use of com.tencent.angel.ml.math2.vector.CompIntIntVector 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.CompIntIntVector 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;
}
use of com.tencent.angel.ml.math2.vector.CompIntIntVector in project angel by Tencent.
the class MixedBinaryInZAExecutor 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 base = 0;
int[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < parts.length; i++) {
IntIntVector part = parts[i];
IntIntVectorStorage resPart = (IntIntVectorStorage) resParts[i];
if (part.isDense()) {
int[] resPartValues = resPart.getValues();
int[] partValues = part.getStorage().getValues();
for (int j = 0; j < partValues.length; j++) {
resPartValues[j] = op.apply(partValues[j], v2Values[base + j]);
}
} else if (part.isSparse()) {
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 {
// sorted
if (op.isKeepStorage()) {
int[] resPartIndices = resPart.getIndices();
int[] resPartValues = resPart.getValues();
int[] partIndices = part.getStorage().getIndices();
int[] partValues = part.getStorage().getValues();
for (int j = 0; j < partIndices.length; j++) {
int idx = partIndices[j];
resPartIndices[j] = idx;
resPartValues[j] = op.apply(partValues[j], v2Values[idx + base]);
}
} else {
int[] partIndices = part.getStorage().getIndices();
int[] partValues = part.getStorage().getValues();
for (int j = 0; j < partIndices.length; j++) {
int idx = partIndices[j];
resPart.set(idx, op.apply(partValues[j], v2Values[idx + base]));
}
}
}
base += part.getDim();
}
} else if (v2.isSparse()) {
ObjectIterator<Int2IntMap.Entry> iter = v2.getStorage().entryIterator();
if (v1.size() > v2.size()) {
int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
while (iter.hasNext()) {
Int2IntMap.Entry entry = iter.next();
int idx = entry.getIntKey();
int pidx = (int) (idx / subDim);
int subidx = idx % subDim;
if (parts[pidx].hasKey(subidx)) {
((IntIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), entry.getIntValue()));
}
}
} else {
int base = 0;
for (int i = 0; i < parts.length; i++) {
IntIntVector part = parts[i];
IntIntVectorStorage resPart = (IntIntVectorStorage) resParts[i];
if (part.isDense()) {
int[] partValues = part.getStorage().getValues();
int[] 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<Int2IntMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Int2IntMap.Entry entry = piter.next();
int idx = entry.getIntKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getIntValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
int[] partIndices = part.getStorage().getIndices();
int[] partValues = part.getStorage().getValues();
int[] resPartIndices = resPart.getIndices();
int[] resPartValues = resPart.getValues();
for (int j = 0; j < partIndices.length; j++) {
int idx = partIndices[j];
if (v2.hasKey(idx + base)) {
resPartIndices[j] = idx;
resPartValues[j] = op.apply(partValues[j], v2.get(idx + base));
}
}
} else {
int[] partIndices = part.getStorage().getIndices();
int[] partValues = part.getStorage().getValues();
for (int j = 0; j < partIndices.length; j++) {
int 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()) {
int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
int[] v2Indices = v2.getStorage().getIndices();
int[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < v2Indices.length; i++) {
int idx = v2Indices[i];
int pidx = (int) (idx / subDim);
int subidx = idx % subDim;
if (parts[pidx].hasKey(subidx)) {
((IntIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2Values[i]));
}
}
} else {
int base = 0;
for (int i = 0; i < parts.length; i++) {
IntIntVector part = parts[i];
IntIntVectorStorage resPart = (IntIntVectorStorage) resParts[i];
if (part.isDense()) {
int[] partValues = part.getStorage().getValues();
int[] 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<Int2IntMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Int2IntMap.Entry entry = piter.next();
int idx = entry.getIntKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getIntValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
int[] partIndices = part.getStorage().getIndices();
int[] partValues = part.getStorage().getValues();
int[] resPartIndices = resPart.getIndices();
int[] resPartValues = resPart.getValues();
for (int j = 0; j < partIndices.length; j++) {
int idx = partIndices[j];
if (v2.hasKey(idx + base)) {
resPartIndices[j] = idx;
resPartValues[j] = op.apply(partValues[j], v2.get(idx + base));
}
}
} else {
int[] partIndices = part.getStorage().getIndices();
int[] partValues = part.getStorage().getValues();
for (int j = 0; j < partIndices.length; j++) {
int idx = partIndices[j];
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(partValues[j], v2.get(idx + base)));
}
}
}
}
base += part.getDim();
}
}
}
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.CompIntIntVector in project angel by Tencent.
the class CompReduceExecutor method apply.
private static UnionEle apply(CompIntIntVector v, ReduceOP op, int start, int end) {
UnionEle res = new UnionEle();
IntIntVector[] 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.CompIntIntVector in project angel by Tencent.
the class MixedDotExecutor method apply.
private static double apply(CompIntIntVector v1, IntIntVector v2) {
double dotValue = 0.0;
if (v2.isDense()) {
int base = 0;
int[] v2Values = v2.getStorage().getValues();
for (IntIntVector part : v1.getPartitions()) {
if (part.isDense()) {
int[] partValues = part.getStorage().getValues();
for (int i = 0; i < partValues.length; i++) {
int idx = base + i;
dotValue += partValues[i] * v2Values[idx];
}
} else if (part.isSparse()) {
ObjectIterator<Int2IntMap.Entry> iter = part.getStorage().entryIterator();
while (iter.hasNext()) {
Int2IntMap.Entry entry = iter.next();
int idx = base + entry.getIntKey();
dotValue += entry.getIntValue() * v2Values[idx];
}
} else {
// isSorted
int[] partIndices = part.getStorage().getIndices();
int[] partValues = part.getStorage().getValues();
for (int i = 0; i < partIndices.length; i++) {
int idx = base + partIndices[i];
dotValue += partValues[i] * v2Values[idx];
}
}
base += part.getDim();
}
} else if (v2.isSparse()) {
ObjectIterator<Int2IntMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Int2IntMap.Entry entry = iter.next();
int idx = entry.getIntKey();
dotValue += v1.get(idx) * entry.getIntValue();
}
} else if (v2.isSorted() && v1.size() > v2.size()) {
// v2 is sorted
int[] v2Indices = v2.getStorage().getIndices();
int[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < v2Indices.length; i++) {
int idx = v2Indices[i];
dotValue += v1.get(idx) * v2Values[i];
}
} else {
int base = 0;
for (IntIntVector part : v1.getPartitions()) {
if (part.isDense()) {
int[] partValues = part.getStorage().getValues();
for (int i = 0; i < partValues.length; i++) {
int idx = base + i;
dotValue += partValues[i] * v2.get(idx);
}
} else if (part.isSparse()) {
ObjectIterator<Int2IntMap.Entry> iter = part.getStorage().entryIterator();
while (iter.hasNext()) {
Int2IntMap.Entry entry = iter.next();
int idx = base + entry.getIntKey();
dotValue += entry.getIntValue() * v2.get(idx);
}
} else {
// isSorted
int[] partIndices = part.getStorage().getIndices();
int[] partValues = part.getStorage().getValues();
for (int i = 0; i < partIndices.length; i++) {
int idx = base + partIndices[i];
dotValue += partValues[i] * v2.get(idx);
}
}
base += part.getDim();
}
}
return dotValue;
}
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