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michael chrisco edited this page Aug 21, 2015
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- Latent semantic analysis (LSA)
search = RSemantic::Search.new(documents, :transforms => [:LSA])
- Term frequency – inverse document frequency (TFIDF)
search = RSemantic::Search.new(documents, :transforms => [:TFIDF])
All matrix transforms live in:
rsemantic/lib/semantic/transform/*Add a new class for your transform here. It requires a single class method which is passed a matrix and it should return a matrix.
So as an example adding a PLSA transform our class would look like this:
module RSemantic
module Transform
class PLSA
def self.transform(matrix)
matrix
end
end
end
endAnd then to use the transform we pass the class name in as a symbol when creating the search object.
search = RSemantic::Search.new(documents, :transforms => [:PLSA])documents = ["The cat in the hat disabled",
"A cat is a fine pet ponies.",
"Do and cats make good pets.",
"I haven't got a hat."]
#Log to stdout how the matrix gets built and transformed
search = RSemantic::Search.new(documents, :verbose => true)
#Defaults to performing :TFIDF and then :LSA
search = RSemantic::Search.new(documents)
#Find documents that are related to documents[0] with a ranking for how related they are.
puts search.related(0)
#Search documents for the word cat.
#Returns a ranking for how relevant the matches where for each document.
puts search.search(["cat"])