module Spark::Mllib
MLlib is Spark’s scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives.
Constants
- MultivariateGaussian
-
This class provides basic functionality for a Multivariate Gaussian (Normal) Distribution. In the event that the covariance matrix is singular, the density will be computed in a reduced dimensional subspace under which the distribution is supported.
Arguments:¶ ↑
- mu
-
The mean vector of the distribution
- sigma
-
The covariance matrix of the distribution
Public Class Methods
autoload(klass, location, import=true)
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Calls superclass method
# File lib/spark/mllib.rb, line 7 def self.autoload(klass, location, import=true) if import @for_importing ||= [] @for_importing << klass end super(klass, location) end
autoload_without_import(klass, location)
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# File lib/spark/mllib.rb, line 16 def self.autoload_without_import(klass, location) autoload(klass, location, false) end
import(to=Object)
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# File lib/spark/mllib.rb, line 83 def self.import(to=Object) @for_importing.each do |klass| to.const_set(klass, const_get(klass)) end nil end
mdarray?()
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# File lib/spark/mllib.rb, line 94 def self.mdarray? Gem::Specification::find_all_by_name('mdarray').any? end
narray?()
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# File lib/spark/mllib.rb, line 90 def self.narray? Gem::Specification::find_all_by_name('narray').any? end
prepare()
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# File lib/spark/mllib.rb, line 62 def self.prepare return if @prepared # if narray? # require 'spark/mllib/narray/vector' # require 'spark/mllib/narray/matrix' # elsif mdarray? # require 'spark/mllib/mdarray/vector' # require 'spark/mllib/mdarray/matrix' # else # require 'spark/mllib/matrix/vector' # require 'spark/mllib/matrix/matrix' # end require 'spark/mllib/ruby_matrix/vector_adapter' require 'spark/mllib/ruby_matrix/matrix_adapter' @prepared = true nil end