机器学习之--梯度下降和最小二乘法算线性回归

o1 = 0
o2 = 0
o3 = 0
a = 0.002
x = [1,3,5,3,5,7,2,6,7,3,6,9,4,8,9,6,5,3,3,2,7,1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,6,6,6,7,7,7,8,8,8,9,9,9]   #生成训练数据
x = x * 250              #将训练数据扩大50倍
y = []
#生成训练数据的理论结果
for i in range(int(len(x)/3)):
    y.append(14*x[i] + 5*x[i+1] + 58*x[i+2])
    # print(‘y[{}]:{}‘.format(i,y[i]))
#进行随机梯度下降 每用一行数据，就下降一次，需要样本足够且，a值合理
for i in range(len(y)):
    o1 = o1 - a * (o1 * x[i] + o2 * x[i+1] + o3 * x[i+2] - y[i]) * x[i]
    o2 = o2 - a * (o1 * x[i] + o2 * x[i+1] + o3 * x[i+2] - y[i]) * x[i+1]
    o3 = o3 - a * (o1 * x[i] + o2 * x[i+1] + o3 * x[i+2] - y[i]) * x[i+2]
    print(o1 * x[i] + o2 * x[i+1] + o3 * x[i+2],y[i])
print(o1,o2,o3)