semvec/pypi-packages huggingface dataset:
proxynova-scraper 0.1
Package to get proxies from proxynova.com via web scraping
License: MIT
Requires Python: >=3

# proxynova_scraper

Get free proxies for your projects from [proxinova.com](https://proxinova.com)

## Getting started


**Installing the package**

```text
pip install proxynova-scrapper
```

**Use**

You have two ways to get the proxies:

- Get the latest ones
- Get proxies by country

**Get the latest ones from different countries**

It will scrap the [main page](https://www.proxynova.com/proxy-server-list) from proxynova 

```python
from proxynova_scraper import get_proxies
proxies = get_proxies()
```

**Get proxies by country**

It will scrap [proxynova by specifying the country](https://www.proxynova.com/proxy-server-list/country-mx) (2 char code), if the table from proxynova doesn't contain any proxy it will return an empty array

```python
from proxynova_scraper import get_proxies_by_country
mexico_proxies = get_proxies_by_country('mx')
```

**Proxy item** 

This is an example of the content returned by `get_proxies` or `get_proxies_by_country`

```python
{
    'proxyIp': '45.174.77.241', 
    'proxyPort': '999', 
    'lastCheck': '2022-07-23 22:32:03Z', 
    'proxySpeed': 2735, 
    'Uptime': '5114', 
    'proxyCountry': 'Mexico - Chihuahua City', 
    'anonymity': 'Transparent'
}

```
proxynova-scrapper 0.2
Package to get proxies from proxynova.com via web scraping
License: MIT
Requires Python: >=3

# proxynova_scraper

Get free proxies for your projects from [proxinova.com](https://proxinova.com)

## Getting started


**Installing the package**

```text
pip install proxynova-scrapper
```

**Use**

You have two ways to get the proxies:

- Get the latest ones
- Get proxies by country

**Get the latest ones from different countries**

It will scrap the [main page](https://www.proxynova.com/proxy-server-list) from proxynova 

```python
from proxynova_scraper import get_proxies
proxies = get_proxies()
```

**Get proxies by country**

It will scrap [proxynova by specifying the country](https://www.proxynova.com/proxy-server-list/country-mx) (2 char code), if the table from proxynova doesn't contain any proxy it will return an empty array

```python
from proxynova_scraper import get_proxies_by_country
mexico_proxies = get_proxies_by_country('mx')
```

**Proxy item** 

This is an example of the content returned by `get_proxies` or `get_proxies_by_country`

```python
{
    'proxyIp': '45.174.77.241', 
    'proxyPort': '999', 
    'lastCheck': '2022-07-23 22:32:03Z', 
    'proxySpeed': 2735, 
    'Uptime': '5114', 
    'proxyCountry': 'Mexico - Chihuahua City', 
    'anonymity': 'Transparent'
}

```
clustermil 0.2.0
clustermil - clustering based multiple instance learning
License: MIT
Requires Python: >=3.8,<4.0
Keywords: machine learning,multiple instance learning,scikit-learn

# clustermil

[![Build Status](https://app.travis-ci.com/inoueakimitsu/clustermil.svg?branch=main)](https://app.travis-ci.com/inoueakimitsu/clustermil)
<a href="https://github.com/inoueakimitsu/clustermil/issues"><img alt="GitHub issues" src="https://img.shields.io/github/issues/inoueakimitsu/clustermil"></a> 

Python package for multiple instance learning (MIL) for large n_instance dataset.

## Features

- support count-based multiple instance assumptions (see [wikipedia](https://en.wikipedia.org/wiki/Multiple_instance_learning#:~:text=Presence-%2C%20threshold-%2C%20and%20count-based%20assumptions%5Bedit%5D))
- support multi-class setting
- support scikit-learn Clustering algorithms (such as `MiniBatchKMeans`)
- fast even if n_instance is large

## Installation

```bash
pip install clustermil
```

## Usage

```python
# Prepare follwing dataset
#
# - bags ... list of np.ndarray
#            (num_instance_in_the_bag * num_features)
# - lower_threshold ... np.ndarray (num_bags * num_classes)
# - upper_threshold ... np.ndarray (num_bags * num_classes)
#
# bags[i_bag] contains not less than lower_thrshold[i_bag, i_class]
# i_class instances.

# Prepare single-instance clustering algorithms
from sklearn.cluster import MiniBatchKMeans
n_clusters = 100
clustering = MiniBatchKMeans(n_clusters=n_clusters)
clusters = clustering.fit_predict(np.vstack(bags)) # flatten bags into instances

# Prepare one-hot encoder
from sklearn.preprocessing import OneHotEncoder
onehot_encoder = OneHotEncoder()
onehot_encoder.fit(clusters)

# generate ClusterMilClassifier with helper function
from clustermil import generate_mil_classifier

milclassifier = generate_mil_classifier(
            clustering,
            onehot_encoder,
            bags,
            lower_threshold,
            upper_threshold,
            n_clusters)

# after multiple instance learning,
# you can predict instance class
milclassifier.predict([instance_feature])
```

See `tests/test_classification.py` for an example of a fully working test data generation process.

## License

clustermil is available under the MIT License.
syn-db-cli 1.0.0
A simple command line interface for syndb
License: MIT
Keywords: cli,db,sqlite,sql,syncli

# Syn

Syn is a simple, fast, and powerful database management cli for SynDB.


## Installation

Install Syn with git

```bash
  git https://github.com/Synterra-Technologies/SynTech-CLI.git
  cd Syn-CLI
  pyinstaller -onefile main.py
```
    
## Deployment

To run the tool

```bash
  main help
```


## Used By

This project is used by the following companie(s):

- SynTech


## Features

- Database Management (Coming Soon)
- Easy To Learn
- Cross platform


## Author(s)

- [@DeveloperJosh](https://www.github.com/DeveloperJosh)


## Support

For support, email blue@syntech.dev or join our [discord](https://discord.gg/kV8HP2qa2j)