public static Vector3 ComputeNormal(Matrix <float> eigenVectors, MNLA.Vector <Complex> eigenValue)
{
// Keep the smallest eigenvector as surface normal.
int smallestIndex = 0;
float smallestValue = float.MaxValue;
for (int j = 0; j < eigenVectors.ColumnCount; j++)
{
float lambda = (float)eigenValue[j].Real;
if (lambda < smallestValue)
{
smallestIndex = j;
smallestValue = lambda;
}
}
var normalVector = eigenVectors.Column(smallestIndex);
return(normalVector.Normalize(2).ToVector3());
}
public static MNLA.Vector <float> CrossProduct(this MNLA.Vector <float> left, MNLA.Vector <float> right, bool normalize = true)
{
if ((left.Count != 3 || right.Count != 3))
{
throw new Exception("Vectors must have a length of 3.");
}
MNLA.Vector <float> result = MNLA.Vector <float> .Build.Dense(3);
result[0] = left[1] * right[2] - left[2] * right[1];
result[1] = -left[0] * right[2] + left[2] * right[0];
result[2] = left[0] * right[1] - left[1] * right[0];
if (normalize)
{
result = result.Normalize(2);
}
return(result);
}
public static Vector3 ToVector3(this MNLA.Vector <float> v)
{
return(new Vector3(v[0], v[1], v[2]));
}
