code: def lowess(x, y, f=2.0 / 3.0, iterations=3):
      
      n = len(x)
      r = int(ceil(f * n))
      h = [np.sort(np.abs(x - x[i]))[r] for i in range(n)]
      w = np.clip(np.abs((x[:, None] - x[None, :]) / h), 0.0, 1.0)
      w = (1 - w ** 3) ** 3
      yest = np.zeros(n)
      delta = np.ones(n)
      for _ in range(iterations):
          for i in range(n):
              weights = delta * w[:, i]
              b = np.array([np.sum(weights * y), np.sum(weights * y * x)])
              A = np.array([[np.sum(weights), np.sum(weights * x)], [np.sum(weights * x), np.sum(weights * x * x)]])
              # I think it is safe to assume this.
              # pylint: disable=unexpected-keyword-arg
              beta = linalg.solve(A, b, assume_a="pos", check_finite=False)
              yest[i] = beta[0] + beta[1] * x[i]
  
          residuals = y - yest
          s = np.median(np.abs(residuals))
          delta = np.clip(residuals / (6.0 * s), -1, 1)
          delta = (1 - delta ** 2) ** 2
  
      return yest
docs: lowess(x, y, f=2./3., iter=3) -> yest
      Lowess smoother: Robust locally weighted regression.
      The lowess function fits a nonparametric regression curve to a scatterplot.
      The arrays x and y contain an equal number of elements; each pair
      (x[i], y[i]) defines a data point in the scatterplot. The function returns
      the estimated (smooth) values of y.
      The smoothing span is given by f. A larger value for f will result in a
      smoother curve. The number of robustifying iterations is given by iter. The
      function will run faster with a smaller number of iterations.