https://github.com/foldfelis/algorithm101

This repository is not just an implement yet a learning process to fully understand the algorithms
https://github.com/foldfelis/algorithm101

Last synced: 1 day ago
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This repository is not just an implement yet a learning process to fully understand the algorithms

• Host: GitHub
• URL: https://github.com/foldfelis/algorithm101
• Owner: foldfelis
• License: mit
• Created: 2020-01-13T05:18:31.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2021-04-12T07:05:12.000Z (over 3 years ago)
• Last Synced: 2024-11-11T08:51:46.343Z (2 months ago)
• Language: Julia
• Homepage:
• Size: 71.3 KB
• Stars: 5
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Algorithm101

[![CI](https://github.com/foldfelis/Algorithm101/actions/workflows/ci.yml/badge.svg)](https://github.com/foldfelis/Algorithm101/actions/workflows/ci.yml)

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[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)

This repository is not just an implement yet a learning process to fully understand the algorithms.

In this reop, I implement some common and useful algorithms.

## Divide and conquer

- [x] Find max subarray

## Sorting

- [x] Merge sort

- [x] Quick sort

- [x] Topological sort

## Searching

- [x] Binary search

- [x] Binary search tree

## Approximation

- [x] Monte Carlo integral

- [ ] Metropolis–Hastings algorithm approximate Gamma(k=2.0,theta=2.0)

## Dynamic programming

- [x] Matrix chain multiplication

- [ ] Longest common subsequence

## Compression

- [ ] Huffman coding (100 days: 14)

## Tree

- [x] Kruskal's algorithm (Minimum spanning tree)

- [x] Prim's algorithm (Minimum spanning tree)

- [x] Tree traverse: pre-order, in-order, post-order

- [ ] JSON Parser

## Graph

- [x] Depth first search on graph

- [x] Breadth first search on graph

- [ ] Dijkstra's algorithm (100 days: 48)

- [ ] PageRank (100 days: 32)

- [ ] Strongly connected components (100 days: 95)

## Machine learning

- [ ] Perceptron learning algorithm (100 days: 14)

- [ ] Linear regression with linear algebra solution

- [ ] Linear regression with gradient descent

- [ ] Conjugate gradient method

- [ ] Logistic regression with IRLS (100 days: 79)

- [ ] Markov chain

- [ ] Hidden Markov model ([ref](http://www.csie.ntnu.edu.tw/~u91029/HiddenMarkovModel.html#2))

- [ ] K nearest neighbor classifier

- [ ] K-means (100 days: 21)

- [ ] Principle component analysis (100 days: 92)

- [ ] Naive Bayes classifier

- [ ] Decision tree classifier (ID3)

## Applications

- [ ] POS system

- [ ] Interpreter for arithmetics

- [ ] MVC pattern for machine learning platform