use of com.tencent.angel.ml.math2.vector.IntIntVector in project angel by Tencent.
the class MixedBinaryInAllExecutor method apply.
private static Vector apply(CompIntIntVector v1, IntIntVector v2, Binary op) {
IntIntVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v2.isDense()) {
int[] v2Values = v2.getStorage().getValues();
int base = 0, k = 0;
for (IntIntVector part : parts) {
IntIntVectorStorage resPart = (IntIntVectorStorage) resParts[k];
if (part.isDense()) {
int[] partValue = part.getStorage().getValues();
int[] resPartValues = resPart.getValues();
for (int i = 0; i < partValue.length; i++) {
int idx = i + base;
resPartValues[i] = op.apply(partValue[i], v2Values[idx]);
}
} else if (part.isSparse()) {
int[] resPartValues = resPart.getValues();
if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
for (int i = 0; i < part.getDim(); i++) {
resPart.set(i, op.apply(0, v2Values[i + base]));
}
ObjectIterator<Int2IntMap.Entry> iter = part.getStorage().entryIterator();
while (iter.hasNext()) {
Int2IntMap.Entry entry = iter.next();
int idx = entry.getIntKey();
resPart.set(idx, op.apply(entry.getIntValue(), v2Values[idx + base]));
}
} else {
for (int i = 0; i < resPartValues.length; i++) {
if (part.getStorage().hasKey(i)) {
resPart.set(i, op.apply(part.get(i), v2Values[i + base]));
} else {
resPart.set(i, op.apply(0, v2Values[i + base]));
}
}
}
} else {
// sorted
int[] resPartIndices = resPart.getIndices();
int[] resPartValues = resPart.getValues();
if (op.isKeepStorage()) {
if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
int[] partIndices = part.getStorage().getIndices();
int[] partValues = part.getStorage().getValues();
for (int i = 0; i < part.getDim(); i++) {
resPartIndices[i] = i;
resPartValues[i] = op.apply(0, v2Values[i + base]);
}
int size = part.size();
for (int i = 0; i < size; i++) {
int idx = partIndices[i];
resPartValues[idx] = op.apply(partValues[i], v2Values[idx + base]);
}
} else {
IntIntVectorStorage partStorage = part.getStorage();
for (int i = 0; i < resPartValues.length; i++) {
if (partStorage.hasKey(i)) {
resPartIndices[i] = i;
resPartValues[i] = op.apply(partStorage.get(i), v2Values[i + base]);
} else {
resPartIndices[i] = i;
resPartValues[i] = op.apply(0, v2Values[i + base]);
}
}
}
} else {
if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
int[] partIndices = part.getStorage().getIndices();
int[] partValues = part.getStorage().getValues();
for (int i = 0; i < part.getDim(); i++) {
resPartValues[i] = op.apply(0, v2Values[i + base]);
}
int size = part.size();
for (int i = 0; i < size; i++) {
int idx = partIndices[i];
resPartValues[idx] = op.apply(partValues[i], v2Values[idx + base]);
}
} else {
IntIntVectorStorage partStorage = part.getStorage();
for (int i = 0; i < resPartValues.length; i++) {
if (partStorage.hasKey(i)) {
resPartValues[i] = op.apply(partStorage.get(i), v2Values[i + base]);
} else {
resPartValues[i] = op.apply(0, v2Values[i + base]);
}
}
}
}
}
base += part.getDim();
k++;
}
} else {
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof IntIntSortedVectorStorage) {
resParts[i] = new IntIntSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
for (int i = 0; i < v1.getDim(); i++) {
int pidx = (int) (i / subDim);
int subidx = i % subDim;
if (v2.getStorage().hasKey(i)) {
((IntIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
} else {
((IntIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 0));
}
}
}
IntIntVector[] res = new IntIntVector[parts.length];
int i = 0;
for (IntIntVector part : parts) {
res[i] = new IntIntVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntIntVectorStorage) resParts[i]);
i++;
}
v1.setPartitions(res);
return v1;
}
use of com.tencent.angel.ml.math2.vector.IntIntVector in project angel by Tencent.
the class MixedBinaryInAllExecutor method apply.
private static Vector apply(CompIntLongVector v1, IntIntVector v2, Binary op) {
IntLongVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v2.isDense()) {
int[] v2Values = v2.getStorage().getValues();
int base = 0, k = 0;
for (IntLongVector part : parts) {
IntLongVectorStorage resPart = (IntLongVectorStorage) resParts[k];
if (part.isDense()) {
long[] partValue = part.getStorage().getValues();
long[] resPartValues = resPart.getValues();
for (int i = 0; i < partValue.length; i++) {
int idx = i + base;
resPartValues[i] = op.apply(partValue[i], v2Values[idx]);
}
} else if (part.isSparse()) {
long[] resPartValues = resPart.getValues();
if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
for (int i = 0; i < part.getDim(); i++) {
resPart.set(i, op.apply(0, v2Values[i + base]));
}
ObjectIterator<Int2LongMap.Entry> iter = part.getStorage().entryIterator();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
int idx = entry.getIntKey();
resPart.set(idx, op.apply(entry.getLongValue(), v2Values[idx + base]));
}
} else {
for (int i = 0; i < resPartValues.length; i++) {
if (part.getStorage().hasKey(i)) {
resPart.set(i, op.apply(part.get(i), v2Values[i + base]));
} else {
resPart.set(i, op.apply(0, v2Values[i + base]));
}
}
}
} else {
// sorted
int[] resPartIndices = resPart.getIndices();
long[] resPartValues = resPart.getValues();
if (op.isKeepStorage()) {
if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
int[] partIndices = part.getStorage().getIndices();
long[] partValues = part.getStorage().getValues();
for (int i = 0; i < part.getDim(); i++) {
resPartIndices[i] = i;
resPartValues[i] = op.apply(0, v2Values[i + base]);
}
int size = part.size();
for (int i = 0; i < size; i++) {
int idx = partIndices[i];
resPartValues[idx] = op.apply(partValues[i], v2Values[idx + base]);
}
} else {
IntLongVectorStorage partStorage = part.getStorage();
for (int i = 0; i < resPartValues.length; i++) {
if (partStorage.hasKey(i)) {
resPartIndices[i] = i;
resPartValues[i] = op.apply(partStorage.get(i), v2Values[i + base]);
} else {
resPartIndices[i] = i;
resPartValues[i] = op.apply(0, v2Values[i + base]);
}
}
}
} else {
if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
int[] partIndices = part.getStorage().getIndices();
long[] partValues = part.getStorage().getValues();
for (int i = 0; i < part.getDim(); i++) {
resPartValues[i] = op.apply(0, v2Values[i + base]);
}
int size = part.size();
for (int i = 0; i < size; i++) {
int idx = partIndices[i];
resPartValues[idx] = op.apply(partValues[i], v2Values[idx + base]);
}
} else {
IntLongVectorStorage partStorage = part.getStorage();
for (int i = 0; i < resPartValues.length; i++) {
if (partStorage.hasKey(i)) {
resPartValues[i] = op.apply(partStorage.get(i), v2Values[i + base]);
} else {
resPartValues[i] = op.apply(0, v2Values[i + base]);
}
}
}
}
}
base += part.getDim();
k++;
}
} else {
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof IntLongSortedVectorStorage) {
resParts[i] = new IntLongSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
for (int i = 0; i < v1.getDim(); i++) {
int pidx = (int) (i / subDim);
int subidx = i % subDim;
if (v2.getStorage().hasKey(i)) {
((IntLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
} else {
((IntLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 0));
}
}
}
IntLongVector[] res = new IntLongVector[parts.length];
int i = 0;
for (IntLongVector part : parts) {
res[i] = new IntLongVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntLongVectorStorage) resParts[i]);
i++;
}
v1.setPartitions(res);
return v1;
}
use of com.tencent.angel.ml.math2.vector.IntIntVector in project angel by Tencent.
the class MixedBinaryInAllExecutor method apply.
private static Vector apply(CompIntIntVector v1, IntDummyVector v2, Binary op) {
IntIntVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof IntIntSortedVectorStorage) {
resParts[i] = new IntIntSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
for (int i = 0; i < v1.getDim(); i++) {
int pidx = (int) (i / subDim);
int subidx = i % subDim;
((IntIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
}
IntIntVector[] res = new IntIntVector[parts.length];
int i = 0;
for (IntIntVector part : parts) {
res[i] = new IntIntVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntIntVectorStorage) resParts[i]);
i++;
}
v1.setPartitions(res);
return v1;
}
use of com.tencent.angel.ml.math2.vector.IntIntVector in project angel by Tencent.
the class RowSplitCombineUtils method combineServerIntIntRowSplits.
private static Vector combineServerIntIntRowSplits(List<ServerRow> rowSplits, MatrixMeta matrixMeta, int rowIndex) {
int colNum = (int) matrixMeta.getColNum();
int elemNum = 0;
int size = rowSplits.size();
for (int i = 0; i < size; i++) {
elemNum += rowSplits.get(i).size();
}
IntIntVector row;
if (elemNum >= (int) (storageConvFactor * colNum)) {
row = VFactory.denseIntVector(colNum);
} else {
row = VFactory.sparseIntVector(colNum, elemNum);
}
row.setMatrixId(matrixMeta.getId());
row.setRowId(rowIndex);
Collections.sort(rowSplits, serverRowComp);
int clock = Integer.MAX_VALUE;
for (int i = 0; i < size; i++) {
if (rowSplits.get(i) == null) {
continue;
}
if (rowSplits.get(i).getClock() < clock) {
clock = rowSplits.get(i).getClock();
}
((ServerIntIntRow) rowSplits.get(i)).mergeTo(row);
}
row.setClock(clock);
return row;
}
use of com.tencent.angel.ml.math2.vector.IntIntVector in project angel by Tencent.
the class CompIntIntVectorSplitter method split.
@Override
public Map<PartitionKey, RowUpdateSplit> split(Vector vector, List<PartitionKey> parts) {
IntIntVector[] vecParts = ((CompIntIntVector) vector).getPartitions();
assert vecParts.length == parts.size();
Map<PartitionKey, RowUpdateSplit> updateSplitMap = new HashMap<>(parts.size());
for (int i = 0; i < vecParts.length; i++) {
updateSplitMap.put(parts.get(i), new CompIntIntRowUpdateSplit(vector.getRowId(), vecParts[i], (int) (parts.get(i).getEndCol() - parts.get(i).getStartCol())));
}
return updateSplitMap;
}
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