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, LongIntVector v2, Binary op) {
LongLongVector[] 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)) {
((LongLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), entry.getIntValue()));
}
}
} 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();
}
}
} 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)) {
((LongLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2Values[i]));
}
}
} 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(CompLongDoubleVector v1, LongLongVector v2, Binary op) {
LongDoubleVector[] 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)) {
((LongDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), entry.getLongValue()));
}
}
} else {
long base = 0;
for (int i = 0; i < parts.length; i++) {
LongDoubleVector part = parts[i];
LongDoubleVectorStorage resPart = (LongDoubleVectorStorage) resParts[i];
if (part.isDense()) {
double[] partValues = part.getStorage().getValues();
double[] 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<Long2DoubleMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Long2DoubleMap.Entry entry = piter.next();
long idx = entry.getLongKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getDoubleValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
long[] partIndices = part.getStorage().getIndices();
double[] partValues = part.getStorage().getValues();
long[] resPartIndices = resPart.getIndices();
double[] 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();
double[] 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)) {
((LongDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2Values[i]));
}
}
} else {
long base = 0;
for (int i = 0; i < parts.length; i++) {
LongDoubleVector part = parts[i];
LongDoubleVectorStorage resPart = (LongDoubleVectorStorage) resParts[i];
if (part.isDense()) {
double[] partValues = part.getStorage().getValues();
double[] 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<Long2DoubleMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Long2DoubleMap.Entry entry = piter.next();
long idx = entry.getLongKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getDoubleValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
long[] partIndices = part.getStorage().getIndices();
double[] partValues = part.getStorage().getValues();
long[] resPartIndices = resPart.getIndices();
double[] 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();
double[] 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();
}
}
}
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++;
}
return new CompLongDoubleVector(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 MixedBinaryOutNonZAExecutor method apply.
private static Vector apply(CompLongLongVector v1, LongIntVector v2, Binary op) {
LongLongVector[] 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 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();
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;
((LongLongVectorStorage) 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 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.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;
((LongLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2Values[i]));
}
}
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 SimpleBinaryOutAllExecutor method apply.
public static Vector apply(LongLongVector v1, LongLongVector v2, Binary op) {
LongLongVector res;
if (v1.isSparse() && v2.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();
LongLongVectorStorage v2Storage = v2.getStorage();
for (int i = 0; i < v1.getDim(); i++) {
if (v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), v2.get(i)));
} else if (v1Storage.hasKey(i) && !v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), 0));
} else if (!v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(0, v2.get(i)));
} else {
newStorage.set(i, op.apply(0, 0));
}
}
res = new LongLongVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
}
} else if (v1.isSparse() && v2.isSorted()) {
if (op.isKeepStorage()) {
throw new AngelException("operation is not support!");
} else {
LongLongVectorStorage newStorage = v1.getStorage().emptySparse((int) (v1.getDim()));
LongLongVectorStorage v1Storage = v1.getStorage();
LongLongVectorStorage v2Storage = v2.getStorage();
for (int i = 0; i < v1.getDim(); i++) {
if (v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), v2.get(i)));
} else if (v1Storage.hasKey(i) && !v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), 0));
} else if (!v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(0, v2.get(i)));
} else {
newStorage.set(i, op.apply(0, 0));
}
}
res = new LongLongVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
}
} else if (v1.isSorted() && v2.isSparse()) {
if (op.isKeepStorage()) {
throw new AngelException("operation is not support!");
} else {
LongLongVectorStorage newStorage = new LongLongSparseVectorStorage(v1.getDim());
LongLongVectorStorage v1Storage = v1.getStorage();
LongLongVectorStorage v2Storage = v2.getStorage();
for (int i = 0; i < v1.getDim(); i++) {
if (v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), v2.get(i)));
} else if (v1Storage.hasKey(i) && !v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), 0));
} else if (!v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(0, v2.get(i)));
} else {
newStorage.set(i, op.apply(0, 0));
}
}
res = new LongLongVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
}
} else if (v1.isSorted() && v2.isSorted()) {
if (op.isKeepStorage()) {
throw new AngelException("operation is not support!");
} else {
LongLongVectorStorage newStorage = v1.getStorage().emptySorted((int) (v1.getDim()));
long[] resIndices = newStorage.getIndices();
long[] resValues = newStorage.getValues();
int v1Pointor = 0;
int v2Pointor = 0;
long size1 = v1.size();
long size2 = v2.size();
long[] v1Indices = v1.getStorage().getIndices();
long[] v1Values = v1.getStorage().getValues();
long[] v2Indices = v2.getStorage().getIndices();
long[] v2Values = v2.getStorage().getValues();
if (!op.isCompare()) {
if (size1 != v1.getDim() && size2 != v2.getDim()) {
for (int i = 0; i < v1.getDim(); i++) {
resValues[i] = 0 / 0;
}
}
}
int globalPointor = 0;
while (v1Pointor < size1 && v2Pointor < size2) {
if (v1Indices[v1Pointor] == v2Indices[v2Pointor]) {
resIndices[globalPointor] = v1Indices[v1Pointor];
resValues[globalPointor] = op.apply(v1Values[v1Pointor], v2Values[v2Pointor]);
v1Pointor++;
v2Pointor++;
globalPointor++;
} else if (v1Indices[v1Pointor] < v2Indices[v2Pointor]) {
resIndices[globalPointor] = v1Indices[v1Pointor];
resValues[globalPointor] = op.apply(v2Values[v2Pointor], 0);
v1Pointor++;
globalPointor++;
} else {
// v1Indices[v1Pointor] > v2Indices[v2Pointor]
resIndices[globalPointor] = v2Indices[v2Pointor];
resValues[globalPointor] = op.apply(0, v2Values[v2Pointor]);
v2Pointor++;
globalPointor++;
}
}
res = new LongLongVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
}
} else {
throw new AngelException("The operation is not support!");
}
return res;
}
use of com.tencent.angel.ml.math2.vector.LongLongVector in project angel by Tencent.
the class SimpleBinaryOutAllExecutor method apply.
public static Vector apply(LongFloatVector v1, LongLongVector v2, Binary op) {
LongFloatVector res;
if (v1.isSparse() && v2.isSparse()) {
if (op.isKeepStorage()) {
throw new AngelException("operation is not support!");
} else {
// multi-rehash
LongFloatVectorStorage newStorage = v1.getStorage().emptySparse((int) (v1.getDim()));
LongFloatVectorStorage v1Storage = v1.getStorage();
LongLongVectorStorage v2Storage = v2.getStorage();
for (int i = 0; i < v1.getDim(); i++) {
if (v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), v2.get(i)));
} else if (v1Storage.hasKey(i) && !v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), 0));
} else if (!v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(0, v2.get(i)));
} else {
newStorage.set(i, op.apply(0.0f, 0));
}
}
res = new LongFloatVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
}
} else if (v1.isSparse() && v2.isSorted()) {
if (op.isKeepStorage()) {
throw new AngelException("operation is not support!");
} else {
LongFloatVectorStorage newStorage = v1.getStorage().emptySparse((int) (v1.getDim()));
LongFloatVectorStorage v1Storage = v1.getStorage();
LongLongVectorStorage v2Storage = v2.getStorage();
for (int i = 0; i < v1.getDim(); i++) {
if (v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), v2.get(i)));
} else if (v1Storage.hasKey(i) && !v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), 0));
} else if (!v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(0, v2.get(i)));
} else {
newStorage.set(i, op.apply(0.0f, 0));
}
}
res = new LongFloatVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
}
} else if (v1.isSorted() && v2.isSparse()) {
if (op.isKeepStorage()) {
throw new AngelException("operation is not support!");
} else {
LongFloatVectorStorage newStorage = new LongFloatSparseVectorStorage(v1.getDim());
LongFloatVectorStorage v1Storage = v1.getStorage();
LongLongVectorStorage v2Storage = v2.getStorage();
for (int i = 0; i < v1.getDim(); i++) {
if (v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), v2.get(i)));
} else if (v1Storage.hasKey(i) && !v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), 0));
} else if (!v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
newStorage.set(i, op.apply(0, v2.get(i)));
} else {
newStorage.set(i, op.apply(0.0f, 0));
}
}
res = new LongFloatVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
}
} else if (v1.isSorted() && v2.isSorted()) {
if (op.isKeepStorage()) {
throw new AngelException("operation is not support!");
} else {
LongFloatVectorStorage newStorage = v1.getStorage().emptySorted((int) (v1.getDim()));
long[] resIndices = newStorage.getIndices();
float[] resValues = newStorage.getValues();
int v1Pointor = 0;
int v2Pointor = 0;
long size1 = v1.size();
long size2 = v2.size();
long[] v1Indices = v1.getStorage().getIndices();
float[] v1Values = v1.getStorage().getValues();
long[] v2Indices = v2.getStorage().getIndices();
long[] v2Values = v2.getStorage().getValues();
if (!op.isCompare()) {
for (int i = 0; i < resValues.length; i++) {
resValues[i] = Float.NaN;
}
}
int globalPointor = 0;
while (v1Pointor < size1 && v2Pointor < size2) {
if (v1Indices[v1Pointor] == v2Indices[v2Pointor]) {
resIndices[globalPointor] = v1Indices[v1Pointor];
resValues[globalPointor] = op.apply(v1Values[v1Pointor], v2Values[v2Pointor]);
v1Pointor++;
v2Pointor++;
globalPointor++;
} else if (v1Indices[v1Pointor] < v2Indices[v2Pointor]) {
resIndices[globalPointor] = v1Indices[v1Pointor];
resValues[globalPointor] = op.apply(v2Values[v2Pointor], 0);
v1Pointor++;
globalPointor++;
} else {
// v1Indices[v1Pointor] > v2Indices[v2Pointor]
resIndices[globalPointor] = v2Indices[v2Pointor];
resValues[globalPointor] = op.apply(0, v2Values[v2Pointor]);
v2Pointor++;
globalPointor++;
}
}
res = new LongFloatVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
}
} else {
throw new AngelException("The operation is not support!");
}
return res;
}
Aggregations