protected override void calculate(BookData dataset)
{
int nUsers = dataset.getUserCount();
int nRatingValues = 5;
similarityValues = new double[nUsers, nUsers];
if (keepRatingCountMatrix)
{
ratingCountMatrix = new RatingCountMatrix[nUsers, nUsers];
}
// if we want to use mapping from userId to index then generate
// index for every userId
if (useObjIdToIndexMapping)
{
dataset.getUsers().ForEach(delegate(BookUser u)
{
idMapping.getIndex(u.UserId.ToString());
});
}
for (int u = 0; u < nUsers; u++)
{
int userAId = getObjIdFromIndex(u);
BookUser userA = dataset.getUser(userAId);
for (int v = u + 1; v < nUsers; v++)
{
int userBId = getObjIdFromIndex(v);
BookUser userB = dataset.getUser(userBId);
RatingCountMatrix rcm = new RatingCountMatrix(userA, userB, nRatingValues);
int totalCount = rcm.getTotalCount();
int agreementCount = rcm.getAgreementCount();
if (agreementCount > 0)
{
similarityValues[u, v] = (double)agreementCount / (double)totalCount;
}
else
{
similarityValues[u, v] = 0.0;
}
// For large datasets
if (keepRatingCountMatrix)
{
ratingCountMatrix[u, v] = rcm;
}
}
// for u == v assign 1.
// RatingCountMatrix wasn't created for this case+
similarityValues[u, u] = 1.0;
}
}
protected override void calculate(BookData dataset)
{
int nItems = dataset.getBookCount();
int nRatingValues = 5; // ИЛИ 6
similarityValues = new double[nItems, nItems];
if (keepRatingCountMatrix)
{
ratingCountMatrix = new RatingCountMatrix[nItems, nItems];
}
// if we want to use mapping from itemId to index then generate
// index for every itemId
if (useObjIdToIndexMapping)
{
// Для каждой книги из dataSet берем книгу, получаем её Ид и получаем индекс с помощью класса idMapping
List <BookItem> bi = dataset.getBooks();
foreach (BookItem b in bi)
{
idMapping.getIndex(b.getId().ToString());
}
}
// By using these variables we reduce the number of method calls
// inside the double loop.
int totalCount = 0;
int agreementCount = 0;
for (int u = 0; u < nItems; u++)
{
int itemAId = getObjIdFromIndex(u);
BookItem itemA = dataset.getBookItem(itemAId);
// // we only need to calculate elements above the main diagonal.
for (int v = u + 1; v < nItems; v++)
{
int itemBId = getObjIdFromIndex(v);
BookItem itemB = dataset.getBookItem(itemBId);
RatingCountMatrix rcm = new RatingCountMatrix(itemA, itemB, nRatingValues);
totalCount = rcm.getTotalCount();
agreementCount = rcm.getAgreementCount();
if (agreementCount > 0)
{
similarityValues[u, v] = (double)agreementCount / (double)totalCount;
}
else
{
similarityValues[u, v] = 0.0;
}
if (keepRatingCountMatrix)
{
ratingCountMatrix[u, v] = rcm;
}
}
similarityValues[u, u] = 1.0;
}
}