code: def gmm_cause(points, k=4, p1=2, p2=2):
      
      g = GMM(k, covariance_type="spherical")
      g.fit(np.random.randn(300, 1))
  
      g.means_ = p1 * np.random.randn(k, 1)
      g.covars_ = np.power(abs(p2 * np.random.randn(k, 1) + 1), 2)
      g.weights_ = abs(np.random.rand(k))
      g.weights_ = g.weights_ / sum(g.weights_)
      return g.sample(points)[0].reshape(-1)
docs: Init a root cause with a Gaussian Mixture Model w/ a spherical covariance type.