/**
* Build the SVD model.
*
* @return A singular value decomposition recommender model.
*/
@Override
public SVDModel get() {
// Create index mappings of user and item IDs.
// You can use these to find row and columns in the matrix based on user/item IDs.
IdIndexMapping userMapping = IdIndexMapping.create(userDAO.getUserIds());
logger.debug("indexed {} users", userMapping.size());
IdIndexMapping itemMapping = IdIndexMapping.create(itemDAO.getItemIds());
logger.debug("indexed {} items", itemMapping.size());
// We have to do 2 things:
// First, prepare a matrix containing the rating data.
RealMatrix matrix = createRatingMatrix(userMapping, itemMapping);
// Second, compute its factorization
// All the work is done in the constructor
SingularValueDecomposition svd = new SingularValueDecomposition(matrix);
// Third, truncate the decomposed matrix
// TODO Truncate the matrices and construct the SVD model
RealMatrix userMatrix = svd.getU();
RealMatrix weights = svd.getS();
RealMatrix itemMatrix = svd.getV();
userMatrix = userMatrix.getSubMatrix(0, userMatrix.getRowDimension() - 1, 0, featureCount - 1);
weights = weights.getSubMatrix(0, featureCount - 1, 0, featureCount - 1);
itemMatrix = itemMatrix.getSubMatrix(0, itemMatrix.getRowDimension() - 1, 0, featureCount - 1);
return new SVDModel(userMapping, itemMapping, userMatrix, itemMatrix, weights);
}
public static RealMatrix stochasticSubmatrix(RealMatrix data, int batch_size, Random rng) {
// assume all data has the size number_samples by number_features
int num_samples = data.getRowDimension();
int num_features = data.getColumnDimension();
int batch_num = num_samples / batch_size + 1;
// randomly generate a batch index
int batch_index = rng.nextInt(batch_num);
List<Integer> rowIndex_tmp = new ArrayList<Integer>();
for (int i = 0; i < batch_size; i++) {
if (batch_size * batch_index + i >= num_samples) {
break;
} else {
rowIndex_tmp.add(batch_size * batch_index + i);
}
}
int[] rowIndex = TypeConvert.ArrayTointv(rowIndex_tmp);
// System.out.println(rowIndex_tmp);
int[] columnIndex = new int[num_features];
for (int j = 0; j < num_features; j++) {
columnIndex[j] = j;
}
// System.out.println(batch_index);
// return null;
return data.getSubMatrix(rowIndex, columnIndex);
}