import numpy as np
import matplotlib.pyplot as plt
2.读取数据
代码语言：python代码运行次数：0复制
points = np.genfromtxt('data.csv', delimiter=',')
points[0,0]
# 提取points中的两列数据，分别作为x，y
x = points[:, 0]
y = points[:, 1]
# 用plt画出散点图
plt.scatter(x, y)
plt.show()
3.定义损失函数
代码语言：python代码运行次数：0复制
# 损失函数是系数的函数，另外还要传入数据的x，y
def compute_cost(w, b, points):
total_cost = 0
M = len(points)
# 逐点计算平方损失误差，然后求平均数
for i in range(M):
x = points[i, 0]
y = points[i, 1]
total_cost = ( y - w * x - b ) ** 2
return total_cost/M
4.定义算法拟合函数
代码语言：python代码运行次数：0复制
# 先定义一个求均值的函数
def average(data):
sum = 0
num = len(data)
for i in range(num):
sum = data[i]
return sum/num
# 定义核心拟合函数
def fit(points):
M = len(points)
x_bar = average(points[:, 0])
sum_yx = 0
sum_x2 = 0
sum_delta = 0
for i in range(M):
x = points[i, 0]
y = points[i, 1]
sum_yx = y * ( x - x_bar )
sum_x2 = x ** 2
# 根据公式计算w
w = sum_yx / ( sum_x2 - M * (x_bar**2) )
for i in range(M):
x = points[i, 0]
y = points[i, 1]
sum_delta = ( y - w * x )
b = sum_delta / M
return w, b
5.测试
代码语言：python代码运行次数：0复制
w, b = fit(points)
print("w is: ", w)
print("b is: ", b)
cost = compute_cost(w, b, points)
print("cost is: ", cost)
6.画出拟合曲线
代码语言：python代码运行次数：0复制
plt.scatter(x, y)
# 针对每一个x，计算出预测的y值
pred_y = w * x b
plt.plot(x, pred_y, c='r')
plt.show()
