Results for can-map-define

This is a quick little gem that uses Google Maps to determine the distance, duration and fuel costs for driving between two user defined points. Just like Google Maps, the starting and destination points can be full addresses, city/state combinations, or just ZIP codes.

This ActiveRecord extension provides the capability to define or map attributes in your ActiveRecord models that can be easily represented with the necessary Open Graph, Twitter Cards or any other custom meta tags.

==== Ruby Topic Maps (RTM) RTM is a Topic Maps engine written in Ruby. See http://rtm.topicmapslab.de/ for instructions. Several backends and extensions are available as separate gems. ==== Overview From a developer's perspective, RTM is a schema-less database management system. The Topic Maps standard (described below) on which RTM is based provides a way of creating a self-describing schema just by using it. ==== Quickstart require 'rtm' connection = RTM.connect # uses the default Ontopia in-memory backend topic_map = connection.create "http://example.org/my_topic_map/" some_topic = topicmap.get!("identifier_of_the_topic") some_topic["-"] = "default name for the topic" topic_map.to_xtm("my_xtm_file.xtm") ==== Topic Maps Topic Maps is an international industry standard (ISO13250) for interchangeably representing information about the structure of information resources used to define topics, and the relationships between topics. A set of one or more interrelated documents that employs the notation defined by this International Standard is called a topic map. A topic map defines a multidimensional topic space - a space in which the locations are topics, and in which the distances between topics are measurable in terms of the number of intervening topics which must be visited in order to get from one topic to another, and the kinds of relationships that define the path from one topic to another, if any, through the intervening topics, if any. In addition, information objects can have properties, as well as values for those properties, assigned to them. The Topic Maps Data Model which is used in this implementation can be found on http://www.isotopicmaps.org/sam/sam-model/. ==== Backends * rtm-ontopia: JRuby only, recommended, uses Ontopia: http://code.google.com/p/ontopia/ * rtm-tinytim: JRuby only, uses TinyTiM: http://tinytim.sourceforge.net/ * rtm-activerecord: uses a custom ActiveRecord schema ==== Extensions * rtm-tmql: Adds support for the Topic Maps Query Language (TMQL), http://isotopicmaps.org/tmql/ * rtm-tmcl: Adds support for the Topic Maps Constraint Language (TMCL), http://isotopicmaps.org/tmcl/ ==== License Copyright 2009 Topic Maps Lab, University of Leipzig. Apache License, Version 2.0

This gem implements: 1.) a logistic map function (#logistic_map), which is a discrete, non-linear, dynamic equation which can show - with proper parameters - chaotic behaviour with super-sensitivity to the initial parameters. Very small changes to initial parameters cause huge changes in the result (can be used as a PRNG as iterated over and over); 2.) A tent-map version of the logistic map (#logistic_points) which returns an array of Nth iterated values of several logistic maps with their initial X0 parameter ranging from 0 to 1 by user defined steps, showing curve-like properties when plotted.

Vimdo is a ruby gem to automate tasks with vim remote servers. Predefined tasks include diff, merge, etc. You can define your own recipes to run tasks with Vim. For example, you can define `DirDiff` recipe: ```ruby module VimDo class CLI < Thor desc "dirdiff", "directory diff in vim" def dirdiff(from, to) [from, to].each do |f| unless File.directory?(f) raise PathError "#{f} is not directory!" end end from, to = [from, to].map {|f| File.expand_path(f) } commands(%Q{exec 'DirDiff ' fnameescape("#{from}") fnameescape("#{to}")}) end end end ``` Then run `vimdo dirdiff path/to/a path/to/b` from the command line or other tools

==== Topic Maps for Rails (rtm-rails) RTM-Rails is the Rails-Adapter for Ruby Topic Maps. It allows simple configuration of topicmaps in config/topicmaps.yml. ==== Overview From a developer's perspective, RTM is a schema-less database management system. The Topic Maps standard (described below) on which RTM is based provides a way of creating a self-describing schema just by using it. You can use RTM as a complement data storage to ActiveRecord in your Rails apps. ==== Quickstart - existing Rails project jruby script/generate topicmaps Run the command above after installing rtm-rails. This will create * a minimal default configuration: config/topicmaps.yml and * a file with more examples and explanations config/topicmaps.example.yml * a file README.topicmaps.txt which contains more information how to use it and where to find more information * an initializer to load the topicmaps at startup * a rake task to migrate the topic maps backends in your rails application. ==== Quickstart - new Rails project For a new Rails application these are the complete initial steps: jruby -S rails my_topicmaps_app cd my_topicmaps_app jruby -S script/generate jdbc jruby -S script/generate topicmaps # The following lines are necessary because Rails does not have a template # for the H2 database and Ontopia does not support the Rails default SQLite3. sed -e "s/sqlite3/h2/" config/database.yml > config/database.yml.h2 mv config/database.yml.h2 config/database.yml # Prepare the database and then check if all is OK jruby -S rake topicmaps:migrate_backends jruby -S rake topicmaps:check ==== Usage inside the application When everything is fine, let's create our first topic: jruby -S script/console TM[:example].get!("http://example.org/my/first/topic") # and save the topic map TM[:example].commit Access the configured topic maps anywhere in your application like this: TM[:example] To retrieve all topics, you can do TM[:example].topics To retrieve a specific topic by its subject identifier: TM[:example].get("http://example.org/my/topic") Commit the changes to the database permanently: TM[:example].commit ... or abort the transaction: TM[:example].abort More information can be found on http://rtm.topicmapslab.de/ ==== Minimal configuration default: topicmaps: example: http://rtm.topicmapslab.de/example1/ The minimal configuration creates a single topic map, named :example with the locator given. This topic map will be persisted in the same database as your ActiveRecord connection if not specified otherwise. The default backend is OntopiaRDBMS (from the rtm-ontopia gem). A more complete configuration can be found in config/topicmaps.example.yml after running "jruby script/generate topicmaps". It also includes how to specifiy multiple connections to different data stores and so on. ==== Topic Maps Topic Maps is an international industry standard (ISO13250) for interchangeably representing information about the structure of information resources used to define topics, and the relationships between topics. A set of one or more interrelated documents that employs the notation defined by this International Standard is called a topic map. A topic map defines a multidimensional topic space - a space in which the locations are topics, and in which the distances between topics are measurable in terms of the number of intervening topics which must be visited in order to get from one topic to another, and the kinds of relationships that define the path from one topic to another, if any, through the intervening topics, if any. In addition, information objects can have properties, as well as values for those properties, assigned to them. The Topic Maps Data Model which is used in this implementation can be found on http://www.isotopicmaps.org/sam/sam-model/. ==== License Copyright 2009 Topic Maps Lab, University of Leipzig. Apache License, Version 2.0

Mokio is a Content Management System that allows creation of sophisticated websites. It consists maily of administration panel for your desired website. It provides the following types of content: Article (piece of text with pictures, lists, links, etc.), Picture Gallery (easily managed article with a number of photos - thumbs and edition provided), Movie Gallery (article with links to Dailymotion, Vimeo and Youtube movies - thumbs and edition provided), Contact Page with Google map and contact form. Apart from content it is possible to manage menu structure of your website for each language and defined position (part of the screen). If this is not enough for what you need, you can also include pieces of HTML code that should be shared by some (or all) subpages - called HTML Blocks. You can define on which part of the page, these blocks should be displayed. Mokio provides also administration panel for javascripts, languages and backend users. Beside Mokio itself, there are various gems dedicated for Mokio, that extends its core functionality. Visit: http://www.mokio.org Folow us on facebook: https://www.facebook.com/mokioCMS

# Procer **NOTE: Experimental. Use it to experience what a default `to_proc` could have been. For production code, I recommend an explicit transformation, like the one provided by the gem `jgomo3-func`**. A reasonable good default `to_proc` method for all objects. Install with: ``` gem install procer ``` When you require Procer, all objects will have a default `to_proc` method which will try to call one of the following methods, in the given order: - `call` - `[]` - `===` Many methods which receive a block, can benefit greatly from this because you can now pass an object to perform the block role. Think of the Enumerable module and all its methods. Many objects define `===`, but not `to_proc`. So they will be nicely usable in a `case/when` expression, but not in other contexts. This is the case of classes and ranges, which you can use in `case/when` expressions, but they don't define `to_proc`. Now they do define `to_proc` so they are useful in those contexts. Examples: ```ruby require 'procer' [1, 2, '3', '4', 5, 6].filter(&Numeric) # => [1, 2, 5, 6] [-10, 100, -2, 3, 20, -33].filter(&(0..50)) # => [3, 20] ``` Also, Hashes already implement `to_proc` and that is useful with Enumerator. We can use it as a transformation table with `map`: ```ruby table = { 1 => 'one', 2 => 'two', 3 => 'three' } [3, 1, 2].map(&table) # => ['three, 'one, 'two'] ``` Sadly, Arrays, even when they have the same interface as hashes as a function of indices, don't implement `to_proc` and so they can't be used in the same way. Until now. ```ruby table = ['zero', 'one', 'two'] [2, 0, 1].map(&table) # => ['two', 'zero', 'one'] ``` Alternatively, you could have used `values_at`: ```ruby table.values_at([3, 1, 2]) # In the Hash example table.values_at([2, 0, 1]) # In the Array example ``` But the map solution is more generic and `table` can be anything that implements `to_proc` and not something that necessarily implements `values_at`. Notice that if the object implements `[]` that will triumph over `===`. It was unexpected when I tried to use Integers as the object, as they implement `[]` as a way to access their binary form: ```ruby 5 # b101 [5[2], 5[1], 5[0]] # [1, 0, 1] ``` So the proc will work like that: ```ruby [2, 4, 5].map(&5) # Actual => [1, 0, 0] # I was expecting => [false, false, true] ```

==== subj3ct - The DNS for the Semantic Web This is a Ruby adapter for the subj3ct.com webservice. Subj3ct is an infrastructure technology for Web 3.0 applications. These are applications that are organised around subjects and semantics rather than documents and links. Subj3ct provides the technology and services to enable Web 3.0 applications to define and exchange subject definitions. Or in other words: Subj3ct.com is for the Semantic Web what DNS is for the internet. ==== Installing Install the gem: gem install subj3ct ==== Usage Query a specific subject - to be specific: its subject identity record - using it's identifier: Subj3ct.identifier("http://www.topicmapslab.de/publications/TMRA_2009_subj3ct_a_subject_identity_resolution_service") See the README or the github page for more examples. ==== Subj3ct vs. Subject The official name is "Subj3ct", however in this API, you can also use "Subject" which may be easier to remember or to type for normal, n0n-1337 people. It should work for the gem, for the require and for the main module. ==== Contribute! Subj3ct is a young and ambitious service. It's free, will stay free and needs your help. Contribute to this library! Create bindings for other languages! Publish your data as linked data to the web and register it with subj3ct.com. ==== Note on Patches/Pull Requests * Fork the project on http://github.bb/subj3ct * Make your feature addition or bug fix. * Add tests for it. This is important so I don't break it in a future version unintentionally. * Commit, do not mess with rakefile, version, or history. (if you want to have your own version, that is fine but bump version in a commit by itself I can ignore when I pull) * Send me a pull request. Bonus points for topic branches. ==== Copyright Copyright (c) 2010 Benjamin Bock, Topic Maps Lab. See LICENSE for details.

==== subj3ct - The DNS for the Semantic Web This is a Ruby adapter for the subj3ct.com webservice. Subj3ct is an infrastructure technology for Web 3.0 applications. These are applications that are organised around subjects and semantics rather than documents and links. Subj3ct provides the technology and services to enable Web 3.0 applications to define and exchange subject definitions. Or in other words: Subj3ct.com is for the Semantic Web what DNS is for the internet. ==== Installing Install the gem: gem install subj3ct ==== Usage Query a specific subject - to be specific: its subject identity record - using it's identifier: Subj3ct.identifier("http://www.topicmapslab.de/publications/TMRA_2009_subj3ct_a_subject_identity_resolution_service") See the README or the github page for more examples. ==== Subj3ct vs. Subject The official name is "Subj3ct", however in this API, you can also use "Subject" which may be easier to remember or to type for normal, n0n-1337 people. It should work for the gem, for the require and for the main module. ==== Contribute! Subj3ct is a young and ambitious service. It's free, will stay free and needs your help. Contribute to this library! Create bindings for other languages! Publish your data as linked data to the web and register it with subj3ct.com. ==== Note on Patches/Pull Requests * Fork the project on http://github.bb/subj3ct * Make your feature addition or bug fix. * Add tests for it. This is important so I don't break it in a future version unintentionally. * Commit, do not mess with rakefile, version, or history. (if you want to have your own version, that is fine but bump version in a commit by itself I can ignore when I pull) * Send me a pull request. Bonus points for topic branches. ==== Copyright Copyright (c) 2010 Benjamin Bock, Topic Maps Lab. See LICENSE for details.

A simple Gem to enable any `ActiveRecord::Base` object to store a set of attributes in a set like structure represented through a bitfield on the database level. You only have to specify the name of the set to hold the attributes in question an the rest is done for you through some fine selected Ruby magic. Here is a simple example of how you could use the gem: class Person &lt; ActiveRecord::Base has_set :interests end To get this to work you need some additional work done first: 1. You need an unsigned 8-Byte integer column in your database to store the bitfield. It is expected that the column is named after the name of the set with the suffix `_bitfield` appended (e.g. `interests_bitfield`). You can change that default behavior by providing the option `:column_name` (e.g. `has_set :interests, :column_name =&gt; :my_custom_column`). 2. You need a class that provides the valid values to be stored within the set and map the single bits back to something meaningful. The class should be named after the name of the set (you can change this through the `:enum_class` option). This class could be seen as an enumeration and must implement the following simple interface: * There must be a class method `values` to return all valid enumerators in the defined enumeration. * Each enumerator must implement a `name` method to return a literal representation for identification. The literal must be of the type `String`. * Each enumerator must implement a `bitfield_index` method to return the exponent of the number 2 for calculation the position of this enumerator in the bitfield. **Attention** Changing this index afterwards will destroy your data integrity. Here is a simple example of how to implement such a enumeration type while using the the `renum` gem for simplicity. You are free to use anything else that matches the described interface. enum :Interests do attr_reader :bitfield_index Art(0) Golf(1) Sleeping(2) Drinking(3) Dating(4) Shopping(5) def init(bitfield_index) @bitfield_index = bitfield_index end end

README ====== This is a simple API to evaluate information retrieval results. It allows you to load ranked and unranked query results and calculate various evaluation metrics (precision, recall, MAP, kappa) against a previously loaded gold standard. Start this program from the command line with: retreval -l <gold-standard-file> -q <query-results> -f <format> -o <output-prefix> The options are outlined when you pass no arguments and just call retreval You will find further information in the RDOC documentation and the HOWTO section below. If you want to see an example, use this command: retreval -l example/gold_standard.yml -q example/query_results.yml -f yaml -v INSTALLATION ============ If you have RubyGems, just run gem install retreval You can manually download the sources and build the Gem from there by `cd`ing to the folder where this README is saved and calling gem build retreval.gemspec This will create a gem file called which you just have to install with `gem install <file>` and you're done. HOWTO ===== This API supports the following evaluation tasks: - Loading a Gold Standard that takes a set of documents, queries and corresponding judgements of relevancy (i.e. "Is this document relevant for this query?") - Calculation of the _kappa measure_ for the given gold standard - Loading ranked or unranked query results for a certain query - Calculation of _precision_ and _recall_ for each result - Calculation of the _F-measure_ for weighing precision and recall - Calculation of _mean average precision_ for multiple query results - Calculation of the _11-point precision_ and _average precision_ for ranked query results - Printing of summary tables and results Typically, you will want to use this Gem either standalone or within another application's context. Standalone Usage ================ Call parameters --------------- After installing the Gem (see INSTALLATION), you can always call `retreval` from the commandline. The typical call is: retreval -l <gold-standard-file> -q <query-results> -f <format> -o <output-prefix> Where you have to define the following options: - `gold-standard-file` is a file in a specified format that includes all the judgements - `query-results` is a file in a specified format that includes all the query results in a single file - `format` is the format that the files will use (either "yaml" or "plain") - `output-prefix` is the prefix of output files that will be created Formats ------- Right now, we focus on the formats you can use to load data into the API. Currently, we support YAML files that must adhere to a special syntax. So, in order to load a gold standard, we need a file in the following format: * "query" denotes the query * "documents" these are the documents judged for this query * "id" the ID of the document (e.g. its filename, etc.) * "judgements" an array of judgements, each one with: * "relevant" a boolean value of the judgment (relevant or not) * "user" an optional identifier of the user Example file, with one query, two documents, and one judgement: - query: 12th air force germany 1957 documents: - id: g5701s.ict21311 judgements: [] - id: g5701s.ict21313 judgements: - relevant: false user: 2 So, when calling the program, specify the format as `yaml`. For the query results, a similar format is used. Note that it is necessary to specify whether the result sets are ranked or not, as this will heavily influence the calculations. You can specify the score for a document. By "score" we mean the score that your retrieval algorithm has given the document. But this is not necessary. The documents will always be ranked in the order of their appearance, regardless of their score. Thus in the following example, the document with "07" at the end is the first and "25" is the last, regardless of the score. --- query: 12th air force germany 1957 ranked: true documents: - score: 0.44034874 document: g5701s.ict21307 - score: 0.44034874 document: g5701s.ict21309 - score: 0.44034874 document: g5701s.ict21311 - score: 0.44034874 document: g5701s.ict21313 - score: 0.44034874 document: g5701s.ict21315 - score: 0.44034874 document: g5701s.ict21317 - score: 0.44034874 document: g5701s.ict21319 - score: 0.44034874 document: g5701s.ict21321 - score: 0.44034874 document: g5701s.ict21323 - score: 0.44034874 document: g5701s.ict21325 --- query: 1612 ranked: true documents: - score: 1.0174774 document: g3290.np000144 - score: 0.763108 document: g3201b.ct000726 - score: 0.763108 document: g3400.ct000886 - score: 0.6359234 document: g3201s.ct000130 --- **Note**: You can also use the `plain` format, which will load the gold standard in a different way (but not the results): my_query my_document_1 false my_query my_document_2 true See that every query/document/relevancy pair is separated by a tabulator? You can also add the user's ID in the fourth column if necessary. Running the evaluation ----------------------- After you have specified the input files and the format, you can run the program. If needed, the `-v` switch will turn on verbose messages, such as information on how many judgements, documents and users there are, but this shouldn't be necessary. The program will first load the gold standard and then calculate the statistics for each result set. The output files are automatically created and contain a YAML representation of the results. Calculations may take a while depending on the amount of judgements and documents. If there are a thousand judgements, always consider a few seconds for each result set. Interpreting the output files ------------------------------ Two output files will be created: - `output_avg_precision.yml` - `output_statistics.yml` The first lists the average precision for each query in the query result file. The second file lists all supported statistics for each query in the query results file. For example, for a ranked evaluation, the first two entries of such a query result statistic look like this: --- 12th air force germany 1957: - :precision: 0.0 :recall: 0.0 :false_negatives: 1 :false_positives: 1 :true_negatives: 2516 :true_positives: 0 :document: g5701s.ict21313 :relevant: false - :precision: 0.0 :recall: 0.0 :false_negatives: 1 :false_positives: 2 :true_negatives: 2515 :true_positives: 0 :document: g5701s.ict21317 :relevant: false You can see the precision and recall for that specific point and also the number of documents for the contingency table (true/false positives/negatives). Also, the document identifier is given. API Usage ========= Using this API in another ruby application is probably the more common use case. All you have to do is include the Gem in your Ruby or Ruby on Rails application. For details about available methods, please refer to the API documentation generated by RDoc. **Important**: For this implementation, we use the document ID, the query and the user ID as the primary keys for matching objects. This means that your documents and queries are identified by a string and thus the strings should be sanitized first. Loading the Gold Standard ------------------------- Once you have loaded the Gem, you will probably start by creating a new gold standard. gold_standard = GoldStandard.new Then, you can load judgements into this standard, either from a file, or manually: gold_standard.load_from_yaml_file "my-file.yml" gold_standard.add_judgement :document => doc_id, :query => query_string, :relevant => boolean, :user => John There is a nice shortcut for the `add_judgement` method. Both lines are essentially the same: gold_standard.add_judgement :document => doc_id, :query => query_string, :relevant => boolean, :user => John gold_standard << :document => doc_id, :query => query_string, :relevant => boolean, :user => John Note the usage of typical Rails hashes for better readability (also, this Gem was developed to be used in a Rails webapp). Now that you have loaded the gold standard, you can do things like: gold_standard.contains_judgement? :document => "a document", :query => "the query" gold_standard.relevant? :document => "a document", :query => "the query" Loading the Query Results ------------------------- Now we want to create a new `QueryResultSet`. A query result set can contain more than one result, which is what we normally want. It is important that you specify the gold standard it belongs to. query_result_set = QueryResultSet.new :gold_standard => gold_standard Just like the Gold Standard, you can read a query result set from a file: query_result_set.load_from_yaml_file "my-results-file.yml" Alternatively, you can load the query results one by one. To do this, you have to create the results (either ranked or unranked) and then add documents: my_result = RankedQueryResult.new :query => "the query" my_result.add_document :document => "test_document 1", :score => 13 my_result.add_document :document => "test_document 2", :score => 11 my_result.add_document :document => "test_document 3", :score => 3 This result would be ranked, obviously, and contain three documents. Documents can have a score, but this is optional. You can also create an Array of documents first and add them altogether: documents = Array.new documents << ResultDocument.new :id => "test_document 1", :score => 20 documents << ResultDocument.new :id => "test_document 2", :score => 21 my_result = RankedQueryResult.new :query => "the query", :documents => documents The same applies to `UnrankedQueryResult`s, obviously. The order of ranked documents is the same as the order in which they were added to the result. The `QueryResultSet` will now contain all the results. They are stored in an array called `query_results`, which you can access. So, to iterate over each result, you might want to use the following code: query_result_set.query_results.each_with_index do |result, index| # ... end Or, more simply: for result in query_result_set.query_results # ... end Calculating statistics ---------------------- Now to the interesting part: Calculating statistics. As mentioned before, there is a conceptual difference between ranked and unranked results. Unranked results are much easier to calculate and thus take less CPU time. No matter if unranked or ranked, you can get the most important statistics by just calling the `statistics` method. statistics = my_result.statistics In the simple case of an unranked result, you will receive a hash with the following information: * `precision` - the precision of the results * `recall` - the recall of the results * `false_negatives` - number of not retrieved but relevant items * `false_positives` - number of retrieved but nonrelevant * `true_negatives` - number of not retrieved and nonrelevantv items * `true_positives` - number of retrieved and relevant items In case of a ranked result, you will receive an Array that consists of _n_ such Hashes, depending on the number of documents. Each Hash will give you the information at a certain rank, e.g. the following to lines return the recall at the fourth rank. statistics = my_ranked_result.statistics statistics[3][:recall] In addition to the information mentioned above, you can also get for each rank: * `document` - the ID of the document that was returned at this rank * `relevant` - whether the document was relevant or not Calculating statistics with missing judgements ---------------------------------------------- Sometimes, you don't have judgements for all document/query pairs in the gold standard. If this happens, the results will be cleaned up first. This means that every document in the results that doesn't appear to have a judgement will be removed temporarily. As an example, take the following results: * A * B * C * D Our gold standard only contains judgements for A and C. The results will be cleaned up first, thus leading to: * A * C With this approach, we can still provide meaningful results (for precision and recall). Other statistics ---------------- There are several other statistics that can be calculated, for example the **F measure**. The F measure weighs precision and recall and has one parameter, either "alpha" or "beta". Get the F measure like so: my_result.f_measure :beta => 1 If you don't specify either alpha or beta, we will assume that beta = 1. Another interesting measure is **Cohen's Kappa**, which tells us about the inter-agreement of assessors. Get the kappa statistic like this: gold_standard.kappa This will calculate the average kappa for each pairwise combination of users in the gold standard. For ranked results one might also want to calculate an **11-point precision**. Just call the following: my_ranked_result.eleven_point_precision This will return a Hash that has indices at the 11 recall levels from 0 to 1 (with steps of 0.1) and the corresponding precision at that recall level.

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