semvec/pypi-packages huggingface dataset:
vector-shortcuts 0.2.1
Vector and linear algebra toolbelt for NumPy

vx
==

[![version](https://img.shields.io/pypi/v/vector_shortcuts.svg?style=flat-square)][pypi]
[![license](https://img.shields.io/pypi/l/vector_shortcuts.svg?style=flat-square)][pypi]
[![build](https://img.shields.io/circleci/project/github/lace/vx/master.svg?style=flat-square)][build]
[![docs build](https://img.shields.io/readthedocs/vx.svg?style=flat-square)][docs build]
[![code style](https://img.shields.io/badge/code%20style-black-black.svg?style=flat-square)][black]

Vector and linear algebra toolbelt for [NumPy][].

[pypi]: https://pypi.org/project/vector_shortcuts/
[build]: https://circleci.com/gh/lace/vx/tree/master
[docs build]: https://vx.readthedocs.io/en/latest/
[black]: https://black.readthedocs.io/en/stable/
[lace]: https://github.com/metabolize/lace
[numpy]: https://www.numpy.org/


Features
--------

- `normalize` normalizes a vector.
- `sproj` computes the scalar projection of one vector onto another.
- `proj` computes the vector projection of one vector onto another.
- `reject` computes the vector rejection of one vector from another.
- `reject_axis` zeros or squashes one component of a vector.
- `magnitude` computes the magnitude of a vector.
- `angle` computes the unsigned angle between two vectors.
- `signed_angle` computes the signed angle between two vectors.
- `almost_zero` tests if a vector is almost the zero vector.
- `almost_collinear` tests if two vectors are almost collinear.
- `pad_with_ones` adds a column of ones.
- `unpad` strips off a column (e.g. of ones).
- `apply_homogeneous` applies a transformation matrix using homogeneous
  coordinates.
- Complete documentation: http://vx.readthedocs.io/


Installation
------------

```sh
pip install numpy vector_shortcuts
```


Usage
-----

```py
import numpy as np
import vx

projected = vx.sproj(np.array([5.0, -3.0, 1.0]), onto=vx.basis.neg_y)
```


Contribute
----------

- Issue Tracker: https://github.com/metabolize/vx/issues
- Source Code: https://github.com/metabolize/vx

Pull requests welcome!


Support
-------

If you are having issues, please let us know.


Acknowledgements
----------------

This collection was developed at Body Labs by [Paul Melnikow][] and extracted
from the Body Labs codebase and open-sourced as part of [blmath][] by [Alex
Weiss][]. blmath was subsequently [forked][fork] by Paul Melnikow and later
this namespace was broken out into its own package.

[paul melnikow]: https://github.com/paulmelnikow
[blmath]: https://github.com/bodylabs/blmath
[alex weiss]: https://github.com/algrs
[fork]: https://github.com/metabolize/blmath


License
-------

The project is licensed under the two-clause BSD license.
py-agender 0.0.9
Simple opencv & tensorflow based solution to estimate Faces, Age and Gender on pictures

# Face Age & Gender detection tool
Simple opencv & tensorflow solution to estimate Age and Gender in your 
next project. Command line or Python DIY style. 

Based on and forked from: https://github.com/yu4u/age-gender-estimation

Good enough for pet-projects and prototypes, but not ready for production 
real-life applications

## How

```commandline
pip3 install py-agender[cpu]  # for the cpu version of TensorFlow
pip3 install py-agender[gpu]  # for the gpu version of TensorFlow
```

**Warning**: ~190MB download (pretrained network is heavy).  


CLI: 

```commandline
py-agender PATH_TO_IMAGE
```

Python:

```python
from pyagender import PyAgender

agender = PyAgender() 
# see available options in __init__() src

faces = agender.detect_genders_ages(cv2.imread(MY_IMAGE))
# [
#   {left: 34, top: 11, right: 122, bottom: 232, width:(r-l), height: (b-t), gender: 0.67, age: 23.5},
#   ...
# ]

# Additional options & methods in PyAgender source
``` 

Don't forget to download pretrained weights if using source code DIY style.

## TODO: 
- add options (like STDIN input, output formatters etc.) for useful commandline 
applications 
- add help output
- train better network with higher image resolution
- train another (more compact & precise) network architecture

## Tests

```commandline
python3 -m unittest 
```

## Dependencies
- Python 3.5, 3.6, 3.7
- numpy ~> 1.15
- Keras ~> 2.2
- tensorflow or tensorflow-gpu ~> 1.10
- opencv-python ~> 3.4.2+contrib

Tested on:
- MacOS 10.13 high Sierra without GPU (you're welcome to update & contribute!)


# Model

These weigts are from https://github.com/yu4u/age-gender-estimation
on first console version run they are cached in **./pyagender.pretrained_models** folder:

https://github.com/yu4u/age-gender-estimation/releases/download/v0.5/weights.28-3.73.hdf5

# License

This project is released under the MIT license.

However, [the IMDB-Wiki dataset](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/) used for pretrained network above is originally provided under the following conditions:

> Please notice that this dataset is made available for academic research purpose only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform us, we will remove it from our dataset immediately.

Refer to fresh IMDB-Wiki dataset License and act accordingly.
MaLeSMo 0.2.7
Machine Learning Standard Models

# MaLeSMo - Machine Learning Standard Models

Python package with wrappers for standart machine learning models

## Installation

To install the package from PyPI type:

```
pip install malesmo
```

Or from the repo:

```
pip install git+https://github.com/loven-doo/MaLeSMo.git
```

For 'dev' branch version:

```
pip install git+https://github.com/loven-doo/MaLeSMo.git@dev
```

## Package reference

### gboost
ssh-alerts-telegram 0.0.4
Get Instant Alerts on Telegram with complete IP Details, whenever someone login into server using SSH.

# ssh-alerts-telegram

**ssh-alerts-telegram:** Get instant alerts on Telegram, when somemone logs into your server using SSH. 

## Install
pip install ssh-alerts-telegram

## STEPS

To get started
1. Go to your telegrama, serach for 'servergaze_bot' 
2. Select the bot and click 'Start' button to get your UID
3. Run command on server: "python -m ssh-alerts-telegram"
4. Fill in your given UID 
5. Assign name to your server in ServerID 
6. and you're done

```bash
python -m ssh-alerts-telegram
```

## License

MIT licensed. See the
[LICENSE](https://github.com/geetanshjindal/ssh-alerts-telegram/blob/master/LICENSE) file
for more details.