// Token: 0x060002F9 RID: 761 RVA: 0x00030E84 File Offset: 0x0002F084
private bool IsPositiveRank(double[,] M, ref double maxEntry, ref Vector3d maxRow)
{
maxEntry = -1.0;
int i = -1;
for (int j = 0; j < 3; j++)
{
for (int k = j; k < 3; k++)
{
double num = Math.Abs(M[j, k]);
if (num > maxEntry)
{
maxEntry = num;
i = j;
}
}
}
maxRow = MatrixMath.GetRow(M, i).ToVector3d();
return(maxEntry >= 1E-08);
}
// Token: 0x06000324 RID: 804 RVA: 0x00031F98 File Offset: 0x00030198
private static double[] ChordalNodesU(Point3d[,] intPoints)
{
int length = intPoints.GetLength(0);
int length2 = intPoints.GetLength(1);
Point3d[] intPoints2 = new Point3d[length2];
double[,] array = new double[length, length2];
for (int i = 0; i < length; i++)
{
intPoints2 = MatrixMath.GetRow(intPoints, i);
double[] array2 = SplineMath.ChordalNodes(intPoints2);
for (int j = 0; j < length2; j++)
{
array[i, j] = array2[j];
}
}
double[] array3 = new double[length2];
for (int k = 0; k < length2; k++)
{
array3[k] = MathEx.Mean(MatrixMath.GetColumn(array, k));
}
return(array3);
}
/// <summary>
/// 使用二维数组来初始化 Matrix3x3
/// </summary>
/// <param name="orientaition"></param>
public static Matrix3x3 Create(double[,] orientaition)
{
return(Create(MatrixMath.GetRow(orientaition, 0), MatrixMath.GetRow(orientaition, 1), MatrixMath.GetRow(orientaition, 2)));
}
public static Point4d operator *(Point4d point, Matrix4x4 matrix)
{
return(new Point4d(MatrixMath.Multiply(point.ToArray(), matrix.To2DArray())));
}
// Token: 0x060002FA RID: 762 RVA: 0x00030EF0 File Offset: 0x0002F0F0
private void ComputeVectors(double[,] A, Vector3d U2, int i0, int i1, int i2)
{
Vector3d vector;
Vector3d vector2;
GenerateComplementBasis(out vector, out vector2, U2);
Vector3d v = MatrixMath.Multiply(A, vector.ToArray()).ToVector3d();
double num = mEigenvalue[i2] - vector.DotProduct(v);
double num2 = vector2.DotProduct(v);
Vector3d v2 = MatrixMath.Multiply(A, vector2.ToArray()).ToVector3d();
double num3 = mEigenvalue[i2] - vector2.DotProduct(v2);
double num4 = Math.Abs(num);
int num5 = 0;
double num6 = Math.Abs(num2);
if (num6 > num4)
{
num4 = num6;
}
num6 = Math.Abs(num3);
if (num6 > num4)
{
num4 = num6;
num5 = 1;
}
if (num4 >= 1E-08)
{
if (num5 == 0)
{
double num7 = 1.0 / Math.Sqrt(num * num + num2 * num2);
num *= num7;
num2 *= num7;
mEigenvector[i2] = vector.Multiply(num2).Add(vector2.Multiply(num));
}
else
{
double num7 = 1.0 / Math.Sqrt(num3 * num3 + num2 * num2);
num3 *= num7;
num2 *= num7;
mEigenvector[i2] = vector.Multiply(num3).Add(vector2.Multiply(num2));
}
}
else if (num5 == 0)
{
mEigenvector[i2] = vector2;
}
else
{
mEigenvector[i2] = vector;
}
Vector3d vector3 = U2.CrossProduct(mEigenvector[i2]);
v = MatrixMath.Multiply(A, U2.ToArray()).ToVector3d();
num = mEigenvalue[i0] - U2.DotProduct(v);
num2 = vector3.DotProduct(v);
Vector3d v3 = MatrixMath.Multiply(A, vector3.ToArray()).ToVector3d();
num3 = mEigenvalue[i0] - vector3.DotProduct(v3);
num4 = Math.Abs(num);
num5 = 0;
num6 = Math.Abs(num2);
if (num6 > num4)
{
num4 = num6;
}
num6 = Math.Abs(num3);
if (num6 > num4)
{
num4 = num6;
num5 = 1;
}
if (num4 >= 1E-08)
{
if (num5 == 0)
{
double num7 = 1.0 / Math.Sqrt(num * num + num2 * num2);
num *= num7;
num2 *= num7;
mEigenvector[i0] = U2.Multiply(num2).Add(vector3.Multiply(num));
}
else
{
double num7 = 1.0 / Math.Sqrt(num3 * num3 + num2 * num2);
num3 *= num7;
num2 *= num7;
mEigenvector[i0] = U2.Multiply(num3).Add(vector3.Multiply(num2));
}
}
else if (num5 == 0)
{
mEigenvector[i0] = vector3;
}
else
{
mEigenvector[i0] = U2;
}
mEigenvector[i1] = mEigenvector[i2].CrossProduct(mEigenvector[i0]);
}
internal void ComputeEigenStuff(double[,] A)
{
double num = Math.Abs(A[0, 0]);
double num2 = Math.Abs(A[0, 1]);
double num3 = Math.Abs(A[0, 2]);
double num4 = Math.Abs(A[1, 1]);
double num5 = Math.Abs(A[1, 2]);
double num6 = Math.Abs(A[2, 2]);
double num7 = MathEx.Max(new double[]
{
num,
num2,
num3,
num4,
num5,
num6
});
double[,] array = A;
if (num7 > 1.0)
{
double s = 1.0 / num7;
array = MatrixMath.Multiply(A, s);
}
double[] array2 = new double[3];
ComputeRoots(array, array2);
mEigenvalue[0] = array2[0];
mEigenvalue[1] = array2[1];
mEigenvalue[2] = array2[2];
double[] array3 = new double[3];
Vector3d[] array4 = new Vector3d[3];
for (int i = 0; i < 3; i++)
{
double[,] array5 = (double[, ])array.Clone();
array5[0, 0] -= mEigenvalue[i];
array5[1, 1] -= mEigenvalue[i];
array5[2, 2] -= mEigenvalue[i];
if (!IsPositiveRank(array5, ref array3[i], ref array4[i]))
{
if (num7 > 1.0)
{
for (int j = 0; j < 3; j++)
{
mEigenvalue[j] *= num7;
}
}
mEigenvector[0] = Vector3dEx.AxisX;
mEigenvector[1] = Vector3dEx.AxisY;
mEigenvector[2] = Vector3dEx.AxisZ;
return;
}
}
double num8 = array3[0];
int num9 = 0;
if (array3[1] > num8)
{
num8 = array3[1];
num9 = 1;
}
if (array3[2] > num8)
{
num9 = 2;
}
if (num9 == 0)
{
array4[0].Normalize();
ComputeVectors(array, array4[0], 1, 2, 0);
}
else if (num9 == 1)
{
array4[1].Normalize();
ComputeVectors(array, array4[1], 2, 0, 1);
}
else
{
array4[2].Normalize();
ComputeVectors(array, array4[2], 0, 1, 2);
}
if (num7 > 1.0)
{
for (int k = 0; k < 3; k++)
{
mEigenvalue[k] *= num7;
}
}
}
// Token: 0x060002EA RID: 746 RVA: 0x00030528 File Offset: 0x0002E728
public static double[] BackSolve(double[,] a, int[] index, double[] b)
{
double[] array = MatrixMath.Copy(b);
BackSubstitution(a, index, array);
return(array);
}
