/*! Given data (an image) and currently estimated parameters, find cluster
assignments.
@param img - image data. this is assumed to be three channel data. */
void GMM::getClusterAssignments(const vector<float>& img) {
int n = img.size() / 3;
if (assignment.size() != n)
assignment = vector<int>(n, 0);
// for each pixel estimate the assignment.
vector<float> pix(3, 0.0f);
vector<float> d;
for (size_t i = 0; i < n; ++i) {
pix[0] = img[3 * i];
pix[1] = img[3 * i + 1];
pix[2] = img[3 * i + 2];
d = computeClusterDistance(pix);
assignment[i] = min_element(d.begin(), d.end()) - d.begin();
}
}
bool HierarchicalClustering::train_(MatrixFloat &data){
trained = false;
clusters.clear();
distanceMatrix.clear();
if( data.getNumRows() == 0 || data.getNumCols() == 0 ){
return false;
}
//Set the rows and columns
M = data.getNumRows();
N = data.getNumCols();
//Build the distance matrix
distanceMatrix.resize(M,M);
//Build the distance matrix
for(UINT i=0; i<M; i++){
for(UINT j=0; j<M; j++){
if( i== j ) distanceMatrix[i][j] = grt_numeric_limits< Float >::max();
else{
distanceMatrix[i][j] = squaredEuclideanDistance(data[i], data[j]);
}
}
}
//Build the initial clusters, at the start each sample gets its own cluster
UINT uniqueClusterID = 0;
Vector< ClusterInfo > clusterData(M);
for(UINT i=0; i<M; i++){
clusterData[i].uniqueClusterID = uniqueClusterID++;
clusterData[i].addSampleToCluster(i);
}
trainingLog << "Starting clustering..." << std::endl;
//Create the first cluster level, each sample is it's own cluster
UINT level = 0;
ClusterLevel newLevel;
newLevel.level = level;
for(UINT i=0; i<M; i++){
newLevel.clusters.push_back( clusterData[i] );
}
clusters.push_back( newLevel );
//Move to level 1 and start the search
level++;
bool keepClustering = true;
while( keepClustering ){
//Find the closest two clusters within the cluster data
Float minDist = grt_numeric_limits< Float >::max();
Vector< Vector< UINT > > clusterPairs;
UINT K = (UINT)clusterData.size();
for(UINT i=0; i<K; i++){
for(UINT j=0; j<K; j++){
if( i != j ){
Float dist = computeClusterDistance( clusterData[i], clusterData[j] );
if( dist < minDist ){
minDist = dist;
Vector< UINT > clusterPair(2);
clusterPair[0] = i;
clusterPair[1] = j;
clusterPairs.clear();
clusterPairs.push_back( clusterPair );
}
}
}
}
if( minDist == grt_numeric_limits< Float >::max() ){
keepClustering = false;
warningLog << "train_(MatrixFloat &data) - Failed to find any cluster at level: " << level << std::endl;
return false;
}else{
//Merge the two closest clusters together and create a new level
ClusterLevel newLevel;
newLevel.level = level;
//Create the new cluster
ClusterInfo newCluster;
newCluster.uniqueClusterID = uniqueClusterID++;
const UINT numClusterPairs = clusterPairs.getSize();
for(UINT k=0; k<numClusterPairs; k++){
//Add all the samples in the first cluster to the new cluster
UINT numSamplesInClusterA = clusterData[ clusterPairs[k][0] ].getNumSamplesInCluster();
for(UINT i=0; i<numSamplesInClusterA; i++){
UINT index = clusterData[ clusterPairs[k][0] ][ i ];
newCluster.addSampleToCluster( index );
}
//Add all the samples in the second cluster to the new cluster
UINT numSamplesInClusterB = clusterData[ clusterPairs[k][1] ].getNumSamplesInCluster();
for(UINT i=0; i<numSamplesInClusterB; i++){
UINT index = clusterData[ clusterPairs[k][1] ][ i ];
newCluster.addSampleToCluster( index );
}
//Compute the cluster variance
newCluster.clusterVariance = computeClusterVariance( newCluster, data );
//Remove the two cluster pairs (so they will not be used in the next search
UINT idA = clusterData[ clusterPairs[k][0] ].getUniqueClusterID();
UINT idB = clusterData[ clusterPairs[k][1] ].getUniqueClusterID();
UINT numRemoved = 0;
Vector< ClusterInfo >::iterator iter = clusterData.begin();
while( iter != clusterData.end() ){
if( iter->getUniqueClusterID() == idA || iter->getUniqueClusterID() == idB ){
iter = clusterData.erase( iter );
if( ++numRemoved >= 2 ) break;
}else iter++;
}
}
//Add the merged cluster to the clusterData
clusterData.push_back( newCluster );
//Add the new level and cluster data to the main cluster buffer
newLevel.clusters.push_back( newCluster );
clusters.push_back( newLevel );
//Update the level
level++;
}
//Check to see if we should stop clustering
if( level >= M ){
keepClustering = false;
}
if( clusterData.size() == 0 ){
keepClustering = false;
}
trainingLog << "Cluster level: " << level << " Number of clusters: " << clusters.back().getNumClusters() << std::endl;
}
//Flag that the model is trained
trained = true;
//Setup the cluster labels
clusterLabels.resize(numClusters);
for(UINT i=0; i<numClusters; i++){
clusterLabels[i] = i+1;
}
clusterLikelihoods.resize(numClusters,0);
clusterDistances.resize(numClusters,0);
return true;
}
