examples/text.rb in rb-libsvm-1.0.6 vs examples/text.rb in rb-libsvm-1.0.7

@@ -9,26 +9,27 @@
 #
 require 'libsvm'
 
 # Let take our documents and create word vectors out of them.
 # I've included labels for these already.  1 signifies that
-# the document was funny  0 means that it wasn't
+# the document was funny, 0 means that it wasn't.
 #
 documents = [[1, "Why did the chicken cross the road? Because a car was coming"],
              [0, "You're an elevator tech? I bet that job has its ups and downs"]]
 
 # Lets create a dictionary of unique words and then we can
-# create our vectors.  This is a very simple example.  If 
-# you were doing this in a production system you'd do things
-# like stemming and removing all punctuation.
+# create our vectors.  This is a very simple example.  If you
+# were doing this in a production system you'd do things like
+# stemming and removing all punctuation (in a less casual way).
 #
-dictionary = documents.map(&:last).flatten.uniq
+dictionary = documents.map(&:last).map(&:split).flatten.uniq
 dictionary = dictionary.map { |x| x.gsub(/\?|,|\.|\-/,'') }
 
 training_set = []
-docments.each do |doc|
-  training_set << [doc.first, Libsvm::Node.features(dictionary.map { |x| doc.include?(x) ? 1 : 0 })]
+documents.each do |doc|
+  features_array = dictionary.map { |x| doc.last.include?(x) ? 1 : 0 }
+  training_set << [doc.first, Libsvm::Node.features(features_array)]
 end
 
 # Lets set up libsvm so that we can test our prediction
 # using the test set
 #
@@ -37,17 +38,18 @@
 
 parameter.cache_size = 1 # in megabytes
 parameter.eps = 0.001
 parameter.c   = 10
 
-# train classifier using training set
+# Train classifier using training set
 #
 problem.set_examples(training_set.map(&:first), training_set.map(&:last))
 model = Libsvm::Model.train(problem, parameter)
 
 # Now lets test our classifier using the test set
 #
 test_set = [1, "Why did the chicken cross the road? To get the worm"]
-test_document = test_set.last.split(' ').map{ |x| x.gsub(/\?|,|\.|\-/,'') }
+test_document = test_set.last.split.map{ |x| x.gsub(/\?|,|\.|\-/,'') }
 
-pred = model.predict(Libsvm::Node.features(dictionary.map{|x| test_document.include?(x) }))
-puts "Predicted #{pred}"
+doc_features = dictionary.map{|x| test_document.include?(x) ? 1 : 0 }
+pred = model.predict(Libsvm::Node.features(doc_features))
+puts "Predicted #{pred==1 ? 'funny' : 'not funny'}"