def _InitialPoints(self):
"""Generate a grid of starting points for the ensemble of optimizers"""
npts = self._npts
if len(self._strictMax): upper = list(self._strictMax)
else:
upper = list(self._defaultMax)
if len(self._strictMin): lower = list(self._strictMin)
else:
lower = list(self._defaultMin)
# build a grid of starting points
from mystic.math import samplepts
return samplepts(lower, upper, npts, self._dist)
def _InitialPoints(self):
"""Generate a grid of starting points for the ensemble of optimizers"""
npts = self._npts
if len(self._strictMax):
upper = list(self._strictMax)
else:
upper = list(self._defaultMax)
if len(self._strictMin):
lower = list(self._strictMin)
else:
lower = list(self._defaultMin)
# build a grid of starting points
from mystic.math import samplepts
return samplepts(lower, upper, npts, self._dist)
def Solve(self, cost, termination=None, sigint_callback=None,
ExtraArgs=(), **kwds):
"""Minimize a function using buckshot optimization.
Description:
Uses parallel mapping of solvers on randomly selected points
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.
sigint_callback -- callback function for signal handler.
ExtraArgs -- extra arguments for cost.
Further Inputs:
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
settings = self._process_inputs(kwds)
disp=0
# for key in settings:
# exec "%s = settings['%s']" % (key,key)
if disp in ['verbose', 'all']: verbose = True
else: verbose = False
#-------------------------------------------------------------
import signal
#self._EARLYEXIT = False
#FIXME: EvaluationMonitor fails for MPI, throws error for 'pp'
from python_map import python_map
if self._map != python_map:
self._fcalls = [0] #FIXME: temporary patch for removing the following line
else:
self._fcalls, cost = wrap_function(cost, ExtraArgs, self._evalmon)
#generate signal_handler
self._generateHandler(sigint_callback)
if self._handle_sigint: signal.signal(signal.SIGINT,self.signal_handler)
# register termination function
if termination is not None:
self.SetTermination(termination)
# get the nested solver instance
solver = self._AbstractNestedSolver__get_solver_instance()
#-------------------------------------------------------------
npts = self._npts
if len(self._strictMax): upper = list(self._strictMax)
else:
upper = list(self._defaultMax)
if len(self._strictMin): lower = list(self._strictMin)
else:
lower = list(self._defaultMin)
# generate a set of starting points
from mystic.math import samplepts
initial_values = samplepts(lower,upper,npts)
# 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))]
id = range(len(initial_values))
# generate the local_optimize function
def local_optimize(solver, x0, rank=None, disp=verbose):
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)
return solver
# map:: solver = local_optimize(solver, x0, id, verbose)
results = self._map(local_optimize, op, initial_values, id, \
**self._mapconfig)
# save initial state
self._AbstractSolver__save_state()
# get the results with the lowest energy
self._bestSolver = results[0]
bestpath = self._bestSolver._stepmon
besteval = self._bestSolver._evalmon
self._total_evals = self._bestSolver.evaluations
for solver in results[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?
#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])
#-------------------------------------------------------------
signal.signal(signal.SIGINT,signal.default_int_handler)
# log any termination messages
msg = self.CheckTermination(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,
sigint_callback=None,
ExtraArgs=(),
**kwds):
"""Minimize a function using buckshot optimization.
Description:
Uses parallel mapping of solvers on randomly selected points
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.
sigint_callback -- callback function for signal handler.
ExtraArgs -- extra arguments for cost.
Further Inputs:
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
settings = self._process_inputs(kwds)
disp = 0
# for key in settings:
# exec "%s = settings['%s']" % (key,key)
if disp in ['verbose', 'all']: verbose = True
else: verbose = False
#-------------------------------------------------------------
import signal
#self._EARLYEXIT = False
#FIXME: EvaluationMonitor fails for MPI, throws error for 'pp'
from python_map import python_map
if self._map != python_map:
self._fcalls = [
0
] #FIXME: temporary patch for removing the following line
else:
self._fcalls, cost = wrap_function(cost, ExtraArgs, self._evalmon)
#generate signal_handler
self._generateHandler(sigint_callback)
if self._handle_sigint:
signal.signal(signal.SIGINT, self.signal_handler)
# register termination function
if termination is not None:
self.SetTermination(termination)
# get the nested solver instance
solver = self._AbstractNestedSolver__get_solver_instance()
#-------------------------------------------------------------
npts = self._npts
if len(self._strictMax): upper = list(self._strictMax)
else:
upper = list(self._defaultMax)
if len(self._strictMin): lower = list(self._strictMin)
else:
lower = list(self._defaultMin)
# generate a set of starting points
from mystic.math import samplepts
initial_values = samplepts(lower, upper, npts)
# 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))]
id = range(len(initial_values))
# generate the local_optimize function
def local_optimize(solver, x0, rank=None, disp=verbose):
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)
return solver
# map:: solver = local_optimize(solver, x0, id, verbose)
results = self._map(local_optimize, op, initial_values, id, \
**self._mapconfig)
# save initial state
self._AbstractSolver__save_state()
# get the results with the lowest energy
self._bestSolver = results[0]
bestpath = self._bestSolver._stepmon
besteval = self._bestSolver._evalmon
self._total_evals = self._bestSolver.evaluations
for solver in results[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?
#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])
#-------------------------------------------------------------
signal.signal(signal.SIGINT, signal.default_int_handler)
# log any termination messages
msg = self.CheckTermination(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 function using buckshot optimization.
Description:
Uses parallel mapping of solvers on randomly selected points
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
sigint_callback = kwds.pop('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 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
self._generateHandler(sigint_callback)
# activate signal_handler
import signal
if self._handle_sigint:
signal.signal(signal.SIGINT, self.signal_handler)
# register termination function
if termination is not None:
self.SetTermination(termination)
# get the nested solver instance
solver = self._AbstractEnsembleSolver__get_solver_instance()
#-------------------------------------------------------------
npts = self._npts
if len(self._strictMax): upper = list(self._strictMax)
else:
upper = list(self._defaultMax)
if len(self._strictMin): lower = list(self._strictMin)
else:
lower = list(self._defaultMin)
# generate a set of starting points
from mystic.math import samplepts
initial_values = samplepts(lower, upper, npts)
# 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 = 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 store allSolvers? skip this & _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
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 function using buckshot optimization.
Description:
Uses parallel mapping of solvers on randomly selected points
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
sigint_callback = kwds.pop('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 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
self._generateHandler(sigint_callback)
# activate signal_handler
import signal
if self._handle_sigint: signal.signal(signal.SIGINT,self.signal_handler)
# register termination function
if termination is not None:
self.SetTermination(termination)
# get the nested solver instance
solver = self._AbstractEnsembleSolver__get_solver_instance()
#-------------------------------------------------------------
npts = self._npts
if len(self._strictMax): upper = list(self._strictMax)
else:
upper = list(self._defaultMax)
if len(self._strictMin): lower = list(self._strictMin)
else:
lower = list(self._defaultMin)
# generate a set of starting points
from mystic.math import samplepts
initial_values = samplepts(lower,upper,npts)
# 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 = 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 store allSolvers? skip this & _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
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