def _Step(self, cost=None, ExtraArgs=None, **kwds):
"""perform a single optimization iteration
Note that ExtraArgs should be a *tuple* of extra arguments"""
# process and activate input settings
settings = self._process_inputs(kwds)
#(hardwired: due to python3.x exec'ing to locals())
callback = settings['callback'] if 'callback' in settings else None
disp = settings['disp'] if 'disp' in settings else False
strategy = settings[
'strategy'] if 'strategy' in settings else self.strategy
# HACK to enable not explicitly calling _decorate_objective
cost = self._bootstrap_objective(cost, ExtraArgs)
init = False # flag to do 0th iteration 'post-initialization'
if not len(self._stepmon): # do generation = 0
init = True
strategy = None
self.population[0] = asfarray(self.population[0])
# decouple bestSolution from population and bestEnergy from popEnergy
self.bestSolution = self.population[0]
self.bestEnergy = self.popEnergy[0]
for candidate in range(self.nPop):
if not len(self._stepmon):
# generate trialSolution (within valid range)
self.trialSolution[candidate][:] = self.population[candidate]
if strategy:
# generate trialSolution (within valid range)
strategy(self, candidate)
# apply constraints
self.trialSolution[candidate][:] = self._constraints(
self.trialSolution[candidate])
# bind constraints to cost #XXX: apparently imposes constraints poorly
#concost = wrap_nested(cost, self._constraints)
# apply penalty
#trialEnergy = map(self._penalty, self.trialSolution)#,**self._mapconfig)
# calculate cost
trialEnergy = self._map(cost, self.trialSolution, **self._mapconfig)
self._fcalls[0] += len(self.trialSolution) #FIXME: manually increment
# each trialEnergy should be a scalar
if isiterable(trialEnergy[0]) and len(trialEnergy[0]) == 1:
trialEnergy = ravel(trialEnergy)
# for len(trialEnergy) > 1, will throw ValueError below
for candidate in range(self.nPop):
if trialEnergy[candidate] < self.popEnergy[candidate]:
# New low for this candidate
self.popEnergy[candidate] = trialEnergy[candidate]
self.population[candidate][:] = self.trialSolution[candidate]
self.UpdateGenealogyRecords(candidate,
self.trialSolution[candidate][:])
# Check if all-time low
if trialEnergy[candidate] < self.bestEnergy:
self.bestEnergy = trialEnergy[candidate]
self.bestSolution[:] = self.trialSolution[candidate]
# log bestSolution and bestEnergy (includes penalty)
#FIXME: StepMonitor works for 'pp'?
self._stepmon(self.bestSolution[:], self.bestEnergy, self.id)
# if savefrequency matches, then save state
self._AbstractSolver__save_state()
# do callback
if callback is not None: callback(self.bestSolution)
# initialize termination conditions, if needed
if init: self._termination(self) #XXX: at generation 0 or always?
return #XXX: call Terminated ?
def _Step(self, cost=None, ExtraArgs=None, **kwds):
"""perform a single optimization iteration
Note that ExtraArgs should be a *tuple* of extra arguments"""
# process and activate input settings
settings = self._process_inputs(kwds)
for key in settings:
exec "%s = settings['%s']" % (key,key)
# HACK to enable not explicitly calling _decorate_objective
cost = self._bootstrap_objective(cost, ExtraArgs)
init = False # flag to do 0th iteration 'post-initialization'
if not len(self._stepmon): # do generation = 0
init = True
strategy = None
self.population[0] = asfarray(self.population[0])
# decouple bestSolution from population and bestEnergy from popEnergy
self.bestSolution = self.population[0]
self.bestEnergy = self.popEnergy[0]
for candidate in range(self.nPop):
if not len(self._stepmon):
# generate trialSolution (within valid range)
self.trialSolution[candidate][:] = self.population[candidate]
if strategy:
# generate trialSolution (within valid range)
strategy(self, candidate)
# apply constraints
self.trialSolution[candidate][:] = self._constraints(self.trialSolution[candidate])
# bind constraints to cost #XXX: apparently imposes constraints poorly
#concost = wrap_nested(cost, self._constraints)
# apply penalty
#trialEnergy = map(self._penalty, self.trialSolution)#,**self._mapconfig)
# calculate cost
trialEnergy = self._map(cost, self.trialSolution, **self._mapconfig)
self._fcalls[0] += len(self.trialSolution) #FIXME: manually increment
# each trialEnergy should be a scalar
if isiterable(trialEnergy[0]) and len(trialEnergy[0]) == 1:
trialEnergy = ravel(trialEnergy)
# for len(trialEnergy) > 1, will throw ValueError below
for candidate in range(self.nPop):
if trialEnergy[candidate] < self.popEnergy[candidate]:
# New low for this candidate
self.popEnergy[candidate] = trialEnergy[candidate]
self.population[candidate][:] = self.trialSolution[candidate]
self.UpdateGenealogyRecords(candidate, self.trialSolution[candidate][:])
# Check if all-time low
if trialEnergy[candidate] < self.bestEnergy:
self.bestEnergy = trialEnergy[candidate]
self.bestSolution[:] = self.trialSolution[candidate]
# log bestSolution and bestEnergy (includes penalty)
#FIXME: StepMonitor works for 'pp'?
self._stepmon(self.bestSolution[:], self.bestEnergy, self.id)
# if savefrequency matches, then save state
self._AbstractSolver__save_state()
# do callback
if callback is not None: callback(self.bestSolution)
# initialize termination conditions, if needed
if init: self._termination(self) #XXX: at generation 0 or always?
return #XXX: call Terminated ?
def _Step(self, cost=None, ExtraArgs=None, **kwds):
"""perform a single optimization iteration
Note that ExtraArgs should be a *tuple* of extra arguments"""
# process and activate input settings
settings = self._process_inputs(kwds)
for key in settings:
exec "%s = settings['%s']" % (key, key)
# HACK to enable not explicitly calling _decorate_objective
cost = self._bootstrap_objective(cost, ExtraArgs)
init = False # flag to do 0th iteration 'post-initialization'
if not len(self._stepmon): # do generation = 0
init = True
strategy = None
self.population[0] = asfarray(self.population[0])
# decouple bestSolution from population and bestEnergy from popEnergy
self.bestSolution = self.population[0]
self.bestEnergy = self.popEnergy[0]
for candidate in range(self.nPop):
if not len(self._stepmon):
# generate trialSolution (within valid range)
self.trialSolution[:] = self.population[candidate]
if strategy:
# generate trialSolution (within valid range)
strategy(self, candidate)
# apply constraints
self.trialSolution[:] = self._constraints(self.trialSolution)
# apply penalty
#trialEnergy = self._penalty(self.trialSolution)
# calculate cost
trialEnergy = cost(self.trialSolution)
# trialEnergy should be a scalar
if isiterable(trialEnergy) and len(trialEnergy) == 1:
trialEnergy = trialEnergy[0]
# for len(trialEnergy) > 1, will throw ValueError below
if trialEnergy < self.popEnergy[candidate]:
# New low for this candidate
self.popEnergy[candidate] = trialEnergy
self.population[candidate][:] = self.trialSolution
self.UpdateGenealogyRecords(candidate, self.trialSolution[:])
# Check if all-time low
if trialEnergy < self.bestEnergy:
self.bestEnergy = trialEnergy
self.bestSolution[:] = self.trialSolution
# log bestSolution and bestEnergy (includes penalty)
self._stepmon(self.bestSolution[:], self.bestEnergy, self.id)
# if savefrequency matches, then save state
self._AbstractSolver__save_state()
# do callback
if callback is not None: callback(self.bestSolution)
# initialize termination conditions, if needed
if init: self._termination(self) #XXX: at generation 0 or always?
return #XXX: call Terminated ?
def _Step(self, cost=None, ExtraArgs=None, **kwds):
"""perform a single optimization iteration
input::
- cost is the objective function, of the form y = cost(x, *ExtraArgs),
where x is a candidate solution, and ExtraArgs is the tuple of positional
arguments required to evaluate the objective.
note::
ExtraArgs needs to be a *tuple* of extra arguments.
This method accepts additional args that are specific for the current
solver, as detailed in the `_process_inputs` method.
"""
# process and activate input settings
settings = self._process_inputs(kwds)
#(hardwired: due to python3.x exec'ing to locals())
callback = settings['callback'] if 'callback' in settings else None
disp = settings['disp'] if 'disp' in settings else False
strategy = settings[
'strategy'] if 'strategy' in settings else self.strategy
# HACK to enable not explicitly calling _decorate_objective
cost = self._bootstrap_objective(cost, ExtraArgs)
init = False # flag to do 0th iteration 'post-initialization'
if not len(self._stepmon): # do generation = 0
init = True
strategy = None
self.population[0] = asfarray(self.population[0])
# decouple bestSolution from population and bestEnergy from popEnergy
bs = self.population[0]
self.bestSolution = bs.copy() if hasattr(bs, 'copy') else bs[:]
self.bestEnergy = self.popEnergy[0]
del bs
if self._useStrictRange:
from mystic.constraints import and_
constraints = and_(self._constraints,
self._strictbounds,
onfail=self._strictbounds)
else:
constraints = self._constraints
for candidate in range(self.nPop):
if not len(self._stepmon):
# generate trialSolution (within valid range)
self.trialSolution[candidate][:] = self.population[candidate]
if strategy:
# generate trialSolution (within valid range)
strategy(self, candidate)
# apply constraints
self.trialSolution[candidate][:] = constraints(
self.trialSolution[candidate])
# bind constraints to cost #XXX: apparently imposes constraints poorly
#concost = wrap_nested(cost, constraints)
# apply penalty
#trialEnergy = map(self._penalty, self.trialSolution)#,**self._mapconfig)
# calculate cost
trialEnergy = self._map(cost, self.trialSolution, **self._mapconfig)
self._fcalls[0] += len(self.trialSolution) #FIXME: manually increment
# each trialEnergy should be a scalar
if isiterable(trialEnergy[0]) and len(trialEnergy[0]) == 1:
trialEnergy = ravel(trialEnergy)
# for len(trialEnergy) > 1, will throw ValueError below
for candidate in range(self.nPop):
if trialEnergy[candidate] < self.popEnergy[candidate]:
# New low for this candidate
self.popEnergy[candidate] = trialEnergy[candidate]
self.population[candidate][:] = self.trialSolution[candidate]
self.UpdateGenealogyRecords(candidate,
self.trialSolution[candidate][:])
# Check if all-time low
if trialEnergy[candidate] < self.bestEnergy:
self.bestEnergy = trialEnergy[candidate]
self.bestSolution[:] = self.trialSolution[candidate]
# log bestSolution and bestEnergy (includes penalty)
#FIXME: StepMonitor works for 'pp'?
self._stepmon(self.bestSolution[:], self.bestEnergy, self.id)
# if savefrequency matches, then save state
self._AbstractSolver__save_state()
# do callback
if callback is not None: callback(self.bestSolution)
# initialize termination conditions, if needed
if init: self._termination(self) #XXX: at generation 0 or always?
return #XXX: call Terminated ?
def _Step(self, cost=None, ExtraArgs=None, **kwds):
"""perform a single optimization iteration
Note that ExtraArgs should be a *tuple* of extra arguments"""
# process and activate input settings
settings = self._process_inputs(kwds)
#(hardwired: due to python3.x exec'ing to locals())
callback = settings['callback'] if 'callback' in settings else None
disp = settings['disp'] if 'disp' in settings else False
strategy = settings['strategy'] if 'strategy' in settings else self.strategy
# HACK to enable not explicitly calling _decorate_objective
cost = self._bootstrap_objective(cost, ExtraArgs)
init = False # flag to do 0th iteration 'post-initialization'
if not len(self._stepmon): # do generation = 0
init = True
strategy = None
self.population[0] = asfarray(self.population[0])
# decouple bestSolution from population and bestEnergy from popEnergy
self.bestSolution = self.population[0]
self.bestEnergy = self.popEnergy[0]
for candidate in range(self.nPop):
if not len(self._stepmon):
# generate trialSolution (within valid range)
self.trialSolution[:] = self.population[candidate]
if strategy:
# generate trialSolution (within valid range)
strategy(self, candidate)
# apply constraints
self.trialSolution[:] = self._constraints(self.trialSolution)
# apply penalty
#trialEnergy = self._penalty(self.trialSolution)
# calculate cost
trialEnergy = cost(self.trialSolution)
# trialEnergy should be a scalar
if isiterable(trialEnergy) and len(trialEnergy) == 1:
trialEnergy = trialEnergy[0]
# for len(trialEnergy) > 1, will throw ValueError below
if trialEnergy < self.popEnergy[candidate]:
# New low for this candidate
self.popEnergy[candidate] = trialEnergy
self.population[candidate][:] = self.trialSolution
self.UpdateGenealogyRecords(candidate, self.trialSolution[:])
# Check if all-time low
if trialEnergy < self.bestEnergy:
self.bestEnergy = trialEnergy
self.bestSolution[:] = self.trialSolution
# log bestSolution and bestEnergy (includes penalty)
self._stepmon(self.bestSolution[:], self.bestEnergy, self.id)
# if savefrequency matches, then save state
self._AbstractSolver__save_state()
# do callback
if callback is not None: callback(self.bestSolution)
# initialize termination conditions, if needed
if init: self._termination(self) #XXX: at generation 0 or always?
return #XXX: call Terminated ?
