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Mauro Asprea brutuscat

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brutuscat / index.html
Last active September 29, 2022 17:00 — forked from bellbind/index.html
[chrome][android] BarcodeDetector example
<!doctype html>
<html>
<head>
<script src="https://cdn.jsdelivr.net/npm/@undecaf/zbar-wasm@0.9.11/dist/index.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@undecaf/barcode-detector-polyfill@0.9.13/dist/index.js"></script>
<script>
try {
window['BarcodeDetector'].getSupportedFormats()
} catch {
window['BarcodeDetector'] = barcodeDetectorPolyfill.BarcodeDetectorPolyfill
@brutuscat
brutuscat / gist:1ca90fffb6a03c69faa8
Last active September 8, 2015 10:25 — forked from arichazan/gist:11b579c873e4a043c88d
doesPaymentMatchOptions
/*
* tests whether payment matches options
*
* returns 'true' if matches, 'false' otherwise
*
* $opts is an array of options to filter the payments by
* possible values are
* 'withoutPrefix' => 'prefix' - only select payment without this prefix
* 'withPrefix' => 'prefix' - only select payment with this prefix
* if no options are provided, always returns 'true'
@brutuscat
brutuscat / 99java
Last active August 29, 2015 14:02 — forked from dz0ny/99java
## Setup java
if [ `uname -m` == 'x86_64' ]; then
PATH="/usr/lib64/jvm/java-7-oracle/jre/bin/"
JAVA_HOME="/usr/lib64/jvm/java-7-oracle/"
else
PATH="/usr/lib/jvm/java-7-oracle/jre/bin/"
JAVA_HOME="/usr/lib/jvm/java-7-oracle/"
fi
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brutuscat / prompt.sh
Created March 5, 2014 09:50 — forked from tobiassjosten/prompt.sh
# Configure colors, if available.
if [ -x /usr/bin/tput ] && tput setaf 1 >&/dev/null; then
c_reset='\[\e[0m\]'
c_user='\[\e[0;32m\]'
c_path='\[\e[1;34m\]'
c_git_clean='\[\e[0;37m\]'
c_git_staged='\[\e[0;32m\]'
c_git_unstaged='\[\e[0;31m\]'
else
c_reset=
@brutuscat
brutuscat / gist:3893558
Created October 15, 2012 16:49 — forked from mattb/gist:3888345
Some pointers for Natural Language Processing / Machine Learning

Here are the areas I've been researching, some things I've read and some open source packages...

Nearly all text processing starts by transforming text into vectors: http://en.wikipedia.org/wiki/Vector_space_model

Often it uses transforms such as TFIDF to normalise the data and control for outliers (words that are too frequent or too rare confuse the algorithms): http://en.wikipedia.org/wiki/Tf%E2%80%93idf

Collocations is a technique to detect when two or more words occur more commonly together than separately (e.g. "wishy-washy" in English) - I use this to group words into n-gram tokens because many NLP techniques consider each word as if it's independent of all the others in a document, ignoring order: http://matpalm.com/blog/2011/10/22/collocations_1/