From c85ac721314566686c27fa34e306a5aed07ffc22 Mon Sep 17 00:00:00 2001
From: Ilya Grigorik <ilya@igvita.com>
Date: Sun, 22 Feb 2009 23:55:58 -0500
Subject: [PATCH] added example to readme

---
 README.rdoc | 61 +++++++++++++++++++++++++++++++++++++----------------
 1 file changed, 43 insertions(+), 18 deletions(-)

diff --git a/README.rdoc b/README.rdoc
index ae022c7..cf7a61d 100644
--- a/README.rdoc
+++ b/README.rdoc
@@ -1,18 +1,43 @@
-= Decision Tree
-
-A ruby library which implements ID3 (information gain) algorithm for decision tree learning. Currently, continuous and discrete datasets can be learned.
-
-- Discrete model assumes unique labels & can be graphed and converted into a png for visual analysis
-- Continuous looks at all possible values for a variable and iteratively chooses the best threshold between all possible assignments. This results in a binary tree which is partitioned by the threshold at every step. (e.g. temperate > 20C)
-
-== Features
-- ID3 algorithms for continuous and discrete cases, with support for incosistent datasets.
-- Graphviz component to visualize the learned tree (http://rockit.sourceforge.net/subprojects/graphr/)
-- Support for multiple, and symbolic outputs and graphing of continuos trees.
-- Returns default value when no branches are suitable for input
-
-== Implementation 
-- Ruleset is a class that trains an ID3Tree with 2/3 of the training data, converts it into a set of rules and prunes the rules with the remaining 1/3 of the training data (in a C4.5 way).
-- Bagging is a bagging-based trainer (quite obvious), which trains 10 Ruleset trainers and when predicting chooses the best output based on voting.
-
-Blog post with explanation & examples: http://www.igvita.com/2007/04/16/decision-tree-learning-in-ruby/
+= Decision Tree
+
+A ruby library which implements ID3 (information gain) algorithm for decision tree learning. Currently, continuous and discrete datasets can be learned.
+
+- Discrete model assumes unique labels & can be graphed and converted into a png for visual analysis
+- Continuous looks at all possible values for a variable and iteratively chooses the best threshold between all possible assignments. This results in a binary tree which is partitioned by the threshold at every step. (e.g. temperate > 20C)
+
+== Features
+- ID3 algorithms for continuous and discrete cases, with support for incosistent datasets.
+- Graphviz component to visualize the learned tree (http://rockit.sourceforge.net/subprojects/graphr/)
+- Support for multiple, and symbolic outputs and graphing of continuos trees.
+- Returns default value when no branches are suitable for input
+
+== Implementation 
+- Ruleset is a class that trains an ID3Tree with 2/3 of the training data, converts it into a set of rules and prunes the rules with the remaining 1/3 of the training data (in a C4.5 way).
+- Bagging is a bagging-based trainer (quite obvious), which trains 10 Ruleset trainers and when predicting chooses the best output based on voting.
+
+Blog post with explanation & examples: http://www.igvita.com/2007/04/16/decision-tree-learning-in-ruby/
+
+== Example
+
+  require 'decisiontree'
+
+  attributes = ['Temperature']
+  training = [
+    [36.6, 'healthy'],
+    [37, 'sick'],
+    [38, 'sick'],
+    [36.7, 'healthy'],
+    [40, 'sick'],
+    [50, 'really sick'],
+  ]
+
+  # Instantiate the tree, and train it based on the data (set default to '1')
+  dec_tree = DecisionTree::ID3Tree.new(attributes, training, 'sick', :continuous)
+  dec_tree.train
+
+  test = [37, 'sick']
+
+  decision = dec_tree.predict(test)
+  puts "Predicted: #{decision} ... True decision: #{test.last}";
+
+  => Predicted: sick ... True decision: sick
\ No newline at end of file