def Collapse(self, disp=False):
"""if solver has terminated by collapse, apply the collapse
(unless both collapse and "stop" are simultaneously satisfied)
"""
#XXX: return True for "collapse and continue" and False otherwise?
collapses = self.Collapsed(disp=disp, info=True)
if collapses: # stop if any Termination is not from Collapse
stop = getattr(self, '__stop__', self.Terminated(info=True))
stop = not all(k.startswith("Collapse") for k in stop.split("; "))
if stop: return {} #XXX: self._collapse = False ?
else: stop = True
if collapses: # then stomach a bunch of module imports (yuck)
import mystic.tools as to
import mystic.termination as mt
import mystic.constraints as cn
import mystic.mask as ma
# get collapse conditions #XXX: efficient? 4x loops over collapses
state = mt.state(self._termination)
npts = getattr(self._stepmon, '_npts', None) #XXX: default?
#conditions = [cn.impose_at(*to.select_params(self,collapses[k])) if state[k].get('target') is None else cn.impose_at(collapses[k],state[k].get('target')) for k in collapses if k.startswith('CollapseAt')]
#conditions += [cn.impose_as(collapses[k],state[k].get('offset')) for k in collapses if k.startswith('CollapseAs')]
#randomize = False
conditions = []; _conditions = []; conditions_ = []
for k in collapses:
#FIXME: these should be encapsulted in termination instance
if k.startswith('CollapseAt'):
t = state[k]
t = t['target'] if 'target' in t else None
if t is None:
t = cn.impose_at(*to.select_params(self,collapses[k]))
else:
t = cn.impose_at(collapses[k],t)
conditions.append(t)
elif k.startswith('CollapseAs'):
t = state[k]
t = t['offset'] if 'offset' in t else None
_conditions.append(cn.impose_as(collapses[k],t))
elif k.startswith(('CollapseCost','CollapseGrad')):
t = state[k]
t = t['clip'] if 'clip' in t else True
conditions_.append(cn.impose_bounds(collapses[k],clip=t))
#randomize = True
conditions.extend(_conditions)
conditions.extend(conditions_)
del _conditions; del conditions_
# get measure collapse conditions
if npts: #XXX: faster/better if comes first or last?
conditions += [cn.impose_measure( npts, [collapses[k] for k in collapses if k.startswith('CollapsePosition')], [collapses[k] for k in collapses if k.startswith('CollapseWeight')] )]
# update termination and constraints in solver
constraints = to.chain(*conditions)(self._constraints)
termination = ma.update_mask(self._termination, collapses)
self.SetConstraints(constraints)
self.SetTermination(termination)
#if randomize: self.SetInitialPoints(self.population[0])
#print(mt.state(self._termination).keys())
#return bool(collapses) and not stop
return collapses
def Collapse(self, disp=False):
"""if solver has terminated by collapse, apply the collapse
(unless both collapse and "stop" are simultaneously satisfied)
"""
#XXX: return True for "collapse and continue" and False otherwise?
collapses = self.Collapsed(disp=disp, info=True)
if collapses: # stop if any Termination is not from Collapse
stop = getattr(self, '__stop__', self.Terminated(info=True))
stop = not all(k.startswith("Collapse") for k in stop.split("; "))
if stop: return {} #XXX: self._collapse = False ?
else: stop = True
if collapses: # then stomach a bunch of module imports (yuck)
import mystic.tools as to
import mystic.termination as mt
import mystic.constraints as cn
import mystic.mask as ma
# get collapse conditions #XXX: efficient? 4x loops over collapses
state = mt.state(self._termination)
npts = getattr(self._stepmon, '_npts', None) #XXX: default?
#conditions = [cn.impose_at(*to.select_params(self,collapses[k])) if state[k].get('target') is None else cn.impose_at(collapses[k],state[k].get('target')) for k in collapses if k.startswith('CollapseAt')]
#conditions += [cn.impose_as(collapses[k],state[k].get('offset')) for k in collapses if k.startswith('CollapseAs')]
#randomize = False
conditions = []; _conditions = []; conditions_ = []
for k in collapses:
if k.startswith('CollapseAt'):
t = state[k]
t = t['target'] if 'target' in t else None
if t is None:
t = cn.impose_at(*to.select_params(self,collapses[k]))
else:
t = cn.impose_at(collapses[k],t)
conditions.append(t)
elif k.startswith('CollapseAs'):
t = state[k]
t = t['offset'] if 'offset' in t else None
_conditions.append(cn.impose_as(collapses[k],t))
elif k.startswith('CollapseCost'):
t = state[k]
t = t['clip'] if 'clip' in t else True
conditions_.append(cn.impose_bounds(collapses[k],clip=t))
#randomize = True
conditions.extend(_conditions)
conditions.extend(conditions_)
del _conditions; del conditions_
# get measure collapse conditions
if npts: #XXX: faster/better if comes first or last?
conditions += [cn.impose_measure( npts, [collapses[k] for k in collapses if k.startswith('CollapsePosition')], [collapses[k] for k in collapses if k.startswith('CollapseWeight')] )]
# update termination and constraints in solver
constraints = to.chain(*conditions)(self._constraints)
termination = ma.update_mask(self._termination, collapses)
self.SetConstraints(constraints)
self.SetTermination(termination)
#if randomize: self.SetInitialPoints(self.population[0])
#print(mt.state(self._termination).keys())
#return bool(collapses) and not stop
return collapses
def __collapse_constraints(self, state, collapses):
"""get updated constraints for the given state and collapses"""
import mystic.tools as to
import mystic.constraints as cn
# get collapse conditions #XXX: efficient? 4x loops over collapses
npts = getattr(self._stepmon, '_npts', None) #XXX: default?
#conditions = [cn.impose_at(*to.select_params(self,collapses[k])) if state[k].get('target') is None else cn.impose_at(collapses[k],state[k].get('target')) for k in collapses if k.startswith('CollapseAt')]
#conditions += [cn.impose_as(collapses[k],state[k].get('offset')) for k in collapses if k.startswith('CollapseAs')]
#randomize = False
conditions = []
_conditions = []
conditions_ = []
for k in collapses:
#FIXME: these should be encapsulted in termination instance
if k.startswith('CollapseAt'):
t = state[k]
t = t['target'] if 'target' in t else None
if t is None:
t = cn.impose_at(*to.select_params(self, collapses[k]))
else:
t = cn.impose_at(collapses[k], t)
conditions.append(t)
elif k.startswith('CollapseAs'):
t = state[k]
t = t['offset'] if 'offset' in t else None
_conditions.append(cn.impose_as(collapses[k], t))
elif k.startswith(('CollapseCost', 'CollapseGrad')):
t = state[k]
t = t['clip'] if 'clip' in t else True
conditions_.append(cn.impose_bounds(collapses[k], clip=t))
#randomize = True
conditions.extend(_conditions)
conditions.extend(conditions_)
del _conditions
del conditions_
# get measure collapse conditions
if npts: #XXX: faster/better if comes first or last?
conditions += [
cn.impose_measure(npts, [
collapses[k]
for k in collapses if k.startswith('CollapsePosition')
], [
collapses[k]
for k in collapses if k.startswith('CollapseWeight')
])
]
# get updated constraints
return to.chain(*conditions)(self._constraints)
shift = 0
scale = 0
step = 200
args = {
#'smooth': 0,
'method': 'thin_plate',
'extrap': False,
'arrays': False,
}
#surface.doit(bounds, stop, step=step)
#'multiquadric','inverse','gaussian','linear','cubic','quintic','thin_plate'
# impose data filter (so X >= 0 and Y >= 0)
from mystic.filters import generate_mask, generate_filter
from mystic.constraints import impose_bounds
_bounds = impose_bounds((0.0, None))(lambda x:x)
filter = generate_filter(generate_mask(_bounds, _bounds))
# from numpy import round as _round
#############
# get trajectories
surface.Sample(bounds, stop, all=all, filter=filter)#, constraints=_round)
print("TOOK: %s" % (time.time() - start))
# exit()
# get interpolated function
surface.Interpolate(**args)
# check extrema #XXX: put _min,_max in Interpolate? (downsampled)
f = lambda x,z: (z,surface.surrogate(*x))
print("min: {}; min@f: {}".format(*f(*surface._min())))
print("max: {}; max@f: {}".format(*f(*surface._max())))
