本文整理汇总了Python中scipy.optimize.OptimizeResult.njev方法的典型用法代码示例。如果您正苦于以下问题：Python OptimizeResult.njev方法的具体用法？Python OptimizeResult.njev怎么用？Python OptimizeResult.njev使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类scipy.optimize.OptimizeResult的用法示例。
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示例1: dual_annealing
# 需要导入模块: from scipy.optimize import OptimizeResult [as 别名]
# 或者: from scipy.optimize.OptimizeResult import njev [as 别名]
#.........这里部分代码省略.........
Package for R. The R Journal, Volume 5/1 (2013).
.. [6] Mullen, K. Continuous Global Optimization in R. Journal of
Statistical Software, 60(6), 1 - 45, (2014). DOI:10.18637/jss.v060.i06
Examples
--------
The following example is a 10-dimensional problem, with many local minima.
The function involved is called Rastrigin
(https://en.wikipedia.org/wiki/Rastrigin_function)
>>> from scipy.optimize import dual_annealing
>>> func = lambda x: np.sum(x*x - 10*np.cos(2*np.pi*x)) + 10*np.size(x)
>>> lw = [-5.12] * 10
>>> up = [5.12] * 10
>>> ret = dual_annealing(func, None, bounds=list(zip(lw, up)), seed=1234)
>>> print("global minimum: xmin = {0}, f(xmin) = {1:.6f}".format(
... ret.x, ret.fun))
global minimum: xmin = [-4.26437714e-09 -3.91699361e-09 -1.86149218e-09 -3.97165720e-09
-6.29151648e-09 -6.53145322e-09 -3.93616815e-09 -6.55623025e-09
-6.05775280e-09 -5.00668935e-09], f(xmin) = 0.000000
"""
if x0 is not None and not len(x0) == len(bounds):
raise ValueError('Bounds size does not match x0')
lu = list(zip(*bounds))
lower = np.array(lu[0])
upper = np.array(lu[1])
# Check that restart temperature ratio is correct
if restart_temp_ratio <= 0. or restart_temp_ratio >= 1.:
raise ValueError('Restart temperature ratio has to be in range (0, 1)')
# Checking bounds are valid
if (np.any(np.isinf(lower)) or np.any(np.isinf(upper)) or np.any(
np.isnan(lower)) or np.any(np.isnan(upper))):
raise ValueError('Some bounds values are inf values or nan values')
# Checking that bounds are consistent
if not np.all(lower < upper):
raise ValueError('Bounds are note consistent min < max')
# Wrapper for the objective function
func_wrapper = ObjectiveFunWrapper(func, maxfun, *args)
# Wrapper fot the minimizer
minimizer_wrapper = LocalSearchWrapper(
bounds, func_wrapper, **local_search_options)
# Initialization of RandomState for reproducible runs if seed provided
rand_state = check_random_state(seed)
# Initialization of the energy state
energy_state = EnergyState(lower, upper, callback)
energy_state.reset(func_wrapper, rand_state, x0)
# Minimum value of annealing temperature reached to perform
# re-annealing
temperature_restart = initial_temp * restart_temp_ratio
# VisitingDistribution instance
visit_dist = VisitingDistribution(lower, upper, visit, rand_state)
# Strategy chain instance
strategy_chain = StrategyChain(accept, visit_dist, func_wrapper,
minimizer_wrapper, rand_state, energy_state)
# Run the search loop
need_to_stop = False
iteration = 0
message = []
t1 = np.exp((visit - 1) * np.log(2.0)) - 1.0
while(not need_to_stop):
for i in range(maxiter):
# Compute temperature for this step
s = float(i) + 2.0
t2 = np.exp((visit - 1) * np.log(s)) - 1.0
temperature = initial_temp * t1 / t2
iteration += 1
if iteration >= maxiter:
message.append("Maximum number of iteration reached")
need_to_stop = True
break
# Need a re-annealing process?
if temperature < temperature_restart:
energy_state.reset(func_wrapper, rand_state)
break
# starting strategy chain
val = strategy_chain.run(i, temperature)
if val is not None:
message.append(val)
need_to_stop = True
break
# Possible local search at the end of the strategy chain
if not no_local_search:
val = strategy_chain.local_search()
if val is not None:
message.append(val)
need_to_stop = True
break
# Return the OptimizeResult
res = OptimizeResult()
res.x = energy_state.xbest
res.fun = energy_state.ebest
res.nit = iteration
res.nfev = func_wrapper.nfev
res.njev = func_wrapper.ngev
res.message = message
return res