package com.imsl.test.example.datamining.supportvectormachine;

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

/**
 * <p>
 * Performs goodness-of-fit using the one-class support vector machine.
 *</p>
 * <p>
 * In this example, {@link SVOneClass} is used to detect
 * examples in the test data that do not belong to the target population. The
 * target population is the uniform distribution.
 * </p>
 *
 * @see <a href="SupportVectorMachineEx3.java">Code</a>
 * @see <a href="doc-files/SupportVectorMachineEx3.out">Output</a>
 *
 */
public class SupportVectorMachineEx3 {

    public static void main(String[] args) throws Exception {

        SVOneClass.VariableType[] ex3VarType = {
            SVOneClass.VariableType.ONE_CLASS,
            SVOneClass.VariableType.QUANTITATIVE_CONTINUOUS
        };

        // 1000 training patterns
        int nTrain = 1000;
        // 100 test patterns
        int nTest = 100;
        int nContaminated = 10;
        int nAttributes = 1;

        double[][] xyTrain = new double[nTrain][nAttributes + 1];
        double[][] xyTest = new double[nTest][2];

        // Create the training set from a uniform distribution.
        Random r = new Random(123457);
        r.setMultiplier(16807);
        for (int i = 0; i < nTrain; i++) {
            xyTrain[i][1] = r.nextFloat();
            xyTrain[i][0] = 1;
        }
        //Construct an SVOneClass object.
        SVOneClass svm1 = new SVOneClass(xyTrain, 0, ex3VarType);
        svm1.setNuParameter(0.001);
        // Train with the training data. 
        svm1.fitModel();

        // Create a testing set from a uniform distribution. 
        for (int i = 0; i < nTest; i++) {
            xyTest[i][1] = r.nextFloat();
            xyTest[i][0] = 1;
        }

        //Contaminate the testing set with deviates from a normal distribution.
        for (int i = 0; i < nContaminated; i++) {
            xyTest[i * 10][1] = r.nextNormal() * Math.sqrt(.2) + .1;
        }
        double[] predictedClass = svm1.predict(xyTest);
        System.out.println("\n\n\n                   Classification Results\n");
        for (int i = 0; i < nTest; i++) {
            if (predictedClass[i] != 1.0) {
                System.out.println("\n The " + i + "-th observation may not "
                        + "belong to the target distribution.");
            }
        }
    }
}