package com.imsl.test.example.stat;

import com.imsl.stat.*;
import com.imsl.math.*;

/**
 * <p>
 * Performs a discriminant analysis on Fisher's iris data.
 * </p>
 * <p>
 * This example uses linear discrimination with equal prior probabilities on
 * Fisher's (1936) iris data. </p>
 *
 *
 * @see <a href="DiscriminantAnalysisEx1.java">Code</a>
 * @see <a href="doc-files/DiscriminantAnalysisEx1.out">Output</a>
 */

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(DiscriminantAnalysis.POOLED);
        da.setClassificationMethod(DiscriminantAnalysis.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());
    }
}