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module TextRank
module RankFilter
##
# A rank filter which normalizes the ranked keywords so that the sum of the
# rank values is 1.0 (a "probability" normalization).
#
# = Example
#
# NormalizeProbability.new.filter!(
# {
# "town" => 0.6818754334834477,
# "cities" => 0.6055017128817066,
# "siege" => 0.5411519524982207,
# "arts" => 0.4907977453782612,
# "envy" => 0.4692709808107252,
# "blessings" => 0.4442147897516214,
# "plagues" => 0.3972420789430091,
# "florish" => 0.2746092797528525,
# "devoured" => 0.26867321734332237,
# "anxieties" => 0.2367731719604189,
# "peace" => 0.1905352582752693,
# "inhabitants" => 0.02715120116732137,
# }
# )
# => {
# "town" => 0.1473434248897056,
# "cities" => 0.13084016782478722,
# "siege" => 0.11693511476062682,
# "arts" => 0.10605429845557579,
# "envy" => 0.10140267579486278,
# "blessings" => 0.09598839508602595,
# "plagues" => 0.08583827125543537,
# "florish" => 0.0593390959673909,
# "devoured" => 0.058056398684529435,
# "anxieties" => 0.051163259981992296,
# "peace" => 0.041171915188530236,
# "inhabitants" => 0.005866982110537665,
# }
##
class NormalizeProbability
# Perform the filter on the ranks
# @param ranks [Hash<String, Float>] the results of the PageRank algorithm
# @return [Hash<String, Float>]
def filter!(ranks, **_)
return if ranks.empty?
total = ranks.values.reduce(:+)
Hash[ranks.map { |k, v| [k, v / total] }]
end
end
end
end
Version data entries
6 entries across 6 versions & 1 rubygems