docstr: Computes the weights for the scaled unscented Kalman filter.
  
  Returns
  -------
  
  Wm : ndarray[2n+1]
      weights for mean
  
  Wc : ndarray[2n+1]
      weights for the covariances
docstr_tok: ["Computes","the","weights","for","the","scaled","unscented","Kalman","filter",".","Returns","--","--","--","-","Wm",":","ndarray","[","2n+1","]","weights","for","mean","Wc",":","ndarray","[","2n+1","]","weights","for","the","covariances"]
code: def weights(self):
      """Computes the weights for the scaled unscented Kalman filter.
  
      Returns
      -------
  
      Wm : ndarray[2n+1]
          weights for mean
  
      Wc : ndarray[2n+1]
          weights for the covariances
      """
  
      n = self.n
      lambda_ = self.alpha**2 * (n + self.kappa) - n
  
      c = 0.5 / (n + lambda_)
      Wc = np.full(2 * n + 1, c)
      Wm = np.full(2 * n + 1, c)
      Wc[0] = lambda_ / (n + lambda_) + (
          1 - self.alpha**2 + self.beta
      )
      Wm[0] = lambda_ / (n + lambda_)
  
      return Wm, Wc
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import matplotlib.pyplot
  import numpy.random
  from numpy.random import randn
  from numpy import asarray
  import numpy
  from filterpy.kalman import UnscentedKalmanFilter
  from filterpy.kalman import unscented_transform, MerweScaledSigmaPoints, JulierSigmaPoints, SimplexSigmaPoints
  from filterpy.common import Q_discrete_white_noise
  import filterpy.stats
  from math import cos, sin
  from filterpy.kalman import UnscentedKalmanFilter2
label_desc: Use image node_id 4 for calling the MerweScaledSigmaPoints2 obj's underlying member method code with example usage: obj.weights() and returns: Wm,  Wc