semvec/pypi-packages huggingface dataset:
kmor 1.0.7
K-means clustering with outlier removal numpy implementation
Requires Python: >=3
Keywords: kmor,anomality detection,cleaning,k-mean,data science

# KMOR Numpy

The python implementation for k-means clustering with outlier removal from the paper written by Guojun Gan et al. [[1]](#1)

## Installation

```
pip install kmor
```

You can also install by conda

```
conda install -c ksunhokim kmor
```

## Example

```python
import numpy as np
from kmor import kmor
X = np.array([
    [1,0,0],
    [0,1,0],
    [0,0,1],
    [0,0,100]
])
U = kmor(X, 1)
print(U) # [0,0,0,1]
```

The outliers are assigned to the extra cluster k.

## Documentation

kmor(X, k, y, nc0, max_iteration, gamma)

| Parameter     | Description                                                                    |
|---------------|--------------------------------------------------------------------------------|
| X             | Your data.                                                                     |
| k             | Number of clusters.                                                            |
| y             | Parameter for outlier detection. (default=3) Increase this to make outlier removal subtle. |
| nc0           | Maximum percentage of your data that can be assigned to outlier cluster. (default=0.1)    |
| max_iteration | Maximum number of iterations.                                                  |
| gamma         | Used to check the convergence.                                                 |


## References

<a id="1">[1]</a> Gan, Guojun, and Michael Kwok-Po Ng. "K-means clustering with outlier removal." Pattern Recognition Letters 90 (2017): 8-14.
optmatch 1.0.0
command line parsing library
Keywords: args,parsing,easy

**optmatch** - Library to allow an easy and effective parsing of command line options.

Full documentation: https://coderazzi.net/python/optmatch

**optmatch** is based on expressing the actions -with all options and related flags-
that are supported, instead of just listing all possible argument flags.

For example:

    class Example(**OptionMatcher**):

        **@optset**

        def handle_common_flag(self, **mail_option**):

            ...


        **@optmatcher**

        def handle_compression(self, filename, **compress_flag** =False):

            ...


        **@optmatcher(flags='verbose', options='mode')**

        def handle(self, filename, verbose=False, mode='simple', where=None):

            ...


In this example:


1. It is defined a common flag (--mail)

2. It is defined an action, supporting a --compress flag which is False by default,  and requiring a filename parameter

3. It is defined a second action, supporting a --verbose flag and a --mode option, requiring one or two file locations.
ctrl.engine 0.0.3
A universal control engine

<div align="center">
  <img src="https://raw.githubusercontent.com/0xJeremy/ctrl.engine/master/graphics/Logo.png">
</div>

#### A general purpose robotics engine

## Installation

[Python](https://pypi.org/project/ctrl.engine/):
```
pip install ctrl.engine
```

This library is tested with Ubuntu 19.04 and Python 3.7.5. It was developed specifically for use with robotics using a Raspberry Pi.

## Modules

```python
from ctrlengine.controllers import PID

from ctrlengine.filters import simple_moving_average
from ctrlengine.filters import exponentially_weighted_moving_average

from ctrlengine.sensors import camera

from ctrlengine.interface import xbox_ctrl

from ctrlengine.util import logger

from ctrlengine.ai import face_detection
from ctrlengine.ai import image_classificatoin
from ctrlengine.ai import object_detection
from ctrlengine.ai import pose_detection

from ctrlengine.ai.cloud_vision import cloud_vision
from ctrlengine.ai.text_to_speech import text_to_speech

from ctrlengine.ai.azure_vision import azure_vision
```
twsyncer 0.0.4
Tool to sync Taswarrior and remote task trackers
Requires Python: >=3.6

[![Build Status](https://travis-ci.com/sorrowless/twsyncer.svg?branch=master)](https://travis-ci.com/sorrowless/twsyncer)
![PyPI](https://img.shields.io/pypi/v/twsyncer)


# Taskwarrior Sync

Taskwarrior Sync is a small tool to sync local Taskwarrior database with some
remote issues tracker. Currently Github is a priority and it is only supported
issue tracker.


### Installation

`pip install twsyncer`

### Configuration

Create configuration file in `~/.config/twsyncer/config.json` (look at
the [example](`config.example.json`).

If you do not want to create config, you can just export several environment
variables:

`TW_DRY_RUN` should be set to `False` to really sync something

`TW_GITHUB_TOKEN` should be set to Github token with proper access permissions

`TW_GITHUB_ORG_LOGIN` is a login of organization in which project with cards
exists

`TW_GITHUB_PROJECT` is a name of the project

`TW_GITHUB_ISSUES_REPO` is a repo from which issues will be linked to project

`TW_FILTER_PROJECT` is a project name in local Taskwarrior to sync upon

After setting all of there ensure that tw_to_github_states mapped
properly.Keys of that mapping are states in TaskWarrior and values are
possible column names in project. In case there will be any inconsistence
between these, according Github issues will be moved across different columns.
Also all the issues in any of the 'completed' column will be closed
automatically.
sysDB 0.1
A x86 and x64 syscall database

# syscallDB
## x86 and x64 syscall database
Have you ever been doing some assembly and finding yourself looking furiously for some nisch syscall\
Well, that will no longer be a problem with syscallDB's reverse lookup, did you forget rax for execve?
## x86
```py
import sysDB
sysDB.syscall32(0) # gives syscall 0
sysDB.syscall32("exec") # gives all syscalls with "exec" in their name
```
## x64
```py
import sysDB
sysDB.syscall64(0) # gives syscall 0
sysDB.syscall64("exec") # gives all syscalls with "exec" in their name
```