@Override
public final double itemSimilarity(long itemID1, long itemID2) throws TasteException {
PreferenceArray xPrefs = dataModel.getPreferencesForItem(itemID1);
PreferenceArray yPrefs = dataModel.getPreferencesForItem(itemID2);
int xLength = xPrefs.length();
int yLength = yPrefs.length();
if ((xLength == 0) || (yLength == 0)) {
return Double.NaN;
}
long xIndex = xPrefs.getUserID(0);
long yIndex = yPrefs.getUserID(0);
int xPrefIndex = 0;
int yPrefIndex = 0;
double sumX = 0.0;
double sumX2 = 0.0;
double sumY = 0.0;
double sumY2 = 0.0;
double sumXY = 0.0;
double sumXYdiff2 = 0.0;
int count = 0;
// No, pref inferrers and transforms don't appy here. I think.
while (true) {
int compare = xIndex < yIndex ? -1 : xIndex > yIndex ? 1 : 0;
if (compare == 0) {
// Both users expressed a preference for the item
double x = xPrefs.getValue(xPrefIndex);
double y = yPrefs.getValue(yPrefIndex);
sumXY += x * y;
sumX += x;
sumX2 += x * x;
sumY += y;
sumY2 += y * y;
double diff = x - y;
sumXYdiff2 += diff * diff;
count++;
}
if (compare <= 0) {
if (++xPrefIndex == xLength) {
break;
}
xIndex = xPrefs.getUserID(xPrefIndex);
}
if (compare >= 0) {
if (++yPrefIndex == yLength) {
break;
}
yIndex = yPrefs.getUserID(yPrefIndex);
}
}
double result;
if (centerData) {
// See comments above on these computations
double n = (double) count;
double meanX = sumX / n;
double meanY = sumY / n;
// double centeredSumXY = sumXY - meanY * sumX - meanX * sumY + n * meanX * meanY;
double centeredSumXY = sumXY - meanY * sumX;
// double centeredSumX2 = sumX2 - 2.0 * meanX * sumX + n * meanX * meanX;
double centeredSumX2 = sumX2 - meanX * sumX;
// double centeredSumY2 = sumY2 - 2.0 * meanY * sumY + n * meanY * meanY;
double centeredSumY2 = sumY2 - meanY * sumY;
result = computeResult(count, centeredSumXY, centeredSumX2, centeredSumY2, sumXYdiff2);
} else {
result = computeResult(count, sumXY, sumX2, sumY2, sumXYdiff2);
}
if (similarityTransform != null) {
result = similarityTransform.transformSimilarity(itemID1, itemID2, result);
}
if (!Double.isNaN(result)) {
result = normalizeWeightResult(result, count, cachedNumUsers);
}
return result;
}
@Override
public double userSimilarity(long userID1, long userID2) throws TasteException {
PreferenceArray xPrefs = dataModel.getPreferencesFromUser(userID1);
PreferenceArray yPrefs = dataModel.getPreferencesFromUser(userID2);
int xLength = xPrefs.length();
int yLength = yPrefs.length();
if ((xLength == 0) || (yLength == 0)) {
return Double.NaN;
}
long xIndex = xPrefs.getItemID(0);
long yIndex = yPrefs.getItemID(0);
int xPrefIndex = 0;
int yPrefIndex = 0;
double sumX = 0.0;
double sumX2 = 0.0;
double sumY = 0.0;
double sumY2 = 0.0;
double sumXY = 0.0;
double sumXYdiff2 = 0.0;
int count = 0;
boolean hasInferrer = inferrer != null;
boolean hasPrefTransform = prefTransform != null;
while (true) {
int compare = xIndex < yIndex ? -1 : xIndex > yIndex ? 1 : 0;
if (hasInferrer || compare == 0) {
double x;
double y;
if (xIndex == yIndex) {
// Both users expressed a preference for the item
if (hasPrefTransform) {
x = prefTransform.getTransformedValue(xPrefs.get(xPrefIndex));
y = prefTransform.getTransformedValue(yPrefs.get(yPrefIndex));
} else {
x = xPrefs.getValue(xPrefIndex);
y = yPrefs.getValue(yPrefIndex);
}
} else {
// Only one user expressed a preference, but infer the other one's preference and tally
// as if the other user expressed that preference
if (compare < 0) {
// X has a value; infer Y's
x =
hasPrefTransform
? prefTransform.getTransformedValue(xPrefs.get(xPrefIndex))
: xPrefs.getValue(xPrefIndex);
y = inferrer.inferPreference(userID2, xIndex);
} else {
// compare > 0
// Y has a value; infer X's
x = inferrer.inferPreference(userID1, yIndex);
y =
hasPrefTransform
? prefTransform.getTransformedValue(yPrefs.get(yPrefIndex))
: yPrefs.getValue(yPrefIndex);
}
}
sumXY += x * y;
sumX += x;
sumX2 += x * x;
sumY += y;
sumY2 += y * y;
double diff = x - y;
sumXYdiff2 += diff * diff;
count++;
}
if (compare <= 0) {
if (++xPrefIndex >= xLength) {
if (hasInferrer) {
// Must count other Ys; pretend next X is far away
if (yIndex == Long.MAX_VALUE) {
// ... but stop if both are done!
break;
}
xIndex = Long.MAX_VALUE;
} else {
break;
}
} else {
xIndex = xPrefs.getItemID(xPrefIndex);
}
}
if (compare >= 0) {
if (++yPrefIndex >= yLength) {
if (hasInferrer) {
// Must count other Xs; pretend next Y is far away
if (xIndex == Long.MAX_VALUE) {
// ... but stop if both are done!
break;
}
yIndex = Long.MAX_VALUE;
} else {
break;
}
} else {
yIndex = yPrefs.getItemID(yPrefIndex);
}
}
}
// "Center" the data. If my math is correct, this'll do it.
double result;
if (centerData) {
double n = count;
double meanX = sumX / n;
double meanY = sumY / n;
// double centeredSumXY = sumXY - meanY * sumX - meanX * sumY + n * meanX * meanY;
double centeredSumXY = sumXY - meanY * sumX;
// double centeredSumX2 = sumX2 - 2.0 * meanX * sumX + n * meanX * meanX;
double centeredSumX2 = sumX2 - meanX * sumX;
// double centeredSumY2 = sumY2 - 2.0 * meanY * sumY + n * meanY * meanY;
double centeredSumY2 = sumY2 - meanY * sumY;
result = computeResult(count, centeredSumXY, centeredSumX2, centeredSumY2, sumXYdiff2);
} else {
result = computeResult(count, sumXY, sumX2, sumY2, sumXYdiff2);
}
if (similarityTransform != null) {
result = similarityTransform.transformSimilarity(userID1, userID2, result);
}
if (!Double.isNaN(result)) {
result = normalizeWeightResult(result, count, cachedNumItems);
}
return result;
}