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Hendrik Möllersmann
2026-01-13 14:03:40 +01:00
parent b5f640bda1
commit 4b348533bb
2 changed files with 9 additions and 9 deletions
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16
GHA_triaxial/ES_gha2.py
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@@ -29,7 +29,7 @@ def Bogenlaenge(P1: NDArray, P2: NDArray) -> float:
return s return s
def gha2_ES(ell: EllipsoidTriaxial, P0: NDArray, Pk: NDArray, stepLenTarget: float = None, sigmaStep: float = 1e-7, stopeval: int = 1000, maxSteps: int = 10000, all_points: bool = False): def gha2_ES(ell: EllipsoidTriaxial, P0: NDArray, Pk: NDArray, stepLenTarget: float = None, sigmaStep: float = 1e-5, stopeval: int = 1000, maxSteps: int = 10000, all_points: bool = False):
""" """
Berechnen der 2. GHA mithilfe der CMA-ES. Berechnen der 2. GHA mithilfe der CMA-ES.
Die CMA-ES optimiert sukzessive einzelne Punkte, die einen definierten Abstand (stepLenTarget) zum vorherigen und den kürzesten Die CMA-ES optimiert sukzessive einzelne Punkte, die einen definierten Abstand (stepLenTarget) zum vorherigen und den kürzesten
@@ -92,13 +92,12 @@ def gha2_ES(ell: EllipsoidTriaxial, P0: NDArray, Pk: NDArray, stepLenTarget: flo
# sin(v); # sin(v);
# sin(u)] auf # sin(u)] auf
# Einheitskugel # Einheitskugel
# % arg_u = max(-1, min(1, q(3))); #arg_u = max(-1, min(1, q(3)));
# #
# %Quadrantenabfrage # %Quadrantenabfrage
# % u0 = mod(asin(arg_u) + pi / 2, pi) - pi / 2; #u0 = mod(asin(arg_u) + pi / 2, pi) - pi / 2;
# % v0 = atan2(q(2), q(1)); #v0 = atan2(q(2), q(1));
# % xmean_init = [u0; #xmean_init = [u0;v0];
# v0];
xmean_init = ell.point_onto_ellipsoid(P_prev + stepLen * (P_end - P_prev) / np.linalg.norm(P_end - P_prev)) xmean_init = ell.point_onto_ellipsoid(P_prev + stepLen * (P_end - P_prev) / np.linalg.norm(P_end - P_prev))
# [~, ~, aux] = geoLength(xmean_init); # [~, ~, aux] = geoLength(xmean_init);
@@ -218,11 +217,12 @@ if __name__ == '__main__':
beta1, lamb1 = (0.7, 0.3) beta1, lamb1 = (0.7, 0.3)
P1 = ell.ell2cart(beta1, lamb1) P1 = ell.ell2cart(beta1, lamb1)
alpha0, alpha1, s, betas, lambs = gha2_num(ell, beta0, lamb0, beta1, lamb1, n=5000, all_points=True) alpha0, alpha1, s_num, betas, lambs = gha2_num(ell, beta0, lamb0, beta1, lamb1, n=10000, all_points=True)
points_num = [] points_num = []
for beta, lamb in zip(betas, lambs): for beta, lamb in zip(betas, lambs):
points_num.append(ell.ell2cart(beta, lamb)) points_num.append(ell.ell2cart(beta, lamb))
points_num = np.array(points_num) points_num = np.array(points_num)
alpha0, alpha1, s, points = gha2_ES(ell, P0, P1) alpha0, alpha1, s, points = gha2_ES(ell, P0, P1, all_points=True, sigmaStep=1e-5)
print(s - s_num)
show_points(points, points_num, P0, P1) show_points(points, points_num, P0, P1)

2
Hansen_ES_CMA.py
View File

@@ -11,7 +11,7 @@ def felli(x):
def escma(func, *, N=10, xmean=None, sigma=0.5, stopfitness=1e-10, stopeval=None, def escma(func, *, N=10, xmean=None, sigma=0.5, stopfitness=1e-10, stopeval=None,
func_args=(), func_kwargs=None, seed=None, func_args=(), func_kwargs=None, seed=None,
bestEver = np.inf, noImproveGen = 0, absTolImprove = 1e-10, maxNoImproveGen = 100, sigmaImprove = 1e-12): bestEver = np.inf, noImproveGen = 0, absTolImprove = 1e-12, maxNoImproveGen = 100, sigmaImprove = 1e-12):
if func_kwargs is None: if func_kwargs is None:
func_kwargs = {} func_kwargs = {}