# WenDesi/lihang_book_algorithm

致力于将李航博士《统计学习方法》一书中所有算法实现一遍

Repository: https://github.com/WenDesi/lihang_book_algorithm
Canonical: https://ross.abutalabs.com/products/lihang_book_algorithm
Homepage: http://blog.csdn.net/wds2006sdo/article/category/6314784
Language: Python
License Family: other
Last push: 2019-04-29T09:39:05+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3700, "days_push": 2683, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5827, forks 1936 (observed 2026-08-28T04:09:30.119212+00:00)

## What it is
A Python repository implementing the algorithms from Dr. Li Hang's textbook 'Statistical Learning Methods' chapter by chapter, including perceptron, KNN, naive Bayes, decision trees, logistic regression, maximum entropy, SVM, AdaBoost, and HMM. Each implementation is paired with a blog post explaining the approach, mostly demonstrated on the MNIST dataset.

## Use cases
- learn machine learning algorithms by reading clean Python implementations
- study algorithms from the Statistical Learning Methods textbook with code
- see how perceptron, KNN, naive Bayes, and SVM work on MNIST
- find reference implementations of AdaBoost and hidden Markov models
- supplement textbook study with hands-on coding examples

## When to choose
- you are studying Li Hang's Statistical Learning Methods and want code to follow along
- you want simple, readable from-scratch implementations of classic ML algorithms
- you are a student or beginner learning how these algorithms work internally

## When to avoid
- you need production-ready, optimized, or well-tested ML libraries
- you need algorithms beyond the book's coverage or modern deep learning
- you require a maintained project with a license and active support

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning
- domain: machine-learning, tutorials, education
- platform: python
- tags: statistical-learning-methods, lihang, mnist, algorithm-implementations, educational

## Member repositories
- WenDesi/lihang_book_algorithm (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:30.119212+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:52:06.139367+00:00, confidence not recorded.
  - readme: https://github.com/WenDesi/lihang_book_algorithm (fetched 2026-08-28T04:09:30.119212+00:00, sha 17fddf7d2b9e)
  - homepage: http://blog.csdn.net/wds2006sdo/article/category/6314784 (fetched 2026-08-29T08:47:35.875041+00:00, sha 1ea246320807)
- Data as of 2026-08-30T08:39:29.467469+00:00.