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Fix 4 means clustering.

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This commit is contained in:
castano 2008-11-22 11:32:51 +00:00
parent a302475fa6
commit d4a713451e
2 changed files with 121 additions and 13 deletions
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131
src/nvmath/Fitting.cpp
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@ -3,6 +3,7 @@
#include "Fitting.h" #include "Fitting.h"
#include <nvcore/Algorithms.h> // max #include <nvcore/Algorithms.h> // max
#include <nvcore/Containers.h> // swap
#include <float.h> // FLT_MAX #include <float.h> // FLT_MAX
using namespace nv; using namespace nv;
@ -78,7 +79,7 @@ Vector3 nv::ComputePrincipalComponent(int n, const Vector3 * points, const float
void nv::Compute4Means(int n, const Vector3 * points, const float * weights, Vector3::Arg metric, Vector3 * cluster) int nv::Compute4Means(int n, const Vector3 * points, const float * weights, Vector3::Arg metric, Vector3 * cluster)
{ {
Vector3 centroid = ComputeCentroid(n, points, weights, metric); Vector3 centroid = ComputeCentroid(n, points, weights, metric);
@ -90,7 +91,7 @@ void nv::Compute4Means(int n, const Vector3 * points, const float * weights, Vec
mini = maxi = 0; mini = maxi = 0;
float mindps, maxdps; float mindps, maxdps;
mindps = maxdps = dot(points[0], principal); mindps = maxdps = dot(points[0] - centroid, principal);
for (int i = 1; i < n; ++i) for (int i = 1; i < n; ++i)
{ {
@ -106,13 +107,15 @@ void nv::Compute4Means(int n, const Vector3 * points, const float * weights, Vec
} }
} }
//cluster[0] = points[mini];
//cluster[1] = points[maxi];
cluster[0] = centroid + mindps * principal; cluster[0] = centroid + mindps * principal;
cluster[3] = centroid + maxdps * principal; cluster[1] = centroid + maxdps * principal;
cluster[1] = (2 * cluster[0] + cluster[1]) / 3; cluster[2] = (2 * cluster[0] + cluster[1]) / 3;
cluster[2] = (2 * cluster[1] + cluster[0]) / 3; cluster[3] = (2 * cluster[1] + cluster[0]) / 3;
// Now we have to iteratively refine the clusters. // Now we have to iteratively refine the clusters.
while(true) while (true)
{ {
Vector3 newCluster[4] = { Vector3(zero), Vector3(zero), Vector3(zero), Vector3(zero) }; Vector3 newCluster[4] = { Vector3(zero), Vector3(zero), Vector3(zero), Vector3(zero) };
float total[4] = {0, 0, 0, 0}; float total[4] = {0, 0, 0, 0};
@ -141,15 +144,119 @@ void nv::Compute4Means(int n, const Vector3 * points, const float * weights, Vec
newCluster[j] /= total[j]; newCluster[j] /= total[j];
} }
if (equal(cluster[0], newCluster[0]) && equal(cluster[3], newCluster[3])) if ((equal(cluster[0], newCluster[0]) || total[0] == 0) &&
(equal(cluster[1], newCluster[1]) || total[1] == 0) &&
(equal(cluster[2], newCluster[2]) || total[2] == 0) &&
(equal(cluster[3], newCluster[3]) || total[3] == 0))
{ {
break; return (total[0] != 0) + (total[1] != 0) + (total[2] != 0) + (total[3] != 0);
} }
// @@ We should choose the optimal assignment.
cluster[0] = newCluster[0]; cluster[0] = newCluster[0];
cluster[1] = newCluster[1];
cluster[2] = newCluster[2];
cluster[3] = newCluster[3]; cluster[3] = newCluster[3];
cluster[1] = (2 * cluster[0] + cluster[1]) / 3;
cluster[2] = (2 * cluster[1] + cluster[0]) / 3; // Sort clusters by weight.
for (int i = 0; i < 4; i++)
{
for (int j = i; j > 0 && total[j] > total[j - 1]; j--)
{
swap( total[j], total[j - 1] );
swap( cluster[j], cluster[j - 1] );
}
}
} }
} }
/*
int nv::Compute2Means(int n, const Vector3 * points, const float * weights, Vector3::Arg metric, Vector3 * cluster)
{
Vector3 centroid = ComputeCentroid(n, points, weights, metric);
// Compute principal component.
Vector3 principal = ComputePrincipalComponent(n, points, weights, metric);
// Pick initial solution.
int mini, maxi;
mini = maxi = 0;
float mindps, maxdps;
mindps = maxdps = dot(points[0] - centroid, principal);
for (int i = 1; i < n; ++i)
{
float dps = dot(points[i] - centroid, principal);
if (dps < mindps) {
mindps = dps;
mini = i;
}
else {
maxdps = dps;
maxi = i;
}
}
cluster[0] = points[mini];
cluster[3] = points[maxi];
//cluster[0] = centroid + mindps * principal;
//cluster[1] = centroid + maxdps * principal;
cluster[2] = (2 * cluster[0] + cluster[1]) / 3;
cluster[3] = (2 * cluster[1] + cluster[0]) / 3;
// Now we have to iteratively refine the clusters.
while (true)
{
Vector3 newCluster[4] = { Vector3(zero), Vector3(zero), Vector3(zero), Vector3(zero) };
float total[4] = {0, 0, 0, 0};
for (int i = 0; i < n; ++i)
{
// Find nearest cluster.
int nearest = 0;
float mindist = FLT_MAX;
for (int j = 0; j < 4; j++)
{
float dist = length_squared(cluster[j] - points[i]);
if (dist < mindist)
{
mindist = dist;
nearest = j;
}
}
newCluster[nearest] += weights[i] * points[i];
total[nearest] += weights[i];
}
for (int j = 0; j < 4; j++)
{
newCluster[j] /= total[j];
}
if ((equal(cluster[0], newCluster[0]) || total[0] == 0) &&
(equal(cluster[1], newCluster[1]) || total[1] == 0) &&
(equal(cluster[2], newCluster[2]) || total[2] == 0) &&
(equal(cluster[3], newCluster[3]) || total[3] == 0))
{
return (total[0] != 0) + (total[1] != 0) + (total[2] != 0) + (total[3] != 0);
}
cluster[0] = newCluster[0];
cluster[1] = newCluster[1];
cluster[2] = newCluster[2];
cluster[3] = newCluster[3];
// Sort clusters by weight.
for (int i = 0; i < 4; i++)
{
for (int j = i; j > 0 && total[j] > total[j - 1]; j--)
{
swap( total[j], total[j - 1] );
swap( cluster[j], cluster[j - 1] );
}
}
}
}
*/

3
src/nvmath/Fitting.h
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@ -13,7 +13,8 @@ namespace nv
void ComputeCovariance(int n, const Vector3 * points, const float * weights, Vector3::Arg metric, float * covariance); void ComputeCovariance(int n, const Vector3 * points, const float * weights, Vector3::Arg metric, float * covariance);
Vector3 ComputePrincipalComponent(int n, const Vector3 * points, const float * weights, Vector3::Arg metric); Vector3 ComputePrincipalComponent(int n, const Vector3 * points, const float * weights, Vector3::Arg metric);
void Compute4Means(int n, const Vector3 * points, const float * weights, Vector3::Arg metric, Vector3 * cluster); // Returns number of clusters [1-4].
int Compute4Means(int n, const Vector3 * points, const float * weights, Vector3::Arg metric, Vector3 * cluster);
} // nv namespace } // nv namespace