本文整理匯總了Python中scipy.optimize.linesearch.line_search_wolfe2方法的典型用法代碼示例。如果您正苦於以下問題：Python linesearch.line_search_wolfe2方法的具體用法？Python linesearch.line_search_wolfe2怎麽用？Python linesearch.line_search_wolfe2使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類scipy.optimize.linesearch的用法示例。
在下文中一共展示了linesearch.line_search_wolfe2方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_line_search_wolfe2
# 需要導入模塊: from scipy.optimize import linesearch [as 別名]
# 或者: from scipy.optimize.linesearch import line_search_wolfe2 [as 別名]
def test_line_search_wolfe2(self):
c = 0
smax = 100
for name, f, fprime, x, p, old_f in self.line_iter():
f0 = f(x)
g0 = fprime(x)
self.fcount = 0
s, fc, gc, fv, ofv, gv = ls.line_search_wolfe2(f, fprime, x, p,
g0, f0, old_f,
amax=smax)
assert_equal(self.fcount, fc+gc)
assert_fp_equal(ofv, f(x))
assert_fp_equal(fv, f(x + s*p))
if gv is not None:
assert_array_almost_equal(gv, fprime(x + s*p), decimal=14)
if s < smax:
c += 1
assert_line_wolfe(x, p, s, f, fprime, err_msg=name)
assert_(c > 3) # check that the iterator really works... 示例2: test_line_search_wolfe2
# 需要導入模塊: from scipy.optimize import linesearch [as 別名]
# 或者: from scipy.optimize.linesearch import line_search_wolfe2 [as 別名]
def test_line_search_wolfe2(self):
c = 0
smax = 512
for name, f, fprime, x, p, old_f in self.line_iter():
f0 = f(x)
g0 = fprime(x)
self.fcount = 0
with suppress_warnings() as sup:
sup.filter(LineSearchWarning,
"The line search algorithm could not find a solution")
sup.filter(LineSearchWarning,
"The line search algorithm did not converge")
s, fc, gc, fv, ofv, gv = ls.line_search_wolfe2(f, fprime, x, p,
g0, f0, old_f,
amax=smax)
assert_equal(self.fcount, fc+gc)
assert_fp_equal(ofv, f(x))
assert_fp_equal(fv, f(x + s*p))
if gv is not None:
assert_array_almost_equal(gv, fprime(x + s*p), decimal=14)
if s < smax:
c += 1
assert_line_wolfe(x, p, s, f, fprime, err_msg=name)
assert_(c > 3) # check that the iterator really works... 示例3: test_line_search_wolfe2_bounds
# 需要導入模塊: from scipy.optimize import linesearch [as 別名]
# 或者: from scipy.optimize.linesearch import line_search_wolfe2 [as 別名]
def test_line_search_wolfe2_bounds(self):
# See gh-7475
# For this f and p, starting at a point on axis 0, the strong Wolfe
# condition 2 is met if and only if the step length s satisfies
# |x + s| <= c2 * |x|
f = lambda x: np.dot(x, x)
fp = lambda x: 2 * x
p = np.array([1, 0])
# Smallest s satisfying strong Wolfe conditions for these arguments is 30
x = -60 * p
c2 = 0.5
s, _, _, _, _, _ = ls.line_search_wolfe2(f, fp, x, p, amax=30, c2=c2)
assert_line_wolfe(x, p, s, f, fp)
s, _, _, _, _, _ = assert_warns(LineSearchWarning,
ls.line_search_wolfe2, f, fp, x, p,
amax=29, c2=c2)
assert_(s is None)
# s=30 will only be tried on the 6th iteration, so this won't converge
assert_warns(LineSearchWarning, ls.line_search_wolfe2, f, fp, x, p,
c2=c2, maxiter=5) 示例4: _line_search_wolfe12
# 需要導入模塊: from scipy.optimize import linesearch [as 別名]
# 或者: from scipy.optimize.linesearch import line_search_wolfe2 [as 別名]
def _line_search_wolfe12(f, fprime, xk, pk, gfk, old_fval, old_old_fval,
**kwargs):
"""
Same as line_search_wolfe1, but fall back to line_search_wolfe2 if
suitable step length is not found, and raise an exception if a
suitable step length is not found.
Raises
------
_LineSearchError
If no suitable step size is found
"""
ret = line_search_wolfe1(f, fprime, xk, pk, gfk,
old_fval, old_old_fval,
**kwargs)
if ret[0] is None:
# line search failed: try different one.
ret = line_search_wolfe2(f, fprime, xk, pk, gfk,
old_fval, old_old_fval, **kwargs)
if ret[0] is None:
raise _LineSearchError()
return ret