Python利用逻辑回归分类实现模板

def sigmoid(x):
 return 1/(1+np.exp(-x))

def weights(x,y,alpha,thershold):
 #初始化参数
 m,n = x_train.shape
 theta = np.random.rand(n) #参数
 cnt = 0 # 迭代次数
 max_iter = 50000
 #开始迭代
 while cnt < max_iter:
  cnt += 1
  diff = np.full(n,0)
  for i in range(m):
   diff = (y[i]-sigmoid(theta.T @ x[i]))*x[i]
   theta = theta + alpha * diff
  if(abs(diff)<thershold).all():
   break
 return theta

def predict(x_test,theta):
 if sigmoid(theta.T @ x_test)>0.5:
  return 1
 else:return 0

x_train = np.array([[1,2.697,6.254],
     [1,1.872,2.014],
     [1,2.312,0.812],
     [1,1.983,4.990],
     [1,0.932,3.920],
     [1,1.321,5.583],
     [1,2.215,1.560],
     [1,1.659,2.932],
     [1,0.865,7.362],
     [1,1.685,4.763],
     [1,1.786,2.523]])
y_train = np.array([1,0,0,1,0,1,0,0,1,0,1])
alpha = 0.001 # 学习率
thershold = 0.01 # 指定一个阈值，用于检查两次误差
print(weights(x_train,y_train,alpha,thershold))