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add region centroid capability and consolidate into one centroid

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function for polygons, regions and VNFs.
Fix bug with anchors for linear_sweep (due to centerpoint issues)
Fix intersection anchors for vnfs when anchor vector intersects
in a path instead of a single point.
This commit is contained in:
Adrian Mariano
2021-10-20 22:44:55 -04:00
parent a7ca1b1b64
commit 76272d9d9a
10 changed files with 158 additions and 101 deletions
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@@ -958,7 +958,7 @@ function linear_solve(A,b,pivot=true) =
// Description:
// Compute the matrix inverse of the square matrix `A`. If `A` is singular, returns `undef`.
// Note that if you just want to solve a linear system of equations you should NOT use this function.
// Instead use [[`linear_solve()`|linear_solve]], or use [[`qr_factor()`|qr_factor]]. The computation
// Instead use {{linear_solve()}}, or use {{qr_factor}}. The computation
// will be faster and more accurate.
function matrix_inverse(A) =
assert(is_matrix(A) && len(A)==len(A[0]),"Input to matrix_inverse() must be a square matrix")
@@ -1007,7 +1007,7 @@ function null_space(A,eps=1e-12) =
// qr = qr_factor(A,[pivot]);
// Description:
// Calculates the QR factorization of the input matrix A and returns it as the list [Q,R,P]. This factorization can be
// used to solve linear systems of equations. The factorization is A = Q*R*transpose(P). If pivot is false (the default)
// used to solve linear systems of equations. The factorization is `A = Q*R*transpose(P)`. If pivot is false (the default)
// then P is the identity matrix and A = Q*R. If pivot is true then column pivoting results in an R matrix where the diagonal
// is non-decreasing. The use of pivoting is supposed to increase accuracy for poorly conditioned problems, and is necessary
// for rank estimation or computation of the null space, but it may be slower.
@@ -1088,7 +1088,7 @@ function _back_substitute(R, b, x=[]) =
// L = cholesky(A);
// Description:
// Compute the cholesky factor, L, of the symmetric positive definite matrix A.
// The matrix L is lower triangular and L * transpose(L) = A. If the A is
// The matrix L is lower triangular and `L * transpose(L) = A`. If the A is
// not symmetric then an error is displayed. If the matrix is symmetric but
// not positive definite then undef is returned.
function cholesky(A) =