python:线性回归入门例子

#
# 线性回归实验
# y=.1*x1+.3
import numpy as np

num_points = 100

vectors_set = []

for i in range(num_points):
    # 利用正态分布生成x1
    x1 = np.random.normal(loc=0.0, scale=0.66)
    # np.random.normal(.0, .03) 干扰因子
    y1 = x1 * .1 + .3 + np.random.normal(.0, .03)
    vectors_set.append([x1, y1])
# 输入数据
x_data = [v[0] for v in vectors_set]
# 生成输出数据
y_data = [v[1] for v in vectors_set]
print(x_data)
print(y_data)

# 导入可视化依赖库
import matplotlib.pyplot as plt

plt.plot(x_data, y_data, "ro", marker='^', c="green", label="original_data")
# 打印original_data
plt.legend()
# 显示可视化结果
plt.show()