import numpy as np#1采样数据data = []#保存样本集的列表for i in range(100):#循环采样100个点 x = np.random.uniform(-10,10)#随机采样输入X # 采样高斯噪声 eps = np.random.normal(0.,0.1) #得到模型的输出 y = 1.477*x +0.089 +eps data.append([x,y])#保存样本点data = np.array(data)#转换为2维数组print(data)#2计算误差def mse(b,w,points): #根据当前的w，b参数计算军方差损失 totalError = 0 for i in range(0,len(points)):#循环迭代所有点 x = points[i,0]# 获得i号店的输入x y = points[i,1]# 获得i号点的输出y totalError+=(y-(w*x+b))**2 return totalError/float(len(points))#得到均方差#3，计算梯度def step_gradient(b_current,w_current,points,lr): #计算误差函数在所有点上的导数，并更新w，b b_gradient = 0 w_gradient =0 M = float(len(points)) for i in range(0,len(points)): x = points[i,0] y=points[i,1] b_gradient +=(2/M)*((w_current*x+b_current)-y) w_gradient += (2/M) * x*((w_current*x+b_current) - y) #根据梯度下降算法更新w,b，lr为xuexilv new_b = b_current -(lr*b_gradient) new_w = w_current -(lr * w_gradient) return [new_b,new_w]#梯度更新def gradient_descent(points,starting_b,starting_w,lr,num_iterations): b = starting_b w = starting_w for step in range(num_iterations): b,w = step_gradient(b,w,np.array(points),lr) loss = mse(b,w,points) if step%50 == 0: print("iteration:{},loss:{},w:{},b:{}".format(step,loss,w,b)) return [b,w]def main(): lr = 0.01 initial_b=0 initial_w = 0 num_iterations = 1000 [b,w] = gradient_descent(data,initial_b,initial_w,lr,num_iterations) loss = mse(b,w,data) print("final loss:{},w:{},b:{}".format(loss,w,b))
