use of com.tencent.angel.ml.math2.vector.LongLongVector in project angel by Tencent.
the class MixedBinaryOutNonZAExecutor method apply.
private static Vector apply(CompLongLongVector v1, LongDummyVector v2, Binary op) {
LongLongVector[] 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 LongLongSortedVectorStorage) {
resParts[i] = new LongLongSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
long[] v2Indices = v2.getIndices();
for (int i = 0; i < v2Indices.length; i++) {
long gidx = v2Indices[i];
int pidx = (int) (gidx / subDim);
long subidx = gidx % subDim;
((LongLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 1));
}
LongLongVector[] res = new LongLongVector[parts.length];
int i = 0;
for (LongLongVector part : parts) {
res[i] = new LongLongVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (LongLongVectorStorage) resParts[i]);
i++;
}
return new CompLongLongVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}
use of com.tencent.angel.ml.math2.vector.LongLongVector in project angel by Tencent.
the class UpdatePSFTest method testSparseLongLongKeyUDF.
public void testSparseLongLongKeyUDF() throws Exception {
Worker worker = LocalClusterContext.get().getWorker(workerAttempt0Id).getWorker();
MatrixClient client1 = worker.getPSAgent().getMatrixClient(SPARSE_LONG_LONG_MAT, 0);
int matrixW1Id = client1.getMatrixId();
long[] index = genLongIndexs(feaNum, nnz);
LongLongVector deltaVec = new LongLongVector(feaNum, new LongLongSparseVectorStorage(feaNum, nnz));
for (int i = 0; i < nnz; i++) {
deltaVec.set(index[i], index[i]);
}
deltaVec.setRowId(0);
Vector[] updates = new Vector[1];
updates[0] = deltaVec;
client1.asyncUpdate(new IncrementRows(new IncrementRowsParam(matrixW1Id, updates))).get();
LongLongVector row = (LongLongVector) client1.getRow(0);
for (long id : index) {
// System.out.println("id=" + id + ", value=" + row.get(id));
Assert.assertEquals(row.get(id), deltaVec.get(id));
}
// Assert.assertTrue(index.length == row.size());
}
use of com.tencent.angel.ml.math2.vector.LongLongVector in project angel by Tencent.
the class MixedBinaryOutZAExecutor method apply.
private static Vector apply(CompLongLongVector v1, LongDummyVector v2, Binary op) {
LongLongVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v1.size() > v2.size()) {
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
long[] v2Indices = v2.getIndices();
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)) {
((LongLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 1));
}
}
} else {
long base = 0;
for (int i = 0; i < parts.length; i++) {
LongLongVector part = parts[i];
LongLongVectorStorage resPart = (LongLongVectorStorage) resParts[i];
if (part.isDense()) {
long[] partValues = part.getStorage().getValues();
long[] 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<Long2LongMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Long2LongMap.Entry entry = piter.next();
long idx = entry.getLongKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getLongValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
long[] partIndices = part.getStorage().getIndices();
long[] partValues = part.getStorage().getValues();
long[] resPartIndices = resPart.getIndices();
long[] 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();
long[] 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();
}
}
LongLongVector[] res = new LongLongVector[parts.length];
int i = 0;
for (LongLongVector part : parts) {
res[i] = new LongLongVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (LongLongVectorStorage) resParts[i]);
i++;
}
return new CompLongLongVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}
use of com.tencent.angel.ml.math2.vector.LongLongVector in project angel by Tencent.
the class MixedBinaryOutZAExecutor method apply.
private static Vector apply(CompLongFloatVector v1, LongLongVector v2, Binary op) {
LongFloatVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v2.isSparse()) {
ObjectIterator<Long2LongMap.Entry> iter = v2.getStorage().entryIterator();
if (v1.size() > v2.size()) {
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
while (iter.hasNext()) {
Long2LongMap.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.getLongValue()));
}
}
} 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();
long[] 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++;
}
return new CompLongFloatVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}
use of com.tencent.angel.ml.math2.vector.LongLongVector in project angel by Tencent.
the class SimpleBinaryInAllExecutor method apply.
public static Vector apply(LongLongVector v1, LongDummyVector v2, Binary op) {
if (v1.isSparse()) {
if (op.isKeepStorage()) {
throw new AngelException("operation is not support!");
} else {
// multi-rehash
LongLongVectorStorage newStorage = v1.getStorage().emptySparse((int) (v1.getDim()));
LongLongVectorStorage v1Storage = v1.getStorage();
for (int i = 0; i < v1.getDim(); i++) {
if (v1Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), v2.get(i)));
} else {
newStorage.set(i, op.apply(0, v2.get(i)));
}
}
v1.setStorage(newStorage);
}
} else {
// sorted
if (op.isKeepStorage()) {
throw new AngelException("operation is not support!");
} else {
LongLongVectorStorage newStorage = new LongLongSparseVectorStorage(v1.getDim());
LongLongVectorStorage v1Storage = v1.getStorage();
for (int i = 0; i < v1.getDim(); i++) {
if (v1Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), v2.get(i)));
} else {
newStorage.set(i, op.apply(0, v2.get(i)));
}
}
v1.setStorage(newStorage);
}
}
return v1;
}
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