def __init__(self, wsize, threshold, integration=IntegrationType.MEAN, sampling=2.0):
UnaryPredicate1D.__init__(self)
self._wsize = wsize
self._threshold = threshold
self._integration = integration
self._func = DensityF1D(self._wsize, self._integration, sampling)
self._func2 = DensityF1D(self._wsize, IntegrationType.MAX, sampling)
def __call__(self, inter):
result = self._functor(inter) - self._lmin
sigma = (self._sigmaMax - self._sigmaMin) / (self._lmax - self._lmin) * result + self._sigmaMin
t = (self._tmax - self._tmin) / (self._lmax - self._lmin) * result + self._tmin
sigma = max(sigma, self._sigmaMin)
self._func = DensityF1D(sigma, self._integration, self._sampling)
return (self._func(inter) < t)
def __init__(self, wsize, threshold, functor, funcmin=0.0, funcmax=1.0, integration=IntegrationType.MEAN):
UnaryPredicate1D.__init__(self)
self._threshold = float(threshold)
self._functor = functor
self._funcmin = float(funcmin)
self._funcmax = float(funcmax)
self._func = DensityF1D(wsize, integration)
def __call__(self, inter):
func = DensityF1D(self._wsize, self._integration)
res = self._functor(inter)
k = (res - self._funcmin) / (self._funcmax - self._funcmin)
return (func(inter) < (self._threshold * k))