semvec/pypi-packages huggingface dataset:
triangle-calculations-20240801 0.0.1
Triangle calculations
Requires Python: >=3.6

# 三角形计算库triangular calculation library
可以用它计算面积，周长，四心，费马点，拿破仑点

It can be used to calculate area, perimeter, four hearts, Fermat points, Napoleon points

其中费马点是用退火算法逼近

where the Fermat point is approximated by the annealing algorithm
aioymaps 1.2.5
Async client for Yandex Maps

``aioymaps`` is an asynchronous library provides API to fetch json information
about transport stops from Yandex Maps


Requirements
============
``aioymaps`` requires python 3.5 or higher due to async/await syntax and aiohttp
library


Installation
============

Use pip to install the library:
::

    pip install aioymaps

Or install manually.
::

    sudo python ./setup.py install


Usage
=====
::

    from aioymaps import YandexMapsRequester

    requester = YandexMapsRequester()
    data = await requester.get_stop_info('stop__10067199')
    print(data)


Or you can use it in your shell:
::

    python -m aioymaps -s group__219
Triangle-calculations-yes 0.0.1
Triangle calculations
Requires Python: >=3.6

# 三角形计算库triangular calculation library
可以用它计算面积，周长，四心，费马点，拿破仑点

It can be used to calculate area, perimeter, four hearts, Fermat points, Napoleon points

其中费马点是用退火算法逼近

where the Fermat point is approximated by the annealing algorithm
contentbased 0.1.0
Contentbased Recommender
Requires Python: <4.0,>=3.12

# TCC DMS Recommender System

## Introduction
The project is structured as a GitLab repository for the DMS Recommender service. We're providing all types of recommender including, collaborative filtering, content-based, market basket analysis. Developers can choose any type of the recommender based on the use cases and user onboarding time period. For example, new user can apply, such as, content-based and market basket analysis. In later phase, collaborative filtering can be used.

## Installation
```bash
pip install ...
```

## Example Usage

### Content-based recommendation
It is recommended to use when new users are onboarding in the platform.
```python
# set up  the recommender (connect DB and choose tables)

# see all available categories or sub-categories

# prepare user preference for categories and sub-categories with top K (using category or sub-category IDs)
# without K, default is ...

# create top products list for this customers with relevant scores

# Now, apply these list with your app
```

### Market Basket Analysis
It is recommended to use when new users are onboarding in the platform.
```python
# set up recommender (connect DB and choose tables)

# run analysis

# see analysis result

# export analysis result as .csv

# inference the recommendations
```

### Collaborative filtering
Recommended to use when users have purchasing history more than ... months or ... transactions. Also, routine updating model is mandatory.
```python
# set up recommender (connect DB and choose tables)
```
Ensure your database contains the following tables with appropriate data:

- SKUMASTER
- ICCAT
- ICDEPT
- TRANSTKD
- GOODSMASTER
```

# run model training

# see evaluation result

# save model to path

# inference the recommendations
```