new_contents: import matplotlib.pyplot as plt
  import numpy as np
  
  #網路上找的dataset 可以線性分割
  
  dataset = np.array([
  ((1, -0.4, 0.3), -1),
  ((1, -0.3, -0.1), -1),
  ((1, -0.2, 0.4), -1),
  ((1, -0.1, 0.1), -1),
  ((1, 0.9, -0.5), 1),
  ((1, 0.7, -0.9), 1),
  ((1, 0.8, 0.2), 1),
  ((1, 0.4, -0.6), 1)])
  
  #判斷有沒有分類錯誤，並列印錯誤率
  
  def check_error(w, dataset):
      result = None
      error = 0
      for x, s in dataset:
          x = np.array(x)
          if int(np.sign(w.T.dot(x))) != s:
              result =  x, s
              error += 1
      print  "error=%s/%s" % (error, len(dataset))
      return result
  
  #PLA演算法實作
  
  def pla(dataset):
      w = np.zeros(3)
      while check_error(w, dataset) is not None:
          x, s = check_error(w, dataset)
          w += s * x
      return w
  
  
  #執行
  
  w = pla(dataset)
  
  #畫圖
  
  ps = [v[0] for v in dataset]
  fig = plt.figure()
  ax1 = fig.add_subplot(111)
  #dataset前半後半已經分割好 直接畫就是
  
  ax1.scatter([v[1] for v in ps[:4]], [v[2] for v in ps[:4]], s=10, c='b', marker="o", label='O')
  ax1.scatter([v[1] for v in ps[4:]], [v[2] for v in ps[4:]], s=10, c='r', marker="x", label='X')
  l = np.linspace(-2,2)
  a,b = -w[1]/w[2], -w[0]/w[2]
  ax1.plot(l, a*l + b, 'b-')
  plt.legend(loc='upper left');
  plt.show()
  
subject: Add pla example