void PhysicsLoop( void )
{
glClear( GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT );
RenderString( 1, 2, "Left click to spawn a polygon" );
RenderString( 1, 4, "Right click to spawn a circle" );
static float accumulator = 0;
accumulator += clock.Elapsed( );
clock.Start( );
accumulator = Clamp( 0.0f, 0.1f, accumulator );
while(accumulator >= dt)
{
if(!frameStepping)
scene.Step( );
else
{
if(canStep)
{
scene.Step( );
canStep = false;
}
}
accumulator -= dt;
}
clock.Stop( );
scene.Render( );
glutSwapBuffers( );
}
void SpectralClustering::Cluster(const DenseMatrix<double> &S, UINT kMeansClusters, UINT reducedDimensionCount, Method method, Vector<UINT> &clusterIDs, SpectralClusteringCache *cache)
{
const UINT pointCount = S.RowCount();
Console::WriteLine("Spectral clustering on " + String(pointCount) + " points, " + String(kMeansClusters) + " clusters, " + String(reducedDimensionCount) + String(" dimensions"));
DenseMatrix<double> L(pointCount, pointCount);
MakeLaplacian(S, method, L);
Vector<double> eigenvalues;
DenseMatrix<double> eigenvectors;
{
if(cache != NULL && cache->eigenvalues.Length() == pointCount)
{
Console::WriteString("Loading eigensystem from cache...");
eigenvalues = cache->eigenvalues;
eigenvectors = cache->eigenvectors;
}
else
{
Clock timer;
Console::WriteString("Solving eigensystem...");
L.EigenSystemTNT(eigenvalues, eigenvectors);
Console::WriteLine(String(timer.Elapsed()) + "s");
}
if(cache != NULL)
{
cache->eigenvalues = eigenvalues;
cache->eigenvectors = eigenvectors;
}
}
//
// Note that the eigensystem is sorted in decreasing order.
//
const bool useLargestEigenvector = (method == RandomWalk || method == Symmetric);
Vector<VecNf> points(pointCount);
for(UINT pointIndex = 0; pointIndex < pointCount; pointIndex++)
{
VecNf &curPoint = points[pointIndex];
curPoint.v.Allocate(reducedDimensionCount);
for(UINT dimensionIndex = 0; dimensionIndex < reducedDimensionCount; dimensionIndex++)
{
if(useLargestEigenvector)
{
curPoint[dimensionIndex] = float(eigenvectors(pointIndex, pointCount - dimensionIndex - 1));
}
else
{
curPoint[dimensionIndex] = float(eigenvectors(pointIndex, dimensionIndex));
}
}
}
if(method == Symmetric)
{
for(UINT pointIndex = 0; pointIndex < pointCount; pointIndex++)
{
points[pointIndex] = VecNf::Normalize(points[pointIndex]);
}
}
KMeansClustering<VecNf, VecNfKMeansMetric> clustering;
clustering.Cluster(points, kMeansClusters, 100);
clusterIDs.Allocate(pointCount);
for(UINT pointIndex = 0; pointIndex < pointCount; pointIndex++)
{
clusterIDs[pointIndex] = clustering.QuantizeToNearestClusterIndex(points[pointIndex]);
}
}