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# niacin

A Python library for replacing the missing variation in your data.

## Why should I use this?

Data collected for model training necessarily undersamples the likely variance in the input space. This library is a collection of tools for inserting typical kinds of perturbations to better approximate population variance; and, for creating similar-but-incorrect examples to aid in reducing the total size of the hypothesis space. These are commonly known as <small>ENRICHMENT</small> and <small>NEGATIVE SAMPLING</small>, respectively.

## How do I use this?

Functions in niacin are separated into submodules for specific data types. Functions expose a similar API, with two input arguments: the data to be transformed, and the probability of applying a specific transformation.

enrichment:

data = "This is the song that never ends and it goes on and on my friends"

This is teh song tath never ends adn it goes on anbd on my firends


negative sampling:

data = "This is the song that never ends and it goes on and on my friends"

This is the musical composition that never extremity and it exit on and on my person


## How do I install this?

with pip:

pip install niacin


from source:

git clone git@github.com:deniederhut/niacin.git && cd niacin && python setup.py install


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