def __init__(self, w, sx, sy, Mxy):
        r"""
        Parameters
        ----------
        w : float
            statistical weight.
                w = \sum_t w_t
            In most cases, :math:`w_t=1`, and then w is just the number of samples that went into s1, S2.
        s : ndarray(n,)
            sum over samples:
            .. math:
                s = \sum_t w_t x_t
        M : ndarray(n, n)
            .. math:
                M = (X-s)^T (X-s)
        """
        self.w = float(w)
        self.sx = sx
        self.sy = sy
        self.Mxy = Mxy
def __init__ ( self , w , sx , sy , Mxy ) : self . w = float ( w ) self . sx = sx self . sy = sy self . Mxy = Mxy
r"""
        Parameters
        ----------
        w : float
            statistical weight.
                w = \sum_t w_t
            In most cases, :math:`w_t=1`, and then w is just the number of samples that went into s1, S2.
        s : ndarray(n,)
            sum over samples:
            .. math:
                s = \sum_t w_t x_t
        M : ndarray(n, n)
            .. math:
                M = (X-s)^T (X-s)