semvec/pypi-packages huggingface dataset:
aloe-webdriver 0.6.0
Selenium webdriver extension for Aloe

Aloe steps for Web Testing with Selenium
========================================

[![Build status](https://img.shields.io/travis/aloetesting/aloe_webdriver.svg)](https://travis-ci.org/aloetesting/aloe_webdriver)
[![Coverage](https://img.shields.io/coveralls/aloetesting/aloe_webdriver.svg)](https://coveralls.io/github/aloetesting/aloe_webdriver)
[![PyPI](https://img.shields.io/pypi/v/aloe_webdriver.svg)](https://pypi.python.org/pypi/aloe_webdriver)

A set of [Gherkin][gherkin] steps for use with [Aloe][aloe] to test Web
applications using Selenium.

Based on [lettuce_webdriver][lettuce_webdriver] which, in turn, is inspired by
[cucumber_watir][cucumber_watir].

Install:

    pip install aloe_webdriver

Read the [documentation][documentation].

License
-------

The MIT License (MIT)

Copyright (C) <2015> Alexey Kotlyarov <a@koterpillar.com>

Copyright (C) <2015> Danielle Madeley <danielle@madeley.id.au>

Copyright (C) <2013-2014> Infoxchange <devs@ix.org.au>

Copyright (C) <2011> Ben Bangert and all contributors.

Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:

The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

[aloe]: http://aloe.readthedocs.io/
[cucumber_watir]: https://github.com/napcs/cucumber_watir
[gherkin]: https://cucumber.io/
[documentation]: http://aloe.readthedocs.io/projects/aloe-webdriver/
[lettuce_webdriver]: https://github.com/bbangert/lettuce_webdriver
pykaHFM 0.2.2
An implementation of kaHFM algorithm
Keywords: pykaHFM

# pykaHFM

Implementace algoritmu kaHFM.

```python
import numpy as np

from pykaHFM import (
    TFIDFTransformer,
    FactorizationMachine,
    StochasticGradientDescent,
    load_knowledge_base_triples,
)

movies = ["Avengers", "Avengers: Infinity War",
          "Stardust", "Princess Bride", "Last Witchhunter", "Tall Girl"]
users = ["Chandler", "Joey", "Ross", "Monica", "Rachel", "Phoebe"]


n_movies = len(movies)
n_users = len(users)

user_movie_matrix = np.random.randint(1, 6, size=(n_users, n_movies))
knowledge_base = [('Avengers', 'genre', 'Teen'),
 ('Avengers', 'genre', 'Thriller'),
 ('Avengers', 'genre', 'Comedy'),
 ('Avengers', 'genre', 'Fantasy'),
 ('Avengers', 'genre', 'Comedy'),
 ('Avengers', 'genre', 'Adult'),
 ('Avengers', 'genre', 'Adult'),
 ('Avengers: Infinity War', 'genre', 'Adult'),
 ('Avengers: Infinity War', 'genre', 'Teen'),
 ('Avengers: Infinity War', 'genre', 'Adventure'),
 ('Avengers: Infinity War', 'genre', 'Thriller'),
 ('Stardust', 'genre', 'Comedy'),
 ('Stardust', 'genre', 'Adventure'),
 ('Stardust', 'genre', 'Drama'),
 ('Stardust', 'genre', 'Adult'),
 ('Stardust', 'genre', 'Comedy'),
 ('Stardust', 'genre', 'Drama'),
 ('Princess Bride', 'genre', 'Adventure'),
 ('Last Witchhunter', 'genre', 'Fantasy'),
 ('Last Witchhunter', 'genre', 'Drama'),
 ('Last Witchhunter', 'genre', 'Action'),
 ('Tall Girl', 'genre', 'Teen'),
 ('Tall Girl', 'genre', 'Adult'),
 ('Tall Girl', 'genre', 'Thriller'),
 ('Tall Girl', 'genre', 'Adventure'),
 ('Tall Girl', 'genre', 'Teen'),
 ('Tall Girl', 'genre', 'Teen'),
 ('Tall Girl', 'genre', 'Drama'),
 ('Tall Girl', 'genre', 'Sci-fi')
 ]

tfidf = TFIDFTransformer(knowledge_base, user_movie_matrix, users, movies)
tfidf.generate_v_matrix()

fm = FactorizationMachine(users, movies, user_movie_matrix, tfidf.v_matrix)

sgd = StochasticGradientDescent(fm, iterations=10, learning_rate=0.0001)
sgd.fit()

Y_hat = fm.predict_all()

print(Y_hat)


```
CHESS-python 2.0.0
Clustered Hierarchical Entropy-Scaling Search
Requires Python: >=3.6

# Clustered Hierarchical Entropy-Scaling Search of Astrophysical and Biological Data

CHESS is a search algorithm for large data sets when the data exhibits certain geometric properties.
The [paper](https://arxiv.org/abs/1908.08551.pdf) is available on the arXiv.

We have extended CHESS to perform Manifold Learning and Anomaly Detection.
We are working on adding Dimensionality Reduction and Visualization abilities, and on 3-d Object Recognition from point clouds.
One of the major problems with this extension is that we need a new name.
Stay tuned.

[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/nishaq503/CHESS.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/nishaq503/CHESS/context:python)
[![codecov](https://codecov.io/gh/thoward27/chess/branch/master/graph/badge.svg)](https://codecov.io/gh/thoward27/chess)
[![Documentation Status](https://readthedocs.org/projects/clustered-hierarchical-entropy-scaling-search/badge/?version=latest)](https://clustered-hierarchical-entropy-scaling-search.readthedocs.io/en/latest/?badge=latest)

## Installation

```bash
python3 -m pip install CHESS-python
```

## Usage

```python
import numpy as np

from chess.datasets import bullseye
from chess.manifold import Manifold
from chess import criterion

# Get the data.
data, _ = bullseye()
# data is a numpy.ndarray in this case but it could just as easily be a numpy.memmap if your data cannot fit in RAM.
# We used memmaps for the research, though it does impose file-io costs.

manifold = Manifold(data=data, metric='euclidean')
# Any metric allowed by scipy's cdist function is allowed in Manifold.
# You can also define your own distance function. It will work so long as scipy allows it.

manifold.build(criterion.MaxDepth(20), criterion.MinRadius(0.25))
# Manifold.build can optionally take any number of early stopping criteria.
# chess.criterion defines some criteria that we have used in research.
# You are free to define your own.
# Take a look at chess/criterion.py for hints of how to define custom criteria.

# A sample rho-nearest neighbors search query
query, radius = data[0], 0.05
results = manifold.find_points(point=query, radius=radius)
# results is a dictionary of indexes of hits in data and the distance to those hits.

# A sample k-nearest neighbors search query
results = manifold.find_knn(point=query, k=25)
```

chess.Manifold relies on the Graph and Cluster classes.
You can import these and work with them directly if you so choose.
We have written good docs for each class and method.
Go crazy.

## Contributing

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

## License

[MIT](LICENSE)
CHESS-python-nightly 1.1.1.dev41
Clustered Hierarchical Entropy-Scaling Search
Requires Python: >=3.6

# Clustered Hierarchical Entropy-Scaling Search of Astrophysical and Biological Data

CHESS is a search algorithm for large data sets when the data exhibits certain geometric properties.
The [paper](https://arxiv.org/abs/1908.08551.pdf) is available on the arXiv.

We have extended CHESS to perform Manifold Learning and Anomaly Detection.
We are working on adding Dimensionality Reduction and Visualization abilities, and on 3-d Object Recognition from point clouds.
One of the major problems with this extension is that we need a new name.
Stay tuned.

[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/nishaq503/CHESS.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/nishaq503/CHESS/context:python)
[![codecov](https://codecov.io/gh/thoward27/chess/branch/master/graph/badge.svg)](https://codecov.io/gh/thoward27/chess)
[![Documentation Status](https://readthedocs.org/projects/clustered-hierarchical-entropy-scaling-search/badge/?version=latest)](https://clustered-hierarchical-entropy-scaling-search.readthedocs.io/en/latest/?badge=latest)

## Installation

```bash
python3 -m pip install CHESS-python
```

## Usage

```python
import numpy as np

from chess.datasets import bullseye
from chess.manifold import Manifold
from chess import criterion

# Get the data.
data, _ = bullseye()
# data is a numpy.ndarray in this case but it could just as easily be a numpy.memmap if your data cannot fit in RAM.
# We used memmaps for the research, though it does impose file-io costs.

manifold = Manifold(data=data, metric='euclidean')
# Any metric allowed by scipy's cdist function is allowed in Manifold.
# You can also define your own distance function. It will work so long as scipy allows it.

manifold.build(criterion.MaxDepth(20), criterion.MinRadius(0.25))
# Manifold.build can optionally take any number of early stopping criteria.
# chess.criterion defines some criteria that we have used in research.
# You are free to define your own.
# Take a look at chess/criterion.py for hints of how to define custom criteria.

# A sample rho-nearest neighbors search query
query, radius = data[0], 0.05
results = manifold.find_points(point=query, radius=radius)
# results is a dictionary of indexes of hits in data and the distance to those hits.

# A sample k-nearest neighbors search query
results = manifold.find_knn(point=query, k=25)
```

chess.Manifold relies on the Graph and Cluster classes.
You can import these and work with them directly if you so choose.
We have written good docs for each class and method.
Go crazy.

## Contributing

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

## License

[MIT](LICENSE)
etutils 0.1.0
etutils is Python package with helper functions that are used across various other packages. Installing this package on itself may not be so interesting.
Requires Python: >=3

# etutils

[![Python](https://img.shields.io/pypi/pyversions/etutils)](https://img.shields.io/pypi/pyversions/etutils)
[![PyPI Version](https://img.shields.io/pypi/v/etutils)](https://pypi.org/project/etutils/)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/erdogant/etutils/blob/master/LICENSE)

* etutils is Python package with helper functions that are used across various other packages. Installing this package on itself may not be so interesting.

## Contents
- [Installation](#-installation)
- [Requirements](#-Requirements)
- [Quick Start](#-quick-start)
- [Contribute](#-contribute)
- [Citation](#-citation)
- [Maintainers](#-maintainers)
- [License](#-copyright)

## Installation
* Install etutils from PyPI (recommended). etutils is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows. 
* It is distributed under the MIT license.

## Quick Start
```
pip install etutils
```
* Alternatively, install etutils from the GitHub source:
```bash
git clone https://github.com/erdogant/etutils.git
cd etutils
python setup.py install
```  

### Import etutils package
```python
import etutils as etutils
```

## Maintainers
* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)

## Contribute
* Contributions are welcome.

## Â© Copyright
See [LICENSE](LICENSE) for details.