teamcoltra Goto Github PK

followers: 27.0 following: 7.0 repos: 62.0 gists: 6.0

Name: Travis Peacock

Type: User

Bio: PHP Developer. Chinese Sourcing expert. Dad.

Location: Vancouver, BC

Travis Peacock's Projects

5thsrd

5th Edition SRD in the style of d20srd.org

airline-codes

An NPM module containing airline codes (IATA) and other information

beaver-bay

The Beaver Bay is a torrent site which users public trackers.

bookz

python scripts for managing bookz downloadz

chatwoot

Open-source customer engagement suite, an alternative to Intercom, Zendesk, Salesforce Service Cloud etc. 🔥💬

This program is a fun little "game" that allows a community to develop a "choose your own adventure" game. Its very simple, and probably could be recreated easily. However, we hope that our version fits your bill just fine. As a community effort from #Club-Ubuntu on irc.freenode.net

classify

It may eat your laundry or wash your cat.

cloak

Cloak is designed to help protect (third party) websites that need additional levels of privacy as a reverse proxy

cordova-glass

Google Glass plugin for Cordova

emailauthorprediction

For the task of prediction of author from emails, we used Unigram language model. We started out on the problem by finding out the features that would help model the solution. The features that looked important were: • N-grams of the email • Frequency of each N-gram • Out of Vocabulary words (Spelling mistakes) The combination of first two features describes how the particular author chooses his dictionary set for writing text. Therefore, this feature can be termed as the signature of the author as all writers tend to choose only words from some defined subset of the Vocabulary. Also, the out of vocabulary words, generally the spelling mistakes done by the author, depict the style of the writing text, and therefore, comes to be an important aspect of the solution. The solution, thus, comes to be finding the total probability of each Ngram to be written by the particular author in the email.