示例#1
0
    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.fpcurf0(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()
示例#2
0
    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.fpcurf0(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()
示例#3
0
    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.fpcurf0(x, y, k, w=w, xb=bbox[0], xe=bbox[1], s=0)
        self._reset_class()
示例#4
0
    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.fpcurf0(x,y,k,w=w,
                                      xb=bbox[0],xe=bbox[1],s=0)
        self._reset_class()