use of com.tencent.angel.ml.math2.vector.CompIntDoubleVector in project angel by Tencent.
the class MixedBinaryInNonZAExecutor method apply.
private static Vector apply(CompIntDoubleVector v1, IntLongVector v2, Binary op) {
IntDoubleVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v2.isDense()) {
long[] v2Values = v2.getStorage().getValues();
int base = 0, k = 0;
for (IntDoubleVector part : parts) {
IntDoubleVectorStorage resPart = (IntDoubleVectorStorage) resParts[k];
double[] newValues = resPart.getValues();
if (part.isDense()) {
double[] partValue = part.getStorage().getValues();
for (int i = 0; i < partValue.length; i++) {
int idx = i + base;
newValues[i] = op.apply(partValue[i], v2Values[idx]);
}
} else if (part.isSparse()) {
if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
for (int i = 0; i < part.getDim(); i++) {
resPart.set(i, op.apply(0, v2Values[i + base]));
}
ObjectIterator<Int2DoubleMap.Entry> iter = part.getStorage().entryIterator();
while (iter.hasNext()) {
Int2DoubleMap.Entry entry = iter.next();
int idx = entry.getIntKey();
resPart.set(idx, op.apply(entry.getDoubleValue(), v2Values[idx + base]));
}
} else {
for (int i = 0; i < newValues.length; i++) {
if (part.getStorage().hasKey(i)) {
resPart.set(i, op.apply(part.get(i), v2Values[i + base]));
} else {
resPart.set(i, op.apply(0, v2Values[i + base]));
}
}
}
} else {
// sorted
if (op.isKeepStorage()) {
int dim = part.getDim();
int[] resIndices = resPart.getIndices();
double[] resValues = resPart.getValues();
int[] partIndices = part.getStorage().getIndices();
double[] partValues = part.getStorage().getValues();
for (int i = 0; i < dim; i++) {
resIndices[i] = i;
resValues[i] = op.apply(0, v2Values[i]);
}
int size = v1.size();
for (int i = 0; i < size; i++) {
int idx = partIndices[i];
resValues[idx] = op.apply(partValues[i], v2Values[idx + base]);
}
} else {
if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
int[] partIndices = part.getStorage().getIndices();
double[] partValues = part.getStorage().getValues();
for (int i = 0; i < part.getDim(); i++) {
newValues[i] = op.apply(0, v2Values[i + base]);
}
int size = part.size();
for (int i = 0; i < size; i++) {
int idx = partIndices[i];
newValues[idx] = op.apply(partValues[i], v2Values[idx + base]);
}
} else {
IntDoubleVectorStorage partStorage = part.getStorage();
for (int i = 0; i < newValues.length; i++) {
if (partStorage.hasKey(i)) {
newValues[i] = op.apply(partStorage.get(i), v2Values[i + base]);
} else {
newValues[i] = op.apply(0, v2Values[i + base]);
}
}
}
}
}
base += part.getDim();
k++;
}
} else if (v2.isSparse()) {
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof IntDoubleSortedVectorStorage) {
resParts[i] = new IntDoubleSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
ObjectIterator<Int2LongMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
int gidx = entry.getIntKey();
int pidx = (int) (gidx / subDim);
int subidx = gidx % subDim;
((IntDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), entry.getLongValue()));
}
} else {
// sorted
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof IntDoubleSortedVectorStorage) {
resParts[i] = new IntDoubleSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
int[] v2Indices = v2.getStorage().getIndices();
long[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < v2Indices.length; i++) {
int gidx = v2Indices[i];
int pidx = (int) (gidx / subDim);
int subidx = gidx % subDim;
((IntDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2Values[i]));
}
}
IntDoubleVector[] res = new IntDoubleVector[parts.length];
int i = 0;
for (IntDoubleVector part : parts) {
res[i] = new IntDoubleVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntDoubleVectorStorage) resParts[i]);
i++;
}
v1.setPartitions(res);
return v1;
}
use of com.tencent.angel.ml.math2.vector.CompIntDoubleVector in project angel by Tencent.
the class MixedBinaryInZAExecutor method apply.
private static Vector apply(CompIntDoubleVector v1, IntFloatVector v2, Binary op) {
IntDoubleVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v2.isDense()) {
int base = 0;
float[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < parts.length; i++) {
IntDoubleVector part = parts[i];
IntDoubleVectorStorage resPart = (IntDoubleVectorStorage) resParts[i];
if (part.isDense()) {
double[] resPartValues = resPart.getValues();
double[] 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<Int2DoubleMap.Entry> iter = part.getStorage().entryIterator();
while (iter.hasNext()) {
Int2DoubleMap.Entry entry = iter.next();
int idx = entry.getIntKey();
resPart.set(idx, op.apply(entry.getDoubleValue(), v2Values[idx + base]));
}
} else {
// sorted
if (op.isKeepStorage()) {
int[] resPartIndices = resPart.getIndices();
double[] resPartValues = resPart.getValues();
int[] partIndices = part.getStorage().getIndices();
double[] 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();
double[] 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<Int2FloatMap.Entry> iter = v2.getStorage().entryIterator();
if (v1.size() > v2.size()) {
int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
while (iter.hasNext()) {
Int2FloatMap.Entry entry = iter.next();
int idx = entry.getIntKey();
int pidx = (int) (idx / subDim);
int subidx = idx % subDim;
if (parts[pidx].hasKey(subidx)) {
((IntDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), entry.getFloatValue()));
}
}
} else {
int base = 0;
for (int i = 0; i < parts.length; i++) {
IntDoubleVector part = parts[i];
IntDoubleVectorStorage resPart = (IntDoubleVectorStorage) 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<Int2DoubleMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Int2DoubleMap.Entry entry = piter.next();
int idx = entry.getIntKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getDoubleValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
int[] partIndices = part.getStorage().getIndices();
double[] partValues = part.getStorage().getValues();
int[] resPartIndices = resPart.getIndices();
double[] 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();
double[] 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();
float[] 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)) {
((IntDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2Values[i]));
}
}
} else {
int base = 0;
for (int i = 0; i < parts.length; i++) {
IntDoubleVector part = parts[i];
IntDoubleVectorStorage resPart = (IntDoubleVectorStorage) 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<Int2DoubleMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Int2DoubleMap.Entry entry = piter.next();
int idx = entry.getIntKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getDoubleValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
int[] partIndices = part.getStorage().getIndices();
double[] partValues = part.getStorage().getValues();
int[] resPartIndices = resPart.getIndices();
double[] 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();
double[] 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();
}
}
}
IntDoubleVector[] res = new IntDoubleVector[parts.length];
int i = 0;
for (IntDoubleVector part : parts) {
res[i] = new IntDoubleVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntDoubleVectorStorage) resParts[i]);
i++;
}
v1.setPartitions(res);
return v1;
}
use of com.tencent.angel.ml.math2.vector.CompIntDoubleVector in project angel by Tencent.
the class MixedBinaryInAllExecutor method apply.
private static Vector apply(CompIntDoubleVector v1, IntIntVector v2, Binary op) {
IntDoubleVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v2.isDense()) {
int[] v2Values = v2.getStorage().getValues();
int base = 0, k = 0;
for (IntDoubleVector part : parts) {
IntDoubleVectorStorage resPart = (IntDoubleVectorStorage) resParts[k];
if (part.isDense()) {
double[] partValue = part.getStorage().getValues();
double[] resPartValues = resPart.getValues();
for (int i = 0; i < partValue.length; i++) {
int idx = i + base;
resPartValues[i] = op.apply(partValue[i], v2Values[idx]);
}
} else if (part.isSparse()) {
double[] resPartValues = resPart.getValues();
if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
for (int i = 0; i < part.getDim(); i++) {
resPart.set(i, op.apply(0, v2Values[i + base]));
}
ObjectIterator<Int2DoubleMap.Entry> iter = part.getStorage().entryIterator();
while (iter.hasNext()) {
Int2DoubleMap.Entry entry = iter.next();
int idx = entry.getIntKey();
resPart.set(idx, op.apply(entry.getDoubleValue(), v2Values[idx + base]));
}
} else {
for (int i = 0; i < resPartValues.length; i++) {
if (part.getStorage().hasKey(i)) {
resPart.set(i, op.apply(part.get(i), v2Values[i + base]));
} else {
resPart.set(i, op.apply(0, v2Values[i + base]));
}
}
}
} else {
// sorted
int[] resPartIndices = resPart.getIndices();
double[] resPartValues = resPart.getValues();
if (op.isKeepStorage()) {
if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
int[] partIndices = part.getStorage().getIndices();
double[] partValues = part.getStorage().getValues();
for (int i = 0; i < part.getDim(); i++) {
resPartIndices[i] = i;
resPartValues[i] = op.apply(0, v2Values[i + base]);
}
int size = part.size();
for (int i = 0; i < size; i++) {
int idx = partIndices[i];
resPartValues[idx] = op.apply(partValues[i], v2Values[idx + base]);
}
} else {
IntDoubleVectorStorage partStorage = part.getStorage();
for (int i = 0; i < resPartValues.length; i++) {
if (partStorage.hasKey(i)) {
resPartIndices[i] = i;
resPartValues[i] = op.apply(partStorage.get(i), v2Values[i + base]);
} else {
resPartIndices[i] = i;
resPartValues[i] = op.apply(0, v2Values[i + base]);
}
}
}
} else {
if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
int[] partIndices = part.getStorage().getIndices();
double[] partValues = part.getStorage().getValues();
for (int i = 0; i < part.getDim(); i++) {
resPartValues[i] = op.apply(0, v2Values[i + base]);
}
int size = part.size();
for (int i = 0; i < size; i++) {
int idx = partIndices[i];
resPartValues[idx] = op.apply(partValues[i], v2Values[idx + base]);
}
} else {
IntDoubleVectorStorage partStorage = part.getStorage();
for (int i = 0; i < resPartValues.length; i++) {
if (partStorage.hasKey(i)) {
resPartValues[i] = op.apply(partStorage.get(i), v2Values[i + base]);
} else {
resPartValues[i] = op.apply(0, v2Values[i + base]);
}
}
}
}
}
base += part.getDim();
k++;
}
} else {
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof IntDoubleSortedVectorStorage) {
resParts[i] = new IntDoubleSparseVectorStorage(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;
if (v2.getStorage().hasKey(i)) {
((IntDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
} else {
((IntDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 0));
}
}
}
IntDoubleVector[] res = new IntDoubleVector[parts.length];
int i = 0;
for (IntDoubleVector part : parts) {
res[i] = new IntDoubleVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntDoubleVectorStorage) resParts[i]);
i++;
}
v1.setPartitions(res);
return v1;
}
use of com.tencent.angel.ml.math2.vector.CompIntDoubleVector in project angel by Tencent.
the class VectorUtils method emptyLike.
private static ComponentVector emptyLike(ComponentVector v) {
ComponentVector result;
if (v instanceof CompIntDoubleVector) {
IntDoubleVector[] parts = new IntDoubleVector[v.getNumPartitions()];
IntDoubleVector[] refParts = ((CompIntDoubleVector) v).getPartitions();
for (int i = 0; i < refParts.length; i++) {
if (null != refParts[i]) {
parts[i] = (IntDoubleVector) emptyLike((SimpleVector) refParts[i]);
}
}
result = new CompIntDoubleVector(((CompIntDoubleVector) v).getMatrixId(), ((CompIntDoubleVector) v).getRowId(), ((CompIntDoubleVector) v).getClock(), ((CompIntDoubleVector) v).getDim(), parts, ((CompIntDoubleVector) v).getSubDim());
} else if (v instanceof CompIntFloatVector) {
IntFloatVector[] parts = new IntFloatVector[v.getNumPartitions()];
IntFloatVector[] refParts = ((CompIntFloatVector) v).getPartitions();
for (int i = 0; i < refParts.length; i++) {
if (null != refParts[i]) {
parts[i] = (IntFloatVector) emptyLike((SimpleVector) refParts[i]);
}
}
result = new CompIntFloatVector(((CompIntFloatVector) v).getMatrixId(), ((CompIntFloatVector) v).getRowId(), ((CompIntFloatVector) v).getClock(), ((CompIntFloatVector) v).getDim(), parts, ((CompIntFloatVector) v).getSubDim());
} else if (v instanceof CompIntLongVector) {
IntLongVector[] parts = new IntLongVector[v.getNumPartitions()];
IntLongVector[] refParts = ((CompIntLongVector) v).getPartitions();
for (int i = 0; i < refParts.length; i++) {
if (null != refParts[i]) {
parts[i] = (IntLongVector) emptyLike((SimpleVector) refParts[i]);
}
}
result = new CompIntLongVector(((CompIntLongVector) v).getMatrixId(), ((CompIntLongVector) v).getRowId(), ((CompIntLongVector) v).getClock(), ((CompIntLongVector) v).getDim(), parts, ((CompIntLongVector) v).getSubDim());
} else if (v instanceof CompIntIntVector) {
IntIntVector[] parts = new IntIntVector[v.getNumPartitions()];
IntIntVector[] refParts = ((CompIntIntVector) v).getPartitions();
for (int i = 0; i < refParts.length; i++) {
if (null != refParts[i]) {
parts[i] = (IntIntVector) emptyLike((SimpleVector) refParts[i]);
}
}
result = new CompIntIntVector(((CompIntIntVector) v).getMatrixId(), ((CompIntIntVector) v).getRowId(), ((CompIntIntVector) v).getClock(), ((CompIntIntVector) v).getDim(), parts, ((CompIntIntVector) v).getSubDim());
} else if (v instanceof CompLongDoubleVector) {
LongDoubleVector[] parts = new LongDoubleVector[v.getNumPartitions()];
LongDoubleVector[] refParts = ((CompLongDoubleVector) v).getPartitions();
for (int i = 0; i < refParts.length; i++) {
if (null != refParts[i]) {
parts[i] = (LongDoubleVector) emptyLike((SimpleVector) refParts[i]);
}
}
result = new CompLongDoubleVector(((CompLongDoubleVector) v).getMatrixId(), ((CompLongDoubleVector) v).getRowId(), ((CompLongDoubleVector) v).getClock(), ((CompLongDoubleVector) v).getDim(), parts, ((CompLongDoubleVector) v).getSubDim());
} else if (v instanceof CompLongFloatVector) {
LongFloatVector[] parts = new LongFloatVector[v.getNumPartitions()];
LongFloatVector[] refParts = ((CompLongFloatVector) v).getPartitions();
for (int i = 0; i < refParts.length; i++) {
if (null != refParts[i]) {
parts[i] = (LongFloatVector) emptyLike((SimpleVector) refParts[i]);
}
}
result = new CompLongFloatVector(((CompLongFloatVector) v).getMatrixId(), ((CompLongFloatVector) v).getRowId(), ((CompLongFloatVector) v).getClock(), ((CompLongFloatVector) v).getDim(), parts, ((CompLongFloatVector) v).getSubDim());
} else if (v instanceof CompLongLongVector) {
LongLongVector[] parts = new LongLongVector[v.getNumPartitions()];
LongLongVector[] refParts = ((CompLongLongVector) v).getPartitions();
for (int i = 0; i < refParts.length; i++) {
if (null != refParts[i]) {
parts[i] = (LongLongVector) emptyLike((SimpleVector) refParts[i]);
}
}
result = new CompLongLongVector(((CompLongLongVector) v).getMatrixId(), ((CompLongLongVector) v).getRowId(), ((CompLongLongVector) v).getClock(), ((CompLongLongVector) v).getDim(), parts, ((CompLongLongVector) v).getSubDim());
} else if (v instanceof CompLongIntVector) {
LongIntVector[] parts = new LongIntVector[v.getNumPartitions()];
LongIntVector[] refParts = ((CompLongIntVector) v).getPartitions();
for (int i = 0; i < refParts.length; i++) {
if (null != refParts[i]) {
parts[i] = (LongIntVector) emptyLike((SimpleVector) refParts[i]);
}
}
result = new CompLongIntVector(((CompLongIntVector) v).getMatrixId(), ((CompLongIntVector) v).getRowId(), ((CompLongIntVector) v).getClock(), ((CompLongIntVector) v).getDim(), parts, ((CompLongIntVector) v).getSubDim());
} else {
throw new AngelException("The operation is not support!");
}
return result;
}
use of com.tencent.angel.ml.math2.vector.CompIntDoubleVector in project angel by Tencent.
the class RBCompIntDoubleMatrix method initEmpty.
@Override
public void initEmpty(int idx) {
int numComp = (int) ((getDim() + subDim - 1) / subDim);
if (null == rows[idx]) {
IntDoubleVector[] tmpParts = new IntDoubleVector[numComp];
for (int i = 0; i < numComp; i++) {
IntDoubleSparseVectorStorage storage = new IntDoubleSparseVectorStorage(subDim);
tmpParts[i] = new IntDoubleVector(matrixId, idx, clock, (int) getDim(), storage);
}
CompIntDoubleVector tmpVect = new CompIntDoubleVector(matrixId, idx, clock, (int) getDim(), tmpParts, subDim);
rows[idx] = tmpVect;
}
}
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