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

Example 36 with IntDoubleVector

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

the class StreamSerdeUtils method deserializeIntDoubleVector.

private static IntDoubleVector deserializeIntDoubleVector(DataInputStream in, long dim) throws IOException {
    int storageType = deserializeInt(in);
    switch(storageType) {
        case DENSE_STORAGE_TYPE:
            return VFactory.denseDoubleVector(deserializeDoubles(in));
        case SPARSE_STORAGE_TYPE:
            int len = deserializeInt(in);
            Int2DoubleOpenHashMap idToValueMap = new Int2DoubleOpenHashMap(len);
            for (int i = 0; i < len; i++) {
                idToValueMap.put(deserializeInt(in), deserializeDouble(in));
            }
            return new IntDoubleVector((int) dim, new IntDoubleSparseVectorStorage((int) dim, idToValueMap));
        case SORTED_STORAGE_TYPE:
            return VFactory.sortedDoubleVector((int) dim, deserializeInts(in), deserializeDoubles(in));
        default:
            throw new UnsupportedOperationException("Unsupport storage type " + storageType);
    }
}

Example 37 with IntDoubleVector

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

the class DotMatrixExecutor method apply.

private static Vector apply(BlasDoubleMatrix mat, boolean trans, IntDummyVector v) {
    int m = mat.getNumRows(), n = mat.getNumCols();
    double[] resArr;
    if (trans) {
        assert m == v.getDim();
        resArr = new double[n];
    } else {
        assert n == v.getDim();
        resArr = new double[m];
    }
    int r = mat.getNumRows(), c = mat.getNumCols();
    double[] data = mat.getData();
    if (trans) {
        for (int j = 0; j < c; j++) {
            int[] idxs = v.getIndices();
            for (int i : idxs) {
                resArr[j] += data[i * c + j];
            }
        }
    } else {
        for (int i = 0; i < r; i++) {
            int[] idxs = v.getIndices();
            for (int j : idxs) {
                resArr[i] += data[i * c + j];
            }
        }
    }
    IntDoubleDenseVectorStorage storage = new IntDoubleDenseVectorStorage(resArr);
    return new IntDoubleVector(v.getMatrixId(), v.getClock(), 0, resArr.length, storage);
}

Example 38 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, IntLongVector v2, Binary op) {
    IntDoubleVector[] parts = v1.getPartitions();
    Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
    if (v2.isDense()) {
        long[] 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 39 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, 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();
    for (int i = 0; i < v1.getDim(); i++) {
        int pidx = (int) (i / subDim);
        int subidx = i % subDim;
        ((IntDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(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 40 with IntDoubleVector

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

the class DotMatrixExecutor method apply.

private static Vector apply(BlasDoubleMatrix mat, boolean trans, IntIntVector v) {
    int m = mat.getNumRows(), n = mat.getNumCols();
    double[] resArr;
    if (trans) {
        assert m == v.getDim();
        resArr = new double[n];
    } else {
        assert n == v.getDim();
        resArr = new double[m];
    }
    int r = mat.getNumRows(), c = mat.getNumCols();
    double[] data = mat.getData();
    if (v.isDense()) {
        double[] tempArray = ArrayCopy.copy(v.getStorage().getValues(), new double[v.getDim()]);
        if (trans) {
            blas.dgemv("N", c, r, 1.0, data, c, tempArray, 1, 0.0, resArr, 1);
        } else {
            blas.dgemv("T", c, r, 1.0, data, c, tempArray, 1, 0.0, resArr, 1);
        }
    } else if (v.isSparse()) {
        if (trans) {
            for (int j = 0; j < c; j++) {
                ObjectIterator<Int2IntMap.Entry> iter = v.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2IntMap.Entry entry = iter.next();
                    int i = entry.getIntKey();
                    resArr[j] += data[i * c + j] * entry.getIntValue();
                }
            }
        } else {
            for (int i = 0; i < r; i++) {
                ObjectIterator<Int2IntMap.Entry> iter = v.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2IntMap.Entry entry = iter.next();
                    int j = entry.getIntKey();
                    resArr[i] += data[i * c + j] * entry.getIntValue();
                }
            }
        }
    } else {
        // sorted
        if (trans) {
            for (int j = 0; j < r; j++) {
                int[] idxs = v.getStorage().getIndices();
                int[] vals = v.getStorage().getValues();
                for (int k = 0; k < idxs.length; k++) {
                    resArr[j] += data[idxs[k] * c + j] * vals[k];
                }
            }
        } else {
            for (int i = 0; i < r; i++) {
                int[] idxs = v.getStorage().getIndices();
                int[] vals = v.getStorage().getValues();
                for (int k = 0; k < idxs.length; k++) {
                    resArr[i] += data[i * c + idxs[k]] * vals[k];
                }
            }
        }
    }
    IntDoubleDenseVectorStorage storage = new IntDoubleDenseVectorStorage(resArr);
    return new IntDoubleVector(v.getMatrixId(), v.getClock(), 0, resArr.length, storage);
}