# Lihang-Statistical-learning-methods-Code

**Repository Path**: ChangXingJ/Lihang-Statistical-learning-methods-Code

## Basic Information

- **Project Name**: Lihang-Statistical-learning-methods-Code
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-24
- **Last Updated**: 2021-10-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 手写李航《统计学习方法》书中全部算法

> 用Python手写实现（矩阵计算和导数计算使用轮子）了李航《统计学习方法》中绝大部分可以实现的内容。除极少数还未完成内容已列出外，其他内容已全部完成。

#### 第02章 感知机

[【算法 2.1】感知机学习算法的原始形式(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC02%E7%AB%A0%20%E6%84%9F%E7%9F%A5%E6%9C%BA/%E6%84%9F%E7%9F%A5%E6%9C%BA%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E7%9A%84%E5%8E%9F%E5%A7%8B%E5%BD%A2%E5%BC%8F(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 2.2】感知机学习算法的对偶形式(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC02%E7%AB%A0%20%E6%84%9F%E7%9F%A5%E6%9C%BA/%E6%84%9F%E7%9F%A5%E6%9C%BA%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E7%9A%84%E5%AF%B9%E5%81%B6%E5%BD%A2%E5%BC%8F(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

#### 第03章 k近邻法

[【算法 3.1】k近邻计算(原生Python+sklearn的kd树实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC03%E7%AB%A0%20k%E8%BF%91%E9%82%BB%E6%B3%95/k%E8%BF%91%E9%82%BB%E8%AE%A1%E7%AE%97(%E5%8E%9F%E7%94%9FPython%2Bsklearn%E7%9A%84kd%E6%A0%91%E5%AE%9E%E7%8E%B0).py)

[简单交叉验证选择KNN分类器(原生Python+sklearn的KNN分类器)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC03%E7%AB%A0%20k%E8%BF%91%E9%82%BB%E6%B3%95/%E7%AE%80%E5%8D%95%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E9%80%89%E6%8B%A9KNN%E5%88%86%E7%B1%BB%E5%99%A8(%E5%8E%9F%E7%94%9FPython%2Bsklearn%E7%9A%84KNN%E5%88%86%E7%B1%BB%E5%99%A8).py)

[S折交叉验证选择KNN分类器(原生Python+sklearn的KNN分类器)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC03%E7%AB%A0%20k%E8%BF%91%E9%82%BB%E6%B3%95/S%E6%8A%98%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E9%80%89%E6%8B%A9KNN%E5%88%86%E7%B1%BB%E5%99%A8(%E5%8E%9F%E7%94%9FPython%2Bsklearn%E7%9A%84KNN%E5%88%86%E7%B1%BB%E5%99%A8).py)

[线性扫描实现的k近邻计算(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC03%E7%AB%A0%20k%E8%BF%91%E9%82%BB%E6%B3%95/%E7%BA%BF%E6%80%A7%E6%89%AB%E6%8F%8F%E5%AE%9E%E7%8E%B0%E7%9A%84k%E8%BF%91%E9%82%BB%E8%AE%A1%E7%AE%97(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[kd树(原生Python实现)-待优化](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC03%E7%AB%A0%20k%E8%BF%91%E9%82%BB%E6%B3%95/kd%E6%A0%91(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0)-%E5%BE%85%E4%BC%98%E5%8C%96.py)

#### 第04章 朴素贝叶斯法

[【算法 4.1】朴素贝叶斯法(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC04%E7%AB%A0%20%E6%9C%B4%E7%B4%A0%E8%B4%9D%E5%8F%B6%E6%96%AF%E6%B3%95/%E6%9C%B4%E7%B4%A0%E8%B4%9D%E5%8F%B6%E6%96%AF%E6%B3%95(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[拉普拉斯平滑的朴素贝叶斯法(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC04%E7%AB%A0%20%E6%9C%B4%E7%B4%A0%E8%B4%9D%E5%8F%B6%E6%96%AF%E6%B3%95/%E6%8B%89%E6%99%AE%E6%8B%89%E6%96%AF%E5%B9%B3%E6%BB%91%E7%9A%84%E6%9C%B4%E7%B4%A0%E8%B4%9D%E5%8F%B6%E6%96%AF%E6%B3%95(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[支持连续型特征的朴素贝叶斯法(直接调用sklearn实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC04%E7%AB%A0%20%E6%9C%B4%E7%B4%A0%E8%B4%9D%E5%8F%B6%E6%96%AF%E6%B3%95/%E6%94%AF%E6%8C%81%E8%BF%9E%E7%BB%AD%E5%9E%8B%E7%89%B9%E5%BE%81%E7%9A%84%E6%9C%B4%E7%B4%A0%E8%B4%9D%E5%8F%B6%E6%96%AF%E6%B3%95(%E7%9B%B4%E6%8E%A5%E8%B0%83%E7%94%A8sklearn%E5%AE%9E%E7%8E%B0).py)

#### 第05章 决策树

[【算法 5.2】ID3算法生成决策树-不剪枝(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC05%E7%AB%A0%20%E5%86%B3%E7%AD%96%E6%A0%91/ID3%E7%AE%97%E6%B3%95%E7%94%9F%E6%88%90%E5%86%B3%E7%AD%96%E6%A0%91-%E4%B8%8D%E5%89%AA%E6%9E%9D(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 5.3】C4.5的生成算法-不剪枝(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC05%E7%AB%A0%20%E5%86%B3%E7%AD%96%E6%A0%91/C4.5%E7%9A%84%E7%94%9F%E6%88%90%E7%AE%97%E6%B3%95-%E4%B8%8D%E5%89%AA%E6%9E%9D(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 5.4】ID3算法生成决策树-剪枝(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC05%E7%AB%A0%20%E5%86%B3%E7%AD%96%E6%A0%91/ID3%E7%AE%97%E6%B3%95%E7%94%9F%E6%88%90%E5%86%B3%E7%AD%96%E6%A0%91-%E5%89%AA%E6%9E%9D(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 5.4】C4.5的生成算法-剪枝(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC05%E7%AB%A0%20%E5%86%B3%E7%AD%96%E6%A0%91/C4.5%E7%9A%84%E7%94%9F%E6%88%90%E7%AE%97%E6%B3%95-%E5%89%AA%E6%9E%9D(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

CART回归树的生成(原生Python实现)-待实现

[CART回归树(直接调用sklearn实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC05%E7%AB%A0%20%E5%86%B3%E7%AD%96%E6%A0%91/CART%E5%9B%9E%E5%BD%92%E6%A0%91(%E7%9B%B4%E6%8E%A5%E8%B0%83%E7%94%A8sklearn%E5%AE%9E%E7%8E%B0).py)

CART分类树的生成(原生Python实现)-待实现

[CART分类树(直接调用sklearn实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC05%E7%AB%A0%20%E5%86%B3%E7%AD%96%E6%A0%91/CART%E5%88%86%E7%B1%BB%E6%A0%91(%E7%9B%B4%E6%8E%A5%E8%B0%83%E7%94%A8sklearn%E5%AE%9E%E7%8E%B0).py)

#### 第06章 逻辑斯谛回归与最大熵模型

[【算法 6.1】IIS算法实现的最大熵模型(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC06%E7%AB%A0%20%E9%80%BB%E8%BE%91%E6%96%AF%E8%B0%9B%E5%9B%9E%E5%BD%92%E4%B8%8E%E6%9C%80%E5%A4%A7%E7%86%B5%E6%A8%A1%E5%9E%8B/IIS%E7%AE%97%E6%B3%95%E5%AE%9E%E7%8E%B0%E7%9A%84%E6%9C%80%E5%A4%A7%E7%86%B5%E6%A8%A1%E5%9E%8B(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 6.2】拟牛顿法实现的最大熵模型(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC06%E7%AB%A0%20%E9%80%BB%E8%BE%91%E6%96%AF%E8%B0%9B%E5%9B%9E%E5%BD%92%E4%B8%8E%E6%9C%80%E5%A4%A7%E7%86%B5%E6%A8%A1%E5%9E%8B/%E6%8B%9F%E7%89%9B%E9%A1%BF%E6%B3%95%E5%AE%9E%E7%8E%B0%E7%9A%84%E6%9C%80%E5%A4%A7%E7%86%B5%E6%A8%A1%E5%9E%8B(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

#### 第07章 支持向量机

[字符串核函数的动态规划计算(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC07%E7%AB%A0%20%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA/%E5%AD%97%E7%AC%A6%E4%B8%B2%E6%A0%B8%E5%87%BD%E6%95%B0%E7%9A%84%E5%8A%A8%E6%80%81%E8%A7%84%E5%88%92%E8%AE%A1%E7%AE%97(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 7.5】SMO实现的线性&非线性支持向量机(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC07%E7%AB%A0%20%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA/SMO%E5%AE%9E%E7%8E%B0%E7%9A%84%E7%BA%BF%E6%80%A7%26%E9%9D%9E%E7%BA%BF%E6%80%A7%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[线性支持向量机(直接调用sklearn实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC07%E7%AB%A0%20%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA/%E7%BA%BF%E6%80%A7%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA(%E7%9B%B4%E6%8E%A5%E8%B0%83%E7%94%A8sklearn%E5%AE%9E%E7%8E%B0).py)

[非线性支持向量机(直接调用sklearn实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC07%E7%AB%A0%20%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA/%E9%9D%9E%E7%BA%BF%E6%80%A7%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA(%E7%9B%B4%E6%8E%A5%E8%B0%83%E7%94%A8sklearn%E5%AE%9E%E7%8E%B0).py)

#### 第08章 提升方法

[【算法 8.1】AdaBoost算法(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC08%E7%AB%A0%20%E6%8F%90%E5%8D%87%E6%96%B9%E6%B3%95/AdaBoost%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[AdaBoost算法(直接调用sklearn实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC08%E7%AB%A0%20%E6%8F%90%E5%8D%87%E6%96%B9%E6%B3%95/AdaBoost%E7%AE%97%E6%B3%95(%E7%9B%B4%E6%8E%A5%E8%B0%83%E7%94%A8sklearn%E5%AE%9E%E7%8E%B0).py)

[【算法 8.3】回归问题的提升树算法(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC08%E7%AB%A0%20%E6%8F%90%E5%8D%87%E6%96%B9%E6%B3%95/%E5%9B%9E%E5%BD%92%E9%97%AE%E9%A2%98%E7%9A%84%E6%8F%90%E5%8D%87%E6%A0%91%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[回归问题的提升树算法(直接调用sklearn实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC08%E7%AB%A0%20%E6%8F%90%E5%8D%87%E6%96%B9%E6%B3%95/%E5%9B%9E%E5%BD%92%E9%97%AE%E9%A2%98%E7%9A%84%E6%8F%90%E5%8D%87%E6%A0%91%E7%AE%97%E6%B3%95(%E7%9B%B4%E6%8E%A5%E8%B0%83%E7%94%A8sklearn%E5%AE%9E%E7%8E%B0).py)

#### 第09章 EM算法及其推广

[【算法 9.2】EM算法实现的高斯混合模型(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC09%E7%AB%A0%20EM%E7%AE%97%E6%B3%95%E5%8F%8A%E5%85%B6%E6%8E%A8%E5%B9%BF/EM%E7%AE%97%E6%B3%95%E5%AE%9E%E7%8E%B0%E7%9A%84%E9%AB%98%E6%96%AF%E6%B7%B7%E5%90%88%E6%A8%A1%E5%9E%8B(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[高斯混合模型(直接调用sklearn实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC09%E7%AB%A0%20EM%E7%AE%97%E6%B3%95%E5%8F%8A%E5%85%B6%E6%8E%A8%E5%B9%BF/%E9%AB%98%E6%96%AF%E6%B7%B7%E5%90%88%E6%A8%A1%E5%9E%8B(%E7%9B%B4%E6%8E%A5%E8%B0%83%E7%94%A8sklearn%E5%AE%9E%E7%8E%B0).py)

#### 第10章 隐马尔可夫模型

[【算法 10.1】观测序列的生成(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC10%E7%AB%A0%20%E9%9A%90%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E6%A8%A1%E5%9E%8B/%E8%A7%82%E6%B5%8B%E5%BA%8F%E5%88%97%E7%9A%84%E7%94%9F%E6%88%90(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 10.2】观测序列概率的前向算法(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC10%E7%AB%A0%20%E9%9A%90%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E6%A8%A1%E5%9E%8B/%E8%A7%82%E6%B5%8B%E5%BA%8F%E5%88%97%E6%A6%82%E7%8E%87%E7%9A%84%E5%89%8D%E5%90%91%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 10.3】观测序列概率的后向算法(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC10%E7%AB%A0%20%E9%9A%90%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E6%A8%A1%E5%9E%8B/%E8%A7%82%E6%B5%8B%E5%BA%8F%E5%88%97%E6%A6%82%E7%8E%87%E7%9A%84%E5%90%8E%E5%90%91%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 10.4】Baum-Welch算法实现的HMM模型学习(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC10%E7%AB%A0%20%E9%9A%90%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E6%A8%A1%E5%9E%8B/Baum-Welch%E7%AE%97%E6%B3%95%E5%AE%9E%E7%8E%B0%E7%9A%84HMM%E6%A8%A1%E5%9E%8B%E5%AD%A6%E4%B9%A0(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[近似算法实现的HMM模型预测(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC10%E7%AB%A0%20%E9%9A%90%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E6%A8%A1%E5%9E%8B/%E8%BF%91%E4%BC%BC%E7%AE%97%E6%B3%95%E5%AE%9E%E7%8E%B0%E7%9A%84HMM%E6%A8%A1%E5%9E%8B%E9%A2%84%E6%B5%8B(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 10.5】维特比算法实现的HMM模型预测(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC10%E7%AB%A0%20%E9%9A%90%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E6%A8%A1%E5%9E%8B/%E7%BB%B4%E7%89%B9%E6%AF%94%E7%AE%97%E6%B3%95%E5%AE%9E%E7%8E%B0%E7%9A%84HMM%E6%A8%A1%E5%9E%8B%E9%A2%84%E6%B5%8B(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

#### 第11章 条件随机场

[已知模型计算条件概率(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC11%E7%AB%A0%20%E6%9D%A1%E4%BB%B6%E9%9A%8F%E6%9C%BA%E5%9C%BA/%E5%B7%B2%E7%9F%A5%E6%A8%A1%E5%9E%8B%E8%AE%A1%E7%AE%97%E6%9D%A1%E4%BB%B6%E6%A6%82%E7%8E%87(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[已知模型构造随机样本集(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC11%E7%AB%A0%20%E6%9D%A1%E4%BB%B6%E9%9A%8F%E6%9C%BA%E5%9C%BA/%E5%B7%B2%E7%9F%A5%E6%A8%A1%E5%9E%8B%E6%9E%84%E9%80%A0%E9%9A%8F%E6%9C%BA%E6%A0%B7%E6%9C%AC%E9%9B%86(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 11.1】IIS算法实现的条件随机场模型(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC11%E7%AB%A0%20%E6%9D%A1%E4%BB%B6%E9%9A%8F%E6%9C%BA%E5%9C%BA/IIS%E7%AE%97%E6%B3%95%E5%AE%9E%E7%8E%B0%E7%9A%84%E6%9D%A1%E4%BB%B6%E9%9A%8F%E6%9C%BA%E5%9C%BA%E6%A8%A1%E5%9E%8B(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 11.2】拟牛顿法实现的条件随机场模型(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC11%E7%AB%A0%20%E6%9D%A1%E4%BB%B6%E9%9A%8F%E6%9C%BA%E5%9C%BA/%E6%8B%9F%E7%89%9B%E9%A1%BF%E6%B3%95%E5%AE%9E%E7%8E%B0%E7%9A%84%E6%9D%A1%E4%BB%B6%E9%9A%8F%E6%9C%BA%E5%9C%BA%E6%A8%A1%E5%9E%8B(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 11.3】条件随机场预测的维特比算法(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC11%E7%AB%A0%20%E6%9D%A1%E4%BB%B6%E9%9A%8F%E6%9C%BA%E5%9C%BA/%E6%9D%A1%E4%BB%B6%E9%9A%8F%E6%9C%BA%E5%9C%BA%E9%A2%84%E6%B5%8B%E7%9A%84%E7%BB%B4%E7%89%B9%E6%AF%94%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

#### 第14章 聚类方法

[计算马哈拉诺比斯距离矩阵(原生Python+numpy矩阵计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC14%E7%AB%A0%20%E8%81%9A%E7%B1%BB%E6%96%B9%E6%B3%95/%E8%AE%A1%E7%AE%97%E9%A9%AC%E5%93%88%E6%8B%89%E8%AF%BA%E6%AF%94%E6%96%AF%E8%B7%9D%E7%A6%BB%E7%9F%A9%E9%98%B5(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E7%9F%A9%E9%98%B5%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[计算相关系数矩阵(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC14%E7%AB%A0%20%E8%81%9A%E7%B1%BB%E6%96%B9%E6%B3%95/%E8%AE%A1%E7%AE%97%E7%9B%B8%E5%85%B3%E7%B3%BB%E6%95%B0%E7%9F%A9%E9%98%B5(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[计算夹角余弦矩阵(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC14%E7%AB%A0%20%E8%81%9A%E7%B1%BB%E6%96%B9%E6%B3%95/%E8%AE%A1%E7%AE%97%E5%A4%B9%E8%A7%92%E4%BD%99%E5%BC%A6%E7%9F%A9%E9%98%B5(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[计算类的样本散布矩阵与样本协方差矩阵(原生Python+numpy矩阵计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC14%E7%AB%A0%20%E8%81%9A%E7%B1%BB%E6%96%B9%E6%B3%95/%E8%AE%A1%E7%AE%97%E7%B1%BB%E7%9A%84%E6%A0%B7%E6%9C%AC%E6%95%A3%E5%B8%83%E7%9F%A9%E9%98%B5%E4%B8%8E%E6%A0%B7%E6%9C%AC%E5%8D%8F%E6%96%B9%E5%B7%AE%E7%9F%A9%E9%98%B5(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E7%9F%A9%E9%98%B5%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[【算法 14.1】聚合聚类算法(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC14%E7%AB%A0%20%E8%81%9A%E7%B1%BB%E6%96%B9%E6%B3%95/%E8%81%9A%E5%90%88%E8%81%9A%E7%B1%BB%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 14.2】k均值聚类算法(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC14%E7%AB%A0%20%E8%81%9A%E7%B1%BB%E6%96%B9%E6%B3%95/k%E5%9D%87%E5%80%BC%E8%81%9A%E7%B1%BB%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

#### 第15章 奇异值分解

[紧奇异值分解(原生Python+numpy矩阵计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC15%E7%AB%A0%20%E5%A5%87%E5%BC%82%E5%80%BC%E5%88%86%E8%A7%A3/%E7%B4%A7%E5%A5%87%E5%BC%82%E5%80%BC%E5%88%86%E8%A7%A3(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E7%9F%A9%E9%98%B5%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[奇异值分解(直接调用numpy实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC15%E7%AB%A0%20%E5%A5%87%E5%BC%82%E5%80%BC%E5%88%86%E8%A7%A3/%E5%A5%87%E5%BC%82%E5%80%BC%E5%88%86%E8%A7%A3(%E7%9B%B4%E6%8E%A5%E8%B0%83%E7%94%A8numpy%E5%AE%9E%E7%8E%B0).py)

#### 第16章 主成分分析

[相关矩阵的特征值分解实现的主成分分析算法(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC16%E7%AB%A0%20%E4%B8%BB%E6%88%90%E5%88%86%E5%88%86%E6%9E%90/%E7%9B%B8%E5%85%B3%E7%9F%A9%E9%98%B5%E7%9A%84%E7%89%B9%E5%BE%81%E5%80%BC%E5%88%86%E8%A7%A3%E5%AE%9E%E7%8E%B0%E7%9A%84%E4%B8%BB%E6%88%90%E5%88%86%E5%88%86%E6%9E%90%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 16.1】数据矩阵的奇异值分解实现的主成分分析算法(原生Python+numpy矩阵计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC16%E7%AB%A0%20%E4%B8%BB%E6%88%90%E5%88%86%E5%88%86%E6%9E%90/%E6%95%B0%E6%8D%AE%E7%9F%A9%E9%98%B5%E7%9A%84%E5%A5%87%E5%BC%82%E5%80%BC%E5%88%86%E8%A7%A3%E5%AE%9E%E7%8E%B0%E7%9A%84%E4%B8%BB%E6%88%90%E5%88%86%E5%88%86%E6%9E%90%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E7%9F%A9%E9%98%B5%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

#### 第17章 潜在语义分析

[依据词频构造单词文本矩阵(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC17%E7%AB%A0%20%E6%BD%9C%E5%9C%A8%E8%AF%AD%E4%B9%89%E5%88%86%E6%9E%90/%E4%BE%9D%E6%8D%AE%E8%AF%8D%E9%A2%91%E6%9E%84%E9%80%A0%E5%8D%95%E8%AF%8D%E6%96%87%E6%9C%AC%E7%9F%A9%E9%98%B5(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[依据TFIDF构造单词文本矩阵(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC17%E7%AB%A0%20%E6%BD%9C%E5%9C%A8%E8%AF%AD%E4%B9%89%E5%88%86%E6%9E%90/%E4%BE%9D%E6%8D%AETFIDF%E6%9E%84%E9%80%A0%E5%8D%95%E8%AF%8D%E6%96%87%E6%9C%AC%E7%9F%A9%E9%98%B5(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[奇异值分解实现的潜在语义分析算法(原生Python+numpy矩阵计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC17%E7%AB%A0%20%E6%BD%9C%E5%9C%A8%E8%AF%AD%E4%B9%89%E5%88%86%E6%9E%90/%E5%A5%87%E5%BC%82%E5%80%BC%E5%88%86%E8%A7%A3%E5%AE%9E%E7%8E%B0%E7%9A%84%E6%BD%9C%E5%9C%A8%E8%AF%AD%E4%B9%89%E5%88%86%E6%9E%90%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E7%9F%A9%E9%98%B5%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[【算法 17.1 (按式17.24和式17.25更新)】使用平方损失函数的非负矩阵分解算法(原生Python+numpy矩阵计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC17%E7%AB%A0%20%E6%BD%9C%E5%9C%A8%E8%AF%AD%E4%B9%89%E5%88%86%E6%9E%90/%E4%BD%BF%E7%94%A8%E5%B9%B3%E6%96%B9%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0%E7%9A%84%E9%9D%9E%E8%B4%9F%E7%9F%A9%E9%98%B5%E5%88%86%E8%A7%A3%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E7%9F%A9%E9%98%B5%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[【算法 17.1 (按式17.26和式17.27更新)】使用散度损失函数的非负矩阵分解算法(原生Python+numpy矩阵计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC17%E7%AB%A0%20%E6%BD%9C%E5%9C%A8%E8%AF%AD%E4%B9%89%E5%88%86%E6%9E%90/%E4%BD%BF%E7%94%A8%E6%95%A3%E5%BA%A6%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0%E7%9A%84%E9%9D%9E%E8%B4%9F%E7%9F%A9%E9%98%B5%E5%88%86%E8%A7%A3%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E7%9F%A9%E9%98%B5%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

#### 第18章 概率潜在语义分析

[【算法 18.1】EM算法实现的概率潜在语义模型(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC18%E7%AB%A0%20%E6%A6%82%E7%8E%87%E6%BD%9C%E5%9C%A8%E8%AF%AD%E4%B9%89%E5%88%86%E6%9E%90/EM%E7%AE%97%E6%B3%95%E5%AE%9E%E7%8E%B0%E7%9A%84%E6%A6%82%E7%8E%87%E6%BD%9C%E5%9C%A8%E8%AF%AD%E4%B9%89%E6%A8%A1%E5%9E%8B(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

#### 第19章 马尔可夫链蒙特卡罗法

[常用概率分布(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC19%E7%AB%A0%20%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E8%92%99%E7%89%B9%E5%8D%A1%E7%BD%97%E6%B3%95/%E5%B8%B8%E7%94%A8%E6%A6%82%E7%8E%87%E5%88%86%E5%B8%83(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[直接抽样法(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC19%E7%AB%A0%20%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E8%92%99%E7%89%B9%E5%8D%A1%E7%BD%97%E6%B3%95/%E7%9B%B4%E6%8E%A5%E6%8A%BD%E6%A0%B7%E6%B3%95(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 19.1】接受-拒绝法(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC19%E7%AB%A0%20%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E8%92%99%E7%89%B9%E5%8D%A1%E7%BD%97%E6%B3%95/%E6%8E%A5%E5%8F%97-%E6%8B%92%E7%BB%9D%E6%B3%95(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[迭代法求离散有限状态马尔可夫链的平稳分布(原生Python+numpy矩阵计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC19%E7%AB%A0%20%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E8%92%99%E7%89%B9%E5%8D%A1%E7%BD%97%E6%B3%95/%E8%BF%AD%E4%BB%A3%E6%B3%95%E6%B1%82%E7%A6%BB%E6%95%A3%E6%9C%89%E9%99%90%E7%8A%B6%E6%80%81%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E7%9A%84%E5%B9%B3%E7%A8%B3%E5%88%86%E5%B8%83(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E7%9F%A9%E9%98%B5%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[计算马尔可夫链是否不可约(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC19%E7%AB%A0%20%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E8%92%99%E7%89%B9%E5%8D%A1%E7%BD%97%E6%B3%95/%E8%AE%A1%E7%AE%97%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E6%98%AF%E5%90%A6%E4%B8%8D%E5%8F%AF%E7%BA%A6(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[计算马尔可夫链是否有周期性(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC19%E7%AB%A0%20%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E8%92%99%E7%89%B9%E5%8D%A1%E7%BD%97%E6%B3%95/%E8%AE%A1%E7%AE%97%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E6%98%AF%E5%90%A6%E6%9C%89%E5%91%A8%E6%9C%9F%E6%80%A7(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[遍历定理求离散有限状态马尔可夫链的平稳分布(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC19%E7%AB%A0%20%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E8%92%99%E7%89%B9%E5%8D%A1%E7%BD%97%E6%B3%95/%E9%81%8D%E5%8E%86%E5%AE%9A%E7%90%86%E6%B1%82%E7%A6%BB%E6%95%A3%E6%9C%89%E9%99%90%E7%8A%B6%E6%80%81%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E7%9A%84%E5%B9%B3%E7%A8%B3%E5%88%86%E5%B8%83(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[计算有限状态马尔可夫链是否可逆(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC19%E7%AB%A0%20%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E8%92%99%E7%89%B9%E5%8D%A1%E7%BD%97%E6%B3%95/%E8%AE%A1%E7%AE%97%E6%9C%89%E9%99%90%E7%8A%B6%E6%80%81%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E6%98%AF%E5%90%A6%E5%8F%AF%E9%80%86(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 19.2】Metropolis-Hastings算法(原生Python+numpy正态分布抽样实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC19%E7%AB%A0%20%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E8%92%99%E7%89%B9%E5%8D%A1%E7%BD%97%E6%B3%95/Metropolis-Hastings%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83%E6%8A%BD%E6%A0%B7%E5%AE%9E%E7%8E%B0).py)

[单分量Metropolis-Hastings算法(原生Python+numpy正态分布抽样实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC19%E7%AB%A0%20%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E8%92%99%E7%89%B9%E5%8D%A1%E7%BD%97%E6%B3%95/%E5%8D%95%E5%88%86%E9%87%8FMetropolis-Hastings%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83%E6%8A%BD%E6%A0%B7%E5%AE%9E%E7%8E%B0).py)

[【算法 19.3】二元正态分布的吉布斯抽样算法(原生Python+numpy正态分布抽样实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC19%E7%AB%A0%20%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E9%93%BE%E8%92%99%E7%89%B9%E5%8D%A1%E7%BD%97%E6%B3%95/%E4%BA%8C%E5%85%83%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83%E7%9A%84%E5%90%89%E5%B8%83%E6%96%AF%E6%8A%BD%E6%A0%B7%E7%AE%97%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83%E6%8A%BD%E6%A0%B7%E5%AE%9E%E7%8E%B0).py)

#### 第20章 潜在狄利克雷分配

[【算法 20.2】吉布斯抽样算法实现的LDA模型(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC20%E7%AB%A0%20%E6%BD%9C%E5%9C%A8%E7%8B%84%E5%88%A9%E5%85%8B%E9%9B%B7%E5%88%86%E9%85%8D/%E5%90%89%E5%B8%83%E6%96%AF%E6%8A%BD%E6%A0%B7%E7%AE%97%E6%B3%95%E5%AE%9E%E7%8E%B0%E7%9A%84LDA%E6%A8%A1%E5%9E%8B(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

【算法 20.5】变分EM算法实现的LDA模型(原生Python实现)-待实现

[变分EM算法实现的LDA模型(直接调用sklearn实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC20%E7%AB%A0%20%E6%BD%9C%E5%9C%A8%E7%8B%84%E5%88%A9%E5%85%8B%E9%9B%B7%E5%88%86%E9%85%8D/%E5%8F%98%E5%88%86EM%E7%AE%97%E6%B3%95%E5%AE%9E%E7%8E%B0%E7%9A%84LDA%E6%A8%A1%E5%9E%8B(%E7%9B%B4%E6%8E%A5%E8%B0%83%E7%94%A8sklearn%E5%AE%9E%E7%8E%B0).py)

#### 第21章 PageRank算法

[迭代法计算基本定义的PageRank(原生Python+numpy矩阵计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC21%E7%AB%A0%20PageRank%E7%AE%97%E6%B3%95/%E8%BF%AD%E4%BB%A3%E6%B3%95%E8%AE%A1%E7%AE%97%E5%9F%BA%E6%9C%AC%E5%AE%9A%E4%B9%89%E7%9A%84PageRank(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E7%9F%A9%E9%98%B5%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[【算法 21.1】迭代法计算一般定义的PageRank(原生Python+numpy矩阵计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC21%E7%AB%A0%20PageRank%E7%AE%97%E6%B3%95/%E8%BF%AD%E4%BB%A3%E6%B3%95%E8%AE%A1%E7%AE%97%E4%B8%80%E8%88%AC%E5%AE%9A%E4%B9%89%E7%9A%84PageRank(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E7%9F%A9%E9%98%B5%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[【算法 21.2】幂法计算一般定义的PageRank(原生Python+numpy矩阵计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC21%E7%AB%A0%20PageRank%E7%AE%97%E6%B3%95/%E5%B9%82%E6%B3%95%E8%AE%A1%E7%AE%97%E4%B8%80%E8%88%AC%E5%AE%9A%E4%B9%89%E7%9A%84PageRank(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E7%9F%A9%E9%98%B5%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[代数算法计算一般定义的PageRank(原生Python+numpy矩阵计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E7%AC%AC21%E7%AB%A0%20PageRank%E7%AE%97%E6%B3%95/%E4%BB%A3%E6%95%B0%E7%AE%97%E6%B3%95%E8%AE%A1%E7%AE%97%E4%B8%80%E8%88%AC%E5%AE%9A%E4%B9%89%E7%9A%84PageRank(%E5%8E%9F%E7%94%9FPython%2Bnumpy%E7%9F%A9%E9%98%B5%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

#### 附录A 梯度下降法

[梯度向量计算(原生Python+scipy导数计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E9%99%84%E5%BD%95A%20%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D%E6%B3%95/%E6%A2%AF%E5%BA%A6%E5%90%91%E9%87%8F%E8%AE%A1%E7%AE%97(%E5%8E%9F%E7%94%9FPython%2Bscipy%E5%AF%BC%E6%95%B0%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[基于黄金分割法的一维搜索(原生Python实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E9%99%84%E5%BD%95A%20%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D%E6%B3%95/%E5%9F%BA%E4%BA%8E%E9%BB%84%E9%87%91%E5%88%86%E5%89%B2%E6%B3%95%E7%9A%84%E4%B8%80%E7%BB%B4%E6%90%9C%E7%B4%A2(%E5%8E%9F%E7%94%9FPython%E5%AE%9E%E7%8E%B0).py)

[【算法 A.1(通常称为最速下降法)】最速下降法(原生Python+scipy导数计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E9%99%84%E5%BD%95A%20%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D%E6%B3%95/%E6%9C%80%E9%80%9F%E4%B8%8B%E9%99%8D%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bscipy%E5%AF%BC%E6%95%B0%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[梯度下降法(原生Python+scipy导数计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E9%99%84%E5%BD%95A%20%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D%E6%B3%95/%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bscipy%E5%AF%BC%E6%95%B0%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

#### 附录B 牛顿法和拟牛顿法

[【算法 B.1】牛顿法(原生Python+scipy导数计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E9%99%84%E5%BD%95B%20%E7%89%9B%E9%A1%BF%E6%B3%95%E5%92%8C%E6%8B%9F%E7%89%9B%E9%A1%BF%E6%B3%95/%E7%89%9B%E9%A1%BF%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bscipy%E5%AF%BC%E6%95%B0%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[【算法 B.2】DFP算法的拟牛顿法(原生Python+scipy导数计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E9%99%84%E5%BD%95B%20%E7%89%9B%E9%A1%BF%E6%B3%95%E5%92%8C%E6%8B%9F%E7%89%9B%E9%A1%BF%E6%B3%95/DFP%E7%AE%97%E6%B3%95%E7%9A%84%E6%8B%9F%E7%89%9B%E9%A1%BF%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bscipy%E5%AF%BC%E6%95%B0%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[【算法 B.3】BFGS算法的拟牛顿法(原生Python+scipy导数计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E9%99%84%E5%BD%95B%20%E7%89%9B%E9%A1%BF%E6%B3%95%E5%92%8C%E6%8B%9F%E7%89%9B%E9%A1%BF%E6%B3%95/BFGS%E7%AE%97%E6%B3%95%E7%9A%84%E6%8B%9F%E7%89%9B%E9%A1%BF%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bscipy%E5%AF%BC%E6%95%B0%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[使用Sherman-Morrison公式的BFGS算法的拟牛顿法(原生Python+scipy导数计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E9%99%84%E5%BD%95B%20%E7%89%9B%E9%A1%BF%E6%B3%95%E5%92%8C%E6%8B%9F%E7%89%9B%E9%A1%BF%E6%B3%95/%E4%BD%BF%E7%94%A8Sherman-Morrison%E5%85%AC%E5%BC%8F%E7%9A%84BFGS%E7%AE%97%E6%B3%95%E7%9A%84%E6%8B%9F%E7%89%9B%E9%A1%BF%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bscipy%E5%AF%BC%E6%95%B0%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

[Broyden算法的拟牛顿法(原生Python+scipy导数计算实现)](https://github.com/ChangxingJiang/Lihang-Statistical-learning-methods-Code/blob/main/%E9%99%84%E5%BD%95B%20%E7%89%9B%E9%A1%BF%E6%B3%95%E5%92%8C%E6%8B%9F%E7%89%9B%E9%A1%BF%E6%B3%95/Broyden%E7%AE%97%E6%B3%95%E7%9A%84%E6%8B%9F%E7%89%9B%E9%A1%BF%E6%B3%95(%E5%8E%9F%E7%94%9FPython%2Bscipy%E5%AF%BC%E6%95%B0%E8%AE%A1%E7%AE%97%E5%AE%9E%E7%8E%B0).py)

> 如果你认为还是更多值得实现的内容，或认为有更好的实现方法，都欢迎反馈给我，我会慢慢优化的。