use of com.tencent.angel.ml.math2.vector.LongIntVector in project angel by Tencent.
the class MixedBinaryInNonZAExecutor method apply.
private static Vector apply(CompLongIntVector v1, LongIntVector v2, Binary op) {
LongIntVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v2.isSparse()) {
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof LongIntSortedVectorStorage) {
resParts[i] = new LongIntSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
ObjectIterator<Long2IntMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Long2IntMap.Entry entry = iter.next();
long gidx = entry.getLongKey();
int pidx = (int) (gidx / subDim);
long subidx = gidx % subDim;
((LongIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), entry.getIntValue()));
}
} else {
// sorted
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof LongIntSortedVectorStorage) {
resParts[i] = new LongIntSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
long[] v2Indices = v2.getStorage().getIndices();
int[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < v2Indices.length; i++) {
long gidx = v2Indices[i];
int pidx = (int) (gidx / subDim);
long subidx = gidx % subDim;
((LongIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2Values[i]));
}
}
LongIntVector[] res = new LongIntVector[parts.length];
int i = 0;
for (LongIntVector part : parts) {
res[i] = new LongIntVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (LongIntVectorStorage) resParts[i]);
i++;
}
v1.setPartitions(res);
return v1;
}
use of com.tencent.angel.ml.math2.vector.LongIntVector in project angel by Tencent.
the class MixedBinaryInNonZAExecutor method apply.
private static Vector apply(CompLongDoubleVector v1, LongIntVector v2, Binary op) {
LongDoubleVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v2.isSparse()) {
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof LongDoubleSortedVectorStorage) {
resParts[i] = new LongDoubleSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
ObjectIterator<Long2IntMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Long2IntMap.Entry entry = iter.next();
long gidx = entry.getLongKey();
int pidx = (int) (gidx / subDim);
long subidx = gidx % subDim;
((LongDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), entry.getIntValue()));
}
} else {
// sorted
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof LongDoubleSortedVectorStorage) {
resParts[i] = new LongDoubleSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
long[] v2Indices = v2.getStorage().getIndices();
int[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < v2Indices.length; i++) {
long gidx = v2Indices[i];
int pidx = (int) (gidx / subDim);
long subidx = gidx % subDim;
((LongDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2Values[i]));
}
}
LongDoubleVector[] res = new LongDoubleVector[parts.length];
int i = 0;
for (LongDoubleVector part : parts) {
res[i] = new LongDoubleVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (LongDoubleVectorStorage) resParts[i]);
i++;
}
v1.setPartitions(res);
return v1;
}
use of com.tencent.angel.ml.math2.vector.LongIntVector in project angel by Tencent.
the class MixedBinaryInZAExecutor method apply.
private static Vector apply(CompLongFloatVector v1, LongIntVector v2, Binary op) {
LongFloatVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v2.isSparse()) {
ObjectIterator<Long2IntMap.Entry> iter = v2.getStorage().entryIterator();
if (v1.size() > v2.size()) {
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
while (iter.hasNext()) {
Long2IntMap.Entry entry = iter.next();
long idx = entry.getLongKey();
int pidx = (int) (idx / subDim);
long subidx = idx % subDim;
if (parts[pidx].hasKey(subidx)) {
((LongFloatVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), entry.getIntValue()));
}
}
} else {
long base = 0;
for (int i = 0; i < parts.length; i++) {
LongFloatVector part = parts[i];
LongFloatVectorStorage resPart = (LongFloatVectorStorage) resParts[i];
if (part.isDense()) {
float[] partValues = part.getStorage().getValues();
float[] resPartValues = resPart.getValues();
for (int j = 0; j < partValues.length; j++) {
if (v2.hasKey(j + base)) {
resPartValues[j] = op.apply(partValues[j], v2.get(j + base));
}
}
} else if (part.isSparse()) {
ObjectIterator<Long2FloatMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Long2FloatMap.Entry entry = piter.next();
long idx = entry.getLongKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getFloatValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
long[] partIndices = part.getStorage().getIndices();
float[] partValues = part.getStorage().getValues();
long[] resPartIndices = resPart.getIndices();
float[] resPartValues = resPart.getValues();
for (int j = 0; j < partIndices.length; j++) {
long idx = partIndices[j];
if (v2.hasKey(idx + base)) {
resPartIndices[j] = idx;
resPartValues[j] = op.apply(partValues[j], v2.get(idx + base));
}
}
} else {
long[] partIndices = part.getStorage().getIndices();
float[] partValues = part.getStorage().getValues();
for (int j = 0; j < partIndices.length; j++) {
long idx = partIndices[j];
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(partValues[j], v2.get(idx + base)));
}
}
}
}
base += part.getDim();
}
}
} else {
// sorted
if (v1.size() > v2.size()) {
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
long[] v2Indices = v2.getStorage().getIndices();
int[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < v2Indices.length; i++) {
long idx = v2Indices[i];
int pidx = (int) (idx / subDim);
long subidx = idx % subDim;
if (parts[pidx].hasKey(subidx)) {
((LongFloatVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2Values[i]));
}
}
} else {
long base = 0;
for (int i = 0; i < parts.length; i++) {
LongFloatVector part = parts[i];
LongFloatVectorStorage resPart = (LongFloatVectorStorage) resParts[i];
if (part.isDense()) {
float[] partValues = part.getStorage().getValues();
float[] resPartValues = resPart.getValues();
for (int j = 0; j < partValues.length; j++) {
if (v2.hasKey(j + base)) {
resPartValues[j] = op.apply(partValues[j], v2.get(j + base));
}
}
} else if (part.isSparse()) {
ObjectIterator<Long2FloatMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Long2FloatMap.Entry entry = piter.next();
long idx = entry.getLongKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getFloatValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
long[] partIndices = part.getStorage().getIndices();
float[] partValues = part.getStorage().getValues();
long[] resPartIndices = resPart.getIndices();
float[] resPartValues = resPart.getValues();
for (int j = 0; j < partIndices.length; j++) {
long idx = partIndices[j];
if (v2.hasKey(idx + base)) {
resPartIndices[j] = idx;
resPartValues[j] = op.apply(partValues[j], v2.get(idx + base));
}
}
} else {
long[] partIndices = part.getStorage().getIndices();
float[] partValues = part.getStorage().getValues();
for (int j = 0; j < partIndices.length; j++) {
long idx = partIndices[j];
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(partValues[j], v2.get(idx + base)));
}
}
}
}
base += part.getDim();
}
}
}
LongFloatVector[] res = new LongFloatVector[parts.length];
int i = 0;
for (LongFloatVector part : parts) {
res[i] = new LongFloatVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (LongFloatVectorStorage) resParts[i]);
i++;
}
v1.setPartitions(res);
return v1;
}
use of com.tencent.angel.ml.math2.vector.LongIntVector in project angel by Tencent.
the class CompReduceExecutor method apply.
private static UnionEle apply(CompLongIntVector v, ReduceOP op, int start, int end) {
UnionEle res = new UnionEle();
LongIntVector[] parts = v.getPartitions();
switch(op) {
case Sum:
for (int i = start; i <= end; i++) {
res.setDouble1(res.getDouble1() + parts[i].sum());
}
break;
case Avg:
for (int i = start; i <= end; i++) {
res.setDouble1(res.getDouble1() + parts[i].sum());
res.setLong1(res.getLong1() + parts[i].getDim());
}
break;
case Std:
for (int i = start; i <= end; i++) {
res.setDouble1(res.getDouble1() + parts[i].sum());
double norm = parts[i].norm();
res.setDouble2(res.getDouble2() + norm * norm);
res.setLong1(res.getLong1() + parts[i].getDim());
}
break;
case Norm:
for (int i = start; i <= end; i++) {
double norm = parts[i].norm();
res.setDouble2(res.getDouble2() + norm * norm);
}
break;
case Min:
res.setDouble1(Double.MAX_VALUE);
for (int i = start; i <= end; i++) {
res.setDouble1(Math.min(res.getDouble1(), parts[i].min()));
}
break;
case Max:
res.setDouble1(Double.MIN_VALUE);
for (int i = start; i <= end; i++) {
res.setDouble1(Math.max(res.getDouble1(), parts[i].max()));
}
break;
case Size:
for (int i = start; i <= end; i++) {
res.setLong1(res.getLong1() + parts[i].size());
}
break;
case Numzeros:
for (int i = start; i <= end; i++) {
res.setLong1(res.getLong1() + parts[i].numZeros());
}
break;
}
return res;
}
use of com.tencent.angel.ml.math2.vector.LongIntVector in project angel by Tencent.
the class CompLongIntVectorSplitter method split.
@Override
public Map<PartitionKey, RowUpdateSplit> split(Vector vector, List<PartitionKey> parts) {
LongIntVector[] vecParts = ((CompLongIntVector) 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 CompLongIntRowUpdateSplit(vector.getRowId(), vecParts[i]));
}
return updateSplitMap;
}
Aggregations