import numpy as np

def computer_error_for_give_point(w, b, points): # 計(jì)算出 觀測(cè)值與計(jì)算值 之間的誤差， 并累加,最后返回 平均誤差

loss = 0

for i in range(len(points)):

x = points[i, 0]

y = points[i, 1]

loss += ((w * x + b ) - y) ** 2

return loss/float(len(points))

# 下面函數(shù)只求導(dǎo)一次更新 w 和 b, 后面可用迭代方法多次更新 w， b

def get_gradient(w_current, b_current, points, LearningRate):

N = len(points)

w_gradient = 0

b_gradient = 0

for i in range(N):

x = points[i, 0]

y = points[i, 1]

w_gradient += 2/N * ((w_currentx+b_current)-y)x

b_gradient += 2/N * ((w_currentx+b_current)-y)

new_w = w_current - LearningRate w_gradient

new_b = b_current - LearningRate * b_gradient

return new_w,new_b # 以列表的形式返回

def gradeient_descent_run(w, b, points, learn_rate, iteration):

points = np.array(points)

LearnRate = learn_rate

for i in range(iteration):

w, b = get_gradient(w, b, points, LearnRate)

return w, b

if name == "main":

initialize_w = 0

initialize_b = 0

points = [[10, 10], [9, 9], [8, 8], [7, 7], [6, 6], [5, 5], [4, 4], [3, 3], [2, 2], [1, 1]]

w, b = gradeient_descent_run(initialize_w, initialize_b, points, 0.005, 100)

print(w)

print(b)

原文：https://www.cnblogs.com/Salted-fish-turn-over/p/11448221.html

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