/*
* File: ConfidenceWeightedDiagonalVarianceProjectTest.java
* Authors: Justin Basilico
* Company: Sandia National Laboratories
* Project: Cognitive Foundry Learning Core
*
* Copyright April 13, 2011, 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.
*/
package gov.sandia.cognition.learning.algorithm.confidence;
import gov.sandia.cognition.learning.data.DefaultInputOutputPair;
import gov.sandia.cognition.learning.data.InputOutputPair;
import gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer;
import gov.sandia.cognition.learning.function.categorization.DiagonalConfidenceWeightedBinaryCategorizer;
import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.math.matrix.VectorFactory;
import gov.sandia.cognition.math.matrix.mtj.Vector2;
import gov.sandia.cognition.statistics.distribution.MultivariateGaussian;
import java.util.ArrayList;
import java.util.Random;
import org.junit.Test;
import static org.junit.Assert.*;
/**
* Unit tests for class ConfidenceWeightedDiagonalVarianceProject.
*
* @author Justin Basilico
* @since 3.3.0
*/
public class ConfidenceWeightedDiagonalVarianceProjectTest
{
protected Random random = new Random(211);
/**
* Creates a new test.
*/
public ConfidenceWeightedDiagonalVarianceProjectTest()
{
}
/**
* Test of constructors of class ConfidenceWeightedDiagonalVariance.
*/
@Test
public void testConstructors()
{
double confidence = ConfidenceWeightedDiagonalVarianceProject.DEFAULT_CONFIDENCE;
double defaultVariance = ConfidenceWeightedDiagonalVarianceProject.DEFAULT_DEFAULT_VARIANCE;
ConfidenceWeightedDiagonalVarianceProject instance = new ConfidenceWeightedDiagonalVarianceProject();
assertEquals(confidence, instance.getConfidence(), 0.0);
assertEquals(defaultVariance, instance.getDefaultVariance(), 0.0);
confidence = random.nextDouble();
defaultVariance = random.nextDouble();
instance = new ConfidenceWeightedDiagonalVarianceProject(confidence, defaultVariance);
assertEquals(confidence, instance.getConfidence(), 0.0);
assertEquals(defaultVariance, instance.getDefaultVariance(), 0.0);
}
/**
* Test of update method, of class ConfidenceWeightedDiagonalVarianceProject.
*/
@Test
public void testUpdate()
{
ConfidenceWeightedDiagonalVarianceProject instance = new ConfidenceWeightedDiagonalVarianceProject();
DiagonalConfidenceWeightedBinaryCategorizer result = instance.createInitialLearnedObject();
assertNull(result.getMean());
assertNull(result.getVariance());
assertEquals(0.0, result.getBias(), 0.0);
Vector input = new Vector2(2.0, 3.0);
Boolean output = true;
instance.update(result, DefaultInputOutputPair.create(input, output));
assertEquals(output, result.evaluate(input));
input = new Vector2(4.0, 4.0);
output = true;
instance.update(result, DefaultInputOutputPair.create(input, output));
assertEquals(output, result.evaluate(input));
input = new Vector2(1.0, 1.0);
output = false;
instance.update(result, DefaultInputOutputPair.create(input, output));
assertEquals(output, result.evaluate(input));
input = new Vector2(1.0, 1.0);
output = false;
instance.update(result, DefaultInputOutputPair.create(input, output));
assertEquals(output, result.evaluate(input));
input = new Vector2(2.0, 3.0);
output = true;
instance.update(result, DefaultInputOutputPair.create(input, output));
assertEquals(output, result.evaluate(input));
result = instance.createInitialLearnedObject();
MultivariateGaussian positive = new MultivariateGaussian(2);
positive.setMean(new Vector2(1.0, 1.0));
positive.getCovariance().setElement(0, 0, 0.2);
positive.getCovariance().setElement(1, 1, 2.0);
MultivariateGaussian negative = new MultivariateGaussian(2);
negative.setMean(new Vector2(-1.0, -1.0));
negative.getCovariance().setElement(0, 0, 0.2);
negative.getCovariance().setElement(1, 1, 2.0);
for (int i = 0; i < 4000; i++)
{
output = random.nextBoolean();
input = (output ? positive : negative).sample(random);
double prediction = result.evaluateAsDouble(input);
assertEquals(prediction >= 0.0, result.evaluateAsBernoulli(input).getP() >= 0.5);
instance.update(result, DefaultInputOutputPair.create(input, output));
assertEquals(output, result.evaluate(input));
}
}
/**
* Test learning a linearly separable example.
*/
@Test
public void testLearnSeparable()
{
System.out.println("testLearnSeparable");
int d = 10;
int trainCount = 1000;
int testCount = 100;
LinearBinaryCategorizer real = new LinearBinaryCategorizer(
VectorFactory.getDenseDefault().createUniformRandom(d, -1, +1, random), 0.0);
ArrayList<InputOutputPair<Vector, Boolean>> trainData =
new ArrayList<InputOutputPair<Vector, Boolean>>(trainCount);
for (int i = 0; i < trainCount; i++)
{
Vector input = VectorFactory.getDenseDefault().createUniformRandom(
d, -1, +1, random);
boolean output = real.evaluate(input);
trainData.add(DefaultInputOutputPair.create(input, output));
}
ArrayList<InputOutputPair<Vector, Boolean>> testData =
new ArrayList<InputOutputPair<Vector, Boolean>>(testCount);
for (int i = 0; i < testCount; i++)
{
Vector input = VectorFactory.getDenseDefault().createUniformRandom(
d, -1, +1, random);
boolean actual = real.evaluate(input);
testData.add(DefaultInputOutputPair.create(input, actual));
}
ConfidenceWeightedDiagonalVarianceProject instance = new ConfidenceWeightedDiagonalVarianceProject();
DiagonalConfidenceWeightedBinaryCategorizer learned = instance.createInitialLearnedObject();
for (InputOutputPair<Vector, Boolean> example : trainData)
{
instance.update(learned, example);
}
int correctCount = 0;
for (InputOutputPair<Vector, Boolean> example : testData)
{
boolean actual = example.getOutput();
boolean predicted = learned.evaluate(example.getInput());
if (actual == predicted)
{
correctCount++;
}
}
double accuracy = (double) correctCount / testData.size();
System.out.println("Accuracy: " + accuracy);
assertTrue(accuracy >= 0.95);
double cosine = learned.getMean().unitVector().cosine(real.getWeights().unitVector());
System.out.println("Cosine: " + cosine);
assertTrue(cosine >= 0.95);
}
}