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

Example 61 with IntDoubleVector

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

the class ByteBufSerdeUtils method serializeIntDoubleVector.

// IntDoubleVector
private static void serializeIntDoubleVector(ByteBuf out, IntDoubleVector vector) {
    IntDoubleVectorStorage storage = vector.getStorage();
    if (storage.isDense()) {
        serializeInt(out, DENSE_STORAGE_TYPE);
        serializeDoubles(out, storage.getValues());
    } else if (storage.isSparse()) {
        serializeInt(out, SPARSE_STORAGE_TYPE);
        serializeInt(out, storage.size());
        ObjectIterator<Entry> iter = storage.entryIterator();
        while (iter.hasNext()) {
            Entry e = iter.next();
            serializeInt(out, e.getIntKey());
            serializeDouble(out, e.getDoubleValue());
        }
    } else if (storage.isSorted()) {
        serializeInt(out, SORTED_STORAGE_TYPE);
        int[] indices = vector.getStorage().getIndices();
        double[] values = vector.getStorage().getValues();
        serializeInts(out, indices);
        serializeDoubles(out, values);
    } else {
        throw new UnsupportedOperationException("Unsupport storage type " + vector.getStorage().getClass());
    }
}

Example 62 with IntDoubleVector

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

the class SnapshotFormat method save.

private void save(ServerIntDoubleRow row, PSMatrixSaveContext saveContext, MatrixPartitionMeta meta, DataOutputStream out) throws IOException {
    int startCol = (int) meta.getStartCol();
    IntDoubleVector vector = ServerRowUtils.getVector(row);
    if (vector.isDense()) {
        double[] data = vector.getStorage().getValues();
        for (int i = 0; i < data.length; i++) {
            out.writeDouble(data[i]);
        }
    } else if (vector.isSorted()) {
        int[] indices = vector.getStorage().getIndices();
        double[] values = vector.getStorage().getValues();
        for (int i = 0; i < indices.length; i++) {
            out.writeInt(indices[i] + startCol);
            out.writeDouble(values[i]);
        }
    } else {
        ObjectIterator<Int2DoubleMap.Entry> iter = vector.getStorage().entryIterator();
        Int2DoubleMap.Entry entry;
        while (iter.hasNext()) {
            entry = iter.next();
            out.writeInt(entry.getIntKey() + startCol);
            out.writeDouble(entry.getDoubleValue());
        }
    }
}

Example 63 with IntDoubleVector

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

the class HashRouterUtils method splitIntDoubleVector.

public static void splitIntDoubleVector(KeyHash hasher, MatrixMeta matrixMeta, IntDoubleVector vector, KeyValuePart[] dataParts) {
    int dataPartNum = dataParts.length;
    int dataPartNumMinus1 = dataPartNum - 1;
    if (isPow2(dataPartNum)) {
        IntDoubleVectorStorage storage = vector.getStorage();
        if (storage.isSparse()) {
            // Use iterator
            IntDoubleSparseVectorStorage sparseStorage = (IntDoubleSparseVectorStorage) storage;
            ObjectIterator<Int2DoubleMap.Entry> iter = sparseStorage.entryIterator();
            while (iter.hasNext()) {
                Int2DoubleMap.Entry keyValue = iter.next();
                int partId = computeHashCode(hasher, keyValue.getIntKey()) & dataPartNumMinus1;
                ((HashIntKeysDoubleValuesPart) dataParts[partId]).add(keyValue.getIntKey(), keyValue.getDoubleValue());
            }
        } else if (storage.isDense()) {
            // Get values
            IntDoubleDenseVectorStorage denseStorage = (IntDoubleDenseVectorStorage) storage;
            double[] values = denseStorage.getValues();
            for (int i = 0; i < values.length; i++) {
                int partId = computeHashCode(hasher, i) & dataPartNumMinus1;
                ((HashIntKeysDoubleValuesPart) dataParts[partId]).add(i, values[i]);
            }
        } else {
            // Key and value array pair
            IntDoubleSortedVectorStorage sortStorage = (IntDoubleSortedVectorStorage) storage;
            int[] keys = sortStorage.getIndices();
            double[] values = sortStorage.getValues();
            for (int i = 0; i < keys.length; i++) {
                int partId = computeHashCode(hasher, keys[i]) & dataPartNumMinus1;
                ((HashIntKeysDoubleValuesPart) dataParts[partId]).add(keys[i], values[i]);
            }
        }
    } else {
        IntDoubleVectorStorage storage = vector.getStorage();
        if (storage.isSparse()) {
            // Use iterator
            IntDoubleSparseVectorStorage sparseStorage = (IntDoubleSparseVectorStorage) storage;
            ObjectIterator<Int2DoubleMap.Entry> iter = sparseStorage.entryIterator();
            while (iter.hasNext()) {
                Int2DoubleMap.Entry keyValue = iter.next();
                int partId = computeHashCode(hasher, keyValue.getIntKey()) % dataPartNum;
                ((HashIntKeysDoubleValuesPart) dataParts[partId]).add(keyValue.getIntKey(), keyValue.getDoubleValue());
            }
        } else if (storage.isDense()) {
            // Get values
            IntDoubleDenseVectorStorage denseStorage = (IntDoubleDenseVectorStorage) storage;
            double[] values = denseStorage.getValues();
            for (int i = 0; i < values.length; i++) {
                int partId = computeHashCode(hasher, i) % dataPartNum;
                ((HashIntKeysDoubleValuesPart) dataParts[partId]).add(i, values[i]);
            }
        } else {
            // Key and value array pair
            IntDoubleSortedVectorStorage sortStorage = (IntDoubleSortedVectorStorage) storage;
            int[] keys = sortStorage.getIndices();
            double[] values = sortStorage.getValues();
            for (int i = 0; i < keys.length; i++) {
                int partId = computeHashCode(hasher, keys[i]) % dataPartNum;
                ((HashIntKeysDoubleValuesPart) dataParts[partId]).add(keys[i], values[i]);
            }
        }
    }
}

Example 64 with IntDoubleVector

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

the class HashRouterUtils method split.

/**
 * Split keys by matrix partition
 *
 * @param matrixMeta matrix meta data
 * @param vector Matrix vector
 * @return partition key to key partition map
 */
public static KeyValuePart[] split(MatrixMeta matrixMeta, Vector vector) {
    KeyHash hasher = HasherFactory.getHasher(matrixMeta.getRouterHash());
    PartitionKey[] matrixParts = matrixMeta.getPartitionKeys();
    KeyValuePart[] dataParts = new KeyValuePart[matrixParts.length];
    int estSize = (int) (vector.getSize() / matrixMeta.getPartitionNum());
    for (int i = 0; i < dataParts.length; i++) {
        dataParts[i] = generateDataPart(vector.getRowId(), vector.getType(), estSize);
    }
    switch(vector.getType()) {
        case T_DOUBLE_DENSE:
        case T_DOUBLE_SPARSE:
            {
                splitIntDoubleVector(hasher, matrixMeta, (IntDoubleVector) vector, dataParts);
                break;
            }
        case T_FLOAT_DENSE:
        case T_FLOAT_SPARSE:
            {
                splitIntFloatVector(hasher, matrixMeta, (IntFloatVector) vector, dataParts);
                break;
            }
        case T_INT_DENSE:
        case T_INT_SPARSE:
            {
                splitIntIntVector(hasher, matrixMeta, (IntIntVector) vector, dataParts);
                break;
            }
        case T_LONG_DENSE:
        case T_LONG_SPARSE:
            {
                splitIntLongVector(hasher, matrixMeta, (IntLongVector) vector, dataParts);
                break;
            }
        case T_DOUBLE_SPARSE_LONGKEY:
            {
                splitLongDoubleVector(hasher, matrixMeta, (LongDoubleVector) vector, dataParts);
                break;
            }
        case T_FLOAT_SPARSE_LONGKEY:
            {
                splitLongFloatVector(hasher, matrixMeta, (LongFloatVector) vector, dataParts);
                break;
            }
        case T_INT_SPARSE_LONGKEY:
            {
                splitLongIntVector(hasher, matrixMeta, (LongIntVector) vector, dataParts);
                break;
            }
        case T_LONG_SPARSE_LONGKEY:
            {
                splitLongLongVector(hasher, matrixMeta, (LongLongVector) vector, dataParts);
                break;
            }
        default:
            {
                throw new UnsupportedOperationException("Unsupport vector type " + vector.getType());
            }
    }
    for (int i = 0; i < dataParts.length; i++) {
        if (dataParts[i] != null) {
            dataParts[i].setRowId(vector.getRowId());
        }
    }
    return dataParts;
}

Example 65 with IntDoubleVector

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

the class MixedBinaryInAllExecutor method apply.

private static Vector apply(CompIntDoubleVector v1, IntDoubleVector v2, Binary op) {
    IntDoubleVector[] parts = v1.getPartitions();
    Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
    if (v2.isDense()) {
        double[] 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;
}