BACtaki commented on code in PR #1650:
URL: https://github.com/apache/systemds/pull/1650#discussion_r934874887
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src/main/java/org/apache/sysds/runtime/matrix/data/LibMatrixCountDistinct.java:
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@@ -102,66 +114,255 @@ else if(in.getNonZeros() < minimumSize) {
*
* Benefit: precise, but uses memory, on the scale of inputs number of
distinct values.
*
- * @param in The input matrix to count number distinct values in
- * @return The absolute distinct count
+ * @param blkIn The input matrix to count number distinct values in
+ * @return A matrix block containing the absolute distinct count for
the entire input or along given row/col axis
*/
- private static int countDistinctValuesNaive(MatrixBlock in) {
+ private static MatrixBlock countDistinctValuesNaive(MatrixBlock blkIn,
CountDistinctOperator op) {
+
+ if (blkIn.isEmpty()) {
+ return new MatrixBlock(1);
+ }
+ else if(blkIn instanceof CompressedMatrixBlock) {
+ throw new NotImplementedException("countDistinct() does
not support CompressedMatrixBlock");
+ }
+
Set<Double> distinct = new HashSet<>();
+ MatrixBlock blkOut;
double[] data;
- if(in.isEmpty())
- return 1;
- else if(in instanceof CompressedMatrixBlock)
- throw new NotImplementedException();
- long nonZeros = in.getNonZeros();
+ if (op.getDirection().isRowCol()) {
+ blkOut = new MatrixBlock(1, 1, false);
- if(nonZeros != -1 && nonZeros < in.getNumColumns() *
in.getNumRows()) {
- distinct.add(0d);
- }
+ long distinctCount = 0;
+ long nonZeros = blkIn.getNonZeros();
- if(in.sparseBlock != null) {
- SparseBlock sb = in.sparseBlock;
+ // Check if input matrix contains any 0 values for
RowCol case.
+ // This does not apply to row/col case, where we count
nnz per row or col during iteration.
+ if(nonZeros != -1 && nonZeros < blkIn.getNumColumns() *
blkIn.getNumRows()) {
+ distinct.add(0d);
+ }
- if(in.sparseBlock.isContiguous()) {
- data = sb.values(0);
- countDistinctValuesNaive(data, distinct);
+ if(blkIn.getSparseBlock() != null) {
+ SparseBlock sb = blkIn.getSparseBlock();
+ if(blkIn.getSparseBlock().isContiguous()) {
+ // COO, CSR
+ data = sb.values(0);
+ distinctCount =
countDistinctValuesNaive(data, distinct);
+ } else {
+ // MCSR
+ for(int i = 0; i < blkIn.getNumRows();
i++) {
+ if(!sb.isEmpty(i)) {
+ data =
blkIn.getSparseBlock().values(i);
+ distinctCount =
countDistinctValuesNaive(data, distinct);
+ }
+ }
+ }
+ } else if(blkIn.getDenseBlock() != null) {
+ DenseBlock db = blkIn.getDenseBlock();
+ for (int i = 0; i <= db.numBlocks(); i++) {
+ data = db.valuesAt(i);
+ distinctCount =
countDistinctValuesNaive(data, distinct);
+ }
}
- else {
- for(int i = 0; i < in.getNumRows(); i++) {
- if(!sb.isEmpty(i)) {
- data = in.sparseBlock.values(i);
+
+ blkOut.setValue(0, 0, distinctCount);
+ } else if (op.getDirection().isRow()) {
+ blkOut = blkIn.slice(0, blkIn.getNumRows() - 1, 0, 0);
Review Comment:
>
// Result is a Mx1 matrix block.
// TODO We are reusing memory allocated for input
matrix block here - explore whether it would be better to
// allocate new
MatrixBlock resultMatrix = in.slice(0, in.getNumRows()
- 1, 0, 0);
I was trying to save on new memory allocations by reusing previously
allocated, but you are right: it is probably unnecessary, not to mention that
it is likely more expensive performance-wise to compute slice than to allocate
new- fixed in next iteration.
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