File:   examples/ex_wine.pl
	Author: Josiah Bryan, <jdb@wcoil.com>
    Desc:
		
		This demonstrates wine cultivar prediction using the
		AI::NeuralNet::Mesh module.
		
        This script uses the data that is the results of a chemical analysis 
        of wines grown in the same region in Italy but derived from three
	    different cultivars. The analysis determined the quantities 
	    of 13 constituents found in each of the three types of wines. 

		The inputs of the net represent 13 seperate attributes
		of the wine's chemical analysis, as follows:
		
		 	1)  Alcohol
		 	2)  Malic acid
		 	3)  Ash
			4)  Alcalinity of ash  
		 	5)  Magnesium
			6)  Total phenols
		 	7)  Flavanoids
		 	8)  Nonflavanoid phenols
		 	9)  Proanthocyanins
			10) Color intensity
		 	11) Hue
		 	12) OD280/OD315 of diluted wines
		 	13) Proline            
		
		There are 168 total examples, with the class distrubution
		as follows:
		
			class 1: 59 instances
			class 2: 71 instances
			class 3: 48 instances
			
		The datasets are stored in wine.dat, and the first
		column on every row is the class attribute for that
		row.