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

Example 21 with CompIntDoubleVector

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

the class MixedDotExecutor method apply.

private static double apply(CompIntDoubleVector v1, IntLongVector v2) {
    double dotValue = 0.0;
    if (v2.isDense()) {
        int base = 0;
        long[] v2Values = v2.getStorage().getValues();
        for (IntDoubleVector part : v1.getPartitions()) {
            if (part.isDense()) {
                double[] partValues = part.getStorage().getValues();
                for (int i = 0; i < partValues.length; i++) {
                    int idx = base + i;
                    dotValue += partValues[i] * v2Values[idx];
                }
            } else if (part.isSparse()) {
                ObjectIterator<Int2DoubleMap.Entry> iter = part.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2DoubleMap.Entry entry = iter.next();
                    int idx = base + entry.getIntKey();
                    dotValue += entry.getDoubleValue() * v2Values[idx];
                }
            } else {
                // isSorted
                int[] partIndices = part.getStorage().getIndices();
                double[] partValues = part.getStorage().getValues();
                for (int i = 0; i < partIndices.length; i++) {
                    int idx = base + partIndices[i];
                    dotValue += partValues[i] * v2Values[idx];
                }
            }
            base += part.getDim();
        }
    } else if (v2.isSparse()) {
        ObjectIterator<Int2LongMap.Entry> iter = v2.getStorage().entryIterator();
        while (iter.hasNext()) {
            Int2LongMap.Entry entry = iter.next();
            int idx = entry.getIntKey();
            dotValue += v1.get(idx) * entry.getLongValue();
        }
    } else if (v2.isSorted() && v1.size() > v2.size()) {
        // v2 is sorted
        int[] v2Indices = v2.getStorage().getIndices();
        long[] v2Values = v2.getStorage().getValues();
        for (int i = 0; i < v2Indices.length; i++) {
            int idx = v2Indices[i];
            dotValue += v1.get(idx) * v2Values[i];
        }
    } else {
        int base = 0;
        for (IntDoubleVector part : v1.getPartitions()) {
            if (part.isDense()) {
                double[] partValues = part.getStorage().getValues();
                for (int i = 0; i < partValues.length; i++) {
                    int idx = base + i;
                    dotValue += partValues[i] * v2.get(idx);
                }
            } else if (part.isSparse()) {
                ObjectIterator<Int2DoubleMap.Entry> iter = part.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2DoubleMap.Entry entry = iter.next();
                    int idx = base + entry.getIntKey();
                    dotValue += entry.getDoubleValue() * v2.get(idx);
                }
            } else {
                // isSorted
                int[] partIndices = part.getStorage().getIndices();
                double[] partValues = part.getStorage().getValues();
                for (int i = 0; i < partIndices.length; i++) {
                    int idx = base + partIndices[i];
                    dotValue += partValues[i] * v2.get(idx);
                }
            }
            base += part.getDim();
        }
    }
    return dotValue;
}

Example 22 with CompIntDoubleVector

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

the class MixedDotExecutor method apply.

private static double apply(CompIntDoubleVector v1, IntDoubleVector v2) {
    double dotValue = 0.0;
    if (v2.isDense()) {
        int base = 0;
        double[] v2Values = v2.getStorage().getValues();
        for (IntDoubleVector part : v1.getPartitions()) {
            if (part.isDense()) {
                double[] partValues = part.getStorage().getValues();
                for (int i = 0; i < partValues.length; i++) {
                    int idx = base + i;
                    dotValue += partValues[i] * v2Values[idx];
                }
            } else if (part.isSparse()) {
                ObjectIterator<Int2DoubleMap.Entry> iter = part.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2DoubleMap.Entry entry = iter.next();
                    int idx = base + entry.getIntKey();
                    dotValue += entry.getDoubleValue() * v2Values[idx];
                }
            } else {
                // isSorted
                int[] partIndices = part.getStorage().getIndices();
                double[] partValues = part.getStorage().getValues();
                for (int i = 0; i < partIndices.length; i++) {
                    int idx = base + partIndices[i];
                    dotValue += partValues[i] * v2Values[idx];
                }
            }
            base += part.getDim();
        }
    } else if (v2.isSparse()) {
        ObjectIterator<Int2DoubleMap.Entry> iter = v2.getStorage().entryIterator();
        while (iter.hasNext()) {
            Int2DoubleMap.Entry entry = iter.next();
            int idx = entry.getIntKey();
            dotValue += v1.get(idx) * entry.getDoubleValue();
        }
    } else if (v2.isSorted() && v1.size() > v2.size()) {
        // v2 is sorted
        int[] v2Indices = v2.getStorage().getIndices();
        double[] v2Values = v2.getStorage().getValues();
        for (int i = 0; i < v2Indices.length; i++) {
            int idx = v2Indices[i];
            dotValue += v1.get(idx) * v2Values[i];
        }
    } else {
        int base = 0;
        for (IntDoubleVector part : v1.getPartitions()) {
            if (part.isDense()) {
                double[] partValues = part.getStorage().getValues();
                for (int i = 0; i < partValues.length; i++) {
                    int idx = base + i;
                    dotValue += partValues[i] * v2.get(idx);
                }
            } else if (part.isSparse()) {
                ObjectIterator<Int2DoubleMap.Entry> iter = part.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2DoubleMap.Entry entry = iter.next();
                    int idx = base + entry.getIntKey();
                    dotValue += entry.getDoubleValue() * v2.get(idx);
                }
            } else {
                // isSorted
                int[] partIndices = part.getStorage().getIndices();
                double[] partValues = part.getStorage().getValues();
                for (int i = 0; i < partIndices.length; i++) {
                    int idx = base + partIndices[i];
                    dotValue += partValues[i] * v2.get(idx);
                }
            }
            base += part.getDim();
        }
    }
    return dotValue;
}

Example 23 with CompIntDoubleVector

use of com.tencent.angel.ml.math2.vector.CompIntDoubleVector 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 24 with CompIntDoubleVector

use of com.tencent.angel.ml.math2.vector.CompIntDoubleVector 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 25 with CompIntDoubleVector

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

the class MatrixUtils method rbCompDense2Blas.

public static BlasDoubleMatrix rbCompDense2Blas(RBCompIntDoubleMatrix mat) {
    assert mat != null;
    int dim = (int) mat.getDim();
    int subDim = mat.getSubDim();
    CompIntDoubleVector[] rows = mat.getRows();
    double[] data = new double[rows.length * dim];
    int rowId = 0;
    for (CompIntDoubleVector row : rows) {
        IntDoubleVector[] partitions = row.getPartitions();
        int partId = 0;
        for (IntDoubleVector part : partitions) {
            assert part.isDense();
            double[] src = part.getStorage().getValues();
            System.arraycopy(src, 0, data, rowId * dim + partId * subDim, src.length);
            partId += 1;
        }
        rowId += 1;
    }
    return MFactory.denseDoubleMatrix(rows.length, dim, data);
}