Carlos Agarie agarie

## queue-threadsafe.c
/*
** queue-threadsafe.c
**
** A thread-safe (for 2 threads) implementation of a queue. Based on the
** implementation from the book Embedded Systems, by Jonathan W. Valvano,
** chapter 3, page 130.
*/

#include <stdlib.h>

## using_rdoc.c
/*
 * This comment WON'T be processed.
 */
static VALUE undocumented_method(VALUE self) {
  return Qnil;
}

/*
 * This comment WILL be processed.
 */

## gist:4753170
# Use Homebrew and be happy.
brew update
brew install opencv

# Give the correct pathnames. Of course, `2.4.3` is the current version at this time, change it accordingly.
gem install ruby-opencv -- --with-opencv-lib=/usr/local/Cellar/opencv/2.4.3/lib \
                           --with-opencv-include=/usr/local/Cellar/opencv/2.4.3/include/opencv \
                           --with-opencv-include=/usr/local/Cellar/opencv/2.4.3/include/opencv2

## naive-bayes.rb
# A very simple naive bayes classifier for english text.
#
# Author: Carlos Agarie <carlos@onox.com.br>

# Some assumptions:
# - The stop words, dataset and test data are hardcoded.
# - Binary classification (so each phrase has a corresponding boolean value).
# - If there's a missing feature, it's ignored.
#
# Of course this isn't a finished "product". I'm studying classifiers, so this

## f1-score.rb

# The F1 score is a measure of how accurate an algorithm is.
#
# Precision is the probability that a (randomly selected) retrieved document is relevant:
# precision = true positives / (true positives + false positives)
#
# Recall is the probability that a (randomly selected) relevant document is retrieved in a search:
# recall = true positives / (true positives + false negatives)
def f1_score(precision, recall)
  (2 * (precision * recall)) / (precision + recall)

## composite.rb
# Small experiment in which I try to implement the composite pattern without
# resorting to two classes.

class Composite
  attr_accessor :action, :subcomposites

  # Cool thing: if we don't provide a block, it'll be passed nil. So there's no
  # need to make it a "default parameter" (you can't do it, actually).
  def initialize(&action)
    @action = action

## tokyotosho.rb
#!/usr/bin/env ruby -w

require 'open-uri'
require 'uri'
require 'nokogiri'

DEFAULT_DIR = "/Users/carlosagarie/torrents"
CATEGORY = 1 # Anime.

# Regular expressions and constants for extraction.

## bootable-flashdrive.sh
# From: http://www.ubuntu.com/download/desktop/create-a-usb-stick-on-mac-osx

hdiutil convert -format UDRW -o ~/path/to/target.img ~/path/to/iso.iso

# Run diskutil list and determine the device node assigned to your flash media (e.g. /dev/disk2).
diskutil list

# Replace N with the disk number from the last command.
diskutil unmountDisk /dev/diskN

## stop_words.en
a
about
above
across
after
afterwards
again
against
all
almost

## stop_words.pt
abaixo
aca
acaso
acerca
acima
acola
acula
ademais
adentro
adiante
	/*
	** queue-threadsafe.c
	**
	** A thread-safe (for 2 threads) implementation of a queue. Based on the
	** implementation from the book Embedded Systems, by Jonathan W. Valvano,
	** chapter 3, page 130.
	*/

	#include <stdlib.h>
	/*
	* This comment WON'T be processed.
	*/
	static VALUE undocumented_method(VALUE self) {
	return Qnil;
	}

	/*
	* This comment WILL be processed.
	*/
	# Use Homebrew and be happy.
	brew update
	brew install opencv

	# Give the correct pathnames. Of course, `2.4.3` is the current version at this time, change it accordingly.
	gem install ruby-opencv -- --with-opencv-lib=/usr/local/Cellar/opencv/2.4.3/lib \
	--with-opencv-include=/usr/local/Cellar/opencv/2.4.3/include/opencv \
	--with-opencv-include=/usr/local/Cellar/opencv/2.4.3/include/opencv2
	# A very simple naive bayes classifier for english text.
	#
	# Author: Carlos Agarie <carlos@onox.com.br>

	# Some assumptions:
	# - The stop words, dataset and test data are hardcoded.
	# - Binary classification (so each phrase has a corresponding boolean value).
	# - If there's a missing feature, it's ignored.
	#
	# Of course this isn't a finished "product". I'm studying classifiers, so this

	# The F1 score is a measure of how accurate an algorithm is.
	#
	# Precision is the probability that a (randomly selected) retrieved document is relevant:
	# precision = true positives / (true positives + false positives)
	#
	# Recall is the probability that a (randomly selected) relevant document is retrieved in a search:
	# recall = true positives / (true positives + false negatives)
	def f1_score(precision, recall)
	(2 * (precision * recall)) / (precision + recall)
	# Small experiment in which I try to implement the composite pattern without
	# resorting to two classes.

	class Composite
	attr_accessor :action, :subcomposites

	# Cool thing: if we don't provide a block, it'll be passed nil. So there's no
	# need to make it a "default parameter" (you can't do it, actually).
	def initialize(&action)
	@action = action
	#!/usr/bin/env ruby -w

	require 'open-uri'
	require 'uri'
	require 'nokogiri'

	DEFAULT_DIR = "/Users/carlosagarie/torrents"
	CATEGORY = 1 # Anime.

	# Regular expressions and constants for extraction.
	# From: http://www.ubuntu.com/download/desktop/create-a-usb-stick-on-mac-osx

	hdiutil convert -format UDRW -o ~/path/to/target.img ~/path/to/iso.iso

	# Run diskutil list and determine the device node assigned to your flash media (e.g. /dev/disk2).
	diskutil list

	# Replace N with the disk number from the last command.
	diskutil unmountDisk /dev/diskN