README.markdown in classifier-reborn-2.1.0 vs README.markdown in classifier-reborn-2.2.0
- old
+ new
@@ -1,252 +1,82 @@
-## Welcome to Classifier Reborn
+# Classifier Reborn
-[][ruby-gems]
-[][travis]
-[][gemnasium]
-[ruby-gems]: https://rubygems.org/gems/jekyll/classifier-reborn
-[gemnasium]: https://gemnasium.com/jekyll/classifier-reborn
-[travis]: https://travis-ci.org/jekyll/classifier-reborn
+[](https://rubygems.org/gems/classifier-reborn)
+[](https://travis-ci.org/jekyll/classifier-reborn)
+[](https://gemnasium.com/jekyll/classifier-reborn)
-Classifier is a general module to allow Bayesian and other types of classifications.
+---
-Classifier Reborn is a fork of cardmagic/classifier under more active development.
+## [Read the Docs](http://www.classifier-reborn.com/)
-## Download
+## Getting Started
-Add this line to your application's Gemfile:
+Classifier Reborn is a general classifier module to allow Bayesian and other types of classifications.
+It is a fork of [cardmagic/classifier](https://github.com/cardmagic/classifier) under more active development.
+Currently, it has [Bayesian Classifier](https://en.wikipedia.org/wiki/Naive_Bayes_classifier) and [Latent Semantic Indexer (LSI)](https://en.wikipedia.org/wiki/Latent_semantic_analysis) implemented.
- gem 'classifier-reborn'
+Here is a quick illustration of the Bayesian classifier.
-And then execute:
-
- $ bundle
-
-Or install it yourself as:
-
- $ gem install classifier-reborn
-
-## Dependencies
-
-The only runtime dependency you'll need to install is Roman Shterenzon's fast-stemmer gem:
-
- gem install fast-stemmer
-
-This should install automatically with RubyGems.
-
-If you would like to speed up LSI classification by at least 10x, please install the following libraries:
-
-* [GNU GSL](http://www.gnu.org/software/gsl)
-* [rb-gsl](https://rubygems.org/gems/rb-gsl)
-
-Notice that LSI will work without these libraries, but as soon as they are installed, Classifier will make use of them. No configuration changes are needed, we like to keep things ridiculously easy for you.
-
-## Bayes
-
-A Bayesian classifier by Lucas Carlson. Bayesian Classifiers are accurate, fast, and have modest memory requirements.
-
-*Note: Classifier only supports UTF-8 characters.*
-
-### Usage
-
-```ruby
-require 'classifier-reborn'
-classifier = ClassifierReborn::Bayes.new 'Interesting', 'Uninteresting'
-classifier.train_interesting "here are some good words. I hope you love them"
-classifier.train_uninteresting "here are some bad words, I hate you"
-classifier.classify "I hate bad words and you" # returns 'Uninteresting'
-
-classifier_snapshot = Marshal.dump classifier
-# This is a string of bytes, you can persist it anywhere you like
-
-File.open("classifier.dat", "w") {|f| f.write(classifier_snapshot) }
-# Or Redis.current.save "classifier", classifier_snapshot
-
-# This is now saved to a file, and you can safely restart the application
-data = File.read("classifier.dat")
-# Or data = Redis.current.get "classifier"
-trained_classifier = Marshal.load data
-trained_classifier.classify "I love" # returns 'Interesting'
+```bash
+$ gem install classifier-reborn
+$ irb
+irb(main):001:0> require 'classifier-reborn'
+irb(main):002:0> classifier = ClassifierReborn::Bayes.new 'Ham', 'Spam'
+irb(main):003:0> classifier.train "Ham", "Sunday is a holiday. Say no to work on Sunday!"
+irb(main):004:0> classifier.train "Spam", "You are the lucky winner! Claim your holiday prize."
+irb(main):005:0> classifier.classify "What's the plan for Sunday?"
+#=> "Ham"
```
-Beyond the basic example, the constructor and trainer can be used in a more
-flexible way to accommodate non-trival applications. Consider the following
-program:
+Now, let's build an LSI, classify some text, and find a cluster of related documents.
-```ruby
-#!/usr/bin/env ruby
-# classifier_reborn_demo.rb
-
-require 'classifier-reborn'
-
-training_set = DATA.read.split("\n")
-categories = training_set.shift.split(',').map{|c| c.strip}
-
-# pass :auto_categorize option to allow feeding previously unknown categories
-classifier = ClassifierReborn::Bayes.new categories, auto_categorize: true
-
-training_set.each do |a_line|
- next if a_line.empty? || '#' == a_line.strip[0]
- parts = a_line.strip.split(':')
- classifier.train(parts.first, parts.last)
-end
-
-puts classifier.classify "I hate bad words and you" #=> 'Uninteresting'
-puts classifier.classify "I hate javascript" #=> 'Uninteresting'
-puts classifier.classify "javascript is bad" #=> 'Uninteresting'
-
-puts classifier.classify "all you need is ruby" #=> 'Interesting'
-puts classifier.classify "i love ruby" #=> 'Interesting'
-
-puts classifier.classify "which is better dogs or cats" #=> 'dog'
-puts classifier.classify "what do I need to kill rats and mice" #=> 'cat'
-
-__END__
-Interesting, Uninteresting
-interesting: here are some good words. I hope you love them
-interesting: all you need is love
-interesting: the love boat, soon we will be taking another ride
-interesting: ruby don't take your love to town
-
-uninteresting: here are some bad words, I hate you
-uninteresting: bad bad leroy brown badest man in the darn town
-uninteresting: the good the bad and the ugly
-uninteresting: java, javascript, css front-end html
-#
-# train categories that were not pre-described
-#
-dog: dog days of summer
-dog: a man's best friend is his dog
-dog: a good hunting dog is a fine thing
-dog: man my dogs are tired
-dog: dogs are better than cats in soooo many ways
-
-cat: the fuzz ball spilt the milk
-cat: got rats or mice get a cat to kill them
-cat: cats never come when you call them
-cat: That dang cat keeps scratching the furniture
+```bash
+irb(main):006:0> lsi = ClassifierReborn::LSI.new
+irb(main):007:0> lsi.add_item "This text deals with dogs. Dogs.", :dog
+irb(main):008:0> lsi.add_item "This text involves dogs too. Dogs!", :dog
+irb(main):009:0> lsi.add_item "This text revolves around cats. Cats.", :cat
+irb(main):010:0> lsi.add_item "This text also involves cats. Cats!", :cat
+irb(main):011:0> lsi.add_item "This text involves birds. Birds.", :bird
+irb(main):012:0> lsi.classify "This text is about dogs!"
+#=> :dog
+irb(main):013:0> lsi.find_related("This text is around cats!", 2)
+#=> ["This text revolves around cats. Cats.", "This text also involves cats. Cats!"]
```
-#### Knowing the Score
+There is much more that can be done using Bayes and LSI beyond these quick examples.
+For more information read the following documentation topics.
-When you ask a bayesian classifier to classify text against a set of trained categories it does so by generating a score (as a Float) for each possible category. The higher the score the closer the fit your text has with that category. The category with the highest score is returned as the best matching category.
+* [Installation and Dependencies](http://www.classifier-reborn.com/)
+* [Bayesian Classifier](http://www.classifier-reborn.com/bayes)
+* [Latent Semantic Indexer (LSI)](http://www.classifier-reborn.com/lsi)
+* [Classifier Validation](http://www.classifier-reborn.com/validation)
+* [Development and Contributions](http://www.classifier-reborn.com/development) (*Optional Docker instructions included*)
-In *ClassifierReborn* the methods *classifications* and *classify_with_score* give you access to the calculated scores. The method *classify* only returns the best matching category.
+### Notes on JRuby support
-Knowing the score allows you to do some interesting things. For example if your application is to generate tags for a blog post you could use the *classifications* method to get a hash of the categories and their scores. You would sort on score and take only the top 3 or 4 categories as your tags for the blog post.
-
-You could within your application establish the smallest acceptable score and only use those categories whose score is greater than or equal to your smallest acceptable score as your tags for the blog post.
-
-But what if you only use the *classify* method? It does not show you the score of the best category. How do you know that the best category is really any good?
-
-You can use the threshold.
-
-#### Using the Threshold
-
-Some applications can have only one category. The application wants to know if the text being classified is of that category or not. For example consider a list of normal free text responses to some question or maybe a URL string coming to your web application. You know what a normal response looks like; but, you have no idea how people might mis-use the response. So what you want to do is create a bayesian classifier that just has one category, for example 'Good' and you want to know wither your text is classified as Good or Not Good.
-
-Or suppose you just want the ability to have multiple categories and a 'None of the Above' as a possibility.
-
-##### Threshold
-
-When you initialize the *ClassifierReborn::Bayes* classifier there are several options which can be set that control threshold processing.
-
```ruby
-b = ClassifierReborn::Bayes.new(
- 'good', # one or more categories
- enable_threshold: true, # default: false
- threshold: -10.0 # default: 0.0
- )
-b.train_good 'good stuff from Dobie Gillis'
-# ...
-text = 'bad junk from Maynard G. Krebs'
-result = b.classify text
-if result.nil?
- STDERR.puts "ALERT: This is not good: #{text}"
- let_loose_the_dogs_of_war! # method definition left to the reader
-end
-
+gem 'classifier-reborn-jruby', platforms: :java
```
-In the *classify* method when the best category for the text has a score that is either less than the established threshold or is Float::INIFINITY, a nil category is returned. When you see a nil value returned from the *classify* method it means that none of the trained categories (regardless or how many categories were trained) has a score that is above or equal to the established threshold.
+While experimental, this gem should work on JRuby without any kind of additional changes. Unfortunately, you will **not** be able to use C bindings to GNU/GSL or similar performance-enhancing native code. Additionally, we do not use `fast_stemmer`, but rather [an implementation](https://tartarus.org/martin/PorterStemmer/java.txt) of the [Porter Stemming](https://tartarus.org/martin/PorterStemmer/) algorithm. Stemming will differ between MRI and JRuby, however you may choose to [disable stemming](https://tartarus.org/martin/PorterStemmer/) and do your own manual preprocessing (or use some other [popular Java library](https://opennlp.apache.org/)).
-#### Other Threshold-related Convience Methods
+If you encounter a problem, please submit your issue with `[JRuby]` in the title.
-```ruby
-b.threshold # get the current threshold
-b.threshold = -10.0 # set the threshold
-b.threshold_enabled? # Boolean: is the threshold enabled?
-b.threshold_disabled? # Boolean: is the threshold disabled?
-b.enable_threshold # enables threshold processing
-b.disable_threshold # disables threshold processing
-```
-
-Using these convience methods your applications can dynamically adjust threshold processing as required.
-
-### Bayesian Classification
-
-* https://en.wikipedia.org/wiki/Naive_Bayes_classifier
-* http://www.process.com/precisemail/bayesian_filtering.htm
-* http://en.wikipedia.org/wiki/Bayesian_filtering
-* http://www.paulgraham.com/spam.html
-
-## LSI
-
-A Latent Semantic Indexer by David Fayram. Latent Semantic Indexing engines
-are not as fast or as small as Bayesian classifiers, but are more flexible, providing
-fast search and clustering detection as well as semantic analysis of the text that
-theoretically simulates human learning.
-
-### Usage
-
-```ruby
-require 'classifier-reborn'
-lsi = ClassifierReborn::LSI.new
-strings = [ ["This text deals with dogs. Dogs.", :dog],
- ["This text involves dogs too. Dogs! ", :dog],
- ["This text revolves around cats. Cats.", :cat],
- ["This text also involves cats. Cats!", :cat],
- ["This text involves birds. Birds.",:bird ]]
-strings.each {|x| lsi.add_item x.first, x.last}
-
-lsi.search("dog", 3)
-# returns => ["This text deals with dogs. Dogs.", "This text involves dogs too. Dogs! ",
-# "This text also involves cats. Cats!"]
-
-lsi.find_related(strings[2], 2)
-# returns => ["This text revolves around cats. Cats.", "This text also involves cats. Cats!"]
-
-lsi.classify "This text is also about dogs!"
-# returns => :dog
-```
-
-Please see the ClassifierReborn::LSI documentation for more information. It is possible to index, search and classify
-with more than just simple strings.
-
-### Latent Semantic Indexing
-
-* http://www.c2.com/cgi/wiki?LatentSemanticIndexing
-* http://www.chadfowler.com/index.cgi/Computing/LatentSemanticIndexing.rdoc
-* http://en.wikipedia.org/wiki/Latent_semantic_analysis
-
-
## Code of Conduct
-In order to have a more open and welcoming community, Classifier-Reborn adheres to the Jekyll
-[code of conduct](https://github.com/jekyll/jekyll/blob/master/CONDUCT.markdown) adapted from the Ruby on Rails code of
-conduct.
+In order to have a more open and welcoming community, `Classifier Reborn` adheres to the `Jekyll`
+[code of conduct](https://github.com/jekyll/jekyll/blob/master/CODE_OF_CONDUCT.markdown) adapted from the `Ruby on Rails` code of conduct.
-Please adhere to this code of conduct in any interactions you have in the
-Classifier community. If you encounter someone violating
-these terms, please let [@chase](https://github.com/Ch4s3) know and we will address it as soon as possible.
+Please adhere to this code of conduct in any interactions you have in the `Classifier` community.
+If you encounter someone violating these terms, please let [Chase Gilliam](https://github.com/Ch4s3) know and we will address it as soon as possible.
+## Authors and Contributors
-## Authors
+* [Lucas Carlson](mailto:lucas@rufy.com)
+* [David Fayram II](mailto:dfayram@gmail.com)
+* [Cameron McBride](mailto:cameron.mcbride@gmail.com)
+* [Ivan Acosta-Rubio](mailto:ivan@softwarecriollo.com)
+* [Parker Moore](mailto:email@byparker.com)
+* [Chase Gilliam](mailto:chase.gilliam@gmail.com)
+* and [many more](https://github.com/jekyll/classifier-reborn/graphs/contributors)...
-* Lucas Carlson (lucas@rufy.com)
-* David Fayram II (dfayram@gmail.com)
-* Cameron McBride (cameron.mcbride@gmail.com)
-* Ivan Acosta-Rubio (ivan@softwarecriollo.com)
-* Parker Moore (email@byparker.com)
-* Chase Gilliam (chase.gilliam@gmail.com)
-
-This library is released under the terms of the GNU LGPL. See LICENSE for more details.
+The Classifier Reborn library is released under the terms of the [GNU LGPL-2.1](https://github.com/jekyll/classifier-reborn/blob/master/LICENSE).