Example: Discriminant Analysis

This example uses linear discrimination with equal prior probabilities on Fisher's (1936) iris data. This example illustrates the use of the DiscriminantAnalysis class.

import com.imsl.stat.*;
import java.text.*;
import com.imsl.math.PrintMatrix;
import com.imsl.math.PrintMatrixFormat;

public class DiscriminantAnalysisEx1 {

    public static void main(String args[]) throws Exception {
        double[][] xorig = {
            {1.0, 5.1, 3.5, 1.4, .2}, {1.0, 4.9, 3.0, 1.4, .2}, 
            {1.0, 4.7, 3.2, 1.3, .2}, {1.0, 4.6, 3.1, 1.5, .2},
            {1.0, 5.0, 3.6, 1.4, .2}, {1.0, 5.4, 3.9, 1.7, .4},
            {1.0, 4.6, 3.4, 1.4, .3}, {1.0, 5.0, 3.4, 1.5, .2},
            {1.0, 4.4, 2.9, 1.4, .2}, {1.0, 4.9, 3.1, 1.5, .1},
            {1.0, 5.4, 3.7, 1.5, .2}, {1.0, 4.8, 3.4, 1.6, .2},
            {1.0, 4.8, 3.0, 1.4, .1}, {1.0, 4.3, 3.0, 1.1, .1},
            {1.0, 5.8, 4.0, 1.2, .2}, {1.0, 5.7, 4.4, 1.5, .4},
            {1.0, 5.4, 3.9, 1.3, .4}, {1.0, 5.1, 3.5, 1.4, .3},
            {1.0, 5.7, 3.8, 1.7, .3}, {1.0, 5.1, 3.8, 1.5, .3},
            {1.0, 5.4, 3.4, 1.7, .2}, {1.0, 5.1, 3.7, 1.5, .4},
            {1.0, 4.6, 3.6, 1.0, .2}, {1.0, 5.1, 3.3, 1.7, .5},
            {1.0, 4.8, 3.4, 1.9, .2}, {1.0, 5.0, 3.0, 1.6, .2},
            {1.0, 5.0, 3.4, 1.6, .4}, {1.0, 5.2, 3.5, 1.5, .2},
            {1.0, 5.2, 3.4, 1.4, .2}, {1.0, 4.7, 3.2, 1.6, .2},
            {1.0, 4.8, 3.1, 1.6, .2}, {1.0, 5.4, 3.4, 1.5, .4},
            {1.0, 5.2, 4.1, 1.5, .1}, {1.0, 5.5, 4.2, 1.4, .2},
            {1.0, 4.9, 3.1, 1.5, .2}, {1.0, 5.0, 3.2, 1.2, .2},
            {1.0, 5.5, 3.5, 1.3, .2}, {1.0, 4.9, 3.6, 1.4, .1},
            {1.0, 4.4, 3.0, 1.3, .2}, {1.0, 5.1, 3.4, 1.5, .2},
            {1.0, 5.0, 3.5, 1.3, .3}, {1.0, 4.5, 2.3, 1.3, .3},
            {1.0, 4.4, 3.2, 1.3, .2}, {1.0, 5.0, 3.5, 1.6, .6},
            {1.0, 5.1, 3.8, 1.9, .4}, {1.0, 4.8, 3.0, 1.4, .3},
            {1.0, 5.1, 3.8, 1.6, .2}, {1.0, 4.6, 3.2, 1.4, .2},
            {1.0, 5.3, 3.7, 1.5, .2}, {1.0, 5.0, 3.3, 1.4, .2},
            {2.0, 7.0, 3.2, 4.7, 1.4}, {2.0, 6.4, 3.2, 4.5, 1.5},
            {2.0, 6.9, 3.1, 4.9, 1.5}, {2.0, 5.5, 2.3, 4.0, 1.3},
            {2.0, 6.5, 2.8, 4.6, 1.5}, {2.0, 5.7, 2.8, 4.5, 1.3},
            {2.0, 6.3, 3.3, 4.7, 1.6}, {2.0, 4.9, 2.4, 3.3, 1.0},
            {2.0, 6.6, 2.9, 4.6, 1.3}, {2.0, 5.2, 2.7, 3.9, 1.4},
            {2.0, 5.0, 2.0, 3.5, 1.0}, {2.0, 5.9, 3.0, 4.2, 1.5},
            {2.0, 6.0, 2.2, 4.0, 1.0}, {2.0, 6.1, 2.9, 4.7, 1.4},
            {2.0, 5.6, 2.9, 3.6, 1.3}, {2.0, 6.7, 3.1, 4.4, 1.4},
            {2.0, 5.6, 3.0, 4.5, 1.5}, {2.0, 5.8, 2.7, 4.1, 1.0},
            {2.0, 6.2, 2.2, 4.5, 1.5}, {2.0, 5.6, 2.5, 3.9, 1.1},
            {2.0, 5.9, 3.2, 4.8, 1.8}, {2.0, 6.1, 2.8, 4.0, 1.3},
            {2.0, 6.3, 2.5, 4.9, 1.5}, {2.0, 6.1, 2.8, 4.7, 1.2},
            {2.0, 6.4, 2.9, 4.3, 1.3}, {2.0, 6.6, 3.0, 4.4, 1.4},
            {2.0, 6.8, 2.8, 4.8, 1.4}, {2.0, 6.7, 3.0, 5.0, 1.7},
            {2.0, 6.0, 2.9, 4.5, 1.5}, {2.0, 5.7, 2.6, 3.5, 1.0},
            {2.0, 5.5, 2.4, 3.8, 1.1}, {2.0, 5.5, 2.4, 3.7, 1.0},
            {2.0, 5.8, 2.7, 3.9, 1.2}, {2.0, 6.0, 2.7, 5.1, 1.6},
            {2.0, 5.4, 3.0, 4.5, 1.5}, {2.0, 6.0, 3.4, 4.5, 1.6},
            {2.0, 6.7, 3.1, 4.7, 1.5}, {2.0, 6.3, 2.3, 4.4, 1.3},
            {2.0, 5.6, 3.0, 4.1, 1.3}, {2.0, 5.5, 2.5, 4.0, 1.3},
            {2.0, 5.5, 2.6, 4.4, 1.2}, {2.0, 6.1, 3.0, 4.6, 1.4},
            {2.0, 5.8, 2.6, 4.0, 1.2}, {2.0, 5.0, 2.3, 3.3, 1.0},
            {2.0, 5.6, 2.7, 4.2, 1.3}, {2.0, 5.7, 3.0, 4.2, 1.2},
            {2.0, 5.7, 2.9, 4.2, 1.3}, {2.0, 6.2, 2.9, 4.3, 1.3},
            {2.0, 5.1, 2.5, 3.0, 1.1}, {2.0, 5.7, 2.8, 4.1, 1.3},
            {3.0, 6.3, 3.3, 6.0, 2.5}, {3.0, 5.8, 2.7, 5.1, 1.9},
            {3.0, 7.1, 3.0, 5.9, 2.1}, {3.0, 6.3, 2.9, 5.6, 1.8},
            {3.0, 6.5, 3.0, 5.8, 2.2}, {3.0, 7.6, 3.0, 6.6, 2.1},
            {3.0, 4.9, 2.5, 4.5, 1.7}, {3.0, 7.3, 2.9, 6.3, 1.8},
            {3.0, 6.7, 2.5, 5.8, 1.8}, {3.0, 7.2, 3.6, 6.1, 2.5},
            {3.0, 6.5, 3.2, 5.1, 2.0}, {3.0, 6.4, 2.7, 5.3, 1.9},
            {3.0, 6.8, 3.0, 5.5, 2.1}, {3.0, 5.7, 2.5, 5.0, 2.0},
            {3.0, 5.8, 2.8, 5.1, 2.4}, {3.0, 6.4, 3.2, 5.3, 2.3},
            {3.0, 6.5, 3.0, 5.5, 1.8}, {3.0, 7.7, 3.8, 6.7, 2.2},
            {3.0, 7.7, 2.6, 6.9, 2.3}, {3.0, 6.0, 2.2, 5.0, 1.5},
            {3.0, 6.9, 3.2, 5.7, 2.3}, {3.0, 5.6, 2.8, 4.9, 2.0},
            {3.0, 7.7, 2.8, 6.7, 2.0}, {3.0, 6.3, 2.7, 4.9, 1.8},
            {3.0, 6.7, 3.3, 5.7, 2.1}, {3.0, 7.2, 3.2, 6.0, 1.8},
            {3.0, 6.2, 2.8, 4.8, 1.8}, {3.0, 6.1, 3.0, 4.9, 1.8},
            {3.0, 6.4, 2.8, 5.6, 2.1}, {3.0, 7.2, 3.0, 5.8, 1.6},
            {3.0, 7.4, 2.8, 6.1, 1.9}, {3.0, 7.9, 3.8, 6.4, 2.0},
            {3.0, 6.4, 2.8, 5.6, 2.2}, {3.0, 6.3, 2.8, 5.1, 1.5},
            {3.0, 6.1, 2.6, 5.6, 1.4}, {3.0, 7.7, 3.0, 6.1, 2.3},
            {3.0, 6.3, 3.4, 5.6, 2.4}, {3.0, 6.4, 3.1, 5.5, 1.8},
            {3.0, 6.0, 3.0, 4.8, 1.8}, {3.0, 6.9, 3.1, 5.4, 2.1},
            {3.0, 6.7, 3.1, 5.6, 2.4}, {3.0, 6.9, 3.1, 5.1, 2.3},
            {3.0, 5.8, 2.7, 5.1, 1.9}, {3.0, 6.8, 3.2, 5.9, 2.3},
            {3.0, 6.7, 3.3, 5.7, 2.5}, {3.0, 6.7, 3.0, 5.2, 2.3},
            {3.0, 6.3, 2.5, 5.0, 1.9}, {3.0, 6.5, 3.0, 5.2, 2.0},
            {3.0, 6.2, 3.4, 5.4, 2.3}, {3.0, 5.9, 3.0, 5.1, 1.8}};

        int[] group = new int[xorig.length];
        int[] varIndex = {1,2,3,4};

        for (int i = 0; i < xorig.length; i++) {
            group[i] = (int) xorig[i][0];
        }
        int nvar = xorig[0].length -1;
        
        DiscriminantAnalysis da = new DiscriminantAnalysis(nvar, 3);
        da.setCovarianceComputation(da.POOLED);
        da.setClassificationMethod(da.RECLASSIFICATION);
        da.update(xorig, group, varIndex);
        da.classify(xorig, group, varIndex);
        new PrintMatrix("Xmean:  ").print(da.getMeans());
        new PrintMatrix("Coef:  ").print(da.getCoefficients());
        new PrintMatrix("Counts: ").print(da.getGroupCounts());
        new PrintMatrix("Stats: ").print(da.getStatistics());
        int[] cm = da.getClassMembership();
        int[][] cMem = new int[1][cm.length];
        for (int i=0; i<cm.length; i++) {
            cMem[0][i] = cm[i];
        }
        new PrintMatrix("ClassMembership").setPageWidth(50).print(cMem);
        new PrintMatrix("ClassTable: ").print(da.getClassTable());
        double cov[][][] = da.getCovariance();
        for (int i= 0; i<cov.length;i++) {
            new PrintMatrix("Covariance Matrix "+i+" : ").print(cov[i]);
        }
        new PrintMatrix("Prior : ").print(da.getPrior());
        PrintMatrixFormat pmf = new PrintMatrixFormat();
        pmf.setNumberFormat(new java.text.DecimalFormat("0.00"));
        new PrintMatrix("PROB: ").print(pmf, da.getProbability());
        new PrintMatrix("MAHALANOBIS: ").print(da.getMahalanobis());
        System.out.println("nrmiss = " + da.getNumberOfRowsMissing());
    }
}

Output

           Xmean:  
     0      1      2      3    
0  5.006  3.428  1.462  0.246  
1  5.936  2.77   4.26   1.326  
2  6.588  2.974  5.552  2.026  

                    Coef:  
      0        1       2        3        4     
0   -86.308  23.544  23.588  -16.431  -17.398  
1   -72.853  15.698   7.073    5.211    6.434  
2  -104.368  12.446   3.685   12.767   21.079  

Counts: 
   0   
0  50  
1  50  
2  50  

   Stats: 
       0     
 0  147      
 1    ?      
 2    ?      
 3    ?      
 4    ?      
 5    ?      
 6    ?      
 7   -9.959  
 8   50      
 9   50      
10   50      
11  150      

                   ClassMembership
   0  1  2  3  4  5  6  7  8  9  10  11  12  13  14  
0  1  1  1  1  1  1  1  1  1  1  1   1   1   1   1   

   15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  
0  1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   

   30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  
0  1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   

   45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  
0  1   1   1   1   1   2   2   2   2   2   2   2   2   2   2   

   60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  
0  2   2   2   2   2   2   2   2   2   2   3   2   2   2   2   

   75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  
0  2   2   2   2   2   2   2   2   3   2   2   2   2   2   2   

   90  91  92  93  94  95  96  97  98  99  100  101  102  103  104  
0  2   2   2   2   2   2   2   2   2   2    3    3    3    3    3   

   105  106  107  108  109  110  111  112  113  114  115  116  117  118  119  
0   3    3    3    3    3    3    3    3    3    3    3    3    3    3    3   

   120  121  122  123  124  125  126  127  128  129  130  131  132  133  134  
0   3    3    3    3    3    3    3    3    3    3    3    3    3    2    3   

   135  136  137  138  139  140  141  142  143  144  145  146  147  148  149  
0   3    3    3    3    3    3    3    3    3    3    3    3    3    3    3   

 ClassTable: 
   0   1   2   
0  50   0   0  
1   0  48   2  
2   0   1  49  

    Covariance Matrix 0 : 
     0      1      2      3    
0  0.265  0.093  0.168  0.038  
1  0.093  0.115  0.055  0.033  
2  0.168  0.055  0.185  0.043  
3  0.038  0.033  0.043  0.042  

 Prior : 
     0    
0  0.333  
1  0.333  
2  0.333  

        PROB: 
      0     1     2    
  0  1.00  0.00  0.00  
  1  1.00  0.00  0.00  
  2  1.00  0.00  0.00  
  3  1.00  0.00  0.00  
  4  1.00  0.00  0.00  
  5  1.00  0.00  0.00  
  6  1.00  0.00  0.00  
  7  1.00  0.00  0.00  
  8  1.00  0.00  0.00  
  9  1.00  0.00  0.00  
 10  1.00  0.00  0.00  
 11  1.00  0.00  0.00  
 12  1.00  0.00  0.00  
 13  1.00  0.00  0.00  
 14  1.00  0.00  0.00  
 15  1.00  0.00  0.00  
 16  1.00  0.00  0.00  
 17  1.00  0.00  0.00  
 18  1.00  0.00  0.00  
 19  1.00  0.00  0.00  
 20  1.00  0.00  0.00  
 21  1.00  0.00  0.00  
 22  1.00  0.00  0.00  
 23  1.00  0.00  0.00  
 24  1.00  0.00  0.00  
 25  1.00  0.00  0.00  
 26  1.00  0.00  0.00  
 27  1.00  0.00  0.00  
 28  1.00  0.00  0.00  
 29  1.00  0.00  0.00  
 30  1.00  0.00  0.00  
 31  1.00  0.00  0.00  
 32  1.00  0.00  0.00  
 33  1.00  0.00  0.00  
 34  1.00  0.00  0.00  
 35  1.00  0.00  0.00  
 36  1.00  0.00  0.00  
 37  1.00  0.00  0.00  
 38  1.00  0.00  0.00  
 39  1.00  0.00  0.00  
 40  1.00  0.00  0.00  
 41  1.00  0.00  0.00  
 42  1.00  0.00  0.00  
 43  1.00  0.00  0.00  
 44  1.00  0.00  0.00  
 45  1.00  0.00  0.00  
 46  1.00  0.00  0.00  
 47  1.00  0.00  0.00  
 48  1.00  0.00  0.00  
 49  1.00  0.00  0.00  
 50  0.00  1.00  0.00  
 51  0.00  1.00  0.00  
 52  0.00  1.00  0.00  
 53  0.00  1.00  0.00  
 54  0.00  1.00  0.00  
 55  0.00  1.00  0.00  
 56  0.00  0.99  0.01  
 57  0.00  1.00  0.00  
 58  0.00  1.00  0.00  
 59  0.00  1.00  0.00  
 60  0.00  1.00  0.00  
 61  0.00  1.00  0.00  
 62  0.00  1.00  0.00  
 63  0.00  0.99  0.01  
 64  0.00  1.00  0.00  
 65  0.00  1.00  0.00  
 66  0.00  0.98  0.02  
 67  0.00  1.00  0.00  
 68  0.00  0.96  0.04  
 69  0.00  1.00  0.00  
 70  0.00  0.25  0.75  
 71  0.00  1.00  0.00  
 72  0.00  0.82  0.18  
 73  0.00  1.00  0.00  
 74  0.00  1.00  0.00  
 75  0.00  1.00  0.00  
 76  0.00  1.00  0.00  
 77  0.00  0.69  0.31  
 78  0.00  0.99  0.01  
 79  0.00  1.00  0.00  
 80  0.00  1.00  0.00  
 81  0.00  1.00  0.00  
 82  0.00  1.00  0.00  
 83  0.00  0.14  0.86  
 84  0.00  0.96  0.04  
 85  0.00  0.99  0.01  
 86  0.00  1.00  0.00  
 87  0.00  1.00  0.00  
 88  0.00  1.00  0.00  
 89  0.00  1.00  0.00  
 90  0.00  1.00  0.00  
 91  0.00  1.00  0.00  
 92  0.00  1.00  0.00  
 93  0.00  1.00  0.00  
 94  0.00  1.00  0.00  
 95  0.00  1.00  0.00  
 96  0.00  1.00  0.00  
 97  0.00  1.00  0.00  
 98  0.00  1.00  0.00  
 99  0.00  1.00  0.00  
100  0.00  0.00  1.00  
101  0.00  0.00  1.00  
102  0.00  0.00  1.00  
103  0.00  0.00  1.00  
104  0.00  0.00  1.00  
105  0.00  0.00  1.00  
106  0.00  0.05  0.95  
107  0.00  0.00  1.00  
108  0.00  0.00  1.00  
109  0.00  0.00  1.00  
110  0.00  0.01  0.99  
111  0.00  0.00  1.00  
112  0.00  0.00  1.00  
113  0.00  0.00  1.00  
114  0.00  0.00  1.00  
115  0.00  0.00  1.00  
116  0.00  0.01  0.99  
117  0.00  0.00  1.00  
118  0.00  0.00  1.00  
119  0.00  0.22  0.78  
120  0.00  0.00  1.00  
121  0.00  0.00  1.00  
122  0.00  0.00  1.00  
123  0.00  0.10  0.90  
124  0.00  0.00  1.00  
125  0.00  0.00  1.00  
126  0.00  0.19  0.81  
127  0.00  0.13  0.87  
128  0.00  0.00  1.00  
129  0.00  0.10  0.90  
130  0.00  0.00  1.00  
131  0.00  0.00  1.00  
132  0.00  0.00  1.00  
133  0.00  0.73  0.27  
134  0.00  0.07  0.93  
135  0.00  0.00  1.00  
136  0.00  0.00  1.00  
137  0.00  0.01  0.99  
138  0.00  0.19  0.81  
139  0.00  0.00  1.00  
140  0.00  0.00  1.00  
141  0.00  0.00  1.00  
142  0.00  0.00  1.00  
143  0.00  0.00  1.00  
144  0.00  0.00  1.00  
145  0.00  0.00  1.00  
146  0.00  0.01  0.99  
147  0.00  0.00  1.00  
148  0.00  0.00  1.00  
149  0.00  0.02  0.98  

        MAHALANOBIS: 
      0       1        2     
0    0      89.864  179.385  
1   89.864   0       17.201  
2  179.385  17.201    0      

nrmiss = 0
Link to Java source.