Example 31 with IntLongVector
use of com.tencent.angel.ml.math2.vector.IntLongVector in project angel by Tencent.
the class SimpleBinaryOutNonZAExecutor method apply.
public static Vector apply(IntLongVector v1, IntIntVector v2, Binary op) {
IntLongVectorStorage newStorage = (IntLongVectorStorage) StorageSwitch.apply(v1, v2, op);
if (v1.isDense() && v2.isDense()) {
long[] resValues = newStorage.getValues();
long[] v1Values = v1.getStorage().getValues();
int[] v2Values = v2.getStorage().getValues();
for (int idx = 0; idx < resValues.length; idx++) {
resValues[idx] = op.apply(v1Values[idx], v2Values[idx]);
}
} else if (v1.isDense() && v2.isSparse()) {
long[] resValues = newStorage.getValues();
long[] v1Values = v1.getStorage().getValues();
ObjectIterator<Int2IntMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Int2IntMap.Entry entry = iter.next();
int idx = entry.getIntKey();
resValues[idx] = op.apply(v1Values[idx], entry.getIntValue());
}
} else if (v1.isDense() && v2.isSorted()) {
long[] resValues = newStorage.getValues();
long[] v1Values = v1.getStorage().getValues();
int[] v2Indices = v2.getStorage().getIndices();
int[] v2Values = v2.getStorage().getValues();
int size = v2.size();
for (int i = 0; i < size; i++) {
int idx = v2Indices[i];
resValues[idx] = op.apply(v1Values[idx], v2Values[i]);
}
} else if (v1.isSparse() && v2.isDense()) {
if (op.isKeepStorage()) {
int dim = v1.getDim();
int[] v2Values = v2.getStorage().getValues();
if (v1.size() < Constant.denseLoopThreshold * v1.getDim()) {
for (int i = 0; i < dim; i++) {
newStorage.set(i, op.apply(0, v2Values[i]));
}
ObjectIterator<Int2LongMap.Entry> iter = v1.getStorage().entryIterator();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(entry.getLongValue(), v2Values[idx]));
}
} else {
for (int i = 0; i < dim; i++) {
if (v1.getStorage().hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), v2Values[i]));
} else {
newStorage.set(i, op.apply(0, v2Values[i]));
}
}
}
} else {
long[] resValues = newStorage.getValues();
int[] v2Values = v2.getStorage().getValues();
if (v1.size() < Constant.denseLoopThreshold * v1.getDim()) {
for (int i = 0; i < resValues.length; i++) {
resValues[i] = op.apply(0, v2Values[i]);
}
ObjectIterator<Int2LongMap.Entry> iter = v1.getStorage().entryIterator();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
int idx = entry.getIntKey();
resValues[idx] = op.apply(entry.getLongValue(), v2Values[idx]);
}
} else {
for (int i = 0; i < resValues.length; i++) {
if (v1.getStorage().hasKey(i)) {
resValues[i] = op.apply(v1.get(i), v2Values[i]);
} else {
resValues[i] = op.apply(0, v2Values[i]);
}
}
}
}
} else if (v1.isSorted() && v2.isDense()) {
if (op.isKeepStorage()) {
int dim = v1.getDim();
int[] resIndices = newStorage.getIndices();
long[] resValues = newStorage.getValues();
int[] v2Values = v2.getStorage().getValues();
int[] v1Indices = v1.getStorage().getIndices();
long[] v1Values = v1.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 = v1Indices[i];
resValues[idx] = op.apply(v1Values[i], v2Values[idx]);
}
} else {
long[] resValues = newStorage.getValues();
int[] v2Values = v2.getStorage().getValues();
if (v1.size() < Constant.denseLoopThreshold * v1.getDim()) {
int[] v1Indices = v1.getStorage().getIndices();
long[] v1Values = v1.getStorage().getValues();
for (int i = 0; i < resValues.length; i++) {
resValues[i] = op.apply(0, v2Values[i]);
}
int size = v1.size();
for (int i = 0; i < size; i++) {
int idx = v1Indices[i];
resValues[idx] = op.apply(v1Values[i], v2Values[idx]);
}
} else {
IntLongVectorStorage v1Storage = v1.getStorage();
for (int i = 0; i < resValues.length; i++) {
if (v1Storage.hasKey(i)) {
resValues[i] = op.apply(v1.get(i), v2Values[i]);
} else {
resValues[i] = op.apply(0, v2Values[i]);
}
}
}
}
} else if (v1.isSparse() && v2.isSparse()) {
int v1Size = v1.size();
int v2Size = v2.size();
if (v1Size >= v2Size * Constant.sparseThreshold && (v1Size + v2Size) * Constant.intersectionCoeff <= Constant.sparseDenseStorageThreshold * v1.dim()) {
// we gauss the indices of v2 maybe is a subset of v1, or overlap is very large
ObjectIterator<Int2IntMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Int2IntMap.Entry entry = iter.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(v1.get(idx), entry.getIntValue()));
}
} else if ((v1Size + v2Size) * Constant.intersectionCoeff >= Constant.sparseDenseStorageThreshold * v1.dim()) {
// we gauss dense storage is more efficient
ObjectIterator<Int2LongMap.Entry> iter1 = v1.getStorage().entryIterator();
while (iter1.hasNext()) {
Int2LongMap.Entry entry = iter1.next();
int idx = entry.getIntKey();
newStorage.set(idx, entry.getLongValue());
}
ObjectIterator<Int2IntMap.Entry> iter2 = v2.getStorage().entryIterator();
while (iter2.hasNext()) {
Int2IntMap.Entry entry = iter2.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(v1.get(idx), entry.getIntValue()));
}
} else {
// to avoid multi-rehash
int capacity = 1 << (32 - Integer.numberOfLeadingZeros((int) (v1.size() / 0.75)));
if (v1.size() + v2.size() <= 1.5 * capacity) {
// no rehashor one onle rehash is required, nothing to optimization
ObjectIterator<Int2IntMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Int2IntMap.Entry entry = iter.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(v1.get(idx), entry.getIntValue()));
}
} else {
// multi-rehash
ObjectIterator<Int2LongMap.Entry> iter1 = v1.getStorage().entryIterator();
while (iter1.hasNext()) {
Int2LongMap.Entry entry = iter1.next();
int idx = entry.getIntKey();
newStorage.set(idx, entry.getLongValue());
}
ObjectIterator<Int2IntMap.Entry> iter2 = v2.getStorage().entryIterator();
while (iter2.hasNext()) {
Int2IntMap.Entry entry = iter2.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(v1.get(idx), entry.getIntValue()));
}
}
}
} else if (v1.isSparse() && v2.isSorted()) {
int v1Size = v1.size();
int v2Size = v2.size();
if (v1Size >= v2Size * Constant.sparseThreshold && (v1Size + v2Size) * Constant.intersectionCoeff <= Constant.sparseDenseStorageThreshold * v1.dim()) {
// we gauss the indices of v2 maybe is a subset of v1, or overlap is very large
int[] v2Indices = v2.getStorage().getIndices();
int[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < v2.size(); i++) {
int idx = v2Indices[i];
newStorage.set(idx, op.apply(v1.get(idx), v2Values[i]));
}
} else if ((v1Size + v2Size) * Constant.intersectionCoeff >= Constant.sparseDenseStorageThreshold * v1.dim()) {
ObjectIterator<Int2LongMap.Entry> iter1 = v1.getStorage().entryIterator();
while (iter1.hasNext()) {
Int2LongMap.Entry entry = iter1.next();
int idx = entry.getIntKey();
newStorage.set(idx, entry.getLongValue());
}
int[] v2Indices = v2.getStorage().getIndices();
int[] v2Values = v2.getStorage().getValues();
int size = v2.size();
for (int i = 0; i < size; i++) {
int idx = v2Indices[i];
newStorage.set(idx, op.apply(v1.get(idx), v2Values[i]));
}
} else {
// to avoid multi-rehash
int capacity = 1 << (32 - Integer.numberOfLeadingZeros((int) (v1.size() / 0.75)));
if (v1.size() + v2.size() <= 1.5 * capacity) {
int[] v2Indices = v2.getStorage().getIndices();
int[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < v2.size(); i++) {
int idx = v2Indices[i];
newStorage.set(idx, op.apply(v1.get(idx), v2Values[i]));
}
} else {
ObjectIterator<Int2LongMap.Entry> iter1 = v1.getStorage().entryIterator();
while (iter1.hasNext()) {
Int2LongMap.Entry entry = iter1.next();
int idx = entry.getIntKey();
newStorage.set(idx, entry.getLongValue());
}
int[] v2Indices = v2.getStorage().getIndices();
int[] v2Values = v2.getStorage().getValues();
int size = v2.size();
for (int i = 0; i < size; i++) {
int idx = v2Indices[i];
newStorage.set(idx, op.apply(v1.get(idx), v2Values[i]));
}
}
}
} else if (v1.isSorted() && v2.isSparse()) {
int v1Size = v1.size();
int v2Size = v2.size();
if ((v1Size + v2Size) * Constant.intersectionCoeff >= Constant.sortedDenseStorageThreshold * v1.dim()) {
if (op.isKeepStorage()) {
int[] v1Indices = v1.getStorage().getIndices();
int[] idxiter = v2.getStorage().indexIterator().toIntArray();
int[] indices = new int[(int) (v1Size + v2Size)];
System.arraycopy(v1Indices, 0, indices, 0, (int) v1.size());
System.arraycopy(idxiter, 0, indices, (int) v1.size(), (int) v2.size());
IntAVLTreeSet avl = new IntAVLTreeSet(indices);
IntBidirectionalIterator iter = avl.iterator();
long[] values = new long[indices.length];
int i = 0;
while (iter.hasNext()) {
int idx = iter.nextInt();
indices[i] = idx;
values[i] = op.apply(v1.get(idx), v2.get(idx));
i++;
}
while (i < indices.length) {
indices[i] = 0;
i++;
}
newStorage = new IntLongSortedVectorStorage(v1.getDim(), (int) avl.size(), indices, values);
} else {
int[] v1Indices = v1.getStorage().getIndices();
long[] v1Values = v1.getStorage().getValues();
int size = v1.size();
for (int i = 0; i < size; i++) {
int idx = v1Indices[i];
newStorage.set(idx, v1Values[i]);
}
ObjectIterator<Int2IntMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Int2IntMap.Entry entry = iter.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(newStorage.get(idx), entry.getIntValue()));
}
}
} else {
if (op.isKeepStorage()) {
int[] v1Indices = v1.getStorage().getIndices();
int[] idxiter = v2.getStorage().indexIterator().toIntArray();
int[] indices = new int[(int) (v1Size + v2Size)];
System.arraycopy(v1Indices, 0, indices, 0, (int) v1.size());
System.arraycopy(idxiter, 0, indices, (int) v1.size(), (int) v2.size());
IntAVLTreeSet avl = new IntAVLTreeSet(indices);
IntBidirectionalIterator iter = avl.iterator();
long[] values = new long[indices.length];
int i = 0;
while (iter.hasNext()) {
int idx = iter.nextInt();
indices[i] = idx;
values[i] = op.apply(v1.get(idx), v2.get(idx));
i++;
}
while (i < indices.length) {
indices[i] = 0;
i++;
}
newStorage = new IntLongSortedVectorStorage(v1.getDim(), (int) avl.size(), indices, values);
} else {
int[] v1Indices = v1.getStorage().getIndices();
long[] v1Values = v1.getStorage().getValues();
int size = v1.size();
for (int i = 0; i < size; i++) {
int idx = v1Indices[i];
newStorage.set(idx, v1Values[i]);
}
ObjectIterator<Int2IntMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Int2IntMap.Entry entry = iter.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(newStorage.get(idx), entry.getIntValue()));
}
}
}
} else if (v1.isSorted() && v2.isSorted()) {
int v1Pointor = 0;
int v2Pointor = 0;
int size1 = v1.size();
int size2 = v2.size();
int[] v1Indices = v1.getStorage().getIndices();
long[] v1Values = v1.getStorage().getValues();
int[] v2Indices = v2.getStorage().getIndices();
int[] v2Values = v2.getStorage().getValues();
if ((size1 + size2) * Constant.intersectionCoeff >= Constant.sortedDenseStorageThreshold * v1.dim()) {
if (op.isKeepStorage()) {
// sorted
int[] resIndices = newStorage.getIndices();
long[] resValues = newStorage.getValues();
int global = 0;
while (v1Pointor < size1 && v2Pointor < size2) {
if (v1Indices[v1Pointor] == v2Indices[v2Pointor]) {
resIndices[global] = v1Indices[v1Pointor];
resValues[global] = op.apply(v1Values[v1Pointor], v2Values[v2Pointor]);
global++;
v1Pointor++;
v2Pointor++;
} else if (v1Indices[v1Pointor] < v2Indices[v2Pointor]) {
resIndices[global] = v1Indices[v1Pointor];
resValues[global] = v1Values[v1Pointor];
global++;
v1Pointor++;
} else {
// v1Indices[v1Pointor] > v2Indices[v2Pointor]
resIndices[global] = v2Indices[v2Pointor];
resValues[global] = op.apply(0, v2Values[v2Pointor]);
global++;
v2Pointor++;
}
}
} else {
// dense
while (v1Pointor < size1 || v2Pointor < size2) {
if ((v1Pointor < size1 && v2Pointor < size2) && v1Indices[v1Pointor] == v2Indices[v2Pointor]) {
newStorage.set(v1Indices[v1Pointor], op.apply(v1Values[v1Pointor], v2Values[v2Pointor]));
v1Pointor++;
v2Pointor++;
} else if ((v1Pointor < size1 && v2Pointor < size2) && v1Indices[v1Pointor] < v2Indices[v2Pointor] || (v1Pointor < size1 && v2Pointor >= size2)) {
newStorage.set(v1Indices[v1Pointor], v1Values[v1Pointor]);
v1Pointor++;
} else if (((v1Pointor < size1 && v2Pointor < size2) && v1Indices[v1Pointor] >= v2Indices[v2Pointor]) || (v1Pointor >= size1 && v2Pointor < size2)) {
newStorage.set(v2Indices[v2Pointor], op.apply(0, v2Values[v2Pointor]));
v2Pointor++;
}
}
}
} else {
if (op.isKeepStorage()) {
int[] resIndices = newStorage.getIndices();
long[] resValues = newStorage.getValues();
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] = v1Values[v1Pointor];
v1Pointor++;
globalPointor++;
} else {
// v1Indices[v1Pointor] > v2Indices[v2Pointor]
resIndices[globalPointor] = v2Indices[v2Pointor];
resValues[globalPointor] = op.apply(0, v2Values[v2Pointor]);
v2Pointor++;
globalPointor++;
}
}
} else {
while (v1Pointor < size1 || v2Pointor < size2) {
if ((v1Pointor < size1 && v2Pointor < size2) && v1Indices[v1Pointor] == v2Indices[v2Pointor]) {
newStorage.set(v1Indices[v1Pointor], op.apply(v1Values[v1Pointor], v2Values[v2Pointor]));
v1Pointor++;
v2Pointor++;
} else if ((v1Pointor < size1 && v2Pointor < size2) && v1Indices[v1Pointor] < v2Indices[v2Pointor] || (v1Pointor < size1 && v2Pointor >= size2)) {
newStorage.set(v1Indices[v1Pointor], v1Values[v1Pointor]);
v1Pointor++;
} else if (((v1Pointor < size1 && v2Pointor < size2) && v1Indices[v1Pointor] >= v2Indices[v2Pointor]) || (v1Pointor >= size1 && v2Pointor < size2)) {
newStorage.set(v2Indices[v2Pointor], op.apply(0, v2Values[v2Pointor]));
v2Pointor++;
}
}
}
}
} else {
throw new AngelException("The operation is not support!");
}
return new IntLongVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
}
Also used :
AngelException(com.tencent.angel.exception.AngelException)
IntLongVector(com.tencent.angel.ml.math2.vector.IntLongVector)
IntLongSortedVectorStorage(com.tencent.angel.ml.math2.storage.IntLongSortedVectorStorage)
IntBidirectionalIterator(it.unimi.dsi.fastutil.ints.IntBidirectionalIterator)
ObjectIterator(it.unimi.dsi.fastutil.objects.ObjectIterator)
IntLongVectorStorage(com.tencent.angel.ml.math2.storage.IntLongVectorStorage)
Int2LongMap(it.unimi.dsi.fastutil.ints.Int2LongMap)
IntAVLTreeSet(it.unimi.dsi.fastutil.ints.IntAVLTreeSet)
Int2IntMap(it.unimi.dsi.fastutil.ints.Int2IntMap)
Example 32 with IntLongVector
use of com.tencent.angel.ml.math2.vector.IntLongVector in project angel by Tencent.
the class SimpleBinaryOutNonZAExecutor method apply.
public static Vector apply(IntDoubleVector v1, IntLongVector v2, Binary op) {
IntDoubleVectorStorage newStorage = (IntDoubleVectorStorage) StorageSwitch.apply(v1, v2, op);
if (v1.isDense() && v2.isDense()) {
double[] resValues = newStorage.getValues();
double[] v1Values = v1.getStorage().getValues();
long[] v2Values = v2.getStorage().getValues();
for (int idx = 0; idx < resValues.length; idx++) {
resValues[idx] = op.apply(v1Values[idx], v2Values[idx]);
}
} else if (v1.isDense() && v2.isSparse()) {
double[] resValues = newStorage.getValues();
double[] v1Values = v1.getStorage().getValues();
ObjectIterator<Int2LongMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
int idx = entry.getIntKey();
resValues[idx] = op.apply(v1Values[idx], entry.getLongValue());
}
} else if (v1.isDense() && v2.isSorted()) {
double[] resValues = newStorage.getValues();
double[] v1Values = v1.getStorage().getValues();
int[] v2Indices = v2.getStorage().getIndices();
long[] v2Values = v2.getStorage().getValues();
int size = v2.size();
for (int i = 0; i < size; i++) {
int idx = v2Indices[i];
resValues[idx] = op.apply(v1Values[idx], v2Values[i]);
}
} else if (v1.isSparse() && v2.isDense()) {
if (op.isKeepStorage()) {
int dim = v1.getDim();
long[] v2Values = v2.getStorage().getValues();
if (v1.size() < Constant.denseLoopThreshold * v1.getDim()) {
for (int i = 0; i < dim; i++) {
newStorage.set(i, op.apply(0, v2Values[i]));
}
ObjectIterator<Int2DoubleMap.Entry> iter = v1.getStorage().entryIterator();
while (iter.hasNext()) {
Int2DoubleMap.Entry entry = iter.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(entry.getDoubleValue(), v2Values[idx]));
}
} else {
for (int i = 0; i < dim; i++) {
if (v1.getStorage().hasKey(i)) {
newStorage.set(i, op.apply(v1.get(i), v2Values[i]));
} else {
newStorage.set(i, op.apply(0, v2Values[i]));
}
}
}
} else {
double[] resValues = newStorage.getValues();
long[] v2Values = v2.getStorage().getValues();
if (v1.size() < Constant.denseLoopThreshold * v1.getDim()) {
for (int i = 0; i < resValues.length; i++) {
resValues[i] = op.apply(0, v2Values[i]);
}
ObjectIterator<Int2DoubleMap.Entry> iter = v1.getStorage().entryIterator();
while (iter.hasNext()) {
Int2DoubleMap.Entry entry = iter.next();
int idx = entry.getIntKey();
resValues[idx] = op.apply(entry.getDoubleValue(), v2Values[idx]);
}
} else {
for (int i = 0; i < resValues.length; i++) {
if (v1.getStorage().hasKey(i)) {
resValues[i] = op.apply(v1.get(i), v2Values[i]);
} else {
resValues[i] = op.apply(0, v2Values[i]);
}
}
}
}
} else if (v1.isSorted() && v2.isDense()) {
if (op.isKeepStorage()) {
int dim = v1.getDim();
int[] resIndices = newStorage.getIndices();
double[] resValues = newStorage.getValues();
long[] v2Values = v2.getStorage().getValues();
int[] v1Indices = v1.getStorage().getIndices();
double[] v1Values = v1.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 = v1Indices[i];
resValues[idx] = op.apply(v1Values[i], v2Values[idx]);
}
} else {
double[] resValues = newStorage.getValues();
long[] v2Values = v2.getStorage().getValues();
if (v1.size() < Constant.denseLoopThreshold * v1.getDim()) {
int[] v1Indices = v1.getStorage().getIndices();
double[] v1Values = v1.getStorage().getValues();
for (int i = 0; i < resValues.length; i++) {
resValues[i] = op.apply(0, v2Values[i]);
}
int size = v1.size();
for (int i = 0; i < size; i++) {
int idx = v1Indices[i];
resValues[idx] = op.apply(v1Values[i], v2Values[idx]);
}
} else {
IntDoubleVectorStorage v1Storage = v1.getStorage();
for (int i = 0; i < resValues.length; i++) {
if (v1Storage.hasKey(i)) {
resValues[i] = op.apply(v1.get(i), v2Values[i]);
} else {
resValues[i] = op.apply(0, v2Values[i]);
}
}
}
}
} else if (v1.isSparse() && v2.isSparse()) {
int v1Size = v1.size();
int v2Size = v2.size();
if (v1Size >= v2Size * Constant.sparseThreshold && (v1Size + v2Size) * Constant.intersectionCoeff <= Constant.sparseDenseStorageThreshold * v1.dim()) {
// we gauss the indices of v2 maybe is a subset of v1, or overlap is very large
ObjectIterator<Int2LongMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(v1.get(idx), entry.getLongValue()));
}
} else if ((v1Size + v2Size) * Constant.intersectionCoeff >= Constant.sparseDenseStorageThreshold * v1.dim()) {
// we gauss dense storage is more efficient
ObjectIterator<Int2DoubleMap.Entry> iter1 = v1.getStorage().entryIterator();
while (iter1.hasNext()) {
Int2DoubleMap.Entry entry = iter1.next();
int idx = entry.getIntKey();
newStorage.set(idx, entry.getDoubleValue());
}
ObjectIterator<Int2LongMap.Entry> iter2 = v2.getStorage().entryIterator();
while (iter2.hasNext()) {
Int2LongMap.Entry entry = iter2.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(v1.get(idx), entry.getLongValue()));
}
} else {
// to avoid multi-rehash
int capacity = 1 << (32 - Integer.numberOfLeadingZeros((int) (v1.size() / 0.75)));
if (v1.size() + v2.size() <= 1.5 * capacity) {
// no rehashor one onle rehash is required, nothing to optimization
ObjectIterator<Int2LongMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(v1.get(idx), entry.getLongValue()));
}
} else {
// multi-rehash
ObjectIterator<Int2DoubleMap.Entry> iter1 = v1.getStorage().entryIterator();
while (iter1.hasNext()) {
Int2DoubleMap.Entry entry = iter1.next();
int idx = entry.getIntKey();
newStorage.set(idx, entry.getDoubleValue());
}
ObjectIterator<Int2LongMap.Entry> iter2 = v2.getStorage().entryIterator();
while (iter2.hasNext()) {
Int2LongMap.Entry entry = iter2.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(v1.get(idx), entry.getLongValue()));
}
}
}
} else if (v1.isSparse() && v2.isSorted()) {
int v1Size = v1.size();
int v2Size = v2.size();
if (v1Size >= v2Size * Constant.sparseThreshold && (v1Size + v2Size) * Constant.intersectionCoeff <= Constant.sparseDenseStorageThreshold * v1.dim()) {
// we gauss the indices of v2 maybe is a subset of v1, or overlap is very large
int[] v2Indices = v2.getStorage().getIndices();
long[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < v2.size(); i++) {
int idx = v2Indices[i];
newStorage.set(idx, op.apply(v1.get(idx), v2Values[i]));
}
} else if ((v1Size + v2Size) * Constant.intersectionCoeff >= Constant.sparseDenseStorageThreshold * v1.dim()) {
ObjectIterator<Int2DoubleMap.Entry> iter1 = v1.getStorage().entryIterator();
while (iter1.hasNext()) {
Int2DoubleMap.Entry entry = iter1.next();
int idx = entry.getIntKey();
newStorage.set(idx, entry.getDoubleValue());
}
int[] v2Indices = v2.getStorage().getIndices();
long[] v2Values = v2.getStorage().getValues();
int size = v2.size();
for (int i = 0; i < size; i++) {
int idx = v2Indices[i];
newStorage.set(idx, op.apply(v1.get(idx), v2Values[i]));
}
} else {
// to avoid multi-rehash
int capacity = 1 << (32 - Integer.numberOfLeadingZeros((int) (v1.size() / 0.75)));
if (v1.size() + v2.size() <= 1.5 * capacity) {
int[] v2Indices = v2.getStorage().getIndices();
long[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < v2.size(); i++) {
int idx = v2Indices[i];
newStorage.set(idx, op.apply(v1.get(idx), v2Values[i]));
}
} else {
ObjectIterator<Int2DoubleMap.Entry> iter1 = v1.getStorage().entryIterator();
while (iter1.hasNext()) {
Int2DoubleMap.Entry entry = iter1.next();
int idx = entry.getIntKey();
newStorage.set(idx, entry.getDoubleValue());
}
int[] v2Indices = v2.getStorage().getIndices();
long[] v2Values = v2.getStorage().getValues();
int size = v2.size();
for (int i = 0; i < size; i++) {
int idx = v2Indices[i];
newStorage.set(idx, op.apply(v1.get(idx), v2Values[i]));
}
}
}
} else if (v1.isSorted() && v2.isSparse()) {
int v1Size = v1.size();
int v2Size = v2.size();
if ((v1Size + v2Size) * Constant.intersectionCoeff >= Constant.sortedDenseStorageThreshold * v1.dim()) {
if (op.isKeepStorage()) {
int[] v1Indices = v1.getStorage().getIndices();
int[] idxiter = v2.getStorage().indexIterator().toIntArray();
int[] indices = new int[(int) (v1Size + v2Size)];
System.arraycopy(v1Indices, 0, indices, 0, (int) v1.size());
System.arraycopy(idxiter, 0, indices, (int) v1.size(), (int) v2.size());
IntAVLTreeSet avl = new IntAVLTreeSet(indices);
IntBidirectionalIterator iter = avl.iterator();
double[] values = new double[indices.length];
int i = 0;
while (iter.hasNext()) {
int idx = iter.nextInt();
indices[i] = idx;
values[i] = op.apply(v1.get(idx), v2.get(idx));
i++;
}
while (i < indices.length) {
indices[i] = 0;
i++;
}
newStorage = new IntDoubleSortedVectorStorage(v1.getDim(), (int) avl.size(), indices, values);
} else {
int[] v1Indices = v1.getStorage().getIndices();
double[] v1Values = v1.getStorage().getValues();
int size = v1.size();
for (int i = 0; i < size; i++) {
int idx = v1Indices[i];
newStorage.set(idx, v1Values[i]);
}
ObjectIterator<Int2LongMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(newStorage.get(idx), entry.getLongValue()));
}
}
} else {
if (op.isKeepStorage()) {
int[] v1Indices = v1.getStorage().getIndices();
int[] idxiter = v2.getStorage().indexIterator().toIntArray();
int[] indices = new int[(int) (v1Size + v2Size)];
System.arraycopy(v1Indices, 0, indices, 0, (int) v1.size());
System.arraycopy(idxiter, 0, indices, (int) v1.size(), (int) v2.size());
IntAVLTreeSet avl = new IntAVLTreeSet(indices);
IntBidirectionalIterator iter = avl.iterator();
double[] values = new double[indices.length];
int i = 0;
while (iter.hasNext()) {
int idx = iter.nextInt();
indices[i] = idx;
values[i] = op.apply(v1.get(idx), v2.get(idx));
i++;
}
while (i < indices.length) {
indices[i] = 0;
i++;
}
newStorage = new IntDoubleSortedVectorStorage(v1.getDim(), (int) avl.size(), indices, values);
} else {
int[] v1Indices = v1.getStorage().getIndices();
double[] v1Values = v1.getStorage().getValues();
int size = v1.size();
for (int i = 0; i < size; i++) {
int idx = v1Indices[i];
newStorage.set(idx, v1Values[i]);
}
ObjectIterator<Int2LongMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
int idx = entry.getIntKey();
newStorage.set(idx, op.apply(newStorage.get(idx), entry.getLongValue()));
}
}
}
} else if (v1.isSorted() && v2.isSorted()) {
int v1Pointor = 0;
int v2Pointor = 0;
int size1 = v1.size();
int size2 = v2.size();
int[] v1Indices = v1.getStorage().getIndices();
double[] v1Values = v1.getStorage().getValues();
int[] v2Indices = v2.getStorage().getIndices();
long[] v2Values = v2.getStorage().getValues();
if ((size1 + size2) * Constant.intersectionCoeff >= Constant.sortedDenseStorageThreshold * v1.dim()) {
if (op.isKeepStorage()) {
// sorted
int[] resIndices = newStorage.getIndices();
double[] resValues = newStorage.getValues();
int global = 0;
while (v1Pointor < size1 && v2Pointor < size2) {
if (v1Indices[v1Pointor] == v2Indices[v2Pointor]) {
resIndices[global] = v1Indices[v1Pointor];
resValues[global] = op.apply(v1Values[v1Pointor], v2Values[v2Pointor]);
global++;
v1Pointor++;
v2Pointor++;
} else if (v1Indices[v1Pointor] < v2Indices[v2Pointor]) {
resIndices[global] = v1Indices[v1Pointor];
resValues[global] = v1Values[v1Pointor];
global++;
v1Pointor++;
} else {
// v1Indices[v1Pointor] > v2Indices[v2Pointor]
resIndices[global] = v2Indices[v2Pointor];
resValues[global] = op.apply(0, v2Values[v2Pointor]);
global++;
v2Pointor++;
}
}
} else {
// dense
while (v1Pointor < size1 || v2Pointor < size2) {
if ((v1Pointor < size1 && v2Pointor < size2) && v1Indices[v1Pointor] == v2Indices[v2Pointor]) {
newStorage.set(v1Indices[v1Pointor], op.apply(v1Values[v1Pointor], v2Values[v2Pointor]));
v1Pointor++;
v2Pointor++;
} else if ((v1Pointor < size1 && v2Pointor < size2) && v1Indices[v1Pointor] < v2Indices[v2Pointor] || (v1Pointor < size1 && v2Pointor >= size2)) {
newStorage.set(v1Indices[v1Pointor], v1Values[v1Pointor]);
v1Pointor++;
} else if (((v1Pointor < size1 && v2Pointor < size2) && v1Indices[v1Pointor] >= v2Indices[v2Pointor]) || (v1Pointor >= size1 && v2Pointor < size2)) {
newStorage.set(v2Indices[v2Pointor], op.apply(0, v2Values[v2Pointor]));
v2Pointor++;
}
}
}
} else {
if (op.isKeepStorage()) {
int[] resIndices = newStorage.getIndices();
double[] resValues = newStorage.getValues();
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] = v1Values[v1Pointor];
v1Pointor++;
globalPointor++;
} else {
// v1Indices[v1Pointor] > v2Indices[v2Pointor]
resIndices[globalPointor] = v2Indices[v2Pointor];
resValues[globalPointor] = op.apply(0, v2Values[v2Pointor]);
v2Pointor++;
globalPointor++;
}
}
} else {
while (v1Pointor < size1 || v2Pointor < size2) {
if ((v1Pointor < size1 && v2Pointor < size2) && v1Indices[v1Pointor] == v2Indices[v2Pointor]) {
newStorage.set(v1Indices[v1Pointor], op.apply(v1Values[v1Pointor], v2Values[v2Pointor]));
v1Pointor++;
v2Pointor++;
} else if ((v1Pointor < size1 && v2Pointor < size2) && v1Indices[v1Pointor] < v2Indices[v2Pointor] || (v1Pointor < size1 && v2Pointor >= size2)) {
newStorage.set(v1Indices[v1Pointor], v1Values[v1Pointor]);
v1Pointor++;
} else if (((v1Pointor < size1 && v2Pointor < size2) && v1Indices[v1Pointor] >= v2Indices[v2Pointor]) || (v1Pointor >= size1 && v2Pointor < size2)) {
newStorage.set(v2Indices[v2Pointor], op.apply(0, v2Values[v2Pointor]));
v2Pointor++;
}
}
}
}
} else {
throw new AngelException("The operation is not support!");
}
return new IntDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
}
Also used :
AngelException(com.tencent.angel.exception.AngelException)
Int2DoubleMap(it.unimi.dsi.fastutil.ints.Int2DoubleMap)
IntBidirectionalIterator(it.unimi.dsi.fastutil.ints.IntBidirectionalIterator)
ObjectIterator(it.unimi.dsi.fastutil.objects.ObjectIterator)
IntDoubleVector(com.tencent.angel.ml.math2.vector.IntDoubleVector)
IntDoubleVectorStorage(com.tencent.angel.ml.math2.storage.IntDoubleVectorStorage)
Int2LongMap(it.unimi.dsi.fastutil.ints.Int2LongMap)
IntAVLTreeSet(it.unimi.dsi.fastutil.ints.IntAVLTreeSet)
IntDoubleSortedVectorStorage(com.tencent.angel.ml.math2.storage.IntDoubleSortedVectorStorage)
Example 33 with IntLongVector
use of com.tencent.angel.ml.math2.vector.IntLongVector in project angel by Tencent.
the class SimpleUnaryExecutor method apply.
private static Vector apply(IntLongVector v1, Unary op) {
IntLongVector res;
if (op.isOrigin() || v1.isDense()) {
if (!op.isInplace()) {
res = v1.copy();
} else {
res = v1;
}
if (v1.isDense()) {
long[] values = res.getStorage().getValues();
for (int i = 0; i < values.length; i++) {
values[i] = op.apply(values[i]);
}
} else if (v1.isSparse()) {
ObjectIterator<Int2LongMap.Entry> iter = res.getStorage().entryIterator();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
entry.setValue(op.apply(entry.getLongValue()));
}
} else if (v1.isSorted()) {
long[] values = res.getStorage().getValues();
for (int i = 0; i < v1.size(); i++) {
values[i] = op.apply(values[i]);
}
} else {
throw new AngelException("The operation is not support!");
}
} else {
IntLongVectorStorage newstorage = v1.getStorage().emptyDense();
IntLongVectorStorage storage = v1.getStorage();
long[] values = newstorage.getValues();
long tmp = op.apply((long) 0);
int dim = v1.getDim();
for (int i = 0; i < dim; i++) {
values[i] = tmp;
}
if (v1.isSparse()) {
ObjectIterator<Int2LongMap.Entry> iter = storage.entryIterator();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
values[entry.getIntKey()] = op.apply(entry.getLongValue());
}
} else {
// sort
int[] idxs = storage.getIndices();
long[] v1Values = storage.getValues();
for (int k = 0; k < idxs.length; k++) {
values[idxs[k]] = op.apply(v1Values[k]);
}
}
if (op.isInplace()) {
v1.setStorage(newstorage);
res = v1;
} else {
res = new IntLongVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newstorage);
}
}
return res;
}
Also used :
IntLongVector(com.tencent.angel.ml.math2.vector.IntLongVector)
AngelException(com.tencent.angel.exception.AngelException)
IntLongVectorStorage(com.tencent.angel.ml.math2.storage.IntLongVectorStorage)
Int2LongMap(it.unimi.dsi.fastutil.ints.Int2LongMap)
ObjectIterator(it.unimi.dsi.fastutil.objects.ObjectIterator)
Example 34 with IntLongVector
use of com.tencent.angel.ml.math2.vector.IntLongVector in project angel by Tencent.
the class MergeUtils method combineIntLongIndexRowSplits.
// //////////////////////////////////////////////////////////////////////////////
// Combine Int key Long value vector
// //////////////////////////////////////////////////////////////////////////////
public static Vector combineIntLongIndexRowSplits(int matrixId, int rowId, int resultSize, KeyPart[] keyParts, ValuePart[] valueParts, MatrixMeta matrixMeta) {
IntLongVector vector = VFactory.sparseLongVector((int) matrixMeta.getColNum(), resultSize);
for (int i = 0; i < keyParts.length; i++) {
mergeTo(vector, keyParts[i], (LongValuesPart) valueParts[i]);
}
vector.setRowId(rowId);
vector.setMatrixId(matrixId);
return vector;
}
Also used :
IntLongVector(com.tencent.angel.ml.math2.vector.IntLongVector)
Example 35 with IntLongVector
use of com.tencent.angel.ml.math2.vector.IntLongVector in project angel by Tencent.
the class UpdatePSFTest method testSparseLongUDF.
public void testSparseLongUDF() throws Exception {
Worker worker = LocalClusterContext.get().getWorker(workerAttempt0Id).getWorker();
MatrixClient client1 = worker.getPSAgent().getMatrixClient(SPARSE_LONG_MAT, 0);
int matrixW1Id = client1.getMatrixId();
int[] index = genIndexs(feaNum, nnz);
IntLongVector deltaVec = new IntLongVector(feaNum, new IntLongSparseVectorStorage(feaNum, nnz));
for (int i = 0; i < index.length; i++) {
deltaVec.set(index[i], index[i]);
}
// for (int i = 0; i < feaNum; i++) {
// deltaVec.set(i, i);
// }
deltaVec.setRowId(0);
Vector[] updates = new Vector[1];
updates[0] = deltaVec;
client1.asyncUpdate(new IncrementRows(new IncrementRowsParam(matrixW1Id, updates))).get();
IntLongVector row = (IntLongVector) client1.getRow(0);
for (int id : index) {
// System.out.println("id=" + id + ", value=" + row.get(id));
Assert.assertTrue(row.get(id) == deltaVec.get(id));
}
Assert.assertTrue(index.length == row.size());
}
Also used :
IntLongVector(com.tencent.angel.ml.math2.vector.IntLongVector)
IntLongSparseVectorStorage(com.tencent.angel.ml.math2.storage.IntLongSparseVectorStorage)
IncrementRowsParam(com.tencent.angel.ml.matrix.psf.update.update.IncrementRowsParam)
IncrementRows(com.tencent.angel.ml.matrix.psf.update.update.IncrementRows)
Worker(com.tencent.angel.worker.Worker)
MatrixClient(com.tencent.angel.psagent.matrix.MatrixClient)
IntLongVector(com.tencent.angel.ml.math2.vector.IntLongVector)
LongIntVector(com.tencent.angel.ml.math2.vector.LongIntVector)
Vector(com.tencent.angel.ml.math2.vector.Vector)
LongFloatVector(com.tencent.angel.ml.math2.vector.LongFloatVector)
IntDoubleVector(com.tencent.angel.ml.math2.vector.IntDoubleVector)
IntFloatVector(com.tencent.angel.ml.math2.vector.IntFloatVector)
LongDoubleVector(com.tencent.angel.ml.math2.vector.LongDoubleVector)
LongLongVector(com.tencent.angel.ml.math2.vector.LongLongVector)
IntIntVector(com.tencent.angel.ml.math2.vector.IntIntVector)
Aggregations
ObjectIterator (it.unimi.dsi.fastutil.objects.ObjectIterator)55
Int2LongMap (it.unimi.dsi.fastutil.ints.Int2LongMap)52
IntLongVector (com.tencent.angel.ml.math2.vector.IntLongVector)47
IntLongVectorStorage (com.tencent.angel.ml.math2.storage.IntLongVectorStorage)45
IntDoubleVectorStorage (com.tencent.angel.ml.math2.storage.IntDoubleVectorStorage)33
IntFloatVectorStorage (com.tencent.angel.ml.math2.storage.IntFloatVectorStorage)33
IntIntVectorStorage (com.tencent.angel.ml.math2.storage.IntIntVectorStorage)32
LongDoubleVectorStorage (com.tencent.angel.ml.math2.storage.LongDoubleVectorStorage)30
LongFloatVectorStorage (com.tencent.angel.ml.math2.storage.LongFloatVectorStorage)30
LongIntVectorStorage (com.tencent.angel.ml.math2.storage.LongIntVectorStorage)30
LongLongVectorStorage (com.tencent.angel.ml.math2.storage.LongLongVectorStorage)30
Storage (com.tencent.angel.ml.math2.storage.Storage)30
IntLongSparseVectorStorage (com.tencent.angel.ml.math2.storage.IntLongSparseVectorStorage)25
CompIntLongVector (com.tencent.angel.ml.math2.vector.CompIntLongVector)25
IntLongSortedVectorStorage (com.tencent.angel.ml.math2.storage.IntLongSortedVectorStorage)24
IntDoubleSortedVectorStorage (com.tencent.angel.ml.math2.storage.IntDoubleSortedVectorStorage)21
IntFloatSortedVectorStorage (com.tencent.angel.ml.math2.storage.IntFloatSortedVectorStorage)21
IntDoubleSparseVectorStorage (com.tencent.angel.ml.math2.storage.IntDoubleSparseVectorStorage)20
IntFloatSparseVectorStorage (com.tencent.angel.ml.math2.storage.IntFloatSparseVectorStorage)20
IntIntSortedVectorStorage (com.tencent.angel.ml.math2.storage.IntIntSortedVectorStorage)20
