现在二维平面上有一系列点 Point，
我们要找到一个一次函数 $y=wx+b$，使得所有点到这条直线的距离平方和 $∑(wx+b−y)^2$ 最小
因此我们可以定义损失函数 $L=(wx+b−y)^2$
w和b的导数分别为

$$ w' = w - lr* \frac{\partial L}{\partial w} \\ b' = b - lr* \frac{\partial L}{\partial b} \\ $$

其中

$$ w' = 2 * x * (wx + b - y),b' = 2 * (wx + b - y) $$

import numpy as np
from random import random

def compute_error_for_line_given_points(b, w, points):
    totalError = 0
    for i in range(len(points)):
        x = points[i, 0]
        y = points[i, 1]
        totalError += (y - (w * x + b)) ** 2
    return totalError / float(len(points)) # average 计算平均损失
  
def step_gradient(b_current, w_current, points, learningRate):
    b_gradient = 0
    w_gradient = 0
    N = float(len(points))
    for i in range(len(points)):
        x = points[i, 0]
        y = points[i, 1]
        b_gradient += 2 * ((w_current * x) + b_current - y)
        w_gradient += 2 * x * ((w_current * x) + b_current - y)
    b_gradient = b_gradient / N
    w_gradient = w_gradient / N
    new_b = b_current - (learningRate * b_gradient)
    new_w = w_current - (learningRate * w_gradient)
    return [new_b, new_w]
  
def gradient_descent_runner(points, starting_b, starting_w, learning_rate, num_iterations): # num_iteration 迭代次数
    b = starting_b
    w = starting_w
    for i in range(num_iterations):
        b, w = step_gradient(b, w, np.array(points), learning_rate)
    return [b, w]

def run():
    points = np.genfromtxt("data.txt", delimiter=",")
    learning_rate = 0.0001
    initial_b = random()
    initial_w = random()
    num_iterations = 1000
    print("Starting gradient descent at b = {0}, w = {1}, error = {2}"
          .format(initial_b, initial_w, 
                  compute_error_for_line_given_points(initial_b, initial_w, points)))
    print("Running...")
    [b, w] = gradient_descent_runner(points, initial_b, initial_w, learning_rate, num_iterations)
    print("After {0} iterations at b = {1}, w = {2}, error = {3}"
          .format(num_iterations, b, w, 
                  compute_error_for_line_given_points(b, w, points)))