void GA::CycleCrossover(Candidate p1, Candidate p2){
int startingGene = std::rand() % p1.getCandidate().size();
int currentGene = -1;
std::vector<City> child1(p1.getCandidate());
std::vector<City> child2(p2.getCandidate());
CycleCrossover_MixGenes(child1,child2,currentGene,startingGene);
//newPopulation.push_back(p1);
//newPopulation.push_back(p2);
Candidate newChild1 = Candidate(child1,distances);
Candidate newChild2 = Candidate(child2,distances);
if(!newChild1.isUnique() || !newChild2.isUnique())
throw new std::logic_error("Invalid child sequence");
newPopulation.push_back(newChild1);
newPopulation.push_back(newChild2);
//std::cout << "P1 " << p1 << "\n";
//std::cout << "P2 " << p2 << "\n";
//std::cout << "C1 " << newChild1 << "\n";
//std::cout << "C2 " << newChild2 << "\n";
//if(newChild1.getFitness() > p1.getFitness())
}
/*
函数说明:
递归遍历数组,寻找多数元素
输入参数:
int A[] : 输入数组
int n : 数组大小
int m : 从第几个元素开始遍历
输出参数:
0
*/
int Candidate(int A[],int n, int m)
{
int j = m;
int c = A[m];
int count = 1;
for (;j < n-1 && count > 0;)
{
j++;
if (c == A[j])
{
count++;
}
else
{
count--;
}
}
if (j == n-1)
{
return c; //递归结束时,c在小范围内出现的次数至少有%50
}
else
{
return Candidate(A, n, j+1);
}
}
void operator() (const Vector3 &v0, const Vector3 &v1,
const Vector3 &v2, double a)
{
Vector3 c = (v0 + v1 + v2)/3;
double distance = Norm(p - c)/divisor;
double probability = a*lambda*exp(-lambda*distance);
candidate.push_back(Candidate(v0, v1, v2, probability));
}
Candidate GA::PMXCrossover(const Candidate& p1, const Candidate& p2){
// std::cout << "Starting crossover\n";
int startPos = std::rand() % p1.getCandidate().size();
int endPos = -1;
while(endPos == -1){
int tmpPos = std::rand() % p1.getCandidate().size();
if(tmpPos > startPos)
endPos = tmpPos;
else if(tmpPos < startPos){
endPos = startPos;
startPos = tmpPos;
}
}
// std::cout << "swath start: " << startPos << "\nswath end: " << endPos << "\n";
std::vector<City> child(p1.getCandidate().size());
std::vector<bool> childCompletion(p1.getCandidate().size());
//COPY RANDOM SWATH OF GENES
//std::copy(p1.getCandidate().begin() + startPos,
// p1.getCandidate().begin() + endPos,
// child.begin() + startPos);
//
for(int i = startPos; i <= endPos; i++){
child[i] = p1.getCandidate().at(i);
}
//for(const auto& c : child){
// std::cout << c << "\n";
//}
for(int i = startPos; i <= endPos; i++)
childCompletion[i] = true;
//FIND FIRST GENE IN PARENT 2 THAT WASN'T COPIED TO CHILD
for(int i = startPos; i <= endPos; i++){
auto tmpGene = std::find(child.begin(), child.end(), p2.getCandidate().at(i));
if(tmpGene == child.end()){
int destPosition = PMXCrossover_FindDestPosition(p1.getCandidate(),p2.getCandidate(),startPos,endPos,i);
// std::cout << "Place " << p2.getCandidate().at(i) << " at pos " << destPosition << "\n";
child[destPosition] = p2.getCandidate().at(i);
childCompletion[destPosition] = true;
}
}
for(int i = 0; i< child.size(); i++){
if(!childCompletion[i])
child[i] = p2.getCandidate().at(i);
}
Candidate result = Candidate(child, distances);
if(!result.isUnique()){
for(const auto& c : result.getCandidate())
std::cout << c << "\n";
throw new std::logic_error("Invalid child sequence");
}
return result;
}
bool Reprojector::reprojectPoint(FramePtr frame, Point* point)
{
Vector2d px(frame->w2c(point->pos_));
if(frame->cam_->isInFrame(px.cast<int>(), 8)) // 8px is the patch size in the matcher
{
const int k = static_cast<int>(px[1]/grid_.cell_size)*grid_.grid_n_cols
+ static_cast<int>(px[0]/grid_.cell_size);
grid_.cells.at(k)->push_back(Candidate(point, px));
return true;
}
return false;
}
// Explore the neighborhood around a vertex looking for missing overlaps
SGEdgeStatsVisitor::CandidateVector SGEdgeStatsVisitor::getMissingCandidates(StringGraph* /*pGraph*/,
Vertex* pVertex,
int minOverlap) const
{
CandidateVector out;
// Mark the vertices that are reached from this vertex as black to indicate
// they already are overlapping
EdgePtrVec edges = pVertex->getEdges();
for(size_t i = 0; i < edges.size(); ++i)
{
edges[i]->getEnd()->setColor(GC_BLACK);
}
pVertex->setColor(GC_BLACK);
for(size_t i = 0; i < edges.size(); ++i)
{
Edge* pXY = edges[i];
EdgePtrVec neighborEdges = pXY->getEnd()->getEdges();
for(size_t j = 0; j < neighborEdges.size(); ++j)
{
Edge* pYZ = neighborEdges[j];
if(pYZ->getEnd()->getColor() != GC_BLACK)
{
// Infer the overlap object from the edges
Overlap ovrXY = pXY->getOverlap();
Overlap ovrYZ = pYZ->getOverlap();
if(SGAlgorithms::hasTransitiveOverlap(ovrXY, ovrYZ))
{
Overlap ovr_xz = SGAlgorithms::inferTransitiveOverlap(ovrXY, ovrYZ);
if(ovr_xz.match.getMinOverlapLength() >= minOverlap)
{
out.push_back(Candidate(pYZ->getEnd(), ovr_xz));
pYZ->getEnd()->setColor(GC_BLACK);
}
}
}
}
}
// Reset colors
for(size_t i = 0; i < edges.size(); ++i)
edges[i]->getEnd()->setColor(GC_WHITE);
pVertex->setColor(GC_WHITE);
for(size_t i = 0; i < out.size(); ++i)
out[i].pEndpoint->setColor(GC_WHITE);
return out;
}
int main()
{
int c, j, count;
c = Candidate(1);
count = 0;
for(j = 1; j <= N; ++j)
{
if(A[j] == c)
{
count = count + 1;
}
}
if(count > (N / 2))
printf("%d", c);
else
printf("none");
return 0;
}
int main(void)
{
int A[SIZE] = {0};
int c = 0;
int count = 0;
int j = 0;
int n = 0;
printf("please input the size of the array:");
scanf("%d",&n);
printf("please input the number : ");
for (j = 0; j < n ; j++)
{
scanf("%d",&A[j]);
}
c = Candidate(A, n - 1, 0); //得到可能是多少元素的数组元素
count = 0;
for (j = 0;j < n;j++)
{
if (c == A[j])
{
count++;
}
}
if (count > n/2) //检查c出现的次数是否大于n/2,是则是多数元素,否则不是
{
printf("the candidate is %d\n",c);
}
else
{
printf("there is no candidate num!\n");
}
system("pause");
return 0;
}
void GA::Initialize(std::string filePath){
std::string fpath(filePath);
baseCityVector = loadDataFromFile(fpath);
std::cout << "City vector size: " << baseCityVector.size() << "\n";
for(const auto& c : baseCityVector)
std::cout << c << "\n";
distances = createDistanceMatrix(baseCityVector);
for(int i = 0; i < populationSize ; i++){
population.push_back( Candidate(generateRandomCandidate(baseCityVector),distances) );
}
maxXPosition = 0;
maxYPosition = 0;
for(const auto& city : population[0].getCandidate()){
if(city.getX() > maxXPosition)
maxXPosition = city.getX();
if(city.getY() > maxYPosition)
maxYPosition = city.getY();
}
}
int Candidate(int m)
{
int c, j, count;
j = m; c = A[m]; count = 1;
while(j < N && count > 0)
{
j = j + 1;
if(A[j] == c)
{
count = count + 1;
}
else
{
count = count - 1;
}
}
if(j == N)
return c;
else
return Candidate(j + 1);
}
bool MajorityElement(int arr[], int& majority, int sz){
bool result = false;
int numOfCandidate = 0;
int can;
//Names of variables are pretty desriptive
//and the following code speaks for itself
if(Candidate(arr, sz, can))
{
for(int i = 0; i < sz; i++)
{
if(can == arr[i])
numOfCandidate++;
}
if(numOfCandidate > sz/2)
result = true;
}
if(result)
majority = can;
return result;
}
bool Candidate(int arr[], int sz, int& can){
bool result = false;
int temp[sz];
int temp_counter = 0;
// temp is an array to hold new values from
// original array. Temp_counter is needed as
// counter (obviously) and as an array size
// to be passed to another Candidate function
if(sz != 0)
{
if(sz == 1)
{
can = arr[0];
result = true;
}
else
{
for(int i = 0; i < sz; i+=2)
{
if(arr[i] == arr[i+1])
temp[temp_counter++] = arr[i];
temp[temp_counter] = NULL;
}
result = Candidate(temp,temp_counter,can);
if(sz%2 != 0 && !result) // if array size is odd and no candidate
{ // is found then the last number is candidate
can = arr[sz-1];
result = true;
}
}
}
return result;
}
EndCriteria::Type DifferentialEvolution::minimize(Problem& p, const EndCriteria& endCriteria) {
EndCriteria::Type ecType;
upperBound_ = p.constraint().upperBound(p.currentValue());
lowerBound_ = p.constraint().lowerBound(p.currentValue());
currGenSizeWeights_ = Array(configuration().populationMembers,
configuration().stepsizeWeight);
currGenCrossover_ = Array(configuration().populationMembers,
configuration().crossoverProbability);
std::vector<Candidate> population(configuration().populationMembers,
Candidate(p.currentValue().size()));
fillInitialPopulation(population, p);
std::partial_sort(population.begin(), population.begin() + 1, population.end(),
sort_by_cost());
bestMemberEver_ = population.front();
Real fxOld = population.front().cost;
Size iteration = 0, stationaryPointIteration = 0;
// main loop - calculate consecutive emerging populations
while (!endCriteria.checkMaxIterations(iteration++, ecType)) {
calculateNextGeneration(population, p.costFunction());
std::partial_sort(population.begin(), population.begin() + 1, population.end(),
sort_by_cost());
if (population.front().cost < bestMemberEver_.cost)
bestMemberEver_ = population.front();
Real fxNew = population.front().cost;
if (endCriteria.checkStationaryFunctionValue(fxOld, fxNew, stationaryPointIteration,
ecType))
break;
fxOld = fxNew;
};
p.setCurrentValue(bestMemberEver_.values);
p.setFunctionValue(bestMemberEver_.cost);
return ecType;
}
bool Reprojector::reprojectPoint(FramePtr frame, Point* point, int print)
{
// point->pos_[2] = point->pos_[2] * 600;
Vector2d px(frame->w2c(point->pos_));
if(frame->cam_->isInFrame(px.cast<int>(), 8)) // 8px is the patch size in the matcher
{
// cout << "point pos" << point->pos_ << endl;
// cout << "px" << px << endl;
if(print)
// cout << "repro" << endl;
if(print) {
outfile << round(px[0]) << " " << round(px[1]) << " " << point->pos_[2] << " ";
outfile2 << point->pos_[0] << " " << point->pos_[1] << " " << point->pos_[2] << "\n";
}
const int k = static_cast<int>(px[1]/grid_.cell_size)*grid_.grid_n_cols
+ static_cast<int>(px[0]/grid_.cell_size);
grid_.cells.at(k)->push_back(Candidate(point, px));
return true;
}
return false;
}
bool GeometryUtilities::placeRectAtLine(const QRectF &rect, const QLineF &line, double lineOffset, double distance,
const QLineF &intersectionLine, QPointF *placement, Side *horizontalAlignedSide)
{
QMT_CHECK(placement);
QList<Candidate> candidates;
candidates << Candidate(QVector2D(rect.topRight() - rect.topLeft()), QPointF(0.0, 0.0), SideTop)
<< Candidate(QVector2D(rect.topLeft() - rect.topRight()), rect.topRight() - rect.topLeft(), SideTop)
<< Candidate(QVector2D(rect.bottomLeft() - rect.topLeft()), QPointF(0.0, 0.0), SideLeft)
<< Candidate(QVector2D(rect.topLeft() - rect.bottomLeft()), rect.bottomLeft() - rect.topLeft(), SideLeft)
<< Candidate(QVector2D(rect.bottomRight() - rect.bottomLeft()), rect.bottomLeft() - rect.topLeft(), SideBottom)
<< Candidate(QVector2D(rect.bottomLeft() - rect.bottomRight()), rect.bottomRight() - rect.topLeft(), SideBottom)
<< Candidate(QVector2D(rect.bottomRight() - rect.topRight()), rect.topRight() - rect.topLeft(), SideRight)
<< Candidate(QVector2D(rect.topRight() - rect.bottomRight()), rect.bottomRight() - rect.topLeft(), SideRight);
QVector<QVector2D> rectEdgeVectors;
rectEdgeVectors << QVector2D(rect.topLeft() - rect.topLeft())
<< QVector2D(rect.topRight() - rect.topLeft())
<< QVector2D(rect.bottomLeft() - rect.topLeft())
<< QVector2D(rect.bottomRight() -rect.topLeft());
QVector2D directionVector(line.p2() - line.p1());
directionVector.normalize();
QVector2D sideVector(directionVector.y(), -directionVector.x());
QVector2D intersectionVector(intersectionLine.p2() - intersectionLine.p1());
intersectionVector.normalize();
QVector2D outsideVector = QVector2D(intersectionVector.y(), -intersectionVector.x());
double p = QVector2D::dotProduct(directionVector, outsideVector);
if (p < 0.0)
outsideVector = outsideVector * -1.0;
double smallestA = -1.0;
QPointF rectTranslation;
Side side = SideUnspecified;
int bestSign = 0;
foreach (const Candidate &candidate, candidates) {
// solve equation a * directionVector + candidate.first = b * intersectionVector to find smallest a
double r = directionVector.x() * intersectionVector.y() - directionVector.y() * intersectionVector.x();
if (r <= -1e-5 || r >= 1e-5) {
double a = (candidate.first.y() * intersectionVector.x()
- candidate.first.x() * intersectionVector.y()) / r;
if (a >= 0.0 && (smallestA < 0.0 || a < smallestA)) {
// verify that all rectangle edges lay outside of shape (by checking for positiv projection to intersection)
bool ok = true;
int sign = 0;
QVector2D rectOriginVector = directionVector * a - QVector2D(candidate.second);
foreach (const QVector2D &rectEdgeVector, rectEdgeVectors) {
QVector2D edgeVector = rectOriginVector + rectEdgeVector;
double aa = QVector2D::dotProduct(outsideVector, edgeVector);
if (aa < 0.0) {
ok = false;
break;
}
int s = sgn(QVector2D::dotProduct(sideVector, edgeVector));
if (s) {
if (sign) {
if (s != sign) {
ok = false;
break;
}
} else {
sign = s;
}
}
}
bool ccKdTreeForFacetExtraction::FuseCells( ccKdTree* kdTree,
double maxError,
CCLib::DistanceComputationTools::ERROR_MEASURES errorMeasure,
double maxAngle_deg,
PointCoordinateType overlapCoef/*=1*/,
bool closestFirst/*=true*/,
CCLib::GenericProgressCallback* progressCb/*=0*/)
{
if (!kdTree)
return false;
ccGenericPointCloud* associatedGenericCloud = kdTree->associatedGenericCloud();
if (!associatedGenericCloud || !associatedGenericCloud->isA(CC_TYPES::POINT_CLOUD) || maxError < 0.0)
return false;
//get leaves
std::vector<ccKdTree::Leaf*> leaves;
if (!kdTree->getLeaves(leaves) || leaves.empty())
return false;
//progress notification
CCLib::NormalizedProgress nProgress(progressCb, static_cast<unsigned>(leaves.size()));
if (progressCb)
{
progressCb->update(0);
if (progressCb->textCanBeEdited())
{
progressCb->setMethodTitle("Fuse Kd-tree cells");
progressCb->setInfo(qPrintable(QString("Cells: %1\nMax error: %2").arg(leaves.size()).arg(maxError)));
}
progressCb->start();
}
ccPointCloud* pc = static_cast<ccPointCloud*>(associatedGenericCloud);
//sort cells based on their population size (we start by the biggest ones)
SortAlgo(leaves.begin(), leaves.end(), DescendingLeafSizeComparison);
//set all 'userData' to -1 (i.e. unfused cells)
{
for (size_t i=0; i<leaves.size(); ++i)
{
leaves[i]->userData = -1;
//check by the way that the plane normal is unit!
assert(static_cast<double>(fabs(CCVector3(leaves[i]->planeEq).norm2()) - 1.0) < 1.0e-6);
}
}
// cosine of the max angle between fused 'planes'
const double c_minCosNormAngle = cos(maxAngle_deg * CC_DEG_TO_RAD);
//fuse all cells, starting from the ones with the best error
const int unvisitedNeighborValue = -1;
bool cancelled = false;
int macroIndex = 1; //starts at 1 (0 is reserved for cells already above the max error)
{
for (size_t i=0; i<leaves.size(); ++i)
{
ccKdTree::Leaf* currentCell = leaves[i];
if (currentCell->error >= maxError)
currentCell->userData = 0; //0 = special group for cells already above the user defined threshold!
//already fused?
if (currentCell->userData != -1)
{
if (progressCb && !nProgress.oneStep()) //process canceled by user
{
cancelled = true;
break;
}
continue;
}
//we create a new "macro cell" index
currentCell->userData = macroIndex++;
//we init the current set of 'fused' points with the cell's points
CCLib::ReferenceCloud* currentPointSet = currentCell->points;
//get current fused set centroid and normal
CCVector3 currentCentroid = *CCLib::Neighbourhood(currentPointSet).getGravityCenter();
CCVector3 currentNormal(currentCell->planeEq);
//visited neighbors
ccKdTree::LeafSet visitedNeighbors;
//set of candidates
std::list<Candidate> candidates;
//we are going to iteratively look for neighbor cells that could be fused to this one
ccKdTree::LeafVector cellsToTest;
cellsToTest.push_back(currentCell);
if (progressCb && !nProgress.oneStep()) //process canceled by user
{
cancelled = true;
break;
}
while (!cellsToTest.empty() || !candidates.empty())
{
//get all neighbors around the 'waiting' cell(s)
if (!cellsToTest.empty())
{
ccKdTree::LeafSet neighbors;
while (!cellsToTest.empty())
{
if (!kdTree->getNeighborLeaves(cellsToTest.back(), neighbors, &unvisitedNeighborValue)) //we only consider unvisited cells!
{
//an error occurred
return false;
}
cellsToTest.pop_back();
}
//add those (new) neighbors to the 'visitedNeighbors' set
//and to the candidates set by the way if they are not yet there
for (ccKdTree::LeafSet::iterator it=neighbors.begin(); it != neighbors.end(); ++it)
{
ccKdTree::Leaf* neighbor = *it;
std::pair<ccKdTree::LeafSet::iterator,bool> ret = visitedNeighbors.insert(neighbor);
//neighbour not already in the set?
if (ret.second)
{
//we create the corresponding candidate
try
{
candidates.push_back(Candidate(neighbor));
}
catch (const std::bad_alloc&)
{
//not enough memory!
ccLog::Warning("[ccKdTreeForFacetExtraction] Not enough memory!");
return false;
}
}
}
}
//is there remaining candidates?
if (!candidates.empty())
{
//update the set of candidates
if (closestFirst && candidates.size() > 1)
{
for (std::list<Candidate>::iterator it = candidates.begin(); it !=candidates.end(); ++it)
it->dist = (it->centroid-currentCentroid).norm2();
//sort candidates by their distance
candidates.sort(CandidateDistAscendingComparison);
}
//we will keep track of the best fused 'couple' at each pass
std::list<Candidate>::iterator bestIt = candidates.end();
CCLib::ReferenceCloud* bestFused = 0;
CCVector3 bestNormal(0,0,0);
double bestError = -1.0;
unsigned skipCount = 0;
for (std::list<Candidate>::iterator it = candidates.begin(); it != candidates.end(); /*++it*/)
{
assert(it->leaf && it->leaf->points);
assert(currentPointSet->getAssociatedCloud() == it->leaf->points->getAssociatedCloud());
//if the leaf orientation is too different
if (fabs(CCVector3(it->leaf->planeEq).dot(currentNormal)) < c_minCosNormAngle)
{
it = candidates.erase(it);
//++it;
//++skipCount;
continue;
}
//compute the minimum distance between the candidate centroid and the 'currentPointSet'
PointCoordinateType minDistToMainSet = 0.0;
{
for (unsigned j=0; j<currentPointSet->size(); ++j)
{
const CCVector3* P = currentPointSet->getPoint(j);
PointCoordinateType d2 = (*P-it->centroid).norm2();
if (d2 < minDistToMainSet || j == 0)
minDistToMainSet = d2;
}
minDistToMainSet = sqrt(minDistToMainSet);
}
//if the leaf is too far
if (it->radius < minDistToMainSet / overlapCoef)
{
++it;
++skipCount;
continue;
}
//fuse the main set with the current candidate
CCLib::ReferenceCloud* fused = new CCLib::ReferenceCloud(*currentPointSet);
if (!fused->add(*(it->leaf->points)))
{
//not enough memory!
ccLog::Warning("[ccKdTreeForFacetExtraction] Not enough memory!");
delete fused;
if (currentPointSet != currentCell->points)
delete currentPointSet;
return false;
}
//fit a plane and estimate the resulting error
double error = -1.0;
const PointCoordinateType* planeEquation = CCLib::Neighbourhood(fused).getLSPlane();
if (planeEquation)
error = CCLib::DistanceComputationTools::ComputeCloud2PlaneDistance(fused, planeEquation, errorMeasure);
if (error < 0.0 || error > maxError)
{
//candidate is rejected
it = candidates.erase(it);
}
else
{
//otherwise we keep track of the best one!
if (bestError < 0.0 || error < bestError)
{
bestIt = it;
bestError = error;
if (bestFused)
delete bestFused;
bestFused = fused;
bestNormal = CCVector3(planeEquation);
fused = 0;
if (closestFirst)
break; //if we have found a good candidate, we stop here (closest first ;)
}
++it;
}
if (fused)
{
delete fused;
fused = 0;
}
}
//we have a (best) candidate for this pass?
if (bestIt != candidates.end())
{
assert(bestFused && bestError >= 0.0);
if (currentPointSet != currentCell->points)
delete currentPointSet;
currentPointSet = bestFused;
{
//update infos
CCLib::Neighbourhood N(currentPointSet);
//currentCentroid = *N.getGravityCenter(); //if we update it, the search will naturally shift along one dimension!
//currentNormal = bestNormal; //same thing here for normals
}
bestIt->leaf->userData = currentCell->userData;
//bestIt->leaf->userData = macroIndex++; //FIXME TEST
//we will test this cell's neighbors as well
cellsToTest.push_back(bestIt->leaf);
if (progressCb && !nProgress.oneStep()) //process canceled by user
{
//premature end!
candidates.clear();
cellsToTest.clear();
cancelled = true;
break;
}
QApplication::processEvents();
//we also remove it from the candidates list
candidates.erase(bestIt);
}
if (skipCount == candidates.size() && cellsToTest.empty())
{
//only far leaves remain...
candidates.clear();
}
}
} //no more candidates or cells to test
//end of the fusion process for the current leaf
if (currentPointSet != currentCell->points)
delete currentPointSet;
currentPointSet = 0;
if (cancelled)
break;
}
}
//convert fused indexes to SF
if (!cancelled)
{
pc->enableScalarField();
for (size_t i=0; i<leaves.size(); ++i)
{
CCLib::ReferenceCloud* subset = leaves[i]->points;
if (subset)
{
ScalarType scalar = (ScalarType)leaves[i]->userData;
if (leaves[i]->userData <= 0) //for unfused cells, we create new individual groups
scalar = static_cast<ScalarType>(macroIndex++);
//scalar = NAN_VALUE; //FIXME TEST
for (unsigned j=0; j<subset->size(); ++j)
subset->setPointScalarValue(j,scalar);
}
}
//pc->setCurrentDisplayedScalarField(sfIdx);
}
return !cancelled;
}
