Metadata-Version: 2.1
Name: thu_big_data_ml
Version: 0.0.2
Summary: 这是本人学习清华大学70240403-200大数据机器学习课程的开源工作，包括对往期Assignment的实现、对Lecture的笔记与理解、对即将来的Project的实现等，欢迎各位同学一起学习一起讨论，对知识取得更好的理解。
Home-page: https://github.com/Open-Book-Studio/THU-Coursework-Machine-Learning-for-Big-Data
Author: 叶璨铭
Author-email: ycm24@mails.tsinghua.edu.cn
License: Apache Software License 2.0
Keywords: nbdev jupyter notebook python
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: fastcore
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: openpyxl
Requires-Dist: scipy
Requires-Dist: statsmodels
Requires-Dist: ipywidgets
Provides-Extra: dev
Requires-Dist: ipykernel; extra == "dev"
Requires-Dist: nbdev; extra == "dev"
Requires-Dist: ray; extra == "dev"
Requires-Dist: optuna; extra == "dev"
Requires-Dist: plotly; extra == "dev"
Requires-Dist: nbformat; extra == "dev"
Requires-Dist: scikit-posthocs; extra == "dev"
Requires-Dist: scikit-plot; extra == "dev"
Requires-Dist: mlxtend; extra == "dev"

# THU-Coursework-Machine-Learning-for-Big-Data


<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

This file will become your README and also the index of your
documentation.

## Developer Guide

如果你想加入我们一起开源作业，请阅读以下指南。

If you are new to using `nbdev` here are some useful pointers to get you
started.

### 关于Quarto和nbdev一些需要配置的地方

``` sh
nbdev_install_quarto
quarto install tinytex
quarto install chromium
sudo apt-get install librsvg2-bin
```

### 关于nbdev、quarto+pandoc 这一套系统支持和不支持的markdown与latex语法

- latex公式：
  - 不能用””
  - 对于align公式,似乎都失败了 align, aligned和aligned\*,
    [参考](https://tex.stackexchange.com/questions/256920/package-amsmath-error-beginaligned-allowed-only-in-math-mode)
  - MathJax引擎支持的应该支持。https://quarto.org/docs/output-formats/html-basics.html
  - VSCode也用的是 MathJax
    https://stackoverflow.com/questions/62879232/how-do-i-use-latex-in-a-jupyter-notebook-inside-visual-studio-code
- markdown语法：

### Install THU_Coursework_Machine_Learning_for_Big_Data in Development mode

``` sh
# make sure THU_Coursework_Machine_Learning_for_Big_Data package is installed in development mode
$ pip install -e .

# make changes under nbs/ directory
# ...

# compile to have changes apply to THU_Coursework_Machine_Learning_for_Big_Data
$ nbdev_prepare
```

## Usage

我们在学习清华大学《大数据机器学习》以及《大数据分析》两门课程完成作业的同时，也形成了一个简单的机器学习与数据分析库，对李航《统计学习方法》上的部分代码做了实现和可视化，你可以通过安装我们的库来复用我们写的代码逻辑。

### Installation

Install latest from the GitHub
[repository](https://github.com/Open-Book-Studio/THU-Coursework-Machine-Learning-for-Big-Data):

``` sh
$ pip install git+https://github.com/Open-Book-Studio/THU-Coursework-Machine-Learning-for-Big-Data.git
```

<!-- 
or from [conda][conda]
&#10;```sh
$ conda install -c yecanming6666 thu_big_data_ml
``` -->

or from
[pypi](https://pypi.org/project/THU-Coursework-Machine-Learning-for-Big-Data/)

``` sh
$ pip install thu_big_data_ml
```

### Documentation

Documentation can be found hosted on this
https://thu-coursework-machine-learning-for-big-data-docs.vercel.app/ .
Additionally you can find package manager specific guidelines on
[pypi](https://pypi.org/project/THU-Coursework-Machine-Learning-for-Big-Data/)
respectively.

<!-- [conda][conda] and  -->
<!-- [conda]: https://anaconda.org/Open-Book-Studio/THU-Coursework-Machine-Learning-for-Big-Data -->

## How to use

Fill me in please! Don’t forget code examples:

``` python
1+1
```

    2
