import numpy as np

def linearRegression(data_x,data_y,learningRate,loopNum):
  w,b=0,0

  #梯度下降
  for i in range(loopNum):
    w_derivative, b_derivative, cost = 0, 0, 0
    for j in range(len(data_x)):
      wxPlusb=w*data_x[j]+b
      w_derivative+=(wxPlusb-data_y[j])*data_x[j]
      b_derivative+=wxPlusb-data_y[j]
      cost+=(wxPlusb-data_y[j])*(wxPlusb-data_y[j])
    w_derivative=w_derivative/len(data_x)
    b_derivative=b_derivative/len(data_x)

    w = w - learningRate*w_derivative
    b = b - learningRate*b_derivative

    cost = cost/(2*len(data_x))
    if i%100==0:
      print(cost)
  print(w)
  print(b)

if __name__== "__main__": #_x:protected __x:private
  x=np.random.normal(0,10,100)
  noise=np.random.normal(0,0.05,100)
  y=2*x+7+noise
  linearRegression(x,y,0.01,5000)