiveFunction& OF )
{
// save current count of instances of underlying OFs for later
const int init_count = FauxObjectiveFunction::get_instance_count();
// clone the objective function
ObjectiveFunction* clone = OF.clone();
// check that the underlying OFs were also cloned
CPPUNIT_ASSERT( init_count < FauxObjectiveFunction::get_instance_count() );
// check that the value is the same
MsqPrintError err(cout);
double orig_val, clone_val;
bool rval;
rval = OF.evaluate( ObjectiveFunction::CALCULATE, patch(), orig_val, false, err );
ASSERT_NO_ERROR(err);
CPPUNIT_ASSERT(rval);
rval = clone->evaluate( ObjectiveFunction::CALCULATE, patch(), clone_val, false, err );
ASSERT_NO_ERROR(err);
CPPUNIT_ASSERT(rval);
CPPUNIT_ASSERT_DOUBLES_EQUAL( orig_val, clone_val, 1e-6 );
// check that cloned instances of underlying OFs are deleted
delete clone;
CPPUNIT_ASSERT_EQUAL( init_count, FauxObjectiveFunction::get_instance_count() );
}
void generateSimplexTab(const std::vector<Constraint<ArgType>>& constraints,
const ObjectiveFunction<ArgType, RetType, CoeffType>& function,
Matrix<CoeffType>& simplexTab)
{
unsigned long cols = simplexTab.getColumnsNumber(), rows =
simplexTab.getRowsNumber(), constrNum = constraints.size(), argsNum =
function.size();
if(constrNum + 1 != rows || argsNum + constrNum + 2 != cols)
throw "simplexTab has not proper size";
for (unsigned long i = 0; i < constrNum; i++) //generuje część odpowiedzialna za ograniczenia pod argumentami
for (unsigned long j = 0; j < argsNum; j++)
simplexTab(i, j) = constraints[i].getCoeff(j);
for (unsigned long j = 0; j < argsNum; j++) // generuje dolną część tablicy pod argumentami
simplexTab(rows - 1, j) = -function.getCoeff(j);
for (unsigned long j = 0; j < constrNum; j++) // generuje część związaną ze zmiennymi dodatkowymi
simplexTab(j, j + argsNum) = 1;
for (unsigned long j = 0; j < constrNum; j++) // część pod p - same zera
simplexTab(j, argsNum + constrNum) = 0;
for (unsigned long j = 0; j < constrNum; j++) // ostatnia kolumna, kolumna B
simplexTab(j, argsNum + constrNum + 1) = constraints[j].getB();
for (unsigned long j = argsNum; j < argsNum + constrNum - 1; j++) //ostatni wiersz pod zmienn. dodatk. - same zera
simplexTab(rows - 1, j) = 0;
simplexTab(rows - 1, cols - 2) = 1;
simplexTab(rows - 1, cols - 1) = 0;
}
void UnOptimizer::optimize_vertex_positions( PatchData &pd, MsqError &err) {
assert( pd.num_free_vertices() == 1 && pd.vertex_by_index(0).is_free_vertex() );
std::vector<Vector3D> grad(1);
double val, junk, coeff;
bool state;
state = objectiveFunction->evaluate_with_gradient( ObjectiveFunction::CALCULATE,
pd, val, grad, err );
MSQ_ERRRTN(err);
if (!state) {
MSQ_SETERR(err)(MsqError::INVALID_MESH);
return;
}
grad[0] /= grad[0].length();
PatchDataVerticesMemento* memento = pd.create_vertices_memento( err ); MSQ_ERRRTN(err);
std::auto_ptr<PatchDataVerticesMemento> deleter( memento );
pd.get_minmax_edge_length( junk, coeff );
for (int i = 0; i < 100; ++i) {
pd.set_free_vertices_constrained( memento, &grad[0], 1, coeff, err ); MSQ_ERRRTN(err);
state = objectiveFunction->evaluate( ObjectiveFunction::CALCULATE, pd, val, true, err );
MSQ_ERRRTN(err);
if (state)
break;
coeff *= 0.5;
}
if (!state) {
pd.set_to_vertices_memento( memento, err );
}
}
std::vector<double> OptimizerNaive::generateRandomPoint(const ObjectiveFunction & objective_function) const
{
static RandomNumberGenerator random_generator(0., 1.);
std::vector<double> dmin = objective_function.getDomainMin();
std::vector<double> dmax = objective_function.getDomainMax();
int x_dim = objective_function.getDimension();
std::vector<double> x;
for(int i=0 ; i<x_dim ; i++) {
x.push_back(random_generator.generateNumber() * (dmax[i] - dmin[i]) + dmin[i]); // TODO denormalize in dmin dmax
}
return x;
}
// Gradient computations
double DenseCRF::gradient( int n_iterations, const ObjectiveFunction & objective, VectorXf * unary_grad, VectorXf * lbl_cmp_grad, VectorXf * kernel_grad) const {
// Run inference
std::vector< MatrixXf > Q(n_iterations+1);
MatrixXf tmp1, unary( M_, N_ ), tmp2;
unary.fill(0);
if( unary_ )
unary = unary_->get();
expAndNormalize( Q[0], -unary );
for( int it=0; it<n_iterations; it++ ) {
tmp1 = -unary;
for( unsigned int k=0; k<pairwise_.size(); k++ ) {
pairwise_[k]->apply( tmp2, Q[it] );
tmp1 -= tmp2;
}
expAndNormalize( Q[it+1], tmp1 );
}
// Compute the objective value
MatrixXf b( M_, N_ );
double r = objective.evaluate( b, Q[n_iterations] );
sumAndNormalize( b, b, Q[n_iterations] );
// Compute the gradient
if(unary_grad && unary_)
*unary_grad = unary_->gradient( b );
if( lbl_cmp_grad )
*lbl_cmp_grad = 0*labelCompatibilityParameters();
if( kernel_grad )
*kernel_grad = 0*kernelParameters();
for( int it=n_iterations-1; it>=0; it-- ) {
// Do the inverse message passing
tmp1.fill(0);
int ip = 0, ik = 0;
// Add up all pairwise potentials
for( unsigned int k=0; k<pairwise_.size(); k++ ) {
// Compute the pairwise gradient expression
if( lbl_cmp_grad ) {
VectorXf pg = pairwise_[k]->gradient( b, Q[it] );
lbl_cmp_grad->segment( ip, pg.rows() ) += pg;
ip += pg.rows();
}
// Compute the kernel gradient expression
if( kernel_grad ) {
VectorXf pg = pairwise_[k]->kernelGradient( b, Q[it] );
kernel_grad->segment( ik, pg.rows() ) += pg;
ik += pg.rows();
}
// Compute the new b
pairwise_[k]->applyTranspose( tmp2, b );
tmp1 += tmp2;
}
sumAndNormalize( b, tmp1.array()*Q[it].array(), Q[it] );
// Add the gradient
if(unary_grad && unary_)
*unary_grad += unary_->gradient( b );
}
return r;
}
void CompositeOFTest::test_eval_fails( ObjectiveFunction& OF )
{
MsqError err;
double value;
OF.evaluate( ObjectiveFunction::CALCULATE, patch(), value, false, err );
CPPUNIT_ASSERT_EQUAL(MsqError::INTERNAL_ERROR, err.error_code());
}
void CompositeOFTest::test_invalid_eval( ObjectiveFunction& OF )
{
MsqPrintError err(cout);
bool rval;
double value;
rval = OF.evaluate( ObjectiveFunction::CALCULATE, patch(), value, false, err );
ASSERT_NO_ERROR(err);
CPPUNIT_ASSERT(rval == false);
}
void CompositeOFTest::test_evaluate( double expected, ObjectiveFunction& OF )
{
MsqPrintError err(cout);
double value;
bool rval = OF.evaluate( ObjectiveFunction::CALCULATE, patch(), value, false, err );
ASSERT_NO_ERROR(err);
CPPUNIT_ASSERT(rval);
CPPUNIT_ASSERT_DOUBLES_EQUAL( expected, value, 1e-6 );
}
/*!
Numerically Calculates the gradient of the ObjectiveFunction for the
free vertices in the patch. Returns 'false' if the patch is outside
of a required feasible region, returns 'ture' otherwise.
The behavior of the function depends on the value of the boolean
useLocalGradient. If useLocalGradient is set to
'true', compute_numerical_gradient creates a sub-patch around a free
vertex, and then perturbs that vertex in one of the coordinate directions.
Only the ObjectiveFunction value on the local sub-patch is used in the
computation of the gradient. Therefore, useLocalGradient should only
be set to 'true' for ObjectiveFunctions which can use this method. Unless
the concrete ObjectiveFunction sets useLocalGradient to 'true' in its
constructor, the value will be 'false'. In this case, the objective
function value for the entire patch is used in the calculation of the
gradient. This is computationally expensive, but it is numerically
correct for all (C_1) functions.
\param pd PatchData on which the gradient is taken.
\param grad Array of Vector3D of length the number of vertices used to store gradient.
\param OF_val will be set to the objective function value.
*/
bool ObjectiveFunction::evaluate_with_gradient( EvalType eval_type,
PatchData &pd,
double& OF_val,
std::vector<Vector3D>& grad,
MsqError &err )
{
bool b;
grad.resize( pd.num_free_vertices() );
// Fast path for single-free-vertex patch
if (pd.num_free_vertices() == 1) {
const EvalType sub_type = (eval_type == CALCULATE) ? CALCULATE : TEMPORARY;
b = compute_subpatch_numerical_gradient( eval_type, sub_type, pd, OF_val, grad[0], err );
return !MSQ_CHKERR(err) && b;
}
ObjectiveFunction* of = this;
std::auto_ptr<ObjectiveFunction> deleter;
if (eval_type == CALCULATE) {
of->clear();
b = of->evaluate( ACCUMULATE, pd, OF_val, OF_FREE_EVALS_ONLY, err );
if (err) { // OF doesn't support BCD type evals, try slow method
err.clear();
of->clear();
b = compute_patch_numerical_gradient( CALCULATE, CALCULATE, pd, OF_val, grad, err );
return !MSQ_CHKERR(err) && b;
}
else if (!b)
return b;
}
else {
b = this->evaluate( eval_type, pd, OF_val, OF_FREE_EVALS_ONLY, err );
if (MSQ_CHKERR(err) || !b)
return false;
of = this->clone();
deleter = std::auto_ptr<ObjectiveFunction>(of);
}
// Determine number of layers of adjacent elements based on metric type.
unsigned layers = min_patch_layers();
// Create a subpatch for each free vertex and use it to evaluate the
// gradient for that vertex.
double flocal;
PatchData subpatch;
for (size_t i = 0; i < pd.num_free_vertices(); ++i)
{
pd.get_subpatch( i, layers, subpatch, err ); MSQ_ERRZERO(err);
b = of->compute_subpatch_numerical_gradient( SAVE, TEMPORARY, subpatch, flocal, grad[i], err );
if (MSQ_CHKERR(err) || !b) {
of->clear();
return false;
}
}
of->clear();
return true;
}
FunctionArguments<ArgType> simplex(
const std::vector<Constraint<ArgType>>& constraints,
const ObjectiveFunction<ArgType, RetType, CoeffType>& function)
{
if(constraints.size() == 0)
throw "No constraints";
if(constraints[0].size() != function.size())
throw "Size of constraints not equal to function size";
Matrix<CoeffType> simplexTab(constraints.size() + 1,
function.size() + constraints.size() + 2);
unsigned long cols = simplexTab.getColumnsNumber(), rows =
simplexTab.getRowsNumber(), constrNum = constraints.size(), argsNum =
function.size();
generateSimplexTab(constraints, function, simplexTab);
std::map<unsigned long,unsigned long> slackSwaped;
while (simplexTab.countIfInRange(rows - 1, 0, rows - 1, cols - 1,
[](const CoeffType& x)
{ return x<0;}))
{
unsigned long pivotColumn, row;
simplexTab.findMinimumInRange(rows - 1, 0, rows - 1, cols - 3, row,
pivotColumn);
row = findMinInColumnBDiv(simplexTab, pivotColumn);
slackSwaped[row]=pivotColumn;
simplexTab.multiplyRow(1/simplexTab(row,pivotColumn),row);
for(unsigned long j=0;j<rows;j++)
if(j!=row)
simplexTab.addRow(-simplexTab(j,pivotColumn),row,j);
}
std::cout<<simplexTab;
FunctionArguments<ArgType> bestSolution;
bestSolution.size(argsNum);
for(unsigned long j=0;j<constrNum;j++)
bestSolution.setArg(slackSwaped[j],simplexTab(j,cols-1));
return bestSolution;
}
void ProgNmaAlignment::processImage(const FileName &fnImg,
const FileName &fnImgOut, const MDRow &rowIn, MDRow &rowOut) {
static size_t imageCounter = 0;
++imageCounter;
double rhoStart=trustradius_scale*250.;
double rhoEnd=trustradius_scale*50.;
int niter=10000;
ObjectiveFunction *of;
int dim = numberOfModes;
parameters.initZeros(dim + 5);
currentImgName = fnImg;
sprintf(nameTemplate, "_node%d_img%lu_XXXXXX", rangen, (long unsigned int)imageCounter);
trial.initZeros(dim + 5);
trial_best.initZeros(dim + 5);
fitness_min.initZeros(1);
fitness_min(0) = 1000000.0;
currentStage = 1;
#ifdef DEBUG
std::cerr << std::endl << "DEBUG: ===== Node: " << rangen
<<" processing image " << fnImg <<"(" << objId << ")"
<< " at stage: " << currentStage << std::endl;
#endif
of = new ObjFunc_nma_alignment(1, dim);
of->xStart.setSize(dim);
for (int i = 0; i < dim; i++)
of->xStart[i] = 0.;
#ifdef DEBUG
strcpy(of->name,("OF1_"+integerToString(rangen)).c_str());
of->setSaveFile();
#endif
CONDOR(rhoStart, rhoEnd, niter, of);
#ifdef DEBUG
of->printStats();
FILE *ff = fopen(("res1_"+integerToString(rangen)+".xmd").c_str(),"w");
fprintf(ff,"%s & %i & %i & (%i) & %e \\\\\n", of->name, of->dim(), of->getNFE(), of->getNFE2(), of->valueBest);
fclose(ff);
#endif
bestStage1 = trial = parameters = trial_best;
delete of;
currentStage = 2;
#ifdef DEBUG
std::cerr << std::endl << "DEBUG: ===== Node: " << rangen
<<" processing image " << fnImg <<"(" << objId << ")"
<< " at stage: " << currentStage << std::endl;
#endif
fitness_min(0) = 1000000.0;
of = new ObjFunc_nma_alignment(1, dim);
of->xStart.setSize(dim);
for (int i = 0; i < dim; i++)
of->xStart[i] = parameters(i);
#ifdef DEBUG
strcpy(of->name,("OF2_"+integerToString(rangen)).c_str());
of->setSaveFile();
#endif
rhoStart=trustradius_scale*50.-1., rhoEnd=0.5;
CONDOR(rhoStart, rhoEnd, niter, of);
#ifdef DEBUG
of->printStats();
ff=fopen(("res2_"+integerToString(rangen)+".xmd").c_str(),"w");
fprintf(ff,"%s & %i & %i & (%i) & %e \\\\\n", of->name, of->dim(), of->getNFE(), of->getNFE2(), of->valueBest);
fclose(ff);
#endif
#ifdef DEBUG
std::cout << "Best fitness = " << fitness << std::endl;
for (int i=0; i<dim; i++)
{
std::cout << "Best deformations = " << dd[i] << std::endl;
}
#endif
trial = trial_best;
for (int i = dim; i < dim + 5; i++) {
parameters(i - dim) = trial_best(i);
}
for (int i = 0; i < dim; i++) {
parameters(5 + i) = trial_best(i);
}
parameters.resize(VEC_XSIZE(parameters) + 1);
parameters(VEC_XSIZE(parameters) - 1) = fitness_min(0);
writeImageParameters(fnImg);
delete of;
}
