README.md in fuzzy_set-1.0.0 vs README.md in fuzzy_set-1.1.0
- old
+ new
@@ -1,11 +1,27 @@
# FuzzySet
-Welcome to your new gem! In this directory, you'll find the files you need to be able to package up your Ruby library into a gem. Put your Ruby code in the file `lib/fuzzy_set`. To experiment with that code, run `bin/console` for an interactive prompt.
+[](http://badge.fury.io/rb/fuzzy_set)
+[](http://rubydoc.org/gems/fuzzy_set/frames)
+[](https://travis-ci.org/mhutter/fuzzy_set)
+[](https://codeclimate.com/github/mhutter/fuzzy_set)
+[](https://codeclimate.com/github/mhutter/fuzzy_set/coverage)
-TODO: Delete this and the text above, and describe your gem
+FuzzySet represents a set which allows searching its entries by using [Approximate string matching](https://en.wikipedia.org/wiki/Approximate_string_matching).
+
+It allows you to create a fuzzy-search!
+
+## How does it work?
+
+When `add`ing an element to the Set, it first gets indexed. This is, on a very basic level, cutting it up into ngrams and building an index with each ngram pointing to the element.
+
+If you then query the set with `get`, the query itself is also sliced into ngrams. We then select all elements in the set which share at least one common ngram with the query. The results are then ordered by their [cosine string similarity](https://github.com/mhutter/string-similarity) to the query.
+
+**TODO**:
+See [Issues labeled #feature](https://github.com/mhutter/fuzzy_set/labels/feature)
+
## Installation
Add this line to your application's Gemfile:
```ruby
@@ -22,19 +38,48 @@
## Usage
```ruby
require 'fuzzy_set'
-
states = open('states.txt').read.split(/\n/)
-fs = FuzzySet.new(*states)
+# Create a new set and add some elements:
+fs = FuzzySet.new
+fs.add 'Some'
+fs.add 'Words'
+fs.add "or", "even", "multiple", "words!"
+
+# Or provide your elements when creating the set:
+fs = FuzzySet.new(states)
+
+# Use #exact_match to find exact matches (= the normalized query
+# matches a normalized element in the set):
fs.exact_match('michigan!') # => "Michigan"
fs.exact_match('mischigen') # => nil
+# Use #get to get all approximate matches:
fs.get('mischigen')
# => ["Michigan", "Wisconsin", "Mississippi", "Minnesota", "Missouri"]
+
+# With the default settings, #get will always first try to get an
+# exact match (see above), and return if there is one:
+fs.get('mississippi') # => ["Mississippi"]
+
+# set `all_matches` to true, to do a full query, even if there is
+# an exact match:
+fs = FuzzySet.new(states, all_matches: true)
+fs.get('mississippi') # => ["Mississippi", "Missouri", "Michigan", "Minnesota"]
+
+# You can configure more stuff (see below)
+fs = FuzzySet.new(states, all_matches: true, ngram_size_min: 1)
```
+
+### Options
+
+- `:all_matches` - If `false` and there is an exact match for `#get`, return the match immediately. If `true`, do the ngram-query to get more possible matches.
+- `:ngram_size_max` - The maximum Ngram size to use (if there is no match using the max ngram size, try again with a smaller ngran size).
+- `:ngram_size_min` - The minimum Ngram size to use.
+
## Development
After checking out the repo, run `bin/setup` to install dependencies. Then, run `rake test` to run the tests. You can also run `bin/console` for an interactive prompt that will allow you to experiment.