package com.imsl.test.example.datamining;

import com.imsl.datamining.*;
import com.imsl.stat.NormalDistribution;

/**
 *
 * <h4>Trains a classifier to Fisher's Iris data. </h4>
 *
<p>
 * This example trains a <a href="../../../datamining/NaiveBayesClassifier.html">NaiveBayesClassifier</a>
 * on Fisher's Iris data. The training data contains 140 of the 150 continuous patterns and the remaining
 * ten are then used as a test set.
 *
 * Fisher's (1936) Iris data is often used for benchmarking classification
 * algorithms. It consists of the following continuous input attributes and a
 * classification target:
 *
 * <ul type="circle"><li><i>Continuous Attributes Usage</i></li>
 * <ul>
 * <li>Sepal Length</li>
 * <li>Sepal Width</li>
 * <li>Petal Length</li>
 * <li>Petal Width</li></ul>
 * <li>Classification of Iris Type</li>
 * <ul>
 * <li>Setosa</li>
 * <li>Versicolour</li>
 * <li>Virginica</li>
 * </ul>
 * </ul>
 * </p>
 *
 * @see <a href="NaiveBayesClassifierEx1.java">Code</a>
 * @see <a href="doc-files/NaiveBayesClassifierEx1.out">Output</a>
 *
 */
public class NaiveBayesClassifierEx1 {

    private static double[][] irisData = {
        {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}
    };

    public static void main(String[] args) throws Exception {
        /* Data corrections described in the KDD data mining archive */
        irisData[34][4] = 0.1;
        irisData[37][2] = 3.1;
        irisData[37][3] = 1.5;

        /*  Train first 140 patterns of the iris Fisher Data */
        int[] irisClassificationData = new int[irisData.length - 10];
        double[][] irisContinuousData
                = new double[irisData.length - 10][irisData[0].length - 1];

        for (int i = 0; i < irisData.length - 10; i++) {
            irisClassificationData[i] = (int) irisData[i][0] - 1;
            System.arraycopy(irisData[i], 1,
                    irisContinuousData[i], 0, irisData[0].length - 1);
        }

        int nNominal = 0;    // no nominal input attributes
        int nContinuous = 4; // four continuous input attributes
        int nClasses = 3;    // three classification categories  
        
        NaiveBayesClassifier nbTrainer
                = new NaiveBayesClassifier(nContinuous, nNominal, nClasses);

        for (int i = 0; i < nContinuous; i++) {
            nbTrainer.createContinuousAttribute(new NormalDistribution());
        }
        nbTrainer.train(irisContinuousData, irisClassificationData);

        int[][] classErrors = nbTrainer.getTrainingErrors();

        System.out.println("     Iris Classification Training Error Rates");
        System.out.println("------------------------------------------------");
        System.out.println("  Setosa   Versicolour   Virginica    |   Total");
        System.out.println("  " + classErrors[0][0] + "/" + classErrors[0][1]
                + "         " + classErrors[1][0] + "/" + classErrors[1][1]
                + "        " + classErrors[2][0] + "/" + classErrors[2][1]
                + "       |   " + classErrors[3][0]
                + "/" + classErrors[3][1]);
        System.out.println(
                "------------------------------------------------\n\n\n");

        /*  Classify last  10 iris data patterns with the trained classifier */
        double[] continuousInput = new double[(irisData[0].length - 1)];
        double[] classifiedProbabilities = new double[nClasses];

        System.out.println("Probabilities for Incorrect Classifications");
        System.out.println(" Predicted   ");
        System.out.println(
                "   Class     |  Class       |   P(0)     P(1)     P(2) ");
        System.out.println(
                "-------------------------------------------------------");
        for (int i = 0; i < 10; i++) {
            int targetClassification
                    = (int) irisData[(irisData.length - 10) + i][0] - 1;
            System.arraycopy(irisData[(irisData.length - 10) + i],
                    1, continuousInput, 0, (irisData[0].length - 1));

            classifiedProbabilities
                    = nbTrainer.probabilities(continuousInput, null);
            int classification = nbTrainer.predictClass(continuousInput, null);
            if (classification == 0) {
                System.out.print("Setosa       |");
            } else if (classification == 1) {
                System.out.print("Versicolour  |");
            } else if (classification == 2) {
                System.out.print("Virginica    |");
            } else {
                System.out.print("Missing      |");
            }
            if (targetClassification == 0) {
                System.out.print(" Setosa       |");
            } else if (targetClassification == 1) {
                System.out.print(" Versicolour  |");
            } else if (targetClassification == 2) {
                System.out.print(" Virginica    |");
            } else {
                System.out.print(" Missing      |");
            }

            for (int j = 0; j < nClasses; j++) {
                Object[] pArgs = {new Double(classifiedProbabilities[j])};
                System.out.printf("   %2.3f ", pArgs);
            }
            System.out.println();
        }
    }
}