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Style examples
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@@ -2,15 +2,25 @@ require 'rubygems'
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require 'decisiontree'
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include DecisionTree
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# ---Continuous-----------------------------------------------------------------------------------------
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# ---Continuous---
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# Read in the training data
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training, attributes = [], nil
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File.open('data/continuous-training.txt','r').each_line { |line|
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data = line.strip.chomp('.').split(',')
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training = []
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File.open('data/continuous-training.txt', 'r').each_line do |line|
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data = line.strip.chomp('.').split(',')
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attributes ||= data
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training.push(data.collect {|v| (v == 'healthy') || (v == 'colic') ? (v == 'healthy' ? 1 : 0) : v.to_f})
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}
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training_data = data.collect do |v|
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case v
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when 'healthy'
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1
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when 'colic'
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0
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else
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v.to_f
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end
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end
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training.push(training_data)
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end
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# Remove the attribute row from the training data
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training.shift
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@@ -19,15 +29,25 @@ training.shift
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dec_tree = ID3Tree.new(attributes, training, 1, :continuous)
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dec_tree.train
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#---- Test the tree....
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# ---Test the tree---
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# Read in the test cases
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# Note: omit the attribute line (first line), we know the labels from the training data
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# Note: omit the attribute line (first line), we know the labels from the training data
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test = []
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File.open('data/continuous-test.txt','r').each_line { |line|
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data = line.strip.chomp('.').split(',')
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test.push(data.collect {|v| (v == 'healthy') || (v == 'colic') ? (v == 'healthy' ? 1 : 0) : v.to_f})
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}
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File.open('data/continuous-test.txt', 'r').each_line do |line|
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data = line.strip.chomp('.').split(',')
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test_data = data.collect do |v|
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if v == 'healthy' || v == 'colic'
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v == 'healthy' ? 1 : 0
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else
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v.to_f
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end
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end
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test.push(test_data)
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end
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# Let the tree predict the output and compare it to the true specified value
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test.each { |t| predict = dec_tree.predict(t); puts "Predict: #{predict} ... True: #{t.last}"}
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test.each do |t|
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predict = dec_tree.predict(t)
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puts "Predict: #{predict} ... True: #{t.last}"
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end
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@@ -1,15 +1,25 @@
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require 'rubygems'
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require 'decisiontree'
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# ---Discrete-----------------------------------------------------------------------------------------
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# ---Discrete---
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# Read in the training data
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training, attributes = [], nil
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File.open('data/discrete-training.txt','r').each_line { |line|
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training = []
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File.open('data/discrete-training.txt', 'r').each_line do |line|
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data = line.strip.split(',')
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attributes ||= data
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training.push(data.collect {|v| (v == 'will buy') || (v == "won't buy") ? (v == 'will buy' ? 1 : 0) : v})
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}
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training_data = data.collect do |v|
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case v
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when 'will buy'
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1
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when "won't buy"
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0
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else
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v
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end
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end
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training.push(training_data)
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end
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# Remove the attribute row from the training data
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training.shift
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@@ -18,17 +28,31 @@ training.shift
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dec_tree = DecisionTree::ID3Tree.new(attributes, training, 1, :discrete)
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dec_tree.train
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#---- Test the tree....
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# ---Test the tree---
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# Read in the test cases
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# Note: omit the attribute line (first line), we know the labels from the training data
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# Note: omit the attribute line (first line), we know the labels from the training data
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test = []
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File.open('data/discrete-test.txt','r').each_line { |line| data = line.strip.split(',')
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test.push(data.collect {|v| (v == 'will buy') || (v == "won't buy") ? (v == 'will buy' ? 1 : 0) : v})
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}
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File.open('data/discrete-test.txt', 'r').each_line do |line|
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data = line.strip.split(',')
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test_data = data.collect do |v|
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case v
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when 'will buy'
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1
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when "won't buy"
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0
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else
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v
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end
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end
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training.push(test_data)
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end
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# Let the tree predict the output and compare it to the true specified value
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test.each { |t| predict = dec_tree.predict(t); puts "Predict: #{predict} ... True: #{t.last}"; }
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test.each do |t|
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predict = dec_tree.predict(t)
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puts "Predict: #{predict} ... True: #{t.last}"
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end
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# Graph the tree, save to 'discrete.png'
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dec_tree.graph("discrete")
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dec_tree.graph('discrete')
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@@ -2,7 +2,7 @@
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require 'rubygems'
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require 'decisiontree'
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attributes = ['Temperature']
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training = [
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[36.6, 'healthy'],
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@@ -10,19 +10,17 @@ training = [
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[38, 'sick'],
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[36.7, 'healthy'],
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[40, 'sick'],
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[50, 'really sick'],
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[50, 'really sick']
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]
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# Instantiate the tree, and train it based on the data (set default to '1')
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dec_tree = DecisionTree::ID3Tree.new(attributes, training, 'sick', :continuous)
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dec_tree.train
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test = [37, 'sick']
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decision = dec_tree.predict(test)
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puts "Predicted: #{decision} ... True decision: #{test.last}";
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puts "Predicted: #{decision} ... True decision: #{test.last}"
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# Graph the tree, save to 'tree.png'
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dec_tree.graph("tree")
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dec_tree.graph('tree')
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