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.