use of com.tencent.angel.ml.math2.storage.IntDoubleSparseVectorStorage in project angel by Tencent.
the class RangeRouterUtils method splitIntDoubleVector.
public static KeyValuePart[] splitIntDoubleVector(MatrixMeta matrixMeta, IntDoubleVector vector) {
IntDoubleVectorStorage storage = vector.getStorage();
if (storage.isSparse()) {
// Get keys and values
IntDoubleSparseVectorStorage sparseStorage = (IntDoubleSparseVectorStorage) storage;
int[] keys = sparseStorage.getIndices();
double[] values = sparseStorage.getValues();
return split(matrixMeta, vector.getRowId(), keys, values, false);
} else if (storage.isDense()) {
// Get values
IntDoubleDenseVectorStorage denseStorage = (IntDoubleDenseVectorStorage) storage;
double[] values = denseStorage.getValues();
return split(matrixMeta, vector.getRowId(), values);
} else {
// Key and value array pair
IntDoubleSortedVectorStorage sortStorage = (IntDoubleSortedVectorStorage) storage;
int[] keys = sortStorage.getIndices();
double[] values = sortStorage.getValues();
return split(matrixMeta, vector.getRowId(), keys, values, true);
}
}
use of com.tencent.angel.ml.math2.storage.IntDoubleSparseVectorStorage in project angel by Tencent.
the class MixedBinaryOutNonZAExecutor 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];
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 + base]);
}
int size = part.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<Int2IntMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Int2IntMap.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.getIntValue()));
}
} 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();
int[] 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++;
}
return new CompIntDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}
use of com.tencent.angel.ml.math2.storage.IntDoubleSparseVectorStorage in project angel by Tencent.
the class MixedBinaryOutAllExecutor 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];
if (part.isDense()) {
double[] partValue = part.getStorage().getValues();
double[] resPartValues = resPart.getValues();
for (int i = 0; i < partValue.length; i++) {
int idx = i;
resPartValues[i] = op.apply(partValue[i], v2Values[idx + base]);
}
} 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 < resPart.size(); 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[] resPartIndices = resPart.getIndices();
double[] resPartValues = resPart.getValues();
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 {
double[] resPartValues = resPart.getValues();
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++;
}
return new CompIntDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}
use of com.tencent.angel.ml.math2.storage.IntDoubleSparseVectorStorage in project angel by Tencent.
the class MixedBinaryOutAllExecutor method apply.
private static Vector apply(CompIntDoubleVector v1, IntDoubleVector v2, Binary op) {
IntDoubleVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v2.isDense()) {
double[] 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;
resPartValues[i] = op.apply(partValue[i], v2Values[idx + base]);
}
} 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 < resPart.size(); 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[] resPartIndices = resPart.getIndices();
double[] resPartValues = resPart.getValues();
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 {
double[] resPartValues = resPart.getValues();
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++;
}
return new CompIntDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}
use of com.tencent.angel.ml.math2.storage.IntDoubleSparseVectorStorage in project angel by Tencent.
the class MixedBinaryOutAllExecutor 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;
resPartValues[i] = op.apply(partValue[i], v2Values[idx + base]);
}
} 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 < resPart.size(); 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[] resPartIndices = resPart.getIndices();
double[] resPartValues = resPart.getValues();
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 {
double[] resPartValues = resPart.getValues();
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++;
}
return new CompIntDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}
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