def __init__(self, x, y, w=None, bbox=[None] * 2, k=3, s=None):
"""
Input:
x,y - 1-d sequences of data points (x must be
in strictly ascending order)
Optional input:
w - positive 1-d sequence of weights
bbox - 2-sequence specifying the boundary of
the approximation interval.
By default, bbox=[x[0],x[-1]]
k=3 - degree of the univariate spline.
s - positive smoothing factor defined for
estimation condition:
sum((w[i]*(y[i]-s(x[i])))**2,axis=0) <= s
Default s=len(w) which should be a good value
if 1/w[i] is an estimate of the standard
deviation of y[i].
"""
# _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
data = dfitpack.fpcurf_smth0(x, y, k, w=w, xb=bbox[0], xe=bbox[1], s=s)
if data[-1] == 1:
# nest too small, setting to maximum bound
data = self._reset_nest(data)
self._data = data
self._reset_class()
def __init__(self, x, y, w=None, bbox=[None] * 2, k=3):
"""
Input:
x,y - 1-d sequences of data points (x must be
in strictly ascending order)
Optional input:
w - positive 1-d sequence of weights
bbox - 2-sequence specifying the boundary of
the approximation interval.
By default, bbox=[x[0],x[-1]]
k=3 - degree of the univariate spline.
"""
# _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
self._data = dfitpack.fpcurf_smth0(x, y, k, w=w, xb=bbox[0], xe=bbox[1], s=0)
self._reset_class()
