Style examples

examples/continuous-id3.rb

@@ -2,15 +2,25 @@ require 'rubygems'
 require 'decisiontree'
 include DecisionTree
 
-# ---Continuous-----------------------------------------------------------------------------------------
+# ---Continuous---
 
 # Read in the training data
-training, attributes = [], nil
+training = []
-File.open('data/continuous-training.txt','r').each_line { |line| 
+File.open('data/continuous-training.txt', 'r').each_line do |line|
   data = line.strip.chomp('.').split(',')
   attributes ||= data
-  training.push(data.collect {|v| (v == 'healthy') || (v == 'colic') ? (v == 'healthy' ? 1 : 0) : v.to_f})
+  training_data = data.collect do |v|
-}
+    case v
+    when 'healthy'
+      1
+    when 'colic'
+      0
+    else
+      v.to_f
+    end
+  end
+  training.push(training_data)
+end
 
 # Remove the attribute row from the training data
 training.shift
@@ -19,15 +29,25 @@ training.shift
 dec_tree = ID3Tree.new(attributes, training, 1, :continuous)
 dec_tree.train
 
-#---- Test the tree....
+# ---Test the tree---
 
 # Read in the test cases
 # Note: omit the attribute line (first line), we know the labels from the training data
 test = []
-File.open('data/continuous-test.txt','r').each_line { |line| 
+File.open('data/continuous-test.txt', 'r').each_line do |line|
   data = line.strip.chomp('.').split(',')
-  test.push(data.collect {|v| (v == 'healthy') || (v == 'colic') ? (v == 'healthy' ? 1 : 0) : v.to_f})
+  test_data = data.collect do |v|
-}
+    if v == 'healthy' || v == 'colic'
+      v == 'healthy' ? 1 : 0
+    else
+      v.to_f
+    end
+  end
+  test.push(test_data)
+end
 
 # Let the tree predict the output and compare it to the true specified value
-test.each { |t| predict = dec_tree.predict(t);  puts "Predict: #{predict} ... True: #{t.last}"}
+test.each do |t|
+  predict = dec_tree.predict(t)
+  puts "Predict: #{predict} ... True: #{t.last}"
+end

examples/discrete-id3.rb

@@ -1,15 +1,25 @@
 require 'rubygems'
 require 'decisiontree'
 
-# ---Discrete-----------------------------------------------------------------------------------------
+# ---Discrete---
 
 # Read in the training data
-training, attributes = [], nil
+training = []
-File.open('data/discrete-training.txt','r').each_line { |line| 
+File.open('data/discrete-training.txt', 'r').each_line do |line|
   data = line.strip.split(',')
   attributes ||= data
-  training.push(data.collect {|v| (v == 'will buy') || (v == "won't buy") ? (v == 'will buy' ? 1 : 0) : v})
+  training_data = data.collect do |v|
-}
+    case v
+    when 'will buy'
+      1
+    when "won't buy"
+      0
+    else
+      v
+    end
+  end
+  training.push(training_data)
+end
 
 # Remove the attribute row from the training data
 training.shift
@@ -18,17 +28,31 @@ training.shift
 dec_tree = DecisionTree::ID3Tree.new(attributes, training, 1, :discrete)
 dec_tree.train
 
-#---- Test the tree....
+# ---Test the tree---
 
 # Read in the test cases
 # Note: omit the attribute line (first line), we know the labels from the training data
 test = []
-File.open('data/discrete-test.txt','r').each_line { |line| data = line.strip.split(',') 
+File.open('data/discrete-test.txt', 'r').each_line do |line|
-  test.push(data.collect {|v| (v == 'will buy') || (v == "won't buy") ? (v == 'will buy' ? 1 : 0) : v})
+  data = line.strip.split(',')
-}
+  test_data = data.collect do |v|
+    case v
+    when 'will buy'
+      1
+    when "won't buy"
+      0
+    else
+      v
+    end
+  end
+  training.push(test_data)
+end
 
 # Let the tree predict the output and compare it to the true specified value
-test.each { |t|   predict = dec_tree.predict(t); puts "Predict: #{predict} ... True: #{t.last}"; }
+test.each do |t|
+  predict = dec_tree.predict(t)
+  puts "Predict: #{predict} ... True: #{t.last}"
+end
 
 # Graph the tree, save to 'discrete.png'
-dec_tree.graph("discrete")
+dec_tree.graph('discrete')

examples/simple.rb

@@ -10,7 +10,7 @@ training = [
   [38, 'sick'],
   [36.7, 'healthy'],
   [40, 'sick'],
-  [50, 'really sick'],
+  [50, 'really sick']
 ]
 
 # Instantiate the tree, and train it based on the data (set default to '1')
@@ -20,9 +20,7 @@ dec_tree.train
 test = [37, 'sick']
 
 decision = dec_tree.predict(test)
-puts "Predicted: #{decision} ... True decision: #{test.last}";
+puts "Predicted: #{decision} ... True decision: #{test.last}"
 
 # Graph the tree, save to 'tree.png'
-dec_tree.graph("tree")
+dec_tree.graph('tree')
-
-