use of com.tencent.angel.ml.math2.storage.IntLongVectorStorage in project angel by Tencent.
the class IntLongVector method argmin.
public int argmin() {
IntLongVectorStorage idstorage = (IntLongVectorStorage) storage;
if (idstorage.size() == 0)
return -1;
long minval = Long.MAX_VALUE;
int minidx = -1;
if (idstorage.isDense()) {
long[] val = idstorage.getValues();
int length = val.length;
for (int idx = 0; idx < length; idx++) {
if (val[idx] < minval) {
minval = val[idx];
minidx = idx;
}
}
} else if (idstorage.isSparse()) {
ObjectIterator<Int2LongMap.Entry> iter = idstorage.entryIterator();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
int idx = entry.getIntKey();
long val = entry.getLongValue();
if (val < minval) {
minval = val;
minidx = idx;
}
}
} else {
int[] indices = idstorage.getIndices();
long[] val = idstorage.getValues();
int size = idstorage.size();
for (int i = 0; i < size; i++) {
int idx = indices[i];
if (val[i] < minval) {
minval = val[i];
minidx = idx;
}
}
}
return minidx;
}
use of com.tencent.angel.ml.math2.storage.IntLongVectorStorage in project angel by Tencent.
the class IntLongVector method numZeros.
public int numZeros() {
IntLongVectorStorage dstorage = (IntLongVectorStorage) storage;
if (dstorage.size() == 0)
return (int) dim;
int numZero = 0;
if (dstorage.isSparse()) {
LongIterator iter = dstorage.valueIterator();
while (iter.hasNext()) {
if (iter.nextLong() != 0) {
numZero += 1;
}
}
} else {
for (long val : dstorage.getValues()) {
if (val != 0) {
numZero += 1;
}
}
}
return (int) getDim() - numZero;
}
use of com.tencent.angel.ml.math2.storage.IntLongVectorStorage in project angel by Tencent.
the class CompIntLongRowUpdateSplit method serialize.
@Override
public void serialize(ByteBuf buf) {
super.serialize(buf);
IntLongVectorStorage storage = split.getStorage();
if (storage instanceof IntLongSparseVectorStorage) {
buf.writeInt(storage.size());
ObjectIterator<Int2LongMap.Entry> iter = storage.entryIterator();
Int2LongMap.Entry entry;
while (iter.hasNext()) {
entry = iter.next();
buf.writeInt(entry.getIntKey());
buf.writeLong(entry.getLongValue());
}
} else if (storage instanceof IntLongSortedVectorStorage) {
buf.writeInt(storage.size());
int[] indices = storage.getIndices();
long[] values = storage.getValues();
for (int i = 0; i < indices.length; i++) {
buf.writeInt(indices[i]);
buf.writeLong(values[i]);
}
} else if (storage instanceof IntLongDenseVectorStorage) {
long[] values = storage.getValues();
int writeSize = values.length < maxItemNum ? values.length : maxItemNum;
buf.writeInt(writeSize);
for (int i = 0; i < writeSize; i++) {
buf.writeLong(values[i]);
}
} else {
throw new UnsupportedOperationException("unsupport split for storage " + storage.getClass().getName());
}
}
use of com.tencent.angel.ml.math2.storage.IntLongVectorStorage in project angel by Tencent.
the class MixedBinaryOutZAExecutor method apply.
private static Vector apply(CompIntLongVector v1, IntLongVector v2, Binary op) {
IntLongVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v2.isDense()) {
int base = 0;
long[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < parts.length; i++) {
IntLongVector part = parts[i];
IntLongVectorStorage resPart = (IntLongVectorStorage) resParts[i];
if (part.isDense()) {
long[] resPartValues = resPart.getValues();
long[] partValues = part.getStorage().getValues();
for (int j = 0; j < resPartValues.length; j++) {
resPartValues[j] = op.apply(partValues[j], v2Values[base + j]);
}
} else if (part.isSparse()) {
ObjectIterator<Int2LongMap.Entry> iter = part.getStorage().entryIterator();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
int idx = entry.getIntKey();
resPart.set(idx, op.apply(entry.getLongValue(), v2Values[idx + base]));
}
} else {
// sorted
if (op.isKeepStorage()) {
int[] resPartIndices = resPart.getIndices();
long[] resPartValues = resPart.getValues();
int[] partIndices = part.getStorage().getIndices();
long[] 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();
long[] 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<Int2LongMap.Entry> iter = v2.getStorage().entryIterator();
if (v1.size() > v2.size()) {
int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
while (iter.hasNext()) {
Int2LongMap.Entry entry = iter.next();
int idx = entry.getIntKey();
int pidx = (int) (idx / subDim);
int subidx = idx % subDim;
if (parts[pidx].hasKey(subidx)) {
((IntLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), entry.getLongValue()));
}
}
} else {
int base = 0;
for (int i = 0; i < parts.length; i++) {
IntLongVector part = parts[i];
IntLongVectorStorage resPart = (IntLongVectorStorage) 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<Int2LongMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Int2LongMap.Entry entry = piter.next();
int idx = entry.getIntKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getLongValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
int[] partIndices = part.getStorage().getIndices();
long[] partValues = part.getStorage().getValues();
int[] resPartIndices = resPart.getIndices();
long[] 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();
long[] 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();
long[] 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)) {
((IntLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2Values[i]));
}
}
} else {
int base = 0;
for (int i = 0; i < parts.length; i++) {
IntLongVector part = parts[i];
IntLongVectorStorage resPart = (IntLongVectorStorage) 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<Int2LongMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Int2LongMap.Entry entry = piter.next();
int idx = entry.getIntKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getLongValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
int[] partIndices = part.getStorage().getIndices();
long[] partValues = part.getStorage().getValues();
int[] resPartIndices = resPart.getIndices();
long[] 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();
long[] 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();
}
}
}
IntLongVector[] res = new IntLongVector[parts.length];
int i = 0;
for (IntLongVector part : parts) {
res[i] = new IntLongVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntLongVectorStorage) resParts[i]);
i++;
}
return new CompIntLongVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}
use of com.tencent.angel.ml.math2.storage.IntLongVectorStorage in project angel by Tencent.
the class MixedBinaryOutZAExecutor method apply.
private static Vector apply(CompIntLongVector v1, IntDummyVector v2, Binary op) {
IntLongVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v1.size() > v2.size()) {
int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
int[] v2Indices = v2.getIndices();
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)) {
((IntLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 1));
}
}
} else {
int base = 0;
for (int i = 0; i < parts.length; i++) {
IntLongVector part = parts[i];
IntLongVectorStorage resPart = (IntLongVectorStorage) 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<Int2LongMap.Entry> piter = part.getStorage().entryIterator();
while (piter.hasNext()) {
Int2LongMap.Entry entry = piter.next();
int idx = entry.getIntKey();
if (v2.hasKey(idx + base)) {
resPart.set(idx, op.apply(entry.getLongValue(), v2.get(idx + base)));
}
}
} else {
// sorted
if (op.isKeepStorage()) {
int[] partIndices = part.getStorage().getIndices();
long[] partValues = part.getStorage().getValues();
int[] resPartIndices = resPart.getIndices();
long[] 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();
long[] 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();
}
}
IntLongVector[] res = new IntLongVector[parts.length];
int i = 0;
for (IntLongVector part : parts) {
res[i] = new IntLongVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntLongVectorStorage) resParts[i]);
i++;
}
return new CompIntLongVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}
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