semvec/pypi-packages huggingface dataset:
sliced 0.7.0
Toolbox for sufficient dimension reduction (SDR).
License: MIT

.. -*- mode: rst -*-

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.. _scikit-learn: https://github.com/scikit-learn/scikit-learn

sliced
======
sliced is a python package offering a number of sufficient dimension reduction (SDR) techniques commonly used in high-dimensional datasets with a supervised target. It is compatible with scikit-learn_.


Algorithms supported:

- Sliced Inverse Regression (SIR) [1]_
- Sliced Average Variance Estimation (SAVE) [2]_

Documentation / Website: https://joshloyal.github.io/sliced/


Example
-------
Example that shows how to learn a one dimensional subspace from a dataset with ten features:

.. code-block:: python

   from sliced.datasets import make_cubic
   from sliced import SlicedInverseRegression

   # load the 10-dimensional dataset
   X, y = make_cubic(random_state=123)

   # Set the options for SIR
   sir = SlicedInverseRegression(n_directions=1)

   # fit the model
   sir.fit(X, y)

   # transform into the new subspace
   X_sir = sir.transform(X)


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

Dependencies
------------
sliced requires:

- Python (>= 2.7 or >= 3.4)
- NumPy (>= 1.8.2)
- SciPy (>= 0.13.3)
- Scikit-learn (>=0.17)

Additionally, to run examples, you need matplotlib(>=2.0.0).

Installation
------------
You need a working installation of numpy and scipy to install sliced. If you have a working installation of numpy and scipy, the easiest way to install sliced is using ``pip``::

    pip install -U sliced

If you prefer, you can clone the repository and run the setup.py file. Use the following commands to get the copy from GitHub and install all the dependencies::

    git clone https://github.com/joshloyal/sliced.git
    cd sliced
    pip install .

Or install using pip and GitHub::

    pip install -U git+https://github.com/joshloyal/sliced.git

Testing
-------
After installation, you can use pytest to run the test suite via setup.py::

    python setup.py test

References:
-----------
.. [1] : Li, K C. (1991)
        "Sliced Inverse Regression for Dimension Reduction (with discussion)",
        Journal of the American Statistical Association, 86, 316-342.
.. [2] : Shao, Y, Cook, RD and Weisberg, S (2007).
         "Marginal Tests with Sliced Average Variance Estimation",
         Biometrika, 94, 285-296.
videodrome 0.2.4
Static Media Center Generator in Python with HTML5 UI
License: MIT

# Videodrome
Videodrome generates static media centers in Python with HTML5 user interface.

See the [demo](https://p5yb14d3.github.io/videodrome/demo.html)

[![Screenshot](https://i.imgur.com/7xSayQ4.jpg)](https://p5yb14d3.github.io/videodrome/demo.html)

## Installation
Requires Python 2.7 installed with pip.

`$ pip install videodrome`

## Running Videodrome
`$ videodrome generate <path to your video folder>`

## Input Devices
Works with the keyboard, mouse or [TV Remote](https://www.amazon.com/REDGO-Portable-Wireless-Keyboard-Multifunctional/dp/B06XPYT52B/) 

## MIT License

## Author
The project was authored by p5yb14d3