semvec/pypi-packages huggingface dataset:
pshitt 1.0.1
Passwords of SSH Intruders Transferred to Text
Requires Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4
Keywords: ssh intrusion-detection

======
PSHITT
======

Introduction
============

pshitt (for Passwords of SSH Intruders Transferred to Text) is a lightweight
fake SSH server designed to collect authentication data sent by intruders.
It basically collects username and password used by SSH bruteforce software
and writes the extracted data to a file in JSON format.

pshitt is written in Python and use paramiko to implement the SSH layer.

Running pshitt
==============

Go into the source directory and run ::

 ./pshitt -o passwords.json

This will run a fake SSH server listening on port 2200 to catch authentication
data sent by the intruders. Information about SSH connection attempt will be
stored in the ``passwords.json`` using JSON as format ::

 {"username": "root", "src_ip": "116.10.191.184", "password": "P@ssword", \
  "src_port": 41397, "timestamp": "2014-06-25T21:35:21.660303"}

Full options are available via '-h' option ::

 usage: pshitt [-h] [-o OUTPUT] [-k KEY] [-l LOG] [-p PORT] [-t THREADS] [-v]
               [-D]

 Passwords of SSH Intruders Transferred to Text

 optional arguments:
   -h, --help            show this help message and exit
   -o OUTPUT, --output OUTPUT
                         File to export collected data
   -k KEY, --key KEY     Host RSA key
   -l LOG, --log LOG     File to log info and debug
   -p PORT, --port PORT  TCP port to listen to
   -t THREADS, --threads THREADS
                         Maximum number of client threads
   -v, --verbose         Show verbose output, use multiple times increase
                         verbosity
   -D, --daemon          Run as unix daemon

Using pshitt data
=================

As the format is JSON, it is easy to use the data in data analysis
software such as Splunk or Logstash.

Here's a sample configuration for logstash ::

 input {
    file {
       path => [ "/var/log/pshitt.log" ]
       codec =>   json
       type => "json-log"
    }
 }

 filter {
     # warn logstash that timestamp is the one to use
     if [type] == "json-log" {
         date {
             match => [ "timestamp", "ISO8601" ]
         }
     }

     # optional but geoip is interesting
     if [src_ip]  {
         geoip {
             source => "src_ip"
             target => "geoip"
             add_field => [ "[geoip][coordinates]", "%{[geoip][longitude]}" ]
             add_field => [ "[geoip][coordinates]", "%{[geoip][latitude]}"  ]
         }
         mutate {
             convert => [ "[geoip][coordinates]", "float" ]
         }
     }
 }

 output {
   elasticsearch {
        host => "localhost"
   }
 }

Basically, it is just enough to mention that the ``pshitt.log`` file is
using JSON format.
implicit-reca 0.0.1
Package for implicit recommendations
Requires Python: >=3.6

# Implicit_reca

This is an implementation of the ALS algorithm under Apache Spark's mllib library, designed specifically for designing recommendation systems on implicit data, with the recent update on the regularization parameter.

## What is implicit data ?

The standard approach to matrix factorization-based collaborative filtering treats the entries in the user-item matrix as explicit preferences given by the user to the item, for example, users giving ratings to movies.

It is common in many real-world use cases to only have access to implicit feedback (e.g. views, clicks, purchases, likes, shares etc.). This is called implicit data.


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install foobar.

```bash
pip install implicit_reca
```

## Usage

```python
import implicit_reca as ir

ir.create_lookup(dataset)                                         # Create the lookup table for future reference.
ir.create_sparse(dataset,name_of_implicit_feautre)                # Create the sparse matrix of user x items (R).
ir.implicit_als(spmx,alpha,iterations,lambd,features)             # Main function behind the ALS algorithm.
ir.item_recommend(item_ID,item_vecs,item_lookup,no_items_rec)     # Item vs item recommendation.
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)
spy-di 0.4.0
A Simple PYthon Dependency Injection Library
Requires Python: >=3.6
Keywords: dependency-injection di

# Simple PYthon Dependepncy Injection Library (SPYDI)
## What the hell is it?
A dependency injection library which is intended to utilize python's type annotations to build the dependency tree.
No attempt has been made to optimize it since the only intended usage is right on app start so a slight slowness is acceptable in the majority of cases.

## Motivation
A brief research has shown there is no such library in the whole internet. Other solutions either:
 * Rely on manual binding-by-name;
 * Requires deep integration into a source code;
 * Do not play well with python async features and context-managed dependencies;
 * Do not work with my beloved `typing.NamedTuple`;
 * Doesn't work at all;

## How to use it
SPYDI does not require a deep integration into your application. Just design your app following the SOLID principles and SPYDI will handle the binding for you.
A brief example could be found here (or refer to tests)
```python
import typing

from spydi.context import DependencyContext
from spydi.factory import DependencyFactory


class Foo(typing.NamedTuple):
    a: int
    b: int
    c: str


def try_create_me(foo: Foo, bar):
    return foo.c + bar


ctx = DependencyContext()
ctx.bind(2, to='a')
ctx.bind(3, to='b')
ctx.bind('qwe', to='c')
ctx.bind('rty', to='bar')

factory = DependencyFactory(ctx)
result = factory.create(try_create_me)
assert result == 'qwerty'
```

## Licensing
This project is licensed under Apache 2.0 License
pyzemberek 0.0.3
Python implementation of the Turkish NLP library Zemberek.
Requires Python: >=3.5

# pyzemberek (v0.0.1-beta)
Türkçe Doğal Dil İşleme Kütüphanesi olan [Zemberek](https://github.com/ahmetaa/zemberek-nlp)'in, Python entegrasyonu.

## Requirements
- [Cython](https://pypi.org/project/Cython/)
- [pyjnius](https://pypi.org/project/pyjnius/)


## Changelog

-   19.04.2020
    -   tokenizasyon ve stemming örnekleri eklendi.

License
=======
    Copyright 2020 Yavuz Kömeçoğlu @Kodiks Bilişim

    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing, software
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.