Class: Ankusa::NaiveBayesClassifier
- Inherits:
-
Object
- Object
- Ankusa::NaiveBayesClassifier
- Includes:
- Classifier
- Defined in:
- lib/ankusa/naive_bayes.rb
Instance Attribute Summary
Attributes included from Classifier
Instance Method Summary collapse
-
#classifications(text, classnames = nil) ⇒ Object
Classes is an array of classes to look at.
- #classify(text, classes = nil) ⇒ Object
-
#log_likelihoods(text, classnames = nil) ⇒ Object
Classes is an array of classes to look at.
Methods included from Classifier
Instance Method Details
#classifications(text, classnames = nil) ⇒ Object
Classes is an array of classes to look at
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# File 'lib/ankusa/naive_bayes.rb', line 20 def classifications(text, classnames=nil) result = log_likelihoods text, classnames result.keys.each { |k| result[k] = (result[k] == -INFTY) ? 0 : Math.exp(result[k]) } # normalize to get probs sum = result.values.inject{ |x,y| x+y } result.keys.each { |k| result[k] = result[k] / sum } unless sum.zero? result end |
#classify(text, classes = nil) ⇒ Object
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# File 'lib/ankusa/naive_bayes.rb', line 7 def classify(text, classes=nil) # return the most probable class result = log_likelihoods(text, classes) if result.values.uniq.size. === 1 # unless all classes are equally likely, then return nil return nil else result.sort_by { |c| -c[1] }.first.first end end |
#log_likelihoods(text, classnames = nil) ⇒ Object
Classes is an array of classes to look at
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# File 'lib/ankusa/naive_bayes.rb', line 35 def log_likelihoods(text, classnames=nil) classnames ||= @classnames result = Hash.new 0 TextHash.new(text).each { |word, count| probs = get_word_probs(word, classnames) classnames.each { |k| # Choose a really small probability if the word has never been seen before in class k result[k] += Math.log(probs[k] > 0 ? (probs[k] * count) : Float::EPSILON) } } # add the prior doc_counts = doc_count_totals.select { |k,v| classnames.include? k }.map { |k,v| v } doc_count_total = (doc_counts.inject(0){ |x,y| x+y } + classnames.length).to_f classnames.each { |k| result[k] += Math.log((@storage.get_doc_count(k) + 1).to_f / doc_count_total) } result end |