semvec/pypi-packages huggingface dataset:
mkdocs-random-walk-plugin 0.1.1
A MkDocs plugin that generates a random link of pages
License: MIT
Requires Python: >=3.5
Keywords: mkdocs python markdown notes random random_walk

# mkdocs-random_walk-plugin

一个用于 mkdocs 文档漫游的插件，类似 Obsidian 自带的笔记漫游功能的简陋实现。

这只是我的一个 toy project：简单地配合 HTML + JS 前端，以及 Python 后端，实现随机跳转笔记的功能。所以我的代码实现比较粗糙，有些地方可能还可以写的更好，但鉴于本人的代码水平，目前就先这样吧...

其中 Python 部分是根据 [@TonyCrane](https://github.com/TonyCrane) 前辈的 [mkdocs-statistics-plugin](https://github.com/TonyCrane/mkdocs-statistics-plugin) 修改而来的，所以代码的一些部分与其十分相似。

可以把这个插件当作获取灵感的途径 ~~，也可以就把它当个打发闲暇时间的玩具。~~

预览：<https://note.noughtq.top/>（我的笔记首页“笔记漫游”链接）

我的配置（目前暂未更新）：<https://github.com/NoughtQ/notebook/blob/master/mkdocs.yml>


## 安装

可以通过 pypi 直接安装：

```sh
$ pip install mkdocs-random-walk-plugin
```

也可以通过源码安装：

```sh
$ git clone https://github.com/NoughtQ/mkdocs-random_walk-plugin.git
$ cd mkdocs-random-walk-plugin
$ pip install . 
```

## 使用

- 在 mkdocs.yml 中启用插件：

    ```yaml
    plugins:
      - random_walk
    ```

    配置选项及解释（优先级从上到下越来越高）：

    | 选项 | 类型 | 默认值 | 解释 |
    |:----|:----|:----|:----|
    |`include_path`|`list`|`None`|只统计匹配的路径（只包含字符串项的列表），路径相对 docs，为空则不启用）|
    |`exclude_path`|`list`|`None`|不统计匹配的路径（只包含字符串项的列表），路径相对 docs，为空则不启用）|
    |`black_list`|`list`|`None`|黑名单（比 `exclude_path` 更灵活）只包含字符串项的列表），路径相对 docs，为空则不启用）|

- 在被设置笔记漫游功能的 Markdown 文件（页面）上
  - 先在元数据处启用功能：

    ```md
    ---
    random_walk: true
    ---
    ```

  - 再设置 HTML 链接（注意不是 Markdown 链接）

    ```html
    <!-- href: 初始/默认链接 -->
    <!-- id: 指定为 randomLink，不要改成其他值 -->
    <!-- markdown: 设置该属性，便于在 <a> 元素内使用 Markdown 语法 -->
    <a href="#" id="randomLink" markdown="1">  
      笔记漫游
    </a>
    ```
  
  - 在网页点击该链接便可跳转到该页面所在目录下的任一页面，刷新页面后会重新生成随机链接


## 问题

目前在自己电脑上跑没有什么问题，但我不确保在各位的电脑上能够正常运行，所以如果发现 bug 的话请及时在 Issues 里提醒我，十分感谢！
anymethod 0.1.2
@anymethod = instance method and @classmethod
License: MIT
Requires Python: !=3.0,!=3.1,!=3.2,!=3.3,!=3.4,>=2.7
Keywords: classmethod, descriptor, instance-method, polymorphism

# anymethod
> @anymethod = instance method + @classmethod

[![license](https://img.shields.io/github/license/makukha/anymethod.svg)](https://github.com/makukha/anymethod/blob/main/LICENSE)
[![pypi](https://img.shields.io/pypi/v/anymethod.svg#v0.1.2)](https://pypi.python.org/pypi/anymethod)
[![python versions](https://img.shields.io/pypi/pyversions/anymethod.svg)](https://pypi.org/project/anymethod)
[![tests](https://raw.githubusercontent.com/makukha/anymethod/v0.1.2/docs/badge/tests.svg)](https://github.com/makukha/anymethod)
[![coverage](https://raw.githubusercontent.com/makukha/anymethod/v0.1.2/docs/badge/coverage.svg)](https://github.com/makukha/anymethod)
[![tested with multipython](https://img.shields.io/badge/tested_with-multipython-x)](https://github.com/makukha/multipython)
[![uses docsub](https://img.shields.io/badge/uses-docsub-royalblue)](https://github.com/makukha/docsub)

For the cases when class and its instances can be used interchangeably, code duplication can be avoided if the same member function works both as `@classmethod` and instance method.

This can be easily achieved with Python [descriptors](https://docs.python.org/3/howto/descriptor.html).


## Installation

```shell
$ pip install anymethod
```


## Example A

<!-- docsub: begin -->
<!-- docsub: include tests/mypy/test_exampleA.txt -->
<!-- docsub: lines after 1 upto -1 -->
```dectest
>>> from anymethod import anymethod

>>> class FooBar:
...     @anymethod
...     def whoami[O](owner: O) -> O:
...         return owner

>>> FooBar.whoami()
<class '__main__.FooBar'>

>>> FooBar().whoami()
<__main__.FooBar object at 0x...>
```
<!-- docsub: end -->


## Example B

<!-- docsub: begin -->
<!-- docsub: include tests/mypy/exampleB.py -->
<!-- docsub: lines after 1 upto -1 -->
```python
from typing import Any, ClassVar
from anymethod import anymethod


class FooBar:
    _cls: ClassVar[list[Any]] = []
    _obj: list[Any]

    def __init__(self) -> None:
        self._obj = []

    @anymethod
    def add_value(owner, v: Any) -> None:
        if isinstance(owner, type):
            owner._cls.append(v)
        else:
            owner._obj.append(v)
```
<!-- docsub: end -->


## More info

Check the [Project changelog](https://github.com/makukha/anymethod/tree/main/CHANGELOG.md)
GridSearchHelper 0.8.0
Advanced hyperparameter tuning using grid search for ML models
License: MIT
Requires Python: >=3.6
Keywords: machine-learning grid-search hyperparameter-tuning scikit-learn

# 📊 GridSearchHelper: Advanced Hyperparameter Tuning Library

Welcome to **GridSearchHelper**, a powerful and flexible hyperparameter tuning library designed to make model optimization effortless! 🚀

## ✨ Features

- 🔄 **Automated Hyperparameter Grid Generation** for supported models
- 📈 **Seamless Integration** with Scikit-Learn's GridSearchCV
- ⚡ **Supports Classification & Regression Models**
- 🛠️ **Customizable Parameter Grids**
- 🎯 **Easy-to-Use API**

---

## 📌 Installation

```bash
pip install GridSearchHelper
```

---

## 🚀 Quick Start

### Import and Initialize

```python
from GridSearchHelper import perform_grid_search
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_diabetes

# Load dataset
data = load_diabetes()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Scale features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Run Hyperparameter Tuning
best_params, best_score, grid_search = perform_grid_search(
    model_name='Ridge',
    X_train=X_train_scaled,
    y_train=y_train,
    cv_folds=5,
    scoring='neg_mean_squared_error'
)

print(f'Best Parameters: {best_params}')
```

---

## ⚙️ Supported Models

- RandomForestClassifier 🌲
- GradientBoostingClassifier 🔥
- SVC 🛡️
- LogisticRegression 📊
- Ridge 📏
- Many more...

---

## 🔧 Configuration

To add custom hyperparameters, simply pass them as a dictionary:

```python
custom_params = {
    'alpha': [0.01, 0.1, 1, 10],
    'solver': ['auto', 'svd', 'cholesky']
}
perform_grid_search('Ridge', X_train_scaled, y_train, additional_params=custom_params)
```

---

## 📜 License

MIT License © 2025 Abdulla Alimov

---

## 🤝 Contributing

Contributions are welcome! Feel free to submit issues or pull requests.

🌟 **Star this repo if you find it useful!**