def __call__(self,
fcn,
x0,
xmin,
xmax,
tol=EPSILON,
maxnfev=None,
step=None,
finalsimplex=[0, 1, 1],
verbose=0):
x0 = np.asarray(x0)
n = len(x0)
if step is None:
step = abs(x0) + 1.2
maxnfev = self.get_maxnfev(maxnfev, n)
init = 0
x0, fval, nfev, err = \
_saoopt.neldermead(verbose, maxnfev, init, finalsimplex, tol, step,
xmin, xmax, x0, fcn)
iquad = 1
simp = 1.0e-2 * tol
step = n * [0.4]
self.par, self.fmin, tmpnfev, ifault = \
_saoopt.minim(verbose, maxnfev - nfev, init, iquad, simp, tol*10,
step, xmin, xmax, x0, fcn)
self.nfev = nfev + tmpnfev
return self.nfev, self.fmin, self.par
def test_mccormick(self):
from sherpa.optmethods.opt import McCormick
init = 0
iquad = 1
ftol = 1.0e-7
simp = 1.0e-2 * ftol
xmin = [-1.5, -3.0]
xmax = [4.0, 4.0]
x = np.asarray([0, 0])
step = 0.4 * np.ones(x.shape, np.float_)
x, fval, neval, ifalut = _saoopt.minim(0, 1000, init, iquad, simp,
ftol, step, xmin, xmax, x,
McCormick)
fmin = -1.9132229549810362
self.assertEqualWithinTol(fval, fmin, self.tolerance)
def __call__(self,
fcn,
x0,
xmin,
xmax,
tol=1.0e-6,
maxnfev=None,
step=None,
finalsimplex=1,
verbose=0):
init = 0
iquad = 1
simp = 1.0e-2 * tol
x0 = np.asarray(x0)
n = len(x0)
if step is None:
step = n * [0.4]
maxnfev = self.get_maxnfev(maxnfev, n)
par, fmin, nfev, ifault = \
_saoopt.minim(verbose, maxnfev, init, iquad, simp, tol*10,
step, xmin, xmax, x0, fcn)
return nfev, fmin, par