Module: Ankusa::Classifier
- Included in:
- KLDivergenceClassifier, NaiveBayesClassifier
- Defined in:
- lib/ankusa/classifier.rb
Instance Attribute Summary collapse
-
#classnames ⇒ Object
readonly
Returns the value of attribute classnames.
Instance Method Summary collapse
- #initialize(storage) ⇒ Object
-
#train(klass, text) ⇒ Object
text can be either an array of strings or a string klass is a symbol.
-
#untrain(klass, text) ⇒ Object
text can be either an array of strings or a string klass is a symbol.
Instance Attribute Details
#classnames ⇒ Object (readonly)
Returns the value of attribute classnames.
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# File 'lib/ankusa/classifier.rb', line 4 def classnames @classnames end |
Instance Method Details
#initialize(storage) ⇒ Object
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# File 'lib/ankusa/classifier.rb', line 6 def initialize(storage) @storage = storage @storage.init_tables @classnames = @storage.classnames end |
#train(klass, text) ⇒ Object
text can be either an array of strings or a string klass is a symbol
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# File 'lib/ankusa/classifier.rb', line 14 def train(klass, text) th = TextHash.new(text) th.each { |word, count| @storage.incr_word_count klass, word, count yield word, count if block_given? } @storage.incr_total_word_count klass, th.word_count doccount = (text.kind_of? Array) ? text.length : 1 @storage.incr_doc_count klass, doccount @classnames << klass if not @classnames.include? klass # cache is now dirty of these vars @doc_count_totals = nil @vocab_sizes = nil th end |
#untrain(klass, text) ⇒ Object
text can be either an array of strings or a string klass is a symbol
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# File 'lib/ankusa/classifier.rb', line 32 def untrain(klass, text) th = TextHash.new(text) th.each { |word, count| @storage.incr_word_count klass, word, -count yield word, count if block_given? } @storage.incr_total_word_count klass, -th.word_count doccount = (text.kind_of? Array) ? text.length : 1 @storage.incr_doc_count klass, -doccount # cache is now dirty of these vars @doc_count_totals = nil @vocab_sizes = nil th end |