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

Example 21 with LongDoubleVector

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

the class MixedBinaryInAllExecutor method apply.

private static Vector apply(CompLongDoubleVector v1, LongDummyVector v2, Binary op) {
    LongDoubleVector[] parts = v1.getPartitions();
    Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
    if (!op.isKeepStorage()) {
        for (int i = 0; i < parts.length; i++) {
            if (parts[i].getStorage() instanceof LongDoubleSortedVectorStorage) {
                resParts[i] = new LongDoubleSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
            }
        }
    }
    long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
    for (int i = 0; i < v1.getDim(); i++) {
        int pidx = (int) (i / subDim);
        long subidx = i % subDim;
        ((LongDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
    }
    LongDoubleVector[] res = new LongDoubleVector[parts.length];
    int i = 0;
    for (LongDoubleVector part : parts) {
        res[i] = new LongDoubleVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (LongDoubleVectorStorage) resParts[i]);
        i++;
    }
    v1.setPartitions(res);
    return v1;
}

Example 22 with LongDoubleVector

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

the class MixedBinaryInAllExecutor method apply.

private static Vector apply(CompLongDoubleVector v1, LongIntVector v2, Binary op) {
    LongDoubleVector[] parts = v1.getPartitions();
    Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
    if (!op.isKeepStorage()) {
        for (int i = 0; i < parts.length; i++) {
            if (parts[i].getStorage() instanceof LongDoubleSortedVectorStorage) {
                resParts[i] = new LongDoubleSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
            }
        }
    }
    long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
    for (int i = 0; i < v1.getDim(); i++) {
        int pidx = (int) (i / subDim);
        long subidx = i % subDim;
        if (v2.getStorage().hasKey(i)) {
            ((LongDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
        } else {
            ((LongDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 0));
        }
    }
    LongDoubleVector[] res = new LongDoubleVector[parts.length];
    int i = 0;
    for (LongDoubleVector part : parts) {
        res[i] = new LongDoubleVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (LongDoubleVectorStorage) resParts[i]);
        i++;
    }
    v1.setPartitions(res);
    return v1;
}

Example 23 with LongDoubleVector

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

the class SimpleBinaryOutAllExecutor method apply.

public static Vector apply(LongDoubleVector v1, LongDoubleVector v2, Binary op) {
    LongDoubleVector res;
    if (v1.isSparse() && v2.isSparse()) {
        if (op.isKeepStorage()) {
            throw new AngelException("operation is not support!");
        } else {
            // multi-rehash
            LongDoubleVectorStorage newStorage = v1.getStorage().emptySparse((int) (v1.getDim()));
            LongDoubleVectorStorage v1Storage = v1.getStorage();
            LongDoubleVectorStorage v2Storage = v2.getStorage();
            for (int i = 0; i < v1.getDim(); i++) {
                if (v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
                    newStorage.set(i, op.apply(v1.get(i), v2.get(i)));
                } else if (v1Storage.hasKey(i) && !v2Storage.hasKey(i)) {
                    newStorage.set(i, op.apply(v1.get(i), 0));
                } else if (!v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
                    newStorage.set(i, op.apply(0, v2.get(i)));
                } else {
                    newStorage.set(i, op.apply(0.0, 0));
                }
            }
            res = new LongDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
        }
    } else if (v1.isSparse() && v2.isSorted()) {
        if (op.isKeepStorage()) {
            throw new AngelException("operation is not support!");
        } else {
            LongDoubleVectorStorage newStorage = v1.getStorage().emptySparse((int) (v1.getDim()));
            LongDoubleVectorStorage v1Storage = v1.getStorage();
            LongDoubleVectorStorage v2Storage = v2.getStorage();
            for (int i = 0; i < v1.getDim(); i++) {
                if (v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
                    newStorage.set(i, op.apply(v1.get(i), v2.get(i)));
                } else if (v1Storage.hasKey(i) && !v2Storage.hasKey(i)) {
                    newStorage.set(i, op.apply(v1.get(i), 0));
                } else if (!v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
                    newStorage.set(i, op.apply(0, v2.get(i)));
                } else {
                    newStorage.set(i, op.apply(0.0, 0));
                }
            }
            res = new LongDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
        }
    } else if (v1.isSorted() && v2.isSparse()) {
        if (op.isKeepStorage()) {
            throw new AngelException("operation is not support!");
        } else {
            LongDoubleVectorStorage newStorage = new LongDoubleSparseVectorStorage(v1.getDim());
            LongDoubleVectorStorage v1Storage = v1.getStorage();
            LongDoubleVectorStorage v2Storage = v2.getStorage();
            for (int i = 0; i < v1.getDim(); i++) {
                if (v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
                    newStorage.set(i, op.apply(v1.get(i), v2.get(i)));
                } else if (v1Storage.hasKey(i) && !v2Storage.hasKey(i)) {
                    newStorage.set(i, op.apply(v1.get(i), 0));
                } else if (!v1Storage.hasKey(i) && v2Storage.hasKey(i)) {
                    newStorage.set(i, op.apply(0, v2.get(i)));
                } else {
                    newStorage.set(i, op.apply(0.0, 0));
                }
            }
            res = new LongDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
        }
    } else if (v1.isSorted() && v2.isSorted()) {
        if (op.isKeepStorage()) {
            throw new AngelException("operation is not support!");
        } else {
            LongDoubleVectorStorage newStorage = v1.getStorage().emptySorted((int) (v1.getDim()));
            long[] resIndices = newStorage.getIndices();
            double[] resValues = newStorage.getValues();
            int v1Pointor = 0;
            int v2Pointor = 0;
            long size1 = v1.size();
            long size2 = v2.size();
            long[] v1Indices = v1.getStorage().getIndices();
            double[] v1Values = v1.getStorage().getValues();
            long[] v2Indices = v2.getStorage().getIndices();
            double[] v2Values = v2.getStorage().getValues();
            if (!op.isCompare()) {
                for (int i = 0; i < resValues.length; i++) {
                    resValues[i] = Double.NaN;
                }
            }
            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] = op.apply(v2Values[v2Pointor], 0);
                    v1Pointor++;
                    globalPointor++;
                } else {
                    // v1Indices[v1Pointor] > v2Indices[v2Pointor]
                    resIndices[globalPointor] = v2Indices[v2Pointor];
                    resValues[globalPointor] = op.apply(0, v2Values[v2Pointor]);
                    v2Pointor++;
                    globalPointor++;
                }
            }
            res = new LongDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
        }
    } else {
        throw new AngelException("The operation is not support!");
    }
    return res;
}

Example 24 with LongDoubleVector

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

the class SimpleBinaryOutNonZAExecutor method apply.

public static Vector apply(LongDoubleVector v1, LongFloatVector v2, Binary op) {
    LongDoubleVectorStorage newStorage = (LongDoubleVectorStorage) StorageSwitch.apply(v1, v2, op);
    if (v1.isSparse() && v2.isSparse()) {
        long v1Size = v1.size();
        long 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<Long2FloatMap.Entry> iter = v2.getStorage().entryIterator();
            while (iter.hasNext()) {
                Long2FloatMap.Entry entry = iter.next();
                long idx = entry.getLongKey();
                newStorage.set(idx, op.apply(v1.get(idx), entry.getFloatValue()));
            }
        } else if ((v1Size + v2Size) * Constant.intersectionCoeff >= Constant.sparseDenseStorageThreshold * v1.dim()) {
            // we gauss dense storage is more efficient
            ObjectIterator<Long2DoubleMap.Entry> iter1 = v1.getStorage().entryIterator();
            while (iter1.hasNext()) {
                Long2DoubleMap.Entry entry = iter1.next();
                long idx = entry.getLongKey();
                newStorage.set(idx, entry.getDoubleValue());
            }
            ObjectIterator<Long2FloatMap.Entry> iter2 = v2.getStorage().entryIterator();
            while (iter2.hasNext()) {
                Long2FloatMap.Entry entry = iter2.next();
                long idx = entry.getLongKey();
                newStorage.set(idx, op.apply(v1.get(idx), entry.getFloatValue()));
            }
        } 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<Long2FloatMap.Entry> iter = v2.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Long2FloatMap.Entry entry = iter.next();
                    long idx = entry.getLongKey();
                    newStorage.set(idx, op.apply(v1.get(idx), entry.getFloatValue()));
                }
            } else {
                // multi-rehash
                ObjectIterator<Long2DoubleMap.Entry> iter1 = v1.getStorage().entryIterator();
                while (iter1.hasNext()) {
                    Long2DoubleMap.Entry entry = iter1.next();
                    long idx = entry.getLongKey();
                    newStorage.set(idx, entry.getDoubleValue());
                }
                ObjectIterator<Long2FloatMap.Entry> iter2 = v2.getStorage().entryIterator();
                while (iter2.hasNext()) {
                    Long2FloatMap.Entry entry = iter2.next();
                    long idx = entry.getLongKey();
                    newStorage.set(idx, op.apply(v1.get(idx), entry.getFloatValue()));
                }
            }
        }
    } else if (v1.isSparse() && v2.isSorted()) {
        long v1Size = v1.size();
        long 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
            long[] v2Indices = v2.getStorage().getIndices();
            float[] v2Values = v2.getStorage().getValues();
            for (int i = 0; i < v2.size(); i++) {
                long idx = v2Indices[i];
                newStorage.set(idx, op.apply(v1.get(idx), v2Values[i]));
            }
        } else if ((v1Size + v2Size) * Constant.intersectionCoeff >= Constant.sparseDenseStorageThreshold * v1.dim()) {
            ObjectIterator<Long2DoubleMap.Entry> iter1 = v1.getStorage().entryIterator();
            while (iter1.hasNext()) {
                Long2DoubleMap.Entry entry = iter1.next();
                long idx = entry.getLongKey();
                newStorage.set(idx, entry.getDoubleValue());
            }
            long[] v2Indices = v2.getStorage().getIndices();
            float[] v2Values = v2.getStorage().getValues();
            long size = v2.size();
            for (int i = 0; i < size; i++) {
                long 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) {
                long[] v2Indices = v2.getStorage().getIndices();
                float[] v2Values = v2.getStorage().getValues();
                for (int i = 0; i < v2.size(); i++) {
                    long idx = v2Indices[i];
                    newStorage.set(idx, op.apply(v1.get(idx), v2Values[i]));
                }
            } else {
                ObjectIterator<Long2DoubleMap.Entry> iter1 = v1.getStorage().entryIterator();
                while (iter1.hasNext()) {
                    Long2DoubleMap.Entry entry = iter1.next();
                    long idx = entry.getLongKey();
                    newStorage.set(idx, entry.getDoubleValue());
                }
                long[] v2Indices = v2.getStorage().getIndices();
                float[] v2Values = v2.getStorage().getValues();
                long size = v2.size();
                for (int i = 0; i < size; i++) {
                    long idx = v2Indices[i];
                    newStorage.set(idx, op.apply(v1.get(idx), v2Values[i]));
                }
            }
        }
    } else if (v1.isSorted() && v2.isSparse()) {
        long v1Size = v1.size();
        long v2Size = v2.size();
        if ((v1Size + v2Size) * Constant.intersectionCoeff >= Constant.sortedDenseStorageThreshold * v1.dim()) {
            if (op.isKeepStorage()) {
                long[] v1Indices = v1.getStorage().getIndices();
                long[] idxiter = v2.getStorage().indexIterator().toLongArray();
                long[] indices = new long[(int) (v1Size + v2Size)];
                System.arraycopy(v1Indices, 0, indices, 0, (int) v1.size());
                System.arraycopy(idxiter, 0, indices, (int) v1.size(), (int) v2.size());
                LongAVLTreeSet avl = new LongAVLTreeSet(indices);
                LongBidirectionalIterator iter = avl.iterator();
                double[] values = new double[indices.length];
                int i = 0;
                while (iter.hasNext()) {
                    long idx = iter.nextLong();
                    indices[i] = idx;
                    values[i] = op.apply(v1.get(idx), v2.get(idx));
                    i++;
                }
                while (i < indices.length) {
                    indices[i] = 0;
                    i++;
                }
                newStorage = new LongDoubleSortedVectorStorage(v1.getDim(), (int) avl.size(), indices, values);
            } else {
                long[] v1Indices = v1.getStorage().getIndices();
                double[] v1Values = v1.getStorage().getValues();
                long size = v1.size();
                for (int i = 0; i < size; i++) {
                    long idx = v1Indices[i];
                    newStorage.set(idx, v1Values[i]);
                }
                ObjectIterator<Long2FloatMap.Entry> iter = v2.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Long2FloatMap.Entry entry = iter.next();
                    long idx = entry.getLongKey();
                    newStorage.set(idx, op.apply(newStorage.get(idx), entry.getFloatValue()));
                }
            }
        } else {
            if (op.isKeepStorage()) {
                long[] v1Indices = v1.getStorage().getIndices();
                long[] idxiter = v2.getStorage().indexIterator().toLongArray();
                long[] indices = new long[(int) (v1Size + v2Size)];
                System.arraycopy(v1Indices, 0, indices, 0, (int) v1.size());
                System.arraycopy(idxiter, 0, indices, (int) v1.size(), (int) v2.size());
                LongAVLTreeSet avl = new LongAVLTreeSet(indices);
                LongBidirectionalIterator iter = avl.iterator();
                double[] values = new double[indices.length];
                int i = 0;
                while (iter.hasNext()) {
                    long idx = iter.nextLong();
                    indices[i] = idx;
                    values[i] = op.apply(v1.get(idx), v2.get(idx));
                    i++;
                }
                while (i < indices.length) {
                    indices[i] = 0;
                    i++;
                }
                newStorage = new LongDoubleSortedVectorStorage(v1.getDim(), (int) avl.size(), indices, values);
            } else {
                long[] v1Indices = v1.getStorage().getIndices();
                double[] v1Values = v1.getStorage().getValues();
                long size = v1.size();
                for (int i = 0; i < size; i++) {
                    long idx = v1Indices[i];
                    newStorage.set(idx, v1Values[i]);
                }
                ObjectIterator<Long2FloatMap.Entry> iter = v2.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Long2FloatMap.Entry entry = iter.next();
                    long idx = entry.getLongKey();
                    newStorage.set(idx, op.apply(newStorage.get(idx), entry.getFloatValue()));
                }
            }
        }
    } else if (v1.isSorted() && v2.isSorted()) {
        int v1Pointor = 0;
        int v2Pointor = 0;
        long size1 = v1.size();
        long size2 = v2.size();
        long[] v1Indices = v1.getStorage().getIndices();
        double[] v1Values = v1.getStorage().getValues();
        long[] v2Indices = v2.getStorage().getIndices();
        float[] v2Values = v2.getStorage().getValues();
        if ((size1 + size2) * Constant.intersectionCoeff >= Constant.sortedDenseStorageThreshold * v1.dim()) {
            if (op.isKeepStorage()) {
                // sorted
                long[] 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()) {
                long[] 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 LongDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
}

Example 25 with LongDoubleVector

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

the class SimpleBinaryOutAllExecutor method apply.

public static Vector apply(LongDoubleVector v1, LongDummyVector v2, Binary op) {
    LongDoubleVector res;
    if (v1.isSparse()) {
        if (op.isKeepStorage()) {
            throw new AngelException("operation is not support!");
        } else {
            // multi-rehash
            LongDoubleVectorStorage newStorage = v1.getStorage().emptySparse((int) (v1.getDim()));
            LongDoubleVectorStorage v1Storage = v1.getStorage();
            for (int i = 0; i < v1.getDim(); i++) {
                if (v1Storage.hasKey(i)) {
                    newStorage.set(i, op.apply(v1.get(i), v2.get(i)));
                } else {
                    newStorage.set(i, op.apply(0.0, v2.get(i)));
                }
            }
            res = new LongDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
        }
    } else {
        // sorted
        if (op.isKeepStorage()) {
            throw new AngelException("operation is not support!");
        } else {
            LongDoubleVectorStorage newStorage = new LongDoubleSparseVectorStorage(v1.getDim());
            LongDoubleVectorStorage v1Storage = v1.getStorage();
            for (int i = 0; i < v1.getDim(); i++) {
                if (v1Storage.hasKey(i)) {
                    newStorage.set(i, op.apply(v1.get(i), v2.get(i)));
                } else {
                    newStorage.set(i, op.apply(0.0, v2.get(i)));
                }
            }
            res = new LongDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
        }
    }
    return res;
}