/**
* Copies source to dest.
*
* <p>Neither source nor dest can be null.
*
* @param source Mean to copy
* @param dest Mean to copy to
* @throws NullArgumentException if either source or dest is null
*/
public static void copy(Mean source, Mean dest) throws NullArgumentException {
MathUtils.checkNotNull(source);
MathUtils.checkNotNull(dest);
dest.setData(source.getDataRef());
dest.incMoment = source.incMoment;
dest.moment = source.moment.copy();
}
@Test
public void testResults() throws InterruptedException {
TaskUtil.run(d_model.getActivityTask());
assertTrue(d_model.isReady());
final InconsistencyParameter w =
new InconsistencyParameter(Arrays.asList(d_ta, d_tb, d_tc, d_ta));
assertEquals(Collections.singletonList(w), d_model.getInconsistencyFactors());
double[] dAB =
ResultsUtil.getSamples(d_model.getResults(), d_model.getRelativeEffect(d_ta, d_tb), 3);
double[] dBC =
ResultsUtil.getSamples(d_model.getResults(), d_model.getRelativeEffect(d_tb, d_tc), 3);
double[] wABC = ResultsUtil.getSamples(d_model.getResults(), w, 3);
// Values below obtained via a run through regular JAGS with 30k/20k
// iterations. Taking .15 sd as acceptable margin (same as JAGS does
// for testing against WinBUGS results).
double mAB = 0.4713884;
double sAB = 0.4838365;
assertEquals(mAB, d_mean.evaluate(dAB), FACTOR * sAB);
assertEquals(sAB, d_stdDev.evaluate(dAB), FACTOR * sAB);
double mBC = -0.4645146;
double sBC = 0.6111192;
assertEquals(mBC, d_mean.evaluate(dBC), FACTOR * sBC);
assertEquals(sBC, d_stdDev.evaluate(dBC), FACTOR * sBC);
double mABC = -0.1466253;
double sABC = 0.4568596;
assertEquals(mABC, d_mean.evaluate(wABC), FACTOR * sABC);
assertEquals(sABC, d_stdDev.evaluate(wABC), FACTOR * sABC);
double[] dBA =
ResultsUtil.getSamples(d_model.getResults(), d_model.getRelativeEffect(d_tb, d_ta), 3);
assertEquals(-mAB, d_mean.evaluate(dBA), FACTOR * sAB);
assertEquals(sAB, d_stdDev.evaluate(dBA), FACTOR * sAB);
double[] dAC =
ResultsUtil.getSamples(d_model.getResults(), d_model.getRelativeEffect(d_ta, d_tc), 3);
double mAC = 0.1534991;
double sAC = 0.5514409;
assertEquals(mAC, d_mean.evaluate(dAC), FACTOR * sAC);
assertEquals(sAC, d_stdDev.evaluate(dAC), FACTOR * sAC);
}
private void setFactor() {
double[] array = new double[mappingReads.size()];
int i = 0;
for (Long l : mappingReads.values()) {
array[i++] = l;
}
double meanReads = mean.evaluate(array);
for (Map.Entry<String, Long> entry : mappingReads.entrySet()) {
factor.put(entry.getKey(), meanReads / entry.getValue());
}
}
/**
* Computes the covariance between the two arrays.
*
* <p>Array lengths must match and the common length must be at least 2.
*
* @param xArray first data array
* @param yArray second data array
* @param biasCorrected if true, returned value will be bias-corrected
* @return returns the covariance for the two arrays
* @throws MathIllegalArgumentException if the arrays lengths do not match or there is
* insufficient data
*/
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
throws MathIllegalArgumentException {
Mean mean = new Mean();
double result = 0d;
int length = xArray.length;
if (length != yArray.length) {
throw new MathIllegalArgumentException(
LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, length, yArray.length);
} else if (length < 2) {
throw new MathIllegalArgumentException(
LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE, length, 2);
} else {
double xMean = mean.evaluate(xArray);
double yMean = mean.evaluate(yArray);
for (int i = 0; i < length; i++) {
double xDev = xArray[i] - xMean;
double yDev = yArray[i] - yMean;
result += (xDev * yDev - result) / (i + 1);
}
}
return biasCorrected ? result * ((double) length / (double) (length - 1)) : result;
}
private void addControlSamples(String result, Sample sample) {
List<Double> list = new LinkedList<>();
for (String s : controlSamples) {
for (Map.Entry<String, Map<String, Sample>> entry : samples.entrySet()) {
list.add(entry.getValue().get(s).getNorm1b());
}
}
if (!list.isEmpty()) {
double meanVal = mean.evaluate(toDoubleArray(list));
StringBuilder builder = new StringBuilder(result);
builder
.append("\t")
.append(
String.format(
"%.3f%n", meanVal == 0 ? sample.getNorm1b() / meanVal / log.value(2) : 0));
}
}
public static Double mean(double[] values) {
Mean meanCalculator = new Mean();
return meanCalculator.evaluate(values);
}
@Test
public void testLSH() {
RandomGenerator random = RandomManager.getRandom();
Mean avgPercentTopRecsConsidered = new Mean();
Mean avgNDCG = new Mean();
Mean avgPercentAllItemsConsidered = new Mean();
for (int iteration = 0; iteration < ITERATIONS; iteration++) {
LongObjectMap<float[]> Y = new LongObjectMap<float[]>();
for (int i = 0; i < NUM_ITEMS; i++) {
Y.put(i, RandomUtils.randomUnitVector(NUM_FEATURES, random));
}
float[] userVec = RandomUtils.randomUnitVector(NUM_FEATURES, random);
double[] results = doTestRandomVecs(Y, userVec);
double percentTopRecsConsidered = results[0];
double ndcg = results[1];
double percentAllItemsConsidered = results[2];
log.info(
"Considered {}% of all candidates, {} nDCG, got {}% recommendations correct",
100 * percentAllItemsConsidered, ndcg, 100 * percentTopRecsConsidered);
avgPercentTopRecsConsidered.increment(percentTopRecsConsidered);
avgNDCG.increment(ndcg);
avgPercentAllItemsConsidered.increment(percentAllItemsConsidered);
}
log.info("{}", avgPercentTopRecsConsidered.getResult());
log.info("{}", avgNDCG.getResult());
log.info("{}", avgPercentAllItemsConsidered.getResult());
assertTrue(avgPercentTopRecsConsidered.getResult() > 0.8);
assertTrue(avgNDCG.getResult() > 0.8);
assertTrue(avgPercentAllItemsConsidered.getResult() < 0.09);
}
/**
* Mean/mean linking coefficients are computed from the mean item difficulty and mean item
* discrimination. The summary statistics are computed in a storeless manner. This method allows
* for the incremental update to item difficulty summary statistics by combining them with other
* summary statistics.
*
* @param meanDiscrimination item discrimination mean.
* @param meanDifficulty item difficulty mean.
*/
public void incrementMeanMean(Mean meanDiscrimination, Mean meanDifficulty) {
meanDiscrimination.increment(discrimination);
meanDifficulty.increment(difficulty);
}
/**
* Mean/sigma linking coefficients are computed from the mean and standard deviation of item
* difficulty. The summary statistics are computed in a storeless manner. This method allows for
* the incremental update to item difficulty summary statistics by combining them with other
* summary statistics.
*
* @param mean item difficulty mean.
* @param sd item difficulty standard deviation.
*/
public void incrementMeanSigma(Mean mean, StandardDeviation sd) {
mean.increment(difficulty);
sd.increment(difficulty);
}
@Test
public void testLSHEffect() {
RandomGenerator random = RandomManager.getRandom();
PoissonDistribution itemPerUserDist =
new PoissonDistribution(
random,
20,
PoissonDistribution.DEFAULT_EPSILON,
PoissonDistribution.DEFAULT_MAX_ITERATIONS);
int features = 20;
ALSServingModel mainModel = new ALSServingModel(features, true, 1.0, null);
ALSServingModel lshModel = new ALSServingModel(features, true, 0.5, null);
int userItemCount = 20000;
for (int user = 0; user < userItemCount; user++) {
String userID = "U" + user;
float[] vec = VectorMath.randomVectorF(features, random);
mainModel.setUserVector(userID, vec);
lshModel.setUserVector(userID, vec);
int itemsPerUser = itemPerUserDist.sample();
Collection<String> knownIDs = new ArrayList<>(itemsPerUser);
for (int i = 0; i < itemsPerUser; i++) {
knownIDs.add("I" + random.nextInt(userItemCount));
}
mainModel.addKnownItems(userID, knownIDs);
lshModel.addKnownItems(userID, knownIDs);
}
for (int item = 0; item < userItemCount; item++) {
String itemID = "I" + item;
float[] vec = VectorMath.randomVectorF(features, random);
mainModel.setItemVector(itemID, vec);
lshModel.setItemVector(itemID, vec);
}
int numRecs = 10;
Mean meanMatchLength = new Mean();
for (int user = 0; user < userItemCount; user++) {
String userID = "U" + user;
List<Pair<String, Double>> mainRecs =
mainModel.topN(new DotsFunction(mainModel.getUserVector(userID)), null, numRecs, null);
List<Pair<String, Double>> lshRecs =
lshModel.topN(new DotsFunction(lshModel.getUserVector(userID)), null, numRecs, null);
int i = 0;
while (i < lshRecs.size() && i < mainRecs.size() && lshRecs.get(i).equals(mainRecs.get(i))) {
i++;
}
meanMatchLength.increment(i);
}
log.info("Mean matching prefix: {}", meanMatchLength.getResult());
assertTrue(meanMatchLength.getResult() >= 4.0);
meanMatchLength.clear();
for (int item = 0; item < userItemCount; item++) {
String itemID = "I" + item;
List<Pair<String, Double>> mainRecs =
mainModel.topN(
new CosineAverageFunction(mainModel.getItemVector(itemID)), null, numRecs, null);
List<Pair<String, Double>> lshRecs =
lshModel.topN(
new CosineAverageFunction(lshModel.getItemVector(itemID)), null, numRecs, null);
int i = 0;
while (i < lshRecs.size() && i < mainRecs.size() && lshRecs.get(i).equals(mainRecs.get(i))) {
i++;
}
meanMatchLength.increment(i);
}
log.info("Mean matching prefix: {}", meanMatchLength.getResult());
assertTrue(meanMatchLength.getResult() >= 5.0);
}
