逻辑回归代码实现

实现sigmoid函数 def sigmoid(z): return 1/(1+np.exp(-z)) 实现假设函数 def hypothesis(X,theta): z = np.dot(X,theta) return sigmoid(z) 损失函数 def loss(X,y,theta): m = X.shape[0] y_ = hypothesis(X,theta) loss_f = -y*np.log(y_)-(1-y)*np.log(1-y_) return np.sum(loss_f)/m 梯度下降法求解 def gradientDescent(X,y,theta,iterations,alpha): #取数据条数 m = X.shape[0] #在x最前面插入全1的列 X = np.hstack((np.ones((m, 1)), X)) for i in range(iterations): for j in range(len(theta)): theta[j] = theta[j]-(alpha/m)*np.sum((hypothesis(X,theta) - y)*X[:,j].reshape(-1,1)) #每迭代1000次输出一次损失值 if(i%10000==0): print('第',i,'次迭代，当前损失为：',loss(X,y,theta),'theta=',theta) return theta n = X.shape[1]#特征数 theta = np.zeros(n+1).reshape(n+1, 1) # theta是列向量,+1是因为求梯度时X前会增加一个全1列 iterations = 250000 alpha = 0.009 gradientDescent(X,y,theta,iterations,alpha)