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Fix errors.

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This commit is contained in:
castano 2008-11-22 08:35:04 +00:00
parent 4ff8a83f90
commit a4dcd414ca
2 changed files with 24 additions and 19 deletions
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35
src/nvmath/Fitting.cpp
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@ -1,9 +1,14 @@
// This code is in the public domain -- icastano@gmail.com // This code is in the public domain -- icastano@gmail.com
#include "Fitting.h"
#include <nvcore/Algorithms.h> // max
#include <float.h> // FLT_MAX
using namespace nv; using namespace nv;
Vector3 nv::ComputeCentroid(int n, const Vec3 * points, const float * weights, Vector3::Arg metric, float * covariance) Vector3 nv::ComputeCentroid(int n, const Vector3 * points, const float * weights, Vector3::Arg metric)
{ {
Vector3 centroid(zero); Vector3 centroid(zero);
float total = 0.0f; float total = 0.0f;
@ -19,7 +24,7 @@ Vector3 nv::ComputeCentroid(int n, const Vec3 * points, const float * weights, V
} }
void nv::ComputeCovariance(int n, const Vec3 * points, const float * weights, Vector3::Arg metric, float * covariance) void nv::ComputeCovariance(int n, const Vector3 * points, const float * weights, Vector3::Arg metric, float * covariance)
{ {
// compute the centroid // compute the centroid
Vector3 centroid = ComputeCentroid(n, points, weights, metric); Vector3 centroid = ComputeCentroid(n, points, weights, metric);
@ -35,21 +40,21 @@ void nv::ComputeCovariance(int n, const Vec3 * points, const float * weights, Ve
Vector3 a = (points[i] - centroid) * metric; Vector3 a = (points[i] - centroid) * metric;
Vector3 b = weights[i]*a; Vector3 b = weights[i]*a;
covariance[0] += a.X()*b.X(); covariance[0] += a.x()*b.x();
covariance[1] += a.X()*b.Y(); covariance[1] += a.x()*b.y();
covariance[2] += a.X()*b.Z(); covariance[2] += a.x()*b.z();
covariance[3] += a.Y()*b.Y(); covariance[3] += a.y()*b.y();
covariance[4] += a.Y()*b.Z(); covariance[4] += a.y()*b.z();
covariance[5] += a.Z()*b.Z(); covariance[5] += a.z()*b.z();
} }
} }
Vector3 nv::ComputePrincipalComponent(int n, const Vec3 * points, const float * weights, Vector3::Arg metric) Vector3 nv::ComputePrincipalComponent(int n, const Vector3 * points, const float * weights, Vector3::Arg metric)
{ {
float matrix[6]; float matrix[6];
ComputeCovariance(n, points, weights, metric, matrix); ComputeCovariance(n, points, weights, metric, matrix);
if (covariance[0] == 0 || covariance[3] == 0 || covariance[5] == 0) if (matrix[0] == 0 || matrix[3] == 0 || matrix[5] == 0)
{ {
return Vector3(zero); return Vector3(zero);
} }
@ -63,7 +68,7 @@ Vector3 nv::ComputePrincipalComponent(int n, const Vec3 * points, const float *
float y = v.x() * matrix[1] + v.y() * matrix[3] + v.z() * matrix[4]; float y = v.x() * matrix[1] + v.y() * matrix[3] + v.z() * matrix[4];
float z = v.x() * matrix[2] + v.y() * matrix[4] + v.z() * matrix[5]; float z = v.x() * matrix[2] + v.y() * matrix[4] + v.z() * matrix[5];
float norm = std::max(std::max(x, y), z); float norm = max(max(x, y), z);
v = Vector3(x, y, z) / norm; v = Vector3(x, y, z) / norm;
} }
@ -73,7 +78,7 @@ Vector3 nv::ComputePrincipalComponent(int n, const Vec3 * points, const float *
void nv::Compute4Means(int n, const Vec3 * points, const float * weights, Vector3::Arg metric, Vector3 * cluster) void 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);
@ -87,7 +92,7 @@ void nv::Compute4Means(int n, const Vec3 * points, const float * weights, Vector
float mindps, maxdps; float mindps, maxdps;
mindps = maxdps = dot(points[0], principal); mindps = maxdps = dot(points[0], principal);
for (int i = 1; i < count; ++i) for (int i = 1; i < n; ++i)
{ {
float dps = dot(points[i] - centroid, principal); float dps = dot(points[i] - centroid, principal);
@ -112,14 +117,14 @@ void nv::Compute4Means(int n, const Vec3 * points, const float * weights, Vector
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};
for (int i = 0; i < count; ++i) for (int i = 0; i < n; ++i)
{ {
// Find nearest cluster. // Find nearest cluster.
int nearest = 0; int nearest = 0;
float mindist = FLT_MAX; float mindist = FLT_MAX;
for (int j = 0; j < 4; j++) for (int j = 0; j < 4; j++)
{ {
float dist = lengthSquared(cluster[j] - points[i]); float dist = length_squared(cluster[j] - points[i]);
if (dist < mindist) if (dist < mindist)
{ {
mindist = dist; mindist = dist;

8
src/nvmath/Fitting.h
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@ -9,11 +9,11 @@
namespace nv namespace nv
{ {
Vector3 ComputeCentroid(int n, const Vec3 * points, const float * weights, Vector3::Arg metric, float * covariance); Vector3 ComputeCentroid(int n, const Vector3 * points, const float * weights, Vector3::Arg metric);
void ComputeCovariance(int n, const Vec3 * 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 Vec3 * 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 Vec3 * points, const float * weights, Vector3::Arg metric, Vector3 * cluster); void Compute4Means(int n, const Vector3 * points, const float * weights, Vector3::Arg metric, Vector3 * cluster);
} // nv namespace } // nv namespace