Displaying 1 to 19 from 19 results

SubredditSimulator - An automated subreddit with posts created using markov chains

  •    Python

This is the code behind /r/SubredditSimulator, an automated subreddit populated by bots making submissions and comments, primarily using markov chains based on the actual posts in other subreddits. This project is deliberately somewhat difficult to get running (requiring reading of the code and undocumented, manual configuration of some things). Markov chain bots can be hilarious, but they also have the potential to be annoying to real users if released "into the wild", so it is not my intention to make it extremely simple for anyone to start running similar bots.

dissecting-reinforcement-learning - Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog

  •    Python

This repository contains the code and pdf of a series of blog post called "dissecting reinforcement learning" which I published on my blog mpatacchiola.io/blog. Moreover there are links to resources that can be useful for a reinforcement learning practitioner. If you have some good references which may be of interest please send me a pull request and I will integrate them in the README. The source code is contained in src with the name of the subfolders following the post number. In pdf there are the A3 documents of each post for offline reading. In images there are the raw svg file containing the images used in each post.

Media Assistant


Those who loves music spend lot of time to select music to play. What if someone who understands your flavor of music and plays according to that. WOW! What a great life. Media assistant learn from you to understand your choice of music and plays music for you.

gomarkov - Markov chains in golang

  •    Go

Go implementation of markov chains for textual data.

Markov-Chains - Experiments with Markov Chains

  •    Python

Experiments with Markov Chains

markovchain - generates a markov chain of words based on input files

  •    Javascript

markovchain generates a markov chain based on text passed into it. "The" that are 5 words long, and then output to console.

insobot - C99 modular IRC bot with markov chains

  •    C

Module based IRC bot written in C with markov chains and stuff. To run your own insobot instance, see this quickstart guide , other pages in the wiki, and the files 'insobot.sh.example' and 'src/config.h'.

markovifyR - Markovify wrapper for R

  •    R

This package requires Python and markovify to be installed. Here we are going to showcase how to use the package to create new Life Lessons from my favorite professor from college Peter Linneman.

MarkovNameGenerator - :black_nib: Markov process-based procedural name and word generator

  •    Haxe

Markov Namegen is a Markov chain-based procedural name generator written in Haxe. Run it in your browser. Demonstrates the markov-namegen haxelib. Read the docs here.

iterators - Share Market Prediction App using Markov Chains Model

  •    PHP

We have selected one year’s worth data for five different stocks and have applied Markov chain calculations on this data in order to make the predictions. The first step towards applying Markov chains to the data set began with the calculation of moving averages.

presswork - Text generation workbench, starting with Markov Chains

  •    Python

So far, it's all about Markov Chains. Here's a great visual explanation of Markov Chains. Given a bunch of text, model it, and generate "probable" new sentences. I'd like to add other tools to the toolkit, building off of this foundation.

markov - A generic markov chain implementation in Rust.

  •    Rust

A generic implementation of a Markov chain in Rust. It supports all types that implement Eq, Hash, and Clone, and has some specific helpers for working with String as text generation is the most likely use case. You can find up-to-date, ready-to-use documentation online on docs.rs. Note: markov is in passive maintenance mode. It should work well for its intended use case (largely textual generation, especially in chat bots and the like), but will likely not grow to any further use cases. If it does not meet your needs in a broad sense, you should likely fork it or develop a more purpose-built library. Nevertheless, bug reports will still be triaged and fixed.

markov-chain-gan - Code for "Generative Adversarial Training for Markov Chains" (ICLR 2017 Workshop)

  •    Python

TensorFlow code for Generative Adversarial Training for Markov Chains (ICLR 2017 Workshop Track). Work by Jiaming Song, Shengjia Zhao and Stefano Ermon.

a-nice-mc - Code for "A-NICE-MC: Adversarial Training for MCMC"

  •    Jupyter

Tensorflow implementation for the paper A-NICE-MC: Adversarial Training for MCMC, NIPS 2017. A-NICE-MC is a framework that trains a parametric Markov Chain Monte Carlo proposal. It achieves higher performance than traditional nonparametric proposals, such as Hamiltonian Monte Carlo (HMC). This repository provides code to replicate the experiments, as well as providing grounds for further research.

markov - ⛓ A Crystal library for building Markov Chains and running Markov Processes.

  •    Crystal

A Crystal library for building Markov Chains and running Markov Processes. A Markov Chain is essentially a mechanism for guessing probable future events based on a sample of past events. For a great explanation, watch this Khan Academy video.

alfred-pwgen - Generate passwords with Alfred

  •    Python

Generate secure random passwords from Alfred. Uses /dev/urandom as source of entropy. Download from the GitHub releases or Packal and double-click the downloaded file to install.

Temphael - A Tumblr-scraping text post bot

  •    Python

Scrape Tumblr blogs for a corpus, convert it into a Markov probablility matrix, and generate text posts in the style of the original blog. This process consists of two scripts, tscrape.py and genevabot.py. The first scrapes a Tumblr blog for text and tags (because lots of great content is included in Tumblr tags!) and creates a PyMarkovChain probability database from that data. The second simply reconsititues the probability database into memory and generates some sentances from it.

Natural-Language-Processing-Fundamentals - Use Python and NLTK to build out your own text classifiers and solve common NLP problems

  •    Jupyter

If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this course, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.