void CLARANSClustering::startClustering(const ClusterMethodParameters* pParameters, Document *pDocument, ClusteringResult *pClusteringResult)
{
mpDocument = pDocument;
mpClusteringResult = pClusteringResult;
this->mpDocument->setDistanceType(pParameters->dataType);
// retrieve parameters:
const CLARANSParameters *pParams = (const CLARANSParameters*)(pParameters);
std::cout << "CLARANS parameters: numClust = " << pParams->numClust << ", numLocal = " << pParams->numLocal << ", maxNeighbor = " << pParams->maxNeighbor << std::endl;
mpClusteringResult->deleteClustering(); // delete probably old clustering result
const int nSamples = this->mpDocument->nParsedImages();
const int nClusts = pParams->numClust;
if (nSamples < 2) {
throw NoDataException("No data found for clustering!");
}
if (pParameters->dataType == FEATURES_BASED && this->mpDocument->nFeatures() < 2) {
throw NoDataException("No features found for clustering!");
}
// get pointer to image chars:
std::vector<ImageChar*> *imageCharVecPointer = this->mpDocument->getImageCharsVecPointer();
// INITIALIZATION:
// srand(time(NULL)); // FIXME!!!!!!!!!!!!!
double minCost = 1e32f; minCost = 1e32f;
double actCost = 0.0f; double newCost = 0.0f;
std::vector<int> currentNode;
getRandomNode(nSamples, nClusts, currentNode);
actCost = getNodeCost(nSamples, currentNode);
std::cout << "Starting node: "; printNode(currentNode);
// LOOP:
int j = 1; j=1;
std::vector<int> neighborNode;
getRandomNeighbor(nSamples, currentNode, neighborNode); // determine a random neighbor node
newCost = getNodeCost(nSamples, neighborNode);
std::cout << "Finished CLARANS clustering!" << std::endl;
return;
} // end startClustering(...)
// SAH costs, raw and batched
float getCost(int numChildNodes,int numTris) const { return getNodeCost(numChildNodes) + getTriangleCost(numTris); }
