Examples with IntIntVector com.tencent.angel.ml.math2.vector.IntIntVector used on opensource projects

Example 11 with IntIntVector

use of com.tencent.angel.ml.math2.vector.IntIntVector in project angel by Tencent.

the class MixedBinaryInAllExecutor method apply.

private static Vector apply(CompIntFloatVector v1, IntIntVector v2, Binary op) {
    IntFloatVector[] parts = v1.getPartitions();
    Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
    if (v2.isDense()) {
        int[] v2Values = v2.getStorage().getValues();
        int base = 0, k = 0;
        for (IntFloatVector part : parts) {
            IntFloatVectorStorage resPart = (IntFloatVectorStorage) resParts[k];
            if (part.isDense()) {
                float[] partValue = part.getStorage().getValues();
                float[] 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()) {
                float[] 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<Int2FloatMap.Entry> iter = part.getStorage().entryIterator();
                    while (iter.hasNext()) {
                        Int2FloatMap.Entry entry = iter.next();
                        int idx = entry.getIntKey();
                        resPart.set(idx, op.apply(entry.getFloatValue(), 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();
                float[] resPartValues = resPart.getValues();
                if (op.isKeepStorage()) {
                    if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
                        int[] partIndices = part.getStorage().getIndices();
                        float[] 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 {
                        IntFloatVectorStorage 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();
                        float[] 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 {
                        IntFloatVectorStorage 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 IntFloatSortedVectorStorage) {
                    resParts[i] = new IntFloatSparseVectorStorage(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)) {
                ((IntFloatVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
            } else {
                ((IntFloatVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 0));
            }
        }
    }
    IntFloatVector[] res = new IntFloatVector[parts.length];
    int i = 0;
    for (IntFloatVector part : parts) {
        res[i] = new IntFloatVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntFloatVectorStorage) resParts[i]);
        i++;
    }
    v1.setPartitions(res);
    return v1;
}

Example 12 with IntIntVector

use of com.tencent.angel.ml.math2.vector.IntIntVector 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;
}

Example 13 with IntIntVector

use of com.tencent.angel.ml.math2.vector.IntIntVector in project angel by Tencent.

the class BinaryMatrixExecutor method apply.

private static Matrix apply(BlasDoubleMatrix mat, IntIntVector v, boolean onCol, Binary op) {
    double[] data = mat.getData();
    int m = mat.getNumRows(), n = mat.getNumCols();
    int size = v.size();
    byte[] flag = null;
    if (!v.isDense()) {
        flag = new byte[v.getDim()];
    }
    if (onCol && op.isInplace()) {
        if (v.isDense()) {
            int[] values = v.getStorage().getValues();
            for (int i = 0; i < m; i++) {
                int value = values[i];
                for (int j = 0; j < n; j++) {
                    data[i * n + j] = op.apply(data[i * n + j], value);
                }
            }
        } else if (v.isSparse()) {
            ObjectIterator<Int2IntMap.Entry> iter = v.getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2IntMap.Entry entry = iter.next();
                int i = entry.getIntKey();
                flag[i] = 1;
                double value = entry.getIntValue();
                for (int j = 0; j < n; j++) {
                    data[i * n + j] = op.apply(data[i * n + j], value);
                }
            }
        } else {
            // sorted
            int[] idxs = v.getStorage().getIndices();
            int[] values = v.getStorage().getValues();
            for (int k = 0; k < size; k++) {
                int i = idxs[k];
                flag[i] = 1;
                int value = values[k];
                for (int j = 0; j < n; j++) {
                    data[i * n + j] = op.apply(data[i * n + j], value);
                }
            }
        }
        if (!v.isDense()) {
            switch(op.getOpType()) {
                case INTERSECTION:
                    for (int i = 0; i < m; i++) {
                        if (flag[i] == 0) {
                            for (int j = 0; j < n; j++) {
                                data[i * n + j] = 0;
                            }
                        }
                    }
                case UNION:
                    break;
                case ALL:
                    for (int i = 0; i < m; i++) {
                        if (flag[i] == 0) {
                            for (int j = 0; j < n; j++) {
                                data[i * n + j] = op.apply(data[i * n + j], 0);
                            }
                        }
                    }
            }
        }
        return mat;
    } else if (onCol && !op.isInplace()) {
        double[] newData;
        if (op.getOpType() == INTERSECTION) {
            newData = new double[m * n];
        } else {
            newData = ArrayCopy.copy(data);
        }
        if (v.isDense()) {
            int[] values = v.getStorage().getValues();
            for (int i = 0; i < m; i++) {
                int value = values[i];
                for (int j = 0; j < n; j++) {
                    newData[i * n + j] = op.apply(data[i * n + j], value);
                }
            }
        } else if (v.isSparse()) {
            ObjectIterator<Int2IntMap.Entry> iter = v.getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2IntMap.Entry entry = iter.next();
                int i = entry.getIntKey();
                flag[i] = 1;
                double value = entry.getIntValue();
                for (int j = 0; j < n; j++) {
                    newData[i * n + j] = op.apply(data[i * n + j], value);
                }
            }
        } else {
            // sorted
            int[] idxs = v.getStorage().getIndices();
            int[] values = v.getStorage().getValues();
            for (int k = 0; k < size; k++) {
                int i = idxs[k];
                flag[i] = 1;
                int value = values[k];
                for (int j = 0; j < n; j++) {
                    newData[i * n + j] = op.apply(data[i * n + j], value);
                }
            }
        }
        if (!v.isDense()) {
            switch(op.getOpType()) {
                case INTERSECTION:
                    break;
                case UNION:
                    break;
                case ALL:
                    for (int i = 0; i < m; i++) {
                        if (flag[i] == 0) {
                            for (int j = 0; j < n; j++) {
                                newData[i * n + j] = op.apply(data[i * n + j], 0);
                            }
                        }
                    }
            }
        }
        return new BlasDoubleMatrix(mat.getMatrixId(), mat.getClock(), m, n, newData);
    } else if (!onCol && op.isInplace()) {
        if (v.isDense()) {
            int[] values = v.getStorage().getValues();
            for (int i = 0; i < m; i++) {
                for (int j = 0; j < n; j++) {
                    data[i * n + j] = op.apply(data[i * n + j], values[j]);
                }
            }
        } else if (v.isSparse()) {
            ObjectIterator<Int2IntMap.Entry> iter = v.getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2IntMap.Entry entry = iter.next();
                int j = entry.getIntKey();
                double value = entry.getIntValue();
                flag[j] = 1;
                for (int i = 0; i < m; i++) {
                    data[i * n + j] = op.apply(data[i * n + j], value);
                }
            }
        } else {
            // sorted
            int[] idxs = v.getStorage().getIndices();
            int[] values = v.getStorage().getValues();
            for (int k = 0; k < size; k++) {
                int j = idxs[k];
                int value = values[k];
                flag[j] = 1;
                for (int i = 0; i < m; i++) {
                    data[i * n + j] = op.apply(data[i * n + j], value);
                }
            }
        }
        if (!v.isDense()) {
            switch(op.getOpType()) {
                case INTERSECTION:
                    for (int j = 0; j < n; j++) {
                        if (flag[j] == 0) {
                            for (int i = 0; i < m; i++) {
                                data[i * n + j] = 0;
                            }
                        }
                    }
                case UNION:
                    break;
                case ALL:
                    for (int j = 0; j < n; j++) {
                        if (flag[j] == 0) {
                            for (int i = 0; i < m; i++) {
                                data[i * n + j] = op.apply(data[i * n + j], 0);
                            }
                        }
                    }
            }
        }
        return mat;
    } else {
        double[] newData;
        if (op.getOpType() == INTERSECTION) {
            newData = new double[m * n];
        } else {
            newData = ArrayCopy.copy(data);
        }
        if (v.isDense()) {
            int[] values = v.getStorage().getValues();
            for (int j = 0; j < n; j++) {
                int value = values[j];
                for (int i = 0; i < m; i++) {
                    newData[i * n + j] = op.apply(data[i * n + j], value);
                }
            }
        } else if (v.isSparse()) {
            ObjectIterator<Int2IntMap.Entry> iter = v.getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2IntMap.Entry entry = iter.next();
                int j = entry.getIntKey();
                flag[j] = 1;
                double value = entry.getIntValue();
                for (int i = 0; i < m; i++) {
                    newData[i * n + j] = op.apply(data[i * n + j], value);
                }
            }
        } else {
            // sorted
            int[] idxs = v.getStorage().getIndices();
            int[] values = v.getStorage().getValues();
            for (int k = 0; k < size; k++) {
                int j = idxs[k];
                flag[j] = 1;
                int value = values[k];
                for (int i = 0; i < m; i++) {
                    newData[i * n + j] = op.apply(data[i * n + j], value);
                }
            }
        }
        if (!v.isDense()) {
            switch(op.getOpType()) {
                case INTERSECTION:
                    break;
                case UNION:
                    break;
                case ALL:
                    for (int j = 0; j < n; j++) {
                        if (flag[j] == 0) {
                            for (int i = 0; i < m; i++) {
                                newData[i * n + j] = op.apply(data[i * n + j], 0);
                            }
                        }
                    }
            }
        }
        return new BlasDoubleMatrix(mat.getMatrixId(), mat.getClock(), m, n, newData);
    }
}

Example 14 with IntIntVector

use of com.tencent.angel.ml.math2.vector.IntIntVector 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;
}

Example 15 with IntIntVector

use of com.tencent.angel.ml.math2.vector.IntIntVector in project angel by Tencent.

the class SnapshotFormat method save.

private void save(ServerIntIntRow row, PSMatrixSaveContext saveContext, MatrixPartitionMeta meta, DataOutputStream out) throws IOException {
    int startCol = (int) meta.getStartCol();
    IntIntVector vector = ServerRowUtils.getVector(row);
    if (vector.isDense()) {
        int[] data = vector.getStorage().getValues();
        for (int i = 0; i < data.length; i++) {
            out.writeInt(data[i]);
        }
    } else if (vector.isSorted()) {
        int[] indices = vector.getStorage().getIndices();
        int[] values = vector.getStorage().getValues();
        for (int i = 0; i < indices.length; i++) {
            out.writeInt(indices[i] + startCol);
            out.writeInt(values[i]);
        }
    } else {
        ObjectIterator<Int2IntMap.Entry> iter = vector.getStorage().entryIterator();
        Int2IntMap.Entry entry;
        while (iter.hasNext()) {
            entry = iter.next();
            out.writeInt(entry.getIntKey() + startCol);
            out.writeInt(entry.getIntValue());
        }
    }
}