machinelearninglabs.OENaiveBayesClassifier.java Source code

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Here is the source code for machinelearninglabs.OENaiveBayesClassifier.java

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/*
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 * To change this template file, choose Tools | Templates
 * and open the template in the editor.
 */

package machinelearninglabs;

import weka.classifiers.Classifier;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;

/**
 *
 * @author aku12vtu
 */
public class OENaiveBayesClassifier implements Classifier {

    private int[] classCount;
    private double[] classProbs;

    private int[][][] allAttributeCounts;
    private double[][][] conditionalProbabilities;

    @Override
    public void buildClassifier(Instances data) throws Exception {
        allAttributeCounts = new int[data.numAttributes() - 1][][]; // I minused 1 because of the class attr
        conditionalProbabilities = new double[data.numAttributes() - 1][][];

        for (int i = 0; i < data.numAttributes() - 1; i++) {
            allAttributeCounts[i] = attributeCounts(data, i);
        }

        for (int i = 0; i < data.numAttributes() - 1; i++) {
            conditionalProbabilities[i] = attributeProbs(data, i);
            printDoubleMatrix(conditionalProbabilities[i]);
        }

    }

    @Override
    public double classifyInstance(Instance instance) throws Exception {
        double[] probabilities = distributionForInstance(instance);

        double maxSoFar = 0;
        int maxIndex = 0;
        for (int i = 0; i < probabilities.length; i++) {
            if (probabilities[i] > maxSoFar) {
                maxSoFar = probabilities[i];
                maxIndex = i;
            }
        }

        return maxIndex;
    }

    @Override
    public double[] distributionForInstance(Instance instance) throws Exception {
        // return an array with a size of the number of classes
        double[] jointProbabilities = new double[instance.attribute(instance.classIndex()).numValues()];
        double[] result = new double[instance.attribute(instance.classIndex()).numValues()];

        // calculate un-normaalized probs
        for (int cls = 0; cls < jointProbabilities.length; cls++) {
            double p = classProbs[cls];
            for (int att = 0; att < instance.numAttributes() - 1; att++) {
                int value = (int) instance.value(att);
                p *= conditionalProbabilities[att][cls][value];
            }
            jointProbabilities[cls] = p;
        }

        // Find normalized probabilities
        for (int i = 0; i < jointProbabilities.length; i++) {
            double denominator = 0;
            for (int j = 0; j < jointProbabilities.length; j++) {
                denominator += jointProbabilities[j];
            }
            result[i] = jointProbabilities[i] / denominator;
        }
        return result;
    }

    @Override
    public Capabilities getCapabilities() {
        throw new UnsupportedOperationException("Not supported yet."); //To change body of generated methods, choose Tools | Templates.
    }

    /***************************** HELPER METHODS  ************************************/

    public void classDistribution(Instances data) {

        classCount = new int[data.firstInstance().numClasses()];
        classProbs = new double[data.firstInstance().numClasses()];

        // Get the frequency/count of each class in the data
        for (Instance eachInstance : data) {
            double classValue = eachInstance.value(eachInstance.classIndex());
            classCount[(int) classValue]++;
        }

        // Get the probability of the occurence of each class
        for (int i = 0; i < classProbs.length; i++) {
            classProbs[i] = (double) classCount[i] / data.numInstances();
        }

        printIntArray(classCount);
        System.out.println(data.firstInstance().value(0));
        printDoubleArray(classProbs);
    }

    public int[][] attributeCounts(Instances data, int att) {
        int numberOfPossibleValuesForAttribute = data.firstInstance().attribute(att).numValues();
        int[][] result = new int[data.numClasses()][numberOfPossibleValuesForAttribute];

        // for each class
        for (Instance eachInstance : data) {
            double classValue = eachInstance.value(eachInstance.classIndex());
            result[(int) classValue][(int) eachInstance.value(att)]++;
        }
        //printIntMatrix(result);
        return result;
    }

    public double[][] attributeProbs(Instances data, int att) {
        int numberOfPossibleValuesForAttribute = data.firstInstance().attribute(att).numValues();
        double[][] result = new double[data.numClasses()][numberOfPossibleValuesForAttribute];

        // for each class
        for (Instance eachInstance : data) {
            double classValue = eachInstance.value(eachInstance.classIndex());
            result[(int) classValue][(int) eachInstance.value(att)]++;
        }

        // Get conditional probabilities ie probability that attribute = x given some class
        for (int i = 0; i < result.length; i++) {
            for (int j = 0; j < result[i].length; j++) {
                result[i][j] = (double) result[i][j] / classCount[i];
            }
        }
        //printDoubleMatrix(result);
        return result;
    }

    /***************************** PRINT METHODS  ************************************/

    public void printIntArray(int[] array) {
        for (int i = 0; i < array.length; i++) {
            System.out.println(array[i]);
        }
    }

    public void printDoubleArray(double[] array) {
        for (int i = 0; i < array.length; i++) {
            System.out.println(array[i]);
        }
    }

    public void printIntMatrix(int[][] array) {
        for (int i = 0; i < array.length; i++) {
            for (int j = 0; j < array[i].length; j++) {
                System.out.println(array[i][j]);
            }
            System.out.println("");
        }
    }

    public void printDoubleMatrix(double[][] array) {
        for (int i = 0; i < array.length; i++) {
            for (int j = 0; j < array[i].length; j++) {
                System.out.println(array[i][j]);
            }
            System.out.println("");
        }
    }

}