本文整理汇总了Python中scipy.optimize.OptimizeResult.jac方法的典型用法代码示例。如果您正苦于以下问题：Python OptimizeResult.jac方法的具体用法？Python OptimizeResult.jac怎么用？Python OptimizeResult.jac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。
在下文中一共展示了OptimizeResult.jac方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: solve
# 需要导入模块: from scipy.optimize import OptimizeResult [as 别名]
# 或者: from scipy.optimize.OptimizeResult import jac [as 别名]
def solve(self):
"""
Runs the DifferentialEvolutionSolver.
Returns
-------
res : OptimizeResult
The optimization result represented as a ``OptimizeResult`` object.
Important attributes are: ``x`` the solution array, ``success`` a
Boolean flag indicating if the optimizer exited successfully and
``message`` which describes the cause of the termination. See
`OptimizeResult` for a description of other attributes. If polish
was employed, then OptimizeResult also contains the ``hess_inv`` and
``jac`` attributes.
"""
nfev, nit, warning_flag = 0, 0, False
status_message = _status_message['success']
# calculate energies to start with
for index, candidate in enumerate(self.population):
parameters = self._scale_parameters(candidate)
self.population_energies[index] = self.func(parameters,
*self.args)
nfev += 1
if nfev > self.maxfun:
warning_flag = True
status_message = _status_message['maxfev']
break
minval = np.argmin(self.population_energies)
# put the lowest energy into the best solution position.
lowest_energy = self.population_energies[minval]
self.population_energies[minval] = self.population_energies[0]
self.population_energies[0] = lowest_energy
self.population[[0, minval], :] = self.population[[minval, 0], :]
if warning_flag:
return OptimizeResult(
x=self.x,
fun=self.population_energies[0],
nfev=nfev,
nit=nit,
message=status_message,
success=(warning_flag != True))
# do the optimisation.
for nit in range(1, self.maxiter + 1):
if self.dither is not None:
self.scale = self.random_number_generator.rand(
) * (self.dither[1] - self.dither[0]) + self.dither[0]
for candidate in range(np.size(self.population, 0)):
if nfev > self.maxfun:
warning_flag = True
status_message = _status_message['maxfev']
break
trial = self._mutate(candidate)
self._ensure_constraint(trial)
parameters = self._scale_parameters(trial)
energy = self.func(parameters, *self.args)
nfev += 1
if energy < self.population_energies[candidate]:
self.population[candidate] = trial
self.population_energies[candidate] = energy
if energy < self.population_energies[0]:
self.population_energies[0] = energy
self.population[0] = trial
# stop when the fractional s.d. of the population is less than tol
# of the mean energy
convergence = (np.std(self.population_energies) /
np.abs(np.mean(self.population_energies) +
_MACHEPS))
if self.disp:
print("differential_evolution step %d: f(x)= %g"
% (nit,
self.population_energies[0]))
if (self.callback and
self.callback(self._scale_parameters(self.population[0]),
convergence=self.tol / convergence) is True):
warning_flag = True
status_message = ('callback function requested stop early '
'by returning True')
break
if convergence < self.tol or warning_flag:
break
else:
status_message = _status_message['maxiter']
#.........这里部分代码省略.........
示例2: solve
# 需要导入模块: from scipy.optimize import OptimizeResult [as 别名]
# 或者: from scipy.optimize.OptimizeResult import jac [as 别名]
def solve(self):
"""
Runs the DifferentialEvolutionSolver.
Returns
-------
res : OptimizeResult
The optimization result represented as a ``OptimizeResult`` object.
Important attributes are: ``x`` the solution array, ``success`` a
Boolean flag indicating if the optimizer exited successfully and
``message`` which describes the cause of the termination. See
`OptimizeResult` for a description of other attributes. If `polish`
was employed, and a lower minimum was obtained by the polishing,
then OptimizeResult also contains the ``jac`` attribute.
"""
nit, warning_flag = 0, False
status_message = _status_message['success']
# The population may have just been initialized (all entries are
# np.inf). If it has you have to calculate the initial energies.
# Although this is also done in the evolve generator it's possible
# that someone can set maxiter=0, at which point we still want the
# initial energies to be calculated (the following loop isn't run).
if np.all(np.isinf(self.population_energies)):
self.population_energies[:] = self._calculate_population_energies(
self.population)
self._promote_lowest_energy()
# do the optimisation.
for nit in xrange(1, self.maxiter + 1):
# evolve the population by a generation
try:
next(self)
except StopIteration:
warning_flag = True
if self._nfev > self.maxfun:
status_message = _status_message['maxfev']
elif self._nfev == self.maxfun:
status_message = ('Maximum number of function evaluations'
' has been reached.')
break
if self.disp:
print("differential_evolution step %d: f(x)= %g"
% (nit,
self.population_energies[0]))
# should the solver terminate?
convergence = self.convergence
if (self.callback and
self.callback(self._scale_parameters(self.population[0]),
convergence=self.tol / convergence) is True):
warning_flag = True
status_message = ('callback function requested stop early '
'by returning True')
break
if np.any(np.isinf(self.population_energies)):
intol = False
else:
intol = (np.std(self.population_energies) <=
self.atol +
self.tol * np.abs(np.mean(self.population_energies)))
if warning_flag or intol:
break
else:
status_message = _status_message['maxiter']
warning_flag = True
DE_result = OptimizeResult(
x=self.x,
fun=self.population_energies[0],
nfev=self._nfev,
nit=nit,
message=status_message,
success=(warning_flag is not True))
if self.polish:
result = minimize(self.func,
np.copy(DE_result.x),
method='L-BFGS-B',
bounds=self.limits.T)
self._nfev += result.nfev
DE_result.nfev = self._nfev
if result.fun < DE_result.fun:
DE_result.fun = result.fun
DE_result.x = result.x
DE_result.jac = result.jac
# to keep internal state consistent
self.population_energies[0] = result.fun
self.population[0] = self._unscale_parameters(result.x)
return DE_result示例3: solve
# 需要导入模块: from scipy.optimize import OptimizeResult [as 别名]
# 或者: from scipy.optimize.OptimizeResult import jac [as 别名]
#.........这里部分代码省略.........
self.population[[0, minval], :] = self.population[[minval, 0], :]
if warning_flag:
return OptimizeResult(
x=self.x,
fun=self.population_energies[0],
nfev=nfev,
nit=nit,
message=status_message,
success=(warning_flag is not True))
# do the optimisation.
start_time = time.time()
nit = 0
while nit < self.maxiter + 1:
nit += 1
if start_time + self.max_execution_time < time.time():
warning_flag = True
status_message = 'Max execution time reached'
break
if self.dither is not None:
self.scale = self.random_number_generator.rand(
) * (self.dither[1] - self.dither[0]) + self.dither[0]
for candidate in range(np.size(self.population, 0)):
if nfev > self.maxfun:
warning_flag = True
status_message = _status_message['maxfev']
break
trial = self._mutate(candidate)
self._ensure_constraint(trial)
parameters = self._scale_parameters(trial)
energy = self.func(parameters, *self.args)
nfev += 1
if energy < self.population_energies[candidate]:
self.population[candidate] = trial
self.population_energies[candidate] = energy
if energy < self.population_energies[0]:
self.population_energies[0] = energy
self.population[0] = trial
# stop when the fractional s.d. of the population is less than tol
# of the mean energy
convergence = (np.std(self.population_energies) /
np.abs(np.mean(self.population_energies) +
_MACHEPS))
if self.disp:
print("differential_evolution step %d: f(x)= %g"
% (nit,
self.population_energies[0]))
if (self.callback and
self.callback(self._scale_parameters(self.population[0]),
convergence=self.tol / convergence) is True):
warning_flag = True
status_message = ('callback function requested stop early '
'by returning True')
break
if convergence < self.tol or warning_flag:
break
else:
status_message = _status_message['maxiter']
warning_flag = True
DE_result = OptimizeResult(
x=self.x,
fun=self.population_energies[0],
nfev=nfev,
nit=nit,
message=status_message,
success=(warning_flag is not True))
if self.polish:
result = minimize(self.func,
np.copy(DE_result.x),
method='L-BFGS-B',
bounds=self.limits.T,
args=self.args)
nfev += result.nfev
DE_result.nfev = nfev
if result.fun < DE_result.fun:
DE_result.fun = result.fun
DE_result.x = result.x
DE_result.jac = result.jac
# to keep internal state consistent
self.population_energies[0] = result.fun
self.population[0] = self._unscale_parameters(result.x)
return DE_result