系数比之前多了一个分母m
批量梯度下降法,同上一篇方法,下面看随机梯度法,随机梯度通过一个样本更新所有w,类似笔记一
import pandas as pd
import numpy as np
import...= df.iloc[0:100, [0,2]].values
x_std = np.copy(x)
x_std[:, 0] = (x[:,0]-x[:,0].mean())/x[:,0].std()...(-2, 2, n)
# 生成网格数据
X, Y = np.meshgrid(mx, my)
fig, axes = plt.subplots(1,2)
axes0, axes1 = axes.flatten...()
axes0.plot(per.errors, marker='o')
axes0.set_title('errors')
axes1.contourf(X, Y, f(X, Y), 2, alpha...\mathbf{J(w)}=\frac{1}{2m}\sum{(y{(i)}-\phi(z{(i)}))}^2