def _(params):
import mystic._signal as signal
print("plotting params: %s" % params)
pylab.errorbar(binsc, histo, sqrt(histo), fmt='b+')
x = arange(xmin, xmax, (0.1* binwidth))
pylab.plot(x, pdf(x)*N,'b:')
pylab.plot(x, F(params)(x)*N,'r-')
pylab.xlabel('E (GeV)')
pylab.ylabel('Counts')
try: show()
except ImportError: pylab.show()
if solver is not None:
signal.signal(signal.SIGINT, signal.Handler(solver))
def Solve(self, cost=None, termination=None, ExtraArgs=None, **kwds):
"""Minimize a 'cost' function with given termination conditions.
Uses an optimization algorithm to find the minimum of a function of one or
more variables.
Args:
cost (func, default=None): the function to be minimized: ``y = cost(x)``.
termination (termination, default=None): termination conditions.
ExtraArgs (tuple, default=None): extra arguments for cost.
sigint_callback (func, default=None): callback function for signal handler.
callback (func, default=None): function to call after each iteration. The
interface is ``callback(xk)``, with xk the current parameter vector.
disp (bool, default=False): if True, print convergence messages.
Returns:
None
"""
# process and activate input settings
if 'sigint_callback' in kwds:
self.sigint_callback = kwds['sigint_callback']
del kwds['sigint_callback']
else:
self.sigint_callback = None
settings = self._process_inputs(kwds)
# set up signal handler #FIXME: sigint doesn't behave well in parallel
self._EARLYEXIT = False #XXX: why not use EARLYEXIT singleton?
# activate signal handler
#import threading as thread
#mainthread = isinstance(thread.current_thread(), thread._MainThread)
#if mainthread: #XXX: if not mainthread, signal will raise ValueError
import mystic._signal as signal
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.Handler(self))
# register: cost, termination, ExtraArgs
cost = self._bootstrap_objective(cost, ExtraArgs)
if termination is not None: self.SetTermination(termination)
#XXX: self.Step(cost, termination, ExtraArgs, **settings) ?
# run the optimizer to termination
self._Solve(cost, ExtraArgs, **settings)
# restore default handler for signal interrupts
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.default_int_handler)
return
def _(params):
import mystic._signal as signal
print("plotting params: %s" % params)
# because of the log ordinate axis, will draw errorbars the dumb way
plt.semilogy(data[:, 0], data[:, 1], 'k.')
for i, j in data:
plt.semilogy([i, i], [j - sqrt(j), j + sqrt(j)], 'k-')
plt.grid()
plt.xlabel('Time (s)')
plt.ylabel('Number of counts')
x = arange(15, 900, 5)
f = F(params)
plt.plot(x, f(x), linestyle)
if solver is not None:
signal.signal(signal.SIGINT, signal.Hander(solver))
def _(params):
import mystic._signal as signal
print("plotting params: %s" % params)
# because of the log ordinate axis, will draw errorbars the dumb way
plt.semilogy(data[:,0],data[:,1],'k.')
for i, j in data:
plt.semilogy([i, i], [j-sqrt(j), j+sqrt(j)],'k-')
plt.grid()
plt.xlabel('Time (s)')
plt.ylabel('Number of counts')
x = arange(15, 900, 5)
f = F(params)
plt.plot(x, f(x), linestyle)
if solver is not None:
signal.signal(signal.SIGINT, signal.Hander(solver))
def Solve(self, cost=None, termination=None, ExtraArgs=None, **kwds):
"""Minimize a 'cost' function with given termination conditions.
Uses an optimization algorithm to find the minimum of a function of one or
more variables.
Args:
cost (func, default=None): the function to be minimized: ``y = cost(x)``.
termination (termination, default=None): termination conditions.
ExtraArgs (tuple, default=None): extra arguments for cost.
sigint_callback (func, default=None): callback function for signal handler.
callback (func, default=None): function to call after each iteration. The
interface is ``callback(xk)``, with xk the current parameter vector.
disp (bool, default=False): if True, print convergence messages.
Returns:
None
"""
# process and activate input settings
if 'sigint_callback' in kwds:
self.sigint_callback = kwds['sigint_callback']
del kwds['sigint_callback']
else: self.sigint_callback = None
settings = self._process_inputs(kwds)
# set up signal handler #FIXME: sigint doesn't behave well in parallel
self._EARLYEXIT = False #XXX: why not use EARLYEXIT singleton?
# activate signal handler
#import threading as thread
#mainthread = isinstance(thread.current_thread(), thread._MainThread)
#if mainthread: #XXX: if not mainthread, signal will raise ValueError
import mystic._signal as signal
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.Handler(self))
# register: cost, termination, ExtraArgs
cost = self._bootstrap_objective(cost, ExtraArgs)
if termination is not None: self.SetTermination(termination)
#XXX: self.Step(cost, termination, ExtraArgs, **settings) ?
# run the optimizer to termination
self._Solve(cost, ExtraArgs, **settings)
# restore default handler for signal interrupts
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.default_int_handler)
return
def Solve(self, cost=None, termination=None, ExtraArgs=None, **kwds):
"""Minimize a 'cost' function with given termination conditions.
Uses an optimization algorithm to find the minimum of a function of one or
more variables.
Args:
cost (func, default=None): the function to be minimized: ``y = cost(x)``.
termination (termination, default=None): termination conditions.
ExtraArgs (tuple, default=None): extra arguments for cost.
sigint_callback (func, default=None): callback function for signal handler.
callback (func, default=None): function to call after each iteration. The
interface is ``callback(xk)``, with xk the current parameter vector.
disp (bool, default=False): if True, print convergence messages.
Returns:
None
"""
# process and activate input settings
if 'sigint_callback' in kwds:
self.sigint_callback = kwds['sigint_callback']
del kwds['sigint_callback']
else:
self.sigint_callback = None
settings = self._process_inputs(kwds)
disp = settings['disp'] if 'disp' in settings else False
# set up signal handler
self._EARLYEXIT = False #XXX: why not use EARLYEXIT singleton?
# activate signal handler
#import threading as thread
#mainthread = isinstance(thread.current_thread(), thread._MainThread)
#if mainthread: #XXX: if not mainthread, signal will raise ValueError
import mystic._signal as signal
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.Handler(self))
# register: cost, termination, ExtraArgs
cost = self._bootstrap_objective(cost, ExtraArgs)
if termination is not None: self.SetTermination(termination)
#XXX: self.Step(cost, termination, ExtraArgs, **settings) ?
# the main optimization loop
stop = False
while not stop:
stop = self.Step(**settings) #XXX: remove need to pass settings?
continue
# if collapse, then activate any relevant collapses and continue
self.__stop__ = stop #HACK: avoid re-evaluation of Termination
while self._collapse and self.Collapse(disp=disp):
del self.__stop__ #HACK
stop = False
while not stop:
stop = self.Step(**
settings) #XXX: move Collapse inside of Step?
continue
self.__stop__ = stop #HACK
del self.__stop__ #HACK
# restore default handler for signal interrupts
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.default_int_handler)
return
def Solve(self, cost, termination=None, ExtraArgs=(), **kwds):
"""Minimize a 'cost' function with given termination conditions.
Description:
Uses an ensemble of optimizers to find the minimum of
a function of one or more variables.
Inputs:
cost -- the Python function or method to be minimized.
Additional Inputs:
termination -- callable object providing termination conditions.
ExtraArgs -- extra arguments for cost.
Further Inputs:
sigint_callback -- callback function for signal handler.
callback -- an optional user-supplied function to call after each
iteration. It is called as callback(xk), where xk is the
current parameter vector. [default = None]
disp -- non-zero to print convergence messages. [default = 0]
"""
# process and activate input settings
if 'sigint_callback' in kwds:
self.sigint_callback = kwds['sigint_callback']
del kwds['sigint_callback']
else: self.sigint_callback = None
settings = self._process_inputs(kwds)
disp = settings['disp'] if 'disp' in settings else False
echo = settings['callback'] if 'callback' in settings else None
# for key in settings:
# exec "%s = settings['%s']" % (key,key)
if disp in ['verbose', 'all']: verbose = True
else: verbose = False
#-------------------------------------------------------------
from mystic.python_map import python_map
if self._map != python_map:
#FIXME: EvaluationMonitor fails for MPI, throws error for 'pp'
from mystic.monitors import Null
evalmon = Null()
else: evalmon = self._evalmon
fcalls, cost = wrap_function(cost, ExtraArgs, evalmon)
# set up signal handler
#self._EARLYEXIT = False
# activate signal_handler
#import threading as thread
#mainthread = isinstance(thread.current_thread(), thread._MainThread)
#if mainthread: #XXX: if not mainthread, signal will raise ValueError
import mystic._signal as signal
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.Handler(self))
# register termination function
if termination is not None: self.SetTermination(termination)
# get the nested solver instance
solver = self._AbstractEnsembleSolver__get_solver_instance()
#-------------------------------------------------------------
# generate starting points
initial_values = self._InitialPoints()
# run optimizer for each grid point
from copy import deepcopy as _copy
op = [_copy(solver) for i in range(len(initial_values))]
#cf = [cost for i in range(len(initial_values))]
vb = [verbose for i in range(len(initial_values))]
cb = [echo for i in range(len(initial_values))] #XXX: remove?
at = self.id if self.id else 0 # start at self.id
id = range(at,at+len(initial_values))
# generate the local_optimize function
def local_optimize(solver, x0, rank=None, disp=False, callback=None):
from copy import deepcopy as _copy
from mystic.tools import isNull
solver.id = rank
solver.SetInitialPoints(x0)
if solver._useStrictRange: #XXX: always, settable, or sync'd ?
solver.SetStrictRanges(min=solver._strictMin, \
max=solver._strictMax) # or lower,upper ?
solver.Solve(cost, disp=disp, callback=callback)
sm = solver._stepmon
em = solver._evalmon
if isNull(sm): sm = ([],[],[],[])
else: sm = (_copy(sm._x),_copy(sm._y),_copy(sm._id),_copy(sm._info))
if isNull(em): em = ([],[],[],[])
else: em = (_copy(em._x),_copy(em._y),_copy(em._id),_copy(em._info))
return solver, sm, em
# map:: solver = local_optimize(solver, x0, id, verbose)
results = list(self._map(local_optimize, op, initial_values, id, \
vb, cb, **self._mapconfig))
# save initial state
self._AbstractSolver__save_state()
#XXX: HACK TO GET CONTENT OF ALL MONITORS
# reconnect monitors; save all solvers
from mystic.monitors import Monitor
while results: #XXX: option to not save allSolvers? skip this and _copy
_solver, _stepmon, _evalmon = results.pop()
sm = Monitor()
sm._x,sm._y,sm._id,sm._info = _stepmon
_solver._stepmon.extend(sm)
del sm
em = Monitor()
em._x,em._y,em._id,em._info = _evalmon
_solver._evalmon.extend(em)
del em
self._allSolvers[len(results)] = _solver
del results, _solver, _stepmon, _evalmon
#XXX: END HACK
# get the results with the lowest energy
self._bestSolver = self._allSolvers[0]
bestpath = self._bestSolver._stepmon
besteval = self._bestSolver._evalmon
self._total_evals = self._bestSolver.evaluations
for solver in self._allSolvers[1:]:
self._total_evals += solver.evaluations # add func evals
if solver.bestEnergy < self._bestSolver.bestEnergy:
self._bestSolver = solver
bestpath = solver._stepmon
besteval = solver._evalmon
# return results to internals
self.population = self._bestSolver.population #XXX: pointer? copy?
self.popEnergy = self._bestSolver.popEnergy #XXX: pointer? copy?
self.bestSolution = self._bestSolver.bestSolution #XXX: pointer? copy?
self.bestEnergy = self._bestSolver.bestEnergy
self.trialSolution = self._bestSolver.trialSolution #XXX: pointer? copy?
self._fcalls = self._bestSolver._fcalls #XXX: pointer? copy?
self._maxiter = self._bestSolver._maxiter
self._maxfun = self._bestSolver._maxfun
# write 'bests' to monitors #XXX: non-best monitors may be useful too
self._stepmon = bestpath #XXX: pointer? copy?
self._evalmon = besteval #XXX: pointer? copy?
self.energy_history = None
self.solution_history = None
#from mystic.tools import isNull
#if isNull(bestpath):
# self._stepmon = bestpath
#else:
# for i in range(len(bestpath.y)):
# self._stepmon(bestpath.x[i], bestpath.y[i], self.id)
# #XXX: could apply callback here, or in exec'd code
#if isNull(besteval):
# self._evalmon = besteval
#else:
# for i in range(len(besteval.y)):
# self._evalmon(besteval.x[i], besteval.y[i])
#-------------------------------------------------------------
# restore default handler for signal interrupts
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.default_int_handler)
# log any termination messages
msg = self.Terminated(disp=disp, info=True)
if msg: self._stepmon.info('STOP("%s")' % msg)
# save final state
self._AbstractSolver__save_state(force=True)
return
def Solve(self, cost, termination=None, ExtraArgs=(), **kwds):
"""Minimize a 'cost' function with given termination conditions.
Uses an ensemble of optimizers to find the minimum of a function of one or
more variables.
Args:
cost (func, default=None): the function to be minimized: ``y = cost(x)``.
termination (termination, default=None): termination conditions.
ExtraArgs (tuple, default=None): extra arguments for cost.
sigint_callback (func, default=None): callback function for signal handler.
callback (func, default=None): function to call after each iteration. The
interface is ``callback(xk)``, with xk the current parameter vector.
disp (bool, default=False): if True, print convergence messages.
Returns:
None
"""
# process and activate input settings
if 'sigint_callback' in kwds:
self.sigint_callback = kwds['sigint_callback']
del kwds['sigint_callback']
else:
self.sigint_callback = None
settings = self._process_inputs(kwds)
disp = settings['disp'] if 'disp' in settings else False
echo = settings['callback'] if 'callback' in settings else None
# for key in settings:
# exec "%s = settings['%s']" % (key,key)
if disp in ['verbose', 'all']: verbose = True
else: verbose = False
#-------------------------------------------------------------
from mystic.python_map import python_map
if self._map != python_map:
#FIXME: EvaluationMonitor fails for MPI, throws error for 'pp'
from mystic.monitors import Null
evalmon = Null()
else:
evalmon = self._evalmon
fcalls, cost = wrap_function(cost, ExtraArgs, evalmon)
# set up signal handler
#self._EARLYEXIT = False
# activate signal_handler
#import threading as thread
#mainthread = isinstance(thread.current_thread(), thread._MainThread)
#if mainthread: #XXX: if not mainthread, signal will raise ValueError
import mystic._signal as signal
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.Handler(self))
# register termination function
if termination is not None: self.SetTermination(termination)
# get the nested solver instance
solver = self._AbstractEnsembleSolver__get_solver_instance()
#-------------------------------------------------------------
# generate starting points
initial_values = self._InitialPoints()
# run optimizer for each grid point
from copy import deepcopy as _copy
op = [_copy(solver) for i in range(len(initial_values))]
#cf = [cost for i in range(len(initial_values))]
vb = [verbose for i in range(len(initial_values))]
cb = [echo for i in range(len(initial_values))] #XXX: remove?
at = self.id if self.id else 0 # start at self.id
id = range(at, at + len(initial_values))
# generate the local_optimize function
def local_optimize(solver, x0, rank=None, disp=False, callback=None):
from copy import deepcopy as _copy
from mystic.tools import isNull
solver.id = rank
solver.SetInitialPoints(x0)
if solver._useStrictRange: #XXX: always, settable, or sync'd ?
solver.SetStrictRanges(min=solver._strictMin, \
max=solver._strictMax) # or lower,upper ?
solver.Solve(cost, disp=disp, callback=callback)
sm = solver._stepmon
em = solver._evalmon
if isNull(sm): sm = ([], [], [], [])
else:
sm = (_copy(sm._x), _copy(sm._y), _copy(sm._id),
_copy(sm._info))
if isNull(em): em = ([], [], [], [])
else:
em = (_copy(em._x), _copy(em._y), _copy(em._id),
_copy(em._info))
return solver, sm, em
# map:: solver = local_optimize(solver, x0, id, verbose)
results = list(self._map(local_optimize, op, initial_values, id, \
vb, cb, **self._mapconfig))
# save initial state
self._AbstractSolver__save_state()
#XXX: HACK TO GET CONTENT OF ALL MONITORS
# reconnect monitors; save all solvers
from mystic.monitors import Monitor
while results: #XXX: option to not save allSolvers? skip this and _copy
_solver, _stepmon, _evalmon = results.pop()
sm = Monitor()
sm._x, sm._y, sm._id, sm._info = _stepmon
_solver._stepmon.extend(sm)
del sm
em = Monitor()
em._x, em._y, em._id, em._info = _evalmon
_solver._evalmon.extend(em)
del em
self._allSolvers[len(results)] = _solver
del results, _solver, _stepmon, _evalmon
#XXX: END HACK
# get the results with the lowest energy
self._bestSolver = self._allSolvers[0]
bestpath = self._bestSolver._stepmon
besteval = self._bestSolver._evalmon
self._total_evals = self._bestSolver.evaluations
for solver in self._allSolvers[1:]:
self._total_evals += solver.evaluations # add func evals
if solver.bestEnergy < self._bestSolver.bestEnergy:
self._bestSolver = solver
bestpath = solver._stepmon
besteval = solver._evalmon
# return results to internals
self.population = self._bestSolver.population #XXX: pointer? copy?
self.popEnergy = self._bestSolver.popEnergy #XXX: pointer? copy?
self.bestSolution = self._bestSolver.bestSolution #XXX: pointer? copy?
self.bestEnergy = self._bestSolver.bestEnergy
self.trialSolution = self._bestSolver.trialSolution #XXX: pointer? copy?
self._fcalls = self._bestSolver._fcalls #XXX: pointer? copy?
self._maxiter = self._bestSolver._maxiter
self._maxfun = self._bestSolver._maxfun
# write 'bests' to monitors #XXX: non-best monitors may be useful too
self._stepmon = bestpath #XXX: pointer? copy?
self._evalmon = besteval #XXX: pointer? copy?
self.energy_history = None
self.solution_history = None
#from mystic.tools import isNull
#if isNull(bestpath):
# self._stepmon = bestpath
#else:
# for i in range(len(bestpath.y)):
# self._stepmon(bestpath.x[i], bestpath.y[i], self.id)
# #XXX: could apply callback here, or in exec'd code
#if isNull(besteval):
# self._evalmon = besteval
#else:
# for i in range(len(besteval.y)):
# self._evalmon(besteval.x[i], besteval.y[i])
#-------------------------------------------------------------
# restore default handler for signal interrupts
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.default_int_handler)
# log any termination messages
msg = self.Terminated(disp=disp, info=True)
if msg: self._stepmon.info('STOP("%s")' % msg)
# save final state
self._AbstractSolver__save_state(force=True)
return
def Solve(self, cost=None, termination=None, ExtraArgs=None, **kwds):
"""Minimize a 'cost' function with given termination conditions.
Description:
Uses an optimization algorithm to find the minimum of
a function of one or more variables.
Inputs:
cost -- the Python function or method to be minimized.
Additional Inputs:
termination -- callable object providing termination conditions.
ExtraArgs -- extra arguments for cost.
Further Inputs:
sigint_callback -- callback function for signal handler.
callback -- an optional user-supplied function to call after each
iteration. It is called as callback(xk), where xk is
the current parameter vector. [default = None]
disp -- non-zero to print convergence messages.
"""
# process and activate input settings
if 'sigint_callback' in kwds:
self.sigint_callback = kwds['sigint_callback']
del kwds['sigint_callback']
else: self.sigint_callback = None
settings = self._process_inputs(kwds)
disp = settings['disp'] if 'disp' in settings else False
# set up signal handler
self._EARLYEXIT = False #XXX: why not use EARLYEXIT singleton?
# activate signal handler
#import threading as thread
#mainthread = isinstance(thread.current_thread(), thread._MainThread)
#if mainthread: #XXX: if not mainthread, signal will raise ValueError
import mystic._signal as signal
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.Handler(self))
# register: cost, termination, ExtraArgs
cost = self._bootstrap_objective(cost, ExtraArgs)
if termination is not None: self.SetTermination(termination)
#XXX: self.Step(cost, termination, ExtraArgs, **settings) ?
# the main optimization loop
stop = False
while not stop:
stop = self.Step(**settings) #XXX: remove need to pass settings?
continue
# if collapse, then activate any relevant collapses and continue
self.__stop__ = stop #HACK: avoid re-evaluation of Termination
while self._collapse and self.Collapse(disp=disp):
del self.__stop__ #HACK
stop = False
while not stop:
stop = self.Step(**settings) #XXX: move Collapse inside of Step?
continue
self.__stop__ = stop #HACK
del self.__stop__ #HACK
# restore default handler for signal interrupts
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.default_int_handler)
return
def Solve(self, cost=None, termination=None, ExtraArgs=None, **kwds):
"""Minimize a 'cost' function with given termination conditions.
Uses an optimization algorithm to find the minimum of a function of one or
more variables.
Args:
cost (func, default=None): the function to be minimized: ``y = cost(x)``.
termination (termination, default=None): termination conditions.
ExtraArgs (tuple, default=None): extra arguments for cost.
sigint_callback (func, default=None): callback function for signal handler.
callback (func, default=None): function to call after each iteration. The
interface is ``callback(xk)``, with xk the current parameter vector.
disp (bool, default=False): if True, print convergence messages.
Returns:
None
"""
# process and activate input settings
if 'sigint_callback' in kwds:
self.sigint_callback = kwds['sigint_callback']
del kwds['sigint_callback']
else: self.sigint_callback = None
settings = self._process_inputs(kwds)
disp = settings['disp'] if 'disp' in settings else False
# set up signal handler
self._EARLYEXIT = False #XXX: why not use EARLYEXIT singleton?
# activate signal handler
#import threading as thread
#mainthread = isinstance(thread.current_thread(), thread._MainThread)
#if mainthread: #XXX: if not mainthread, signal will raise ValueError
import mystic._signal as signal
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.Handler(self))
# register: cost, termination, ExtraArgs
cost = self._bootstrap_objective(cost, ExtraArgs)
if termination is not None: self.SetTermination(termination)
#XXX: self.Step(cost, termination, ExtraArgs, **settings) ?
# the main optimization loop
stop = False
while not stop:
stop = self.Step(**settings) #XXX: remove need to pass settings?
continue
# if collapse, then activate any relevant collapses and continue
self.__stop__ = stop #HACK: avoid re-evaluation of Termination
while self._collapse and self.Collapse(disp=disp):
del self.__stop__ #HACK
stop = False
while not stop:
stop = self.Step(**settings) #XXX: move Collapse inside of Step?
continue
self.__stop__ = stop #HACK
del self.__stop__ #HACK
# restore default handler for signal interrupts
if self._handle_sigint:
signal.signal(signal.SIGINT, signal.default_int_handler)
return
