lib/ai4r/neural_network/backpropagation.rb in ai4r-1.2 vs lib/ai4r/neural_network/backpropagation.rb in ai4r-1.3

@@ -1,28 +1,5 @@
-# The utility of artificial neural network 
-# models lies in the fact that they can be used 
-# to infer a function from observations. 
-# This is particularly useful in applications 
-# where the complexity of the data or task makes the 
-# design of such a function by hand impractical.
-# Neural Networks are being used in many businesses and applications. Their 
-# ability to learn by example makes them attractive in environments where 
-# the business rules are either not well defined or are hard to enumerate and 
-# define. Many people believe that Neural Networks can only solve toy problems.
-# Give them a try, and let you decide if they are good enough to solve your 
-# needs.
-# 
-# In this module you will find an implementation of neural networks
-# using the Backpropagation is a supervised learning technique (described 
-# by Paul Werbos in 1974, and further developed by David E. 
-# Rumelhart, Geoffrey E. Hinton and Ronald J. Williams in 1986)
-# 
-# More about neural networks and backpropagation:
-# 
-# * http://en.wikipedia.org/wiki/Backpropagation
-# * http://en.wikipedia.org/wiki/Neural_networks
-# 
 # Author::    Sergio Fierens
 # License::   MPL 1.1
 # Project::   ai4r
 # Url::       http://ai4r.rubyforge.org/
 #
@@ -34,10 +11,32 @@
 # Mozilla Foundation at http://www.mozilla.org/MPL/MPL-1.1.txt
 # 
 
 module Ai4r
 
+  # The utility of artificial neural network 
+  # models lies in the fact that they can be used 
+  # to infer a function from observations. 
+  # This is particularly useful in applications 
+  # where the complexity of the data or task makes the 
+  # design of such a function by hand impractical.
+  # Neural Networks are being used in many businesses and applications. Their 
+  # ability to learn by example makes them attractive in environments where 
+  # the business rules are either not well defined or are hard to enumerate and 
+  # define. Many people believe that Neural Networks can only solve toy problems.
+  # Give them a try, and let you decide if they are good enough to solve your 
+  # needs.
+  # 
+  # In this module you will find an implementation of neural networks
+  # using the Backpropagation is a supervised learning technique (described 
+  # by Paul Werbos in 1974, and further developed by David E. 
+  # Rumelhart, Geoffrey E. Hinton and Ronald J. Williams in 1986)
+  # 
+  # More about neural networks and backpropagation:
+  # 
+  # * http://en.wikipedia.org/wiki/Backpropagation
+  # * http://en.wikipedia.org/wiki/Neural_networks
   module NeuralNetwork
 
     # = Introduction
     # 
     # This is an implementation of neural networks
@@ -46,10 +45,10 @@
     # Rumelhart, Geoffrey E. Hinton and Ronald J. Williams in 1986)
     # 
     # = How to use it
     # 
     #   # Create the network
-    #   net = Backpropagation.new([4, 3, 2])  # 4 inputs
+    #   net = Ai4r::NeuralNetwork::Backpropagation.new([4, 3, 2])  # 4 inputs
     #                                         # 1 hidden layer with 3 neurons, 
     #                                         # 2 outputs
     #   # Train the network 
     #   1..upto(100) do |i|
     #     net.train(example[i], result[i])
\ No newline at end of file