def Interpolate(self, **kwds): #XXX: refactor so use self.interpolator ?
"""interpolate data (x,z) to generate response function z=f(*x)
Input:
maxpts: int, maximum number of points to use from (x,z)
noise: float, amplitude of gaussian noise to remove duplicate x
method: string for kind of interpolator
extrap: if True, extrapolate a bounding box (can reduce # of nans)
arrays: if True, return a numpy array; otherwise don't return arrays
Output:
interpolated response function, where z=f(*x.T)
NOTE:
if scipy is not installed, will use np.interp for 1D (non-rbf),
or mystic's rbf otherwise. default method is 'nearest' for
1D and 'linear' otherwise. method can be one of ('rbf','linear',
'nearest','cubic','inverse','gaussian','quintic','thin_plate').
"""
from interpolator import Interpolator
args = self.args.copy()
args.update(kwds)
maxpts, noise = self.maxpts, self.noise
ii = Interpolator(self.x, self.z, maxpts=maxpts, noise=noise, **args)
self.surrogate = ii.Interpolate(**args)
# build the surrogate
self.surrogate.__doc__ = self.objective.__doc__
return self.surrogate
def fit(self, **kwds):
"""generate an interpolated model from data
Input:
data: a mystic legacydata.dataset (or callable model, y = model(x))
rnd: bool, if False, treat the model as deterministic [default: True]
cached: bool, if True, use a mystic.cache [default: False]
NOTE:
any additional keyword arguments will be passed to the interpolator
NOTE:
if data is a model, interpolator will use model's cached archive
"""
self.__kwds__.update(kwds)
cached = self.__kwds__.pop('cached', False)
archive = data = self.__kwds__.pop('data', None)
self.rnd = self.__kwds__.pop('rnd', self.rnd) and (
bool(self.__kwds__.get('noise', True))
) # or callable(data) or isinstance(data, type('')))
if callable(data): #XXX: is a model, allow this?
data = sample(data, bounds=None, pts=0) #XXX: axis? multivalue?
elif isinstance(data, type('')): #XXX: is a name, allow this?
import mystic.cache as mc
import dataset as ds
data = ds.from_archive(mc.archive.read(data))
x = getattr(data, 'coords', getattr(data, 'x', None))
z = getattr(data, 'values', getattr(data, 'y', None))
from interpolator import Interpolator
terp = Interpolator(x, z, **self.__kwds__)
self.__func__ = terp.Interpolate() #XXX: ValueError: zero-size
self.__model__ = _init_axis(terp.model)
self.__model__.__name__ = self.__name__
self.__kwds__['data'] = archive
if cached: #FIXME: clear the archive??? generate new uid name?
self.__kwds__['cached'] = True
self._OUQModel__init_cache()
if hasattr(self.__model__, '__cache__'):
c = self.__model__.__cache__()
c.clear()
else:
self.__kwds__['cached'] = False
return
