Class: Ebooks::MarkovModel
- Inherits:
-
Object
- Object
- Ebooks::MarkovModel
- Defined in:
- lib/twitter_ebooks/markov.rb
Overview
This is an ngram-based Markov model optimized to build from a tokenized sentence list without requiring too much transformation
Class Method Summary collapse
Instance Method Summary collapse
Class Method Details
.build(sentences) ⇒ Object
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# File 'lib/twitter_ebooks/markov.rb', line 11 def self.build(sentences) MarkovModel.new.consume(sentences) end |
Instance Method Details
#chain(tokens) ⇒ Object
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# File 'lib/twitter_ebooks/markov.rb', line 55 def chain(tokens) if tokens.length == 1 matches = @unigrams[tokens[-1]] else matches = @bigrams[tokens[-2]][tokens[-1]] matches = @unigrams[tokens[-1]] if matches.length < 2 end if matches.empty? # This should never happen unless a strange token is # supplied from outside the dataset raise ArgumentError, "Unable to continue chain for: #{tokens.inspect}" end next_token = find_token(matches.sample) if next_token == INTERIM # We chose to end the sentence return tokens else return chain(tokens + [next_token]) end end |
#consume(sentences) ⇒ Object
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# File 'lib/twitter_ebooks/markov.rb', line 15 def consume(sentences) # These models are of the form ngram => [[sentence_pos, token_pos] || INTERIM, ...] # We map by both bigrams and unigrams so we can fall back to the latter in # cases where an input bigram is unavailable, such as starting a sentence @sentences = sentences @unigrams = {} @bigrams = {} sentences.each_with_index do |tokens, i| last_token = INTERIM tokens.each_with_index do |token, j| @unigrams[last_token] ||= [] @unigrams[last_token] << [i, j] @bigrams[last_token] ||= {} @bigrams[last_token][token] ||= [] if j == tokens.length-1 # Mark sentence endings @unigrams[token] ||= [] @unigrams[token] << INTERIM @bigrams[last_token][token] << INTERIM else @bigrams[last_token][token] << [i, j+1] end last_token = token end end self end |
#find_token(index) ⇒ Object
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# File 'lib/twitter_ebooks/markov.rb', line 47 def find_token(index) if index == INTERIM INTERIM else @sentences[index[0]][index[1]] end end |
#generate ⇒ Object
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# File 'lib/twitter_ebooks/markov.rb', line 78 def generate NLP.reconstruct(chain([INTERIM])) end |