semvec/pypi-packages huggingface dataset:
bobotools 0.4.8.0
bobotools
License: MIT
Requires Python: >=3.6

# bobotools
[![OSCS Status](https://www.oscs1024.com/platform/badge/bobo0810/bobotools.svg?size=small)](https://www.oscs1024.com/project/bobo0810/bobotools?ref=badge_small)
- 收录到[PytorchNetHub](https://github.com/bobo0810/PytorchNetHub)

## 安装

```bash
pip install bobotools
or
pip install git+https://github.com/bobo0810/bobotools.git
```

## 工具类

### Pytorch(torch_tools)
|  方法   | 功能  |
|  ----  | ----  |
| get_model_info  | 获取模型信息(模型大小、计算量、参数量、前向推理耗时等) |
| vis_tensor  | 可视化tensor|
| vis_cam  | 可视化注意力图|


### 图像(img_tools)
|  方法   | 功能  |
|  ----  | ----  |
| download_url  | 多进程下载URL图像|
| filter_md5  | 对输入的图像列表，互相去重，返回重复图像列表|
| verify_integrity  | 对输入的图像列表，验证完整性，返回错误图像列表|
| plot_yolo  | 可视化yolo结果|




### 文本(txt_tools)
|  方法   | 功能  |
|  ----  | ----  |
| read_lines  | 批量读取txt，支持指定分隔符 |
| write_lines  | 批量写入,保存为txt |

### 列表(list_tools)
|  方法   | 功能  |
|  ----  | ----  |
| chunk_N  | 列表均分为N块 |
| chunk_per| 列表分块，每块为指定长度M |


## 参考

- [Python打包](https://www.jianshu.com/p/9a5e7c935273)
FlyHash 1.1.1
A novel hashing algorithm based on "A neural algorithm for a fundamental computing problem" by S. Dasgupta, C. F. Stevens, and S. Navlakha (2017)
License: MIT
Requires Python: <3.12,>=3.7

[![Documentation Status](https://readthedocs.org/projects/flyhash/badge/?version=latest)](https://flyhash.readthedocs.io/en/latest/?badge=latest)
[![Coveralls](https://img.shields.io/coveralls/github/TeddyHuang-00/FlyHash/main.svg)](https://coveralls.io/r/TeddyHuang-00/FlyHash)
[![PyPI-Server](https://img.shields.io/pypi/v/FlyHash.svg)](https://pypi.org/project/FlyHash/)
[![Monthly Downloads](https://pepy.tech/badge/FlyHash/month)](https://pepy.tech/project/FlyHash)
[![Project generated with PyScaffold](https://img.shields.io/badge/-PyScaffold-005CA0?logo=pyscaffold)](https://pyscaffold.org/)

# FlyHash

> A novel hashing algorithm based on "A neural algorithm for a fundamental computing problem" by S. Dasgupta, C. F. Stevens, and S. Navlakha (2017)

FlyHash is a LSH algorithm that maps input data to a sparse hash embedding,
where the dimension of the hash embedding is much larger than the input,
and keeps the locality of the input data in the hash embedding.

FlyHash is designed to be cheap to compute, yet not ganranteeing
memory efficiency. It is suitable for hashing small to medium sized data
($d$ ~ 10-1000) to a large hash embedding ($m$ ~ 100-10000).

## Usage

Using a large hash_dim $m=100$ for a small input_dim $d=10$:

```python-repl
>>> import numpy as np
>>> from flyhash import FlyHash
>>> d = 10
>>> m = 100
>>> flyhash = FlyHash(d, m)
>>> data = np.random.randn(5, d)
>>> hashed_data = flyhash(data)
```

For detailed usage, please refer to the [documentation](https://FlyHash.readthedocs.io/en/latest/).