use of com.tencent.angel.ml.math2.vector.CompLongIntVector in project angel by Tencent.
the class MixedBinaryOutAllExecutor method apply.
private static Vector apply(CompLongIntVector v1, LongIntVector v2, Binary op) {
LongIntVector[] 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 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();
for (int i = 0; i < v1.getDim(); i++) {
int pidx = (int) (i / subDim);
long subidx = i % subDim;
if (v2.getStorage().hasKey(i)) {
((LongIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
} else {
((LongIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 0));
}
}
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++;
}
return new CompLongIntVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}
use of com.tencent.angel.ml.math2.vector.CompLongIntVector in project angel by Tencent.
the class MixedBinaryOutAllExecutor method apply.
private static Vector apply(CompLongIntVector v1, LongDummyVector v2, Binary op) {
LongIntVector[] 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 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();
for (int i = 0; i < v1.getDim(); i++) {
int pidx = (int) (i / subDim);
long subidx = i % subDim;
((LongIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(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++;
}
return new CompLongIntVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}
use of com.tencent.angel.ml.math2.vector.CompLongIntVector in project angel by Tencent.
the class MixedBinaryOutZAExecutor 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()) {
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)) {
((LongIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), entry.getIntValue()));
}
}
} else {
long base = 0;
for (int i = 0; i < parts.length; i++) {
LongIntVector part = parts[i];
LongIntVectorStorage resPart = (LongIntVectorStorage) 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<Long2IntMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Long2IntMap.Entry entry = piter.next();
long idx = entry.getLongKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getIntValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
long[] partIndices = part.getStorage().getIndices();
int[] partValues = part.getStorage().getValues();
long[] resPartIndices = resPart.getIndices();
int[] 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();
int[] 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)) {
((LongIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2Values[i]));
}
}
} else {
long base = 0;
for (int i = 0; i < parts.length; i++) {
LongIntVector part = parts[i];
LongIntVectorStorage resPart = (LongIntVectorStorage) 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<Long2IntMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Long2IntMap.Entry entry = piter.next();
long idx = entry.getLongKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getIntValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
long[] partIndices = part.getStorage().getIndices();
int[] partValues = part.getStorage().getValues();
long[] resPartIndices = resPart.getIndices();
int[] 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();
int[] 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();
}
}
}
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++;
}
return new CompLongIntVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}
use of com.tencent.angel.ml.math2.vector.CompLongIntVector in project angel by Tencent.
the class MixedBinaryInAllExecutor method apply.
private static Vector apply(CompLongIntVector v1, LongIntVector v2, Binary op) {
LongIntVector[] 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 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();
for (int i = 0; i < v1.getDim(); i++) {
int pidx = (int) (i / subDim);
long subidx = i % subDim;
if (v2.getStorage().hasKey(i)) {
((LongIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
} else {
((LongIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 0));
}
}
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.CompLongIntVector in project angel by Tencent.
the class MixedBinaryInAllExecutor method apply.
private static Vector apply(CompLongIntVector v1, LongDummyVector v2, Binary op) {
LongIntVector[] 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 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();
for (int i = 0; i < v1.getDim(); i++) {
int pidx = (int) (i / subDim);
long subidx = i % subDim;
((LongIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(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;
}
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