/*
 * File:                SequentialMinimalOptimizationTest.java
 * Authors:             Justin Basilico
 * Company:             Sandia National Laboratories
 * Project:             Cognitive Foundry
 *
 * Copyright August 04, 2010, Sandia Corporation.
 * Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive
 * license for use of this work by or on behalf of the U.S. Government. Export
 * of this program may require a license from the United States Government.
 * See CopyrightHistory.txt for complete details.
 *
 */

package gov.sandia.cognition.learning.algorithm.svm;

import gov.sandia.cognition.learning.data.DefaultInputOutputPair;
import gov.sandia.cognition.learning.data.InputOutputPair;
import gov.sandia.cognition.learning.function.categorization.KernelBinaryCategorizer;
import gov.sandia.cognition.learning.function.kernel.LinearKernel;
import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.math.matrix.VectorFactory;
import java.util.ArrayList;
import java.util.Random;
import junit.framework.TestCase;

/**
 * Unit tests for class SequentialMinimalOptimization.
 *
 * @author  Justin Basilico
 * @since   3.1
 */
public class SequentialMinimalOptimizationTest
    extends TestCase
{
    protected Random random = new Random(211);

    /**
     * Creates a new test.
     *
     * @param   testName The test name.
     */
    public SequentialMinimalOptimizationTest(
        String testName)
    {
        super(testName);
    }

    /**
     * Test of learn method, of class SimplifiedSequentialMinimalOptimization.
     */
    public void testLearn()
    {

        // Generate some data using the example synthetic data from Platt's
        // original SMO paper.
        int d = 300;
        int pointsToGenerate = 100;
        final ArrayList<InputOutputPair<Vector, Boolean>> data =
            new ArrayList<InputOutputPair<Vector, Boolean>>(pointsToGenerate);

        Vector target = VectorFactory.getDenseDefault().createUniformRandom(d, -1.0, 1.0, random);
        while (data.size() < pointsToGenerate)
        {
            Vector input = VectorFactory.getSparseDefault().createVector(d, 0.0);

            for (int i = 0; i < d / 10; i++)
            {
                int index = random.nextInt(d);
                input.setElement(index, 1.0);
            }

            double dotProduct = input.dotProduct(target);
            if (dotProduct < -1.0)
            {
                data.add(DefaultInputOutputPair.create(input, false));
            }
            else if (dotProduct > +1.0)
            {
                data.add(DefaultInputOutputPair.create(input, true));
            }
            // else - The dot product wsa between -1.0 and +1.0, try again.
        }

        SequentialMinimalOptimization<Vector> instance =
            new SequentialMinimalOptimization<Vector>();
        instance.setKernel(new LinearKernel());
        instance.setRandom(random);
        instance.setMaxIterations(1000);
        instance.setMaxPenalty(100.0);
instance.setKernelCacheSize(0);

        final KernelBinaryCategorizer<Vector, ?> result = instance.learn(data);
        assertSame(result, instance.getResult());

        for (InputOutputPair<Vector, Boolean> example : data)
        {
//            System.out.println("" + example.getInput() + " -> " + example.getOutput());
            assertEquals(example.getOutput(), result.evaluate(example.getInput()));
        }
    }

    /**
     * Test of getResult method, of class SimplifiedSequentialMinimalOptimization.
     */
    public void testGetResult()
    {
        // Tested by testLearn.
    }

    /**
     * Test of getRandom method, of class SimplifiedSequentialMinimalOptimization.
     */
    public void testGetRandom()
    {
        this.testSetRandom();
    }

    /**
     * Test of setRandom method, of class SimplifiedSequentialMinimalOptimization.
     */
    public void testSetRandom()
    {
        SimplifiedSequentialMinimalOptimization<String> instance =
            new SimplifiedSequentialMinimalOptimization<String>();
        assertNotNull(instance.getRandom());

        Random random = new Random();
        instance.setRandom(random);
        assertSame(random, instance.getRandom());

        random = new Random();
        instance.setRandom(random);
        assertSame(random, instance.getRandom());

        random = null;
        instance.setRandom(random);
        assertSame(random, instance.getRandom());

        random = new Random();
        instance.setRandom(random);
        assertSame(random, instance.getRandom());
    }

}