public Features(ALSWRFactorizer factorizer)
{
dataModel = factorizer.dataModel;
numFeatures = factorizer.numFeatures;
var random = RandomUtils.getRandom();
M = new double[dataModel.GetNumItems()][]; //numFeatures
var itemIDsIterator = dataModel.GetItemIDs();
while (itemIDsIterator.MoveNext())
{
long itemID = itemIDsIterator.Current;
int itemIDIndex = factorizer.itemIndex(itemID);
M[itemIDIndex] = new double[numFeatures];
M[itemIDIndex][0] = averateRating(itemID);
for (int feature = 1; feature < numFeatures; feature++)
{
M[itemIDIndex][feature] = random.nextDouble() * 0.1;
}
}
U = new double[dataModel.GetNumUsers()][]; //numFeatures
for (int i = 0; i < U.Length; i++)
{
U[i] = new double[numFeatures];
}
}
public Features(ALSWRFactorizer factorizer) {
dataModel = factorizer.dataModel;
numFeatures = factorizer.numFeatures;
var random = RandomUtils.getRandom();
M = new double[dataModel.GetNumItems()][]; //numFeatures
var itemIDsIterator = dataModel.GetItemIDs();
while (itemIDsIterator.MoveNext()) {
long itemID = itemIDsIterator.Current;
int itemIDIndex = factorizer.itemIndex(itemID);
M[itemIDIndex] = new double[numFeatures];
M[itemIDIndex][0] = averateRating(itemID);
for (int feature = 1; feature < numFeatures; feature++) {
M[itemIDIndex][feature] = random.nextDouble() * 0.1;
}
}
U = new double[dataModel.GetNumUsers()][]; //numFeatures
for (int i=0; i<U.Length; i++)
U[i] = new double[numFeatures];
}
