public TextLogTarget(Action <int, LogType, int, string> write, int threadIndex = 0)
{
m_lock = new object();
m_state = new ReportState(threadIndex, m_prefixFun(threadIndex));
m_stateTable = new IntDict <ReportState>();
WriteAct = write;
}
/// <summary>
/// Creates clusters for all points which are within an epsilon
/// distance from each other. This algorithm uses a hash-grid and
/// only works fast if the supplied epsilon is small enough that
/// not too many points fall within each cluster. Thus it is ideal
/// for merging vertices in meshes and point sets, that are different
/// due to numerical inaccuracies.
/// </summary>
public PointEpsilonClustering(V3d[] pa, double epsilon, IRandomUniform rnd = null)
{
rnd = rnd ?? new RandomSystem();
var count = pa.Length;
Alloc(count);
var ca = m_indexArray;
var dict = new IntDict <int>(count, stackDuplicateKeys: true);
int rndBits = 0; int bit = 0;
var ha = new int[8];
var eps2 = epsilon * epsilon;
for (int i = 0; i < count; i++)
{
var p = pa[i]; p.HashCodes8(epsilon, ha);
int ci = ca[i]; if (ca[ci] != ci)
{
do
{
ci = ca[ci];
} while (ca[ci] != ci); ca[i] = ci;
}
for (int hi = 0; hi < 8; hi++)
{
foreach (var j in dict.ValuesWithKey(ha[hi]))
{
int cj = ca[j]; if (ca[cj] != cj)
{
do
{
cj = ca[cj];
} while (ca[cj] != cj); ca[j] = cj;
}
if (ci == cj || V3d.DistanceSquared(p, pa[j]) >= eps2)
{
continue;
}
bit >>= 1; if (bit == 0)
{
rndBits = rnd.UniformInt(); bit = 1 << 30;
}
if ((rndBits & bit) != 0)
{
ca[ci] = cj; ca[i] = cj; ci = cj;
}
else
{
ca[cj] = ci; ca[j] = ci;
}
}
}
dict[ha[0]] = i;
}
Init();
}
/// <summary>
/// Merge clusters of exactly equal points. The supplied epsilon is used to define a
/// grid as acceleration data structure and the algorithm is only fast if not too many
/// points fall within the supplied epsilon.
/// </summary>
public PointEqualClustering(V3d[] pa, double eps, IRandomUniform rnd = null)
{
rnd = rnd ?? new RandomSystem();
var count = pa.Length;
Alloc(count);
var ca = m_indexArray;
var dict = new IntDict <int>(count, stackDuplicateKeys: true);
int rndBits = 0; int bit = 0;
for (int i = 0; i < count; i++)
{
var p = pa[i]; var hc = p.HashCode1(eps);
int ci = ca[i]; if (ca[ci] != ci)
{
do
{
ci = ca[ci];
} while (ca[ci] != ci); ca[i] = ci;
}
foreach (var j in dict.ValuesWithKey(hc))
{
int cj = ca[j]; if (ca[cj] != cj)
{
do
{
cj = ca[cj];
} while (ca[cj] != cj); ca[j] = cj;
}
if (ci == cj || p != pa[j])
{
continue;
}
bit >>= 1; if (bit == 0)
{
rndBits = rnd.UniformInt(); bit = 1 << 30;
}
if ((rndBits & bit) != 0)
{
ca[ci] = cj; ca[i] = cj; ci = cj;
}
else
{
ca[cj] = ci; ca[j] = ci;
}
}
dict[hc] = i;
}
Init();
}
public Tvalue this[TKey key]
{
get
{
if (!IntDict.ContainsKey(key))
{
IntDict[key] = Calulate(key);
}
return(IntDict[key]);
}
set
{
throw new NotImplementedException();
}
}
public void Clear()
{
IntDict.Clear();
}
/// <summary>
/// Creates clusters of planes within a certain epsilon from each other
/// (euclidean distance between normal vectors and between offsets).
/// This algorithm uses a 4d hash-grid and only works fast if the supplied
/// epsilons are small enough that not too many planes fall within each cluster.
/// Thus it is ideal for merging planes with small variations in orientation and
/// offset due to numerical inaccuracies.
/// </summary>
public PlaneEpsilonClustering(
int count, TArray pa,
Func <TArray, int, V3d> getNormal,
Func <TArray, int, double> getDist,
double epsNormal, double epsDist,
double deltaEpsFactor = 0.25, IRandomUniform rnd = null)
{
rnd = rnd ?? new RandomSystem();
Alloc(count);
var ca = m_indexArray;
var dict = new IntDict <int>(count, stackDuplicateKeys: true);
int rndBits = 0; int bit = 0;
var ha = new int[16];
var ne2 = epsNormal * epsNormal;
var de2 = epsDist * epsDist;
var deps = (ne2 + de2) * deltaEpsFactor * deltaEpsFactor;
for (int i = 0; i < count; i++)
{
var ni = getNormal(pa, i); var di = getDist(pa, i);
ni.HashCodes16(di, epsNormal, epsDist, ha);
int ci = ca[i]; if (ca[ci] != ci)
{
do
{
ci = ca[ci];
} while (ca[ci] != ci); ca[i] = ci;
}
double dmin = double.MaxValue;
for (int hi = 0; hi < 16; hi++)
{
foreach (var j in dict.ValuesWithKey(ha[hi]))
{
int cj = ca[j]; if (ca[cj] != cj)
{
do
{
cj = ca[cj];
} while (ca[cj] != cj); ca[j] = cj;
}
if (ci == cj)
{
continue;
}
var dd = Fun.Square(di - getDist(pa, j)); if (dd >= de2)
{
continue;
}
var dn = V3d.DistanceSquared(ni, getNormal(pa, j)); if (dn >= ne2)
{
continue;
}
var d = dn + dd; if (d < dmin)
{
dmin = d;
}
bit >>= 1; if (bit == 0)
{
rnd.UniformInt(); bit = 1 << 30;
}
if ((rndBits & bit) != 0)
{
ca[ci] = cj; ca[i] = cj; ci = cj;
}
else
{
ca[cj] = ci; ca[j] = ci;
}
}
}
if (dmin > deps)
{
dict[ha[0]] = i; // only sparsely populate hashtable for performance reasons
}
}
Init();
}
public PerThreadJobReporter()
{
m_reporterMap = new IntDict <IJobReporter>();
m_threadCount = 0; m_indent = 2;
}
