semvec/pypi-packages huggingface dataset:
adaptationism 0.0.10
Understand the features of language for different kinds of corpora

### Last Updated: 7/1/2018
# Adaptationism is defined as...

_"The belief or assumption, now generally held, that each feature of an organism is the result of evolutionary adaptation for a particular function."_

Our use of language is no different. We are constantly evolving our own group-specific languages (as well as learning the languages of others - both groups and individuals), **the catalyst of development rooted in the functions that they serve.**

Like any scholarly endeavor, the wikipedia article that I pulled this information from defines the traits of an *adaptation* include:
 1. The trait is a variation of an earlier form.
 2. The trait is heritable through the transmission of genes.
 3. The trait enhances reproductive success.

The last metaphorical comparison that I'll make to this idea is that like all of the above, language (and features of language):
 1. Can be derived from variations of a broader parent language,
 2. Are heritable through the groups, communities, and experiences we partake in, and...
 3. The feature enhances the success of achieving a descriptive and/or actionable outcome *through* language.

## So what is this package _actually_ for...?*

"Adaptationism" is meant to help answer the three following questions:
 1. Uses language to describe features of a journey or experience,
 2. How the use of language differs against another group, a broader (parent) group, or a subset of the same group, and finally,
 3. Changes its use of language following some event.

The gap that I hope to fill through the development of this package is not *just* a set of *tools*, but also **a framework for interpretation and action**.

## What is your roadmap?

My roadmap for this package is structured as a pyramid (3 levels), flowing from:
 - **(level 1)** descriptive features of words and phrases, to...
 - **(level 2)** the analysis and description of meta-language related features (i.e. POS, polarity, patterns of POS, named entities... etc.), and finally...
 - **(level 3)** the descriptive statistics of text (word length, statement length, corpus length, average length... etc.).

**While I primarily spend my time analyzing comments...** the types of [corpora](https://wiki.apache.org/spamassassin/PluralOfCorpus) this package can analyze includes:
  1. Comments
  2. Chat / Text Conversations
  3. Books
  4. Speeches

For a full list of different kinds of corpora, [check this out](https://weblearn.ox.ac.uk/access/content/group/3a217dfd-a8cd-4034-8564-c27a58f89b9b/Handouts/CorpusTypes.pdf).

## How did I come up with this idea?

This package has a few origins... mostly stemming from unanswered Stack Overflow posts on NLTK. As I continue to add to my own roadmap, I will add the sets of SO posts that I draw inspiration from.

Current list of TA questions:
  1. [Generating N-Gram Markov Chain Transition Table](https://stackoverflow.com/questions/23374694/n-gram-markov-chain-transition-table)
prism_rest_client 0.2
prism_rest_client
Keywords: web rest client

prism_rest_client README
==================

This client library is meant for interacting with REST APIs implemented using
the prism_rest library. It may work with other REST APIs as well, but has not
been tested against such.

Getting Started
---------------

    >>> import prism_rest_client
    >>> api = prism_rest_client.open('http://example.com/api')
    >>>
    >>> distros = dict(((x.name, x.version), x) for x in api.distros)
    >>>
    >>> distro = distros.get(('centos', 6))
    >>>
    >>> pkgs = distro.query('packages', name='bash')
    >>>
    >>> assert pkgs[0].nevra.name == 'bash'