semvec/pypi-packages huggingface dataset:
multivar-hypergeom 0.0.1
A Python module for sampling from the Multivariate Hypergeometric distribution.
License: MIT
Requires Python: >=3.6


===========================
Multivariate Hypergeometric
===========================






A Python module for sampling from the Multivariate Hypergeometric distribution.


Free Software: MIT License

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

::

        pip install multivar_hypergeom


You can also install the in-development version with: ::

        pip install https://github.com/sarahmorin/multivar_hypergeom/archive/master.zip



Documentation
-------------

`Read The Docs <https://multivar_hypergeom.readthedocs.io/>`_

Development
-----------

To run all tests run: ::

        tox


Changelog
=========

0.0.0 (2020-03-02)
------------------

* First release on PyPI.

0.0.1 (2020-04-27)
------------------

* Bug fixes in CDF
  * Catches empty distribution
  * Uses 64-bit floats
* Improvements to documentation
* Updated dependencies (per PyUp)
on-this-day 1.0.1
What happened on this day on the past
License: MIT

# This Day In History

Find what happened on a particular day in history.

# Usage


```
thiday -m 1 -m 1 Births

This will output the list of famous births in January 1st.
```

#### Arguments
```
thisday -m [month in number] -d [day in number] [occurance]

-m/--month = month (optional argument),
-d/--day = day (optional argument) and
occurrence = postional argument.

If no arguments are provided then the default value for month and day will be the current date and
Holidays_and_observances will be the default value for occurrence.
```

#### Occurrence Choices
* Events
* Births
* Deaths
* Holidays_and_observances
mdrnn 0.3.0
Custom Keras layers for implementing multi-dimensional recurrent neural networks (MDRNNs)
License: MIT

# Multi-Directional Multi-Dimensional Recurrent Neural Networks

A library built on top of TensorFlow implementing the model described in
Alex Graves's paper https://arxiv.org/pdf/0705.2011.pdf.
The library comes with a set of custom Keras layers.
Each layer can be seamlessly used in Keras to build a model and
train it as usual.

# Status: under development

This repository is in its early stages. The code presented here is not stable yet
and it wasn't extensively tested. Use it at your own risk

# Features

Layers available now:
- **MDRNN**: layer analogous to Keras SimpleRNN layer for processing multi-dimensional inputs
- **MDLSTM**: analogous to Keras LSTM layer
- **MultiDirectional**: layer-wrapper analogous to Keras Bidirectional for creating 
multi-directional multi-dimensional RNN

Layers currently under development (coming soon):
- **MDGRU**: analogous to Keras GRU layer

Additional features:
- easy to use with Keras
- Keras-like API for each layer
- option to choose order/direction in which to process inputs
- computations are run on CPU

# Installation
Install the package from PyPI:
```
pip install mdrnn
```

Alternatively, clone the repository and install dependencies:
```
git clone <repo_url>
cd <repo_directory>
pip install -r requirements.txt
```

# Quick Start

Create a 2-dimensional RNN:
```
from mdrnn import MDRNN, MDLSTM, MultiDirectional
import numpy as np
import tensorflow as tf
rnn = MDRNN(units=16, input_shape=(5, 4, 10), activation='tanh', return_sequences=True)
output = rnn(np.zeros((1, 5, 4, 10)))
```

Build a Keras model consisting of 1 MDRNN layer and train it:

```
model = tf.keras.Sequential()
model.add(MDRNN(units=16, input_shape=(2, 3, 6), activation='tanh'))
model.add(tf.keras.layers.Dense(units=10, activation='softmax'))
model.compile(loss='categorical_crossentropy', metrics=['acc'])
model.summary()
x = np.zeros((10, 2, 3, 6))
y = np.zeros((10, 10,))
model.fit(x, y)
```

Similarly, create and train a multi-directional MDRNN
```
x = np.zeros((10, 2, 3, 6))
y = np.zeros((10, 40,))

model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(shape=(2, 3, 6)))
model.add(MultiDirectional(MDRNN(10, input_shape=[2, 3, 6])))

model.compile(loss='categorical_crossentropy', metrics=['acc'])
model.summary()

model.fit(x, y, epochs=1)
```

Similarly, create and train a multi-directional multi-dimensional LSTM (MDLSTM)
```
x = np.zeros((10, 2, 3, 6))
y = np.zeros((10, 40,))

model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(shape=(2, 3, 6)))
model.add(MultiDirectional(MDLSTM(10, input_shape=[2, 3, 6])))

model.compile(loss='categorical_crossentropy', metrics=['acc'])
model.summary()

model.fit(x, y, epochs=1)
```


# Requirements

- TensorFlow version >= 2.0

# References

[1] A. Graves, S. Ferńandez, and J. Schmidhuber. Multidimensional recurrent neural networks.

[2] A. Graves and J. Schmidhuber. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks.
firstlvbo 0.0.2
My short description for my project.
License: MIT
Requires Python: >=3.6.0


�� setup.py (for humans)
=======================

This repo exists to provide [an example setup.py] file, that can be used
to bootstrap your next Python project. It includes some advanced
patterns and best practices for `setup.py`, as well as some
commented–out nice–to–haves.

For example, this `setup.py` provides a `$ python setup.py upload`
command, which creates a *universal wheel* (and *sdist*) and uploads
your package to [PyPi] using [Twine], without the need for an annoying
`setup.cfg` file. It also creates/uploads a new git tag, automatically.

In short, `setup.py` files can be daunting to approach, when first
starting out — even Guido has been heard saying, "everyone cargo cults
thems". It's true — so, I want this repo to be the best place to
copy–paste from :)

[Check out the example!][an example setup.py]

Installation
-----

```bash
cd your_project

# Download the setup.py file:
#  download with wget
wget https://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.py -O setup.py

#  download with curl
curl -O https://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.py
```

To Do
-----

-   Tests via `$ setup.py test` (if it's concise).

Pull requests are encouraged!

More Resources
--------------

-   [What is setup.py?] on Stack Overflow
-   [Official Python Packaging User Guide](https://packaging.python.org)
-   [The Hitchhiker's Guide to Packaging]
-   [Cookiecutter template for a Python package]

License
-------

This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means.

  [an example setup.py]: https://github.com/navdeep-G/setup.py/blob/master/setup.py
  [PyPi]: https://docs.python.org/3/distutils/packageindex.html
  [Twine]: https://pypi.python.org/pypi/twine
  [image]: https://farm1.staticflickr.com/628/33173824932_58add34581_k_d.jpg
  [What is setup.py?]: https://stackoverflow.com/questions/1471994/what-is-setup-py
  [The Hitchhiker's Guide to Packaging]: https://the-hitchhikers-guide-to-packaging.readthedocs.io/en/latest/creation.html
  [Cookiecutter template for a Python package]: https://github.com/audreyr/cookiecutter-pypackage