//----------------------------------------------------------------------------
static public void eigentest()
{
double[,] m = new double[3, 3];
m[0, 0] = 2;
m[0, 1] = 1;
m[0, 2] = 1;
m[1, 0] = 1;
m[1, 1] = 2;
m[1, 2] = 1;
m[2, 0] = 1;
m[2, 1] = 1;
m[2, 2] = 2;
Eigen e = new Eigen(m);
e.EigenStuff();
for (int i = 0; i <= 2; i++)
{
Vectors v = e.GetEigenvector(i);
Debug.WriteLine("" + i + ", eigenvalue=" + e.GetEigenvalue(i) + ";");
Debug.WriteLine("Eigenvector(" + v._X + "," + v._Y + "," + v._Z + ")");
}
}
//------------------------------------------------------------------------------
public void analyseVectors(List <Vectors> p, int number)
{
// calculate point count, centroid and
// then plane by pca
// p[0-2][] are coordinates
// p[3][] if present, is weight
int n = 0; //point count
int i = 0;
// means
_m.set(0.0, 0.0, 0.0, 1.0d);
double Ptx = 0.0d, Pty = 0.0d, Ptz = 0.0d;
while (i < number)
{
_m._W = 1.0d;
Ptx = p[i]._X;
Pty = p[i]._Y;
Ptz = p[i]._Z;
_m._X += Ptx;
_m._Y += Pty;
_m._Z += Ptz;
n++;
i++;
}
_m /= n;
// covariance
double x, y, z;
double xx, yy, zz;
xx = yy = zz = 0.0d;
double xy, yz, xz;
xy = yz = xz = 0.0d;
i = number - 1;
while (i >= 0)
{
_m._W = 1.0;
Ptx = p[i]._X;
Pty = p[i]._Y;
Ptz = p[i]._Z;
x = Ptx - _m._X; //p[0][i] - _m._X;
y = Pty - _m._Y; //p[1][i] - _m._Y;
z = Ptz - _m._Z; //p[2][i] - _m._Z;
xx += x * x;
yy += y * y;
zz += z * z;
xy += x * y;
yz += y * z;
xz += x * z;
i--;
}
// standardize
xx /= (double)n;
yy /= (double)n;
zz /= (double)n;
xy /= (double)n;
yz /= (double)n;
xz /= (double)n;
// setup matrix
double[,] cov = new double[3, 3];
cov[0, 0] = xx;
cov[0, 1] = xy;
cov[0, 2] = xz;
cov[1, 0] = xy;
cov[1, 1] = yy;
cov[1, 2] = yz;
cov[2, 0] = xz;
cov[2, 1] = yz;
cov[2, 2] = zz;
// do pca, smallest eigenvector is normal
_e = new Eigen(cov);
_e.EigenStuff();
_normal = _e.GetEigenvector(0);
_eigenval = _e.GetEigenvalue(0);
_dcent = _normal._X * _m._X + _normal._Y * _m._Y + _normal._Z * _m._Z;
}// analyse _pts
}// analyse _pts
//------------------------------------------------------------------------------
public void analyse(double[][] p, int number)
{
// calculate point count, centroid and
// then plane by pca
// p[0-2][] are coordinates
// p[3][] if present, is weight
int n = 0; //point count
int i = number - 1;
// means
_m.set(0.0, 0.0, 0.0, 0.0);
while (i >= 0)
{
_m._W = 1.0d;
if (p.GetLength(0) == 4)
{
_m._W = p[3][i];
}
_m._X += p[0][i] * _m._W;
_m._Y += p[1][i] * _m._W;
_m._Z += p[2][i] * _m._W;
n += (int)_m._W;
i--;
}
_m /= n;
// covariance
double x, y, z;
double xx, yy, zz;
xx = yy = zz = 0.0d;
double xy, yz, xz;
xy = yz = xz = 0.0d;
i = number - 1;
while (i >= 0)
{
_m._W = 1.0;
if (p.GetLength(0) == 4)
{
_m._W = p[3][i];
}
x = p[0][i] - _m._X;
y = p[1][i] - _m._Y;
z = p[2][i] - _m._Z;
xx += _m._W * x * x;
yy += _m._W * y * y;
zz += _m._W * z * z;
xy += _m._W * x * y;
yz += _m._W * y * z;
xz += _m._W * x * z;
i--;
}
// standardize
xx /= n;
yy /= n;
zz /= n;
xy /= n;
yz /= n;
xz /= n;
// setup matrix
double[,] cov = new double[3, 3];
cov[0, 0] = xx;
cov[0, 1] = xy;
cov[0, 2] = xz;
cov[1, 0] = xy;
cov[1, 1] = yy;
cov[1, 2] = yz;
cov[2, 0] = xz;
cov[2, 1] = yz;
cov[2, 2] = zz;
// do pca, smallest eigenvector is normal
_e = new Eigen(cov);
_e.EigenStuff();
_normal = _e.GetEigenvector(0);
_eigenval = _e.GetEigenvalue(0);
_dcent = _normal._X * _m._X + _normal._Y * _m._Y + _normal._Z * _m._Z;
}// analyse