void JLinkage::clusterPSMatrix(std::vector<std::vector<int> >& result)
{
/* allocate memory */
std::vector<std::vector<double> > distances(line_count,
std::vector<double>(line_count, 0));
std::vector<std::vector<int> > clusters(line_count,
std::vector<int>(line_count, 0));
std::vector<int> indicators(line_count, 1);
/* initialization */
double minDis = 1;
int indexA = 0, indexB = 0;
for(int i = 0; i < line_count; i++)
{
clusters[i][i] = 1;
for(int j = i + 1; j < line_count; j++)
{
const double jd = jaccardDist(PSMatrix[i], PSMatrix[j]);
distances[i][j] = jd;
distances[j][i] = jd;
if(jd < minDis)
{
minDis = jd;
indexA = i;
indexB = j;
}
}
}
while(minDis != 1)
{
/* merge two clusters */
for(int i = 0; i < line_count; i++)
{
if(clusters[indexA][i] || clusters[indexB][i])
clusters[indexA][i] = clusters[indexB][i] = 1;
}
indicators[indexB] = 0;
for(int i = 0; i < JLINKAGE_MODEL_SIZE; i++)
{
PSMatrix[indexA] = PSMatrix[indexB] = PSMatrix[indexA]&PSMatrix[indexB];
}
/* recalculate distance */
for(int i = 0; i < line_count; i++)
{
distances[indexA][i] = jaccardDist(PSMatrix[indexA], PSMatrix[i]);
distances[i][indexA] = distances[indexA][i];
}
/* find minimum distance */
minDis = 1;
for(int i = 0; i < line_count; i++)
{
if(indicators[i] == 0) continue;
for(int j = i + 1; j < line_count; j++)
{
if(indicators[j] == 0) continue;
if(distances[i][j] < minDis)
{
minDis = distances[i][j];
indexA = i;
indexB = j;
}
}
}
}
/* calculate cluster size */
std::vector<int> clusterSizes(line_count);
for(int i = 0; i < line_count; i++)
{
int cs = 0;
if(indicators[i])
{
for(int j = 0; j < line_count; j++)
{
if(clusters[i][j]) ++cs;
}
}
clusterSizes[i] = cs;
}
const int cluster_num = 3;
result.clear();
result.resize(cluster_num, std::vector<int>(line_count)); /* choose the largest three clusters */
int count = 0;
while(count < cluster_num)
{
int max_index = 0;
int max_size = clusterSizes[0];
for(int i = 1; i < line_count; i++)
{
if(max_size < clusterSizes[i])
{
max_size = clusterSizes[i];
max_index = i;
}
}
result[count] = clusters[max_index];
count++;
clusterSizes[max_index] = 0;
}
/* print clusters */
/*
for(int i = 0; i < cluster_num; i++)
{
printf("Cluster %d:\n", i);
for(int j = 0; j < line_count; j++)
{
if(result[i][j])
printf("%d ", j);
}
printf("\n");
}
*/
}
void NetworKit::IntrapartitionDensity::run() {
hasRun = false;
Aux::SignalHandler handler;
minimumValue = std::numeric_limits< double >::max();
maximumValue = std::numeric_limits< double >::lowest();
unweightedAverage = 0;
weightedAverage = 0;
values.clear();
std::vector<count> clusterSizes(P.upperBound(), 0);
std::vector<count> intraEdges(P.upperBound(), 0);
handler.assureRunning();
G.forEdges([&](node u, node v) {
if (P[u] == P[v]) {
++intraEdges[P[u]];
}
});
handler.assureRunning();
G.forNodes([&](node u) {
++clusterSizes[P[u]];
});
handler.assureRunning();
count numClusters = 0;
count intraEdgesSum = 0;
count possibleIntraEdgesSum = 0;
values.resize(P.upperBound(), 0);
for (index i = 0; i < clusterSizes.size(); ++i) {
if (clusterSizes[i] > 0) {
double id = 1;
count possibleEdges = clusterSizes[i] * (clusterSizes[i]-1) / 2;
if (possibleEdges > 0) {
id = intraEdges[i] * 1.0 / possibleEdges;
}
values[i] = id;
unweightedAverage += id;
weightedAverage += id * clusterSizes[i];
minimumValue = std::min(id, minimumValue);
maximumValue = std::max(id, maximumValue);
possibleIntraEdgesSum += possibleEdges;
intraEdgesSum += intraEdges[i];
++numClusters;
} else {
clusterSizes[i] = none;
}
}
handler.assureRunning();
unweightedAverage /= numClusters;
weightedAverage /= G.numberOfNodes();
globalValue = intraEdgesSum * 1.0 / possibleIntraEdgesSum;
hasRun = true;
}
