Examples with IntDoubleVectorStorage com.tencent.angel.ml.math2.storage.IntDoubleVectorStorage used on opensource projects

Example 26 with IntDoubleVectorStorage

use of com.tencent.angel.ml.math2.storage.IntDoubleVectorStorage in project angel by Tencent.

the class ColumnFormat method saveIntDoubleRows.

private void saveIntDoubleRows(ServerPartition part, ServerRow[] rows, MatrixPartitionMeta partMeta, PSMatrixSaveContext saveContext, DataOutputStream output) throws IOException {
    Vector vec = ServerRowUtils.getVector((ServerIntDoubleRow) rows[0]);
    // int size = rows.length;
    int indexOffset = (int) part.getPartitionKey().getStartCol();
    IntDoubleVectorStorage storage = ((IntDoubleVector) vec).getStorage();
    IntDoublesCol col = new IntDoublesCol(0, new double[rows.length]);
    int startCol = (int) rows[0].getStartCol();
    int endCol = (int) rows[0].getEndCol();
    if (storage.isDense()) {
        for (int i = startCol; i < endCol; i++) {
            col.colId = i;
            for (int j = 0; j < rows.length; j++) {
                col.colElems[j] = ((ServerIntDoubleRow) (rows[j])).get(col.colId);
            }
            save(col, output);
        }
    } else {
        if (saveContext.sortFirst()) {
            int[] indices = storage.getIndices();
            Sort.quickSort(indices, 0, indices.length - 1);
            for (int i = 0; i < indices.length; i++) {
                col.colId = indices[i] + indexOffset;
                for (int j = 0; j < rows.length; j++) {
                    col.colElems[j] = ((ServerIntDoubleRow) (rows[j])).get(col.colId);
                }
                save(col, output);
            }
        } else {
            ObjectIterator<Int2DoubleMap.Entry> iter = storage.entryIterator();
            while (iter.hasNext()) {
                col.colId = iter.next().getIntKey() + indexOffset;
                for (int j = 0; j < rows.length; j++) {
                    col.colElems[j] = ((ServerIntDoubleRow) (rows[j])).get(col.colId);
                }
                save(col, output);
            }
        }
    }
}

Example 27 with IntDoubleVectorStorage

use of com.tencent.angel.ml.math2.storage.IntDoubleVectorStorage in project angel by Tencent.

the class ColumnFormat method saveLongDoubleRows.

private void saveLongDoubleRows(ServerPartition part, ServerRow[] rows, MatrixPartitionMeta partMeta, PSMatrixSaveContext saveContext, DataOutputStream output) throws IOException {
    Vector vec = ServerRowUtils.getVector((ServerLongDoubleRow) rows[0]);
    // int size = rows.size();
    long indexOffset = part.getPartitionKey().getStartCol();
    LongDoublesCol col = new LongDoublesCol(0, new double[rows.length]);
    if (vec instanceof IntDoubleVector) {
        IntDoubleVectorStorage storage = ((IntDoubleVector) vec).getStorage();
        long startCol = rows[0].getStartCol();
        long endCol = rows[0].getEndCol();
        if (storage.isDense()) {
            for (long i = startCol; i < endCol; i++) {
                col.colId = i;
                for (int j = 0; j < rows.length; j++) {
                    col.colElems[j] = ((ServerLongDoubleRow) (rows[j])).get(col.colId);
                }
                save(col, output);
            }
        } else {
            if (saveContext.sortFirst()) {
                int[] indices = storage.getIndices();
                Sort.quickSort(indices, 0, indices.length - 1);
                for (int i = 0; i < indices.length; i++) {
                    col.colId = indices[i] + indexOffset;
                    for (int j = 0; j < rows.length; j++) {
                        col.colElems[j] = ((ServerLongDoubleRow) (rows[j])).get(col.colId);
                    }
                    save(col, output);
                }
            } else {
                ObjectIterator<Int2DoubleMap.Entry> iter = storage.entryIterator();
                while (iter.hasNext()) {
                    col.colId = iter.next().getIntKey() + indexOffset;
                    for (int j = 0; j < rows.length; j++) {
                        col.colElems[j] = ((ServerLongDoubleRow) (rows[j])).get(col.colId);
                    }
                    save(col, output);
                }
            }
        }
    } else {
        LongDoubleVectorStorage storage = ((LongDoubleVector) vec).getStorage();
        if (saveContext.sortFirst()) {
            long[] indices = storage.getIndices();
            Sort.quickSort(indices, 0, indices.length - 1);
            for (int i = 0; i < indices.length; i++) {
                col.colId = indices[i] + indexOffset;
                for (int j = 0; j < rows.length; j++) {
                    col.colElems[j] = ((ServerLongDoubleRow) (rows[j])).get(col.colId);
                }
                save(col, output);
            }
        } else {
            ObjectIterator<Long2DoubleMap.Entry> iter = storage.entryIterator();
            while (iter.hasNext()) {
                col.colId = iter.next().getLongKey() + indexOffset;
                for (int j = 0; j < rows.length; j++) {
                    col.colElems[j] = ((ServerLongDoubleRow) (rows[j])).get(col.colId);
                }
                save(col, output);
            }
        }
    }
}

Example 28 with IntDoubleVectorStorage

use of com.tencent.angel.ml.math2.storage.IntDoubleVectorStorage in project angel by Tencent.

the class ByteBufSerdeUtils method serializedIntDoubleVectorLen.

public static int serializedIntDoubleVectorLen(IntDoubleVector vector) {
    int len = 0;
    IntDoubleVectorStorage storage = vector.getStorage();
    if (storage.isDense()) {
        len += serializedIntLen(DENSE_STORAGE_TYPE);
        len += serializedDoublesLen(storage.getValues());
    } else if (storage.isSparse()) {
        len += serializedIntLen(SPARSE_STORAGE_TYPE);
        len += serializedIntLen(storage.size());
        len += storage.size() * (INT_LENGTH + DOUBLE_LENGTH);
    } else if (storage.isSorted()) {
        len += serializedIntLen(SORTED_STORAGE_TYPE);
        len += serializedIntsLen(vector.getStorage().getIndices());
        len += serializedDoublesLen(vector.getStorage().getValues());
    } else {
        throw new UnsupportedOperationException("Unsupport storage type " + vector.getStorage().getClass());
    }
    return len;
}

Example 29 with IntDoubleVectorStorage

use of com.tencent.angel.ml.math2.storage.IntDoubleVectorStorage in project angel by Tencent.

the class MixedBinaryInNonZAExecutor method apply.

private static Vector apply(CompIntDoubleVector v1, IntIntVector v2, Binary op) {
    IntDoubleVector[] parts = v1.getPartitions();
    Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
    if (v2.isDense()) {
        int[] v2Values = v2.getStorage().getValues();
        int base = 0, k = 0;
        for (IntDoubleVector part : parts) {
            IntDoubleVectorStorage resPart = (IntDoubleVectorStorage) resParts[k];
            double[] newValues = resPart.getValues();
            if (part.isDense()) {
                double[] partValue = part.getStorage().getValues();
                for (int i = 0; i < partValue.length; i++) {
                    int idx = i + base;
                    newValues[i] = op.apply(partValue[i], v2Values[idx]);
                }
            } else if (part.isSparse()) {
                if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
                    for (int i = 0; i < part.getDim(); i++) {
                        resPart.set(i, op.apply(0, v2Values[i + base]));
                    }
                    ObjectIterator<Int2DoubleMap.Entry> iter = part.getStorage().entryIterator();
                    while (iter.hasNext()) {
                        Int2DoubleMap.Entry entry = iter.next();
                        int idx = entry.getIntKey();
                        resPart.set(idx, op.apply(entry.getDoubleValue(), v2Values[idx + base]));
                    }
                } else {
                    for (int i = 0; i < newValues.length; i++) {
                        if (part.getStorage().hasKey(i)) {
                            resPart.set(i, op.apply(part.get(i), v2Values[i + base]));
                        } else {
                            resPart.set(i, op.apply(0, v2Values[i + base]));
                        }
                    }
                }
            } else {
                // sorted
                if (op.isKeepStorage()) {
                    int dim = part.getDim();
                    int[] resIndices = resPart.getIndices();
                    double[] resValues = resPart.getValues();
                    int[] partIndices = part.getStorage().getIndices();
                    double[] partValues = part.getStorage().getValues();
                    for (int i = 0; i < dim; i++) {
                        resIndices[i] = i;
                        resValues[i] = op.apply(0, v2Values[i]);
                    }
                    int size = v1.size();
                    for (int i = 0; i < size; i++) {
                        int idx = partIndices[i];
                        resValues[idx] = op.apply(partValues[i], v2Values[idx + base]);
                    }
                } else {
                    if (part.size() < Constant.denseLoopThreshold * part.getDim()) {
                        int[] partIndices = part.getStorage().getIndices();
                        double[] partValues = part.getStorage().getValues();
                        for (int i = 0; i < part.getDim(); i++) {
                            newValues[i] = op.apply(0, v2Values[i + base]);
                        }
                        int size = part.size();
                        for (int i = 0; i < size; i++) {
                            int idx = partIndices[i];
                            newValues[idx] = op.apply(partValues[i], v2Values[idx + base]);
                        }
                    } else {
                        IntDoubleVectorStorage partStorage = part.getStorage();
                        for (int i = 0; i < newValues.length; i++) {
                            if (partStorage.hasKey(i)) {
                                newValues[i] = op.apply(partStorage.get(i), v2Values[i + base]);
                            } else {
                                newValues[i] = op.apply(0, v2Values[i + base]);
                            }
                        }
                    }
                }
            }
            base += part.getDim();
            k++;
        }
    } else if (v2.isSparse()) {
        if (!op.isKeepStorage()) {
            for (int i = 0; i < parts.length; i++) {
                if (parts[i].getStorage() instanceof IntDoubleSortedVectorStorage) {
                    resParts[i] = new IntDoubleSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
                }
            }
        }
        int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
        ObjectIterator<Int2IntMap.Entry> iter = v2.getStorage().entryIterator();
        while (iter.hasNext()) {
            Int2IntMap.Entry entry = iter.next();
            int gidx = entry.getIntKey();
            int pidx = (int) (gidx / subDim);
            int subidx = gidx % subDim;
            ((IntDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), entry.getIntValue()));
        }
    } else {
        // sorted
        if (!op.isKeepStorage()) {
            for (int i = 0; i < parts.length; i++) {
                if (parts[i].getStorage() instanceof IntDoubleSortedVectorStorage) {
                    resParts[i] = new IntDoubleSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
                }
            }
        }
        int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
        int[] v2Indices = v2.getStorage().getIndices();
        int[] v2Values = v2.getStorage().getValues();
        for (int i = 0; i < v2Indices.length; i++) {
            int gidx = v2Indices[i];
            int pidx = (int) (gidx / subDim);
            int subidx = gidx % subDim;
            ((IntDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2Values[i]));
        }
    }
    IntDoubleVector[] res = new IntDoubleVector[parts.length];
    int i = 0;
    for (IntDoubleVector part : parts) {
        res[i] = new IntDoubleVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntDoubleVectorStorage) resParts[i]);
        i++;
    }
    v1.setPartitions(res);
    return v1;
}

Example 30 with IntDoubleVectorStorage

use of com.tencent.angel.ml.math2.storage.IntDoubleVectorStorage in project angel by Tencent.

the class MixedBinaryInNonZAExecutor method apply.

private static Vector apply(CompIntDoubleVector v1, IntDummyVector v2, Binary op) {
    IntDoubleVector[] 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 IntDoubleSortedVectorStorage) {
                resParts[i] = new IntDoubleSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
            }
        }
    }
    int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
    int[] v2Indices = v2.getIndices();
    for (int i = 0; i < v2Indices.length; i++) {
        int gidx = v2Indices[i];
        int pidx = (int) (gidx / subDim);
        int subidx = gidx % subDim;
        ((IntDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 1));
    }
    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;
}