一元线性回归

1、导入库

#!/usr/bin/env python
# -*-coding:utf-8-*-
import numpy as np
import matplotlib.pyplot as plt

2、定义线性回归模型

def predict(x, a, b):
    y_predict = a * x + b
    return y_predict

公式1—一元线性回归模型

公式2—损失函数

公式3—最小化损失函数

根据给定的数据集求解出a和b,即可确定适合该数据集的模型。那么

3、用最二乘法求解线性回归的系数

def train_linear_model(x, y):
    # 最小二乘法求参数
    data_mean_x = np.mean(x)  # x均值
    data_mean_y = np.mean(y)  # y均值
    data_var = np.var(x, ddof=1)  # 方差
    data_cov = np.cov(x, y, ddof=1)[0, 1]  # 协方差.由于np.cov()返回的是协方差矩阵，这里只需要x,y的协方差
    # data_cov = sum((x-data_mean_x)*(y-data_mean_y))/((len(x)-1)*1.0)  # 计算结果等同于上一行
    a = data_cov/data_var
    b = data_mean_y-a*data_mean_x
    return a, b

根据参考文献[1]中介绍的方法，自己用代码实现了一下求解最小二乘法。

公式4—方差

公式5—协方差

公式6—求斜率a

公式7—求截距b

4、画结果图

def draw_picture(x_train, y_train, y_train_predict, x_test=None, y_test_predict=None):
    # 画训练集散点图
    fig = plt.figure()
    ax = fig.add_subplot(111)
    train_point = plt.scatter(x_train, y_train, c='c', s=50, linewidth=0)
    train_predict_line, = plt.plot(x_train, y_train_predict, c='r', linewidth=2)
    train_predict_point = plt.scatter(x_train, y_train_predict, marker='*', s=150, c='g', linewidth=0)
    for i in range(len(x_train)):
        residual, = plt.plot([x_train[i], x_train[i]], [y_train[i], y_train_predict[i]],
                            c='y', linewidth=2)

    # 画测试集散点图
    if x_test!=None and y_test_predict!=None:
        test_predict_point = plt.scatter(x_test, y_test_predict, marker='*', s=150, c='b', linewidth=0)
        plt.legend([train_point, train_predict_line, train_predict_point, residual, test_predict_point],
                   ["train_point", "train_predict_line", "train_predict_point", "residual", "test_predict_point"],
                   loc='lower right')
    else:
        plt.legend([train_point, train_predict_line, train_predict_point, residual],
                   ["train_point", "train_predict_line", "train_predict_point", "residual"], loc='lower right')

    plt.grid(color='b', linewidth=0.3, linestyle='--')
    plt.title('Linear Regression')
    ax.set_ylabel('Y')
    ax.set_xlabel('X')
    fig.show()

5、主函数

def main():
    # 训练集
    x_train = [6, 8, 10, 14, 18]
    y_train = [7, 9, 13, 17.5, 18]
    x_train=np.mat(x_train)
    y_train=np.mat(y_train)

    # 训练
    a, b = train_linear_model(x_train, y_train)

    # 预测
    y_train_predict = predict(x_train, a, b)
    x_test = 12
    y_test_predict = predict(x_test, a, b)

    x_train = x_train.tolist()[0]   #  将矩阵转换为列表，方便接下来的画图操作
    y_train = y_train.tolist()[0]
    y_train_predict = y_train_predict.tolist()[0]

    # 画图
    draw_picture(x_train, y_train, y_train_predict, x_test, y_test_predict)

if __name__ == '__main__':
    main()

结果图.png

参考文献：[1]. [Python 机器学习系列之线性回归篇深度详细 ]（https://www.jianshu.com/p/738f6092ef53）