Module: FeldtRuby

Extended by:
Statistics
Defined in:
lib/feldtruby.rb,
lib/feldtruby/logger.rb,
lib/feldtruby/mongodb.rb,
lib/feldtruby/version.rb,
lib/feldtruby/statistics.rb,
lib/feldtruby/annotations.rb,
lib/feldtruby/mongodb_logger.rb,
lib/feldtruby/statistics/fastmap.rb,
lib/feldtruby/statistics/distance.rb,
lib/feldtruby/statistics/clustering.rb,
lib/feldtruby/statistics/array_archive.rb,
lib/feldtruby/statistics/euclidean_distance.rb

Overview

This is the namespace under which we put things…

Defined Under Namespace

Modules: Annotateable, CompositableDistance, Logging, Normalization, Optimize, SetDistance, Statistics Classes: AllMongoDBLoggers, AverageLinkageMetric, ClusterLinkageMetric, CompleteLinkageMetric, CompositeMetric, Distance, EuclideanDistance, FastMap, HtmlDocGetter, Logger, Metric, MinMaxMeanPerPositionArchive, MongoDBLogger, NgramWordCounter, PositionBasedValueArchive, RCommunicator, SingleLinkageMetric, ValueArchive, WordCounter

Constant Summary collapse

TopDirectory = 
File.dirname(__FILE__).split("/")[0...-1].join("/")
VERSION = 
"0.4.12"

Class Method Summary collapse

Instance Method Summary collapse

Methods included from Statistics

cdm, chi_squared_test, correlation, density_estimation, ncd, probability_of_same_proportions

Class Method Details

.fastmap(objects, distance, k = 2) ⇒ Object

Recursively map n-dimensional objects (given as an Array) into a k-dimensional space while preserving the distances between the objects as well as possible.



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# File 'lib/feldtruby/statistics/fastmap.rb', line 102

def self.fastmap(objects, distance, k = 2)
  FastMap.new(distance, k).run(objects)
end

.is_mongo_running? ⇒ Boolean

Returns:

  • (Boolean) 


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# File 'lib/feldtruby/mongodb.rb', line 7

def self.is_mongo_running?
  begin
    Mongo::MongoClient.new("localhost", 27017)
    return true
  rescue Exception => e
    return false
  end
end

Instance Method Details

#euclidean_distance(o1, o2) ⇒ Object 



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# File 'lib/feldtruby/statistics/euclidean_distance.rb', line 14

def euclidean_distance(o1, o2)
  (@euclidean_distance ||= EuclideanDistance.new).calc(o1, o2)
end