吴恩达471机器学习入门课程1第1周——梯度下降

import math, copy
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('./deeplearning.mplstyle')
from lab_utils_uni import plt_house_x, plt_contour_wgrad, plt_divergence, plt_gradients

1加载数据集

# train data
x = np.array([1.0,2.0])
y = np.array([300.0,500.0])

2计算COST(均值平方差，1/2m(y_pre - y)²)

#通过w*x+b得到y_pre,
def costFun(x,y,w,b):
    m = x.shape[0] #数据长度,一维数组的长度
    cost = 0 #初始化
    for i in range(m):
        f_wb = w*x[i]+b
        cost += (f_wb-y[i])**2
    return 1/(2*m)*cost

3计算梯度

$$\begin{array}{rl}\frac{\partial J(w,b)}{\partial w}& =\frac{1}{m}\sum _{i=0}^{m-1}(f_{w,b}(x^{(i)})-y\\ \frac{\partial J(w,b)}{\partial b}& \end{array}$$