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

Example 11 with IntDummyVector

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

the class BlasFloatMatrix method setRow.

public Matrix setRow(int i, Vector v) {
    if (v instanceof IntFloatVector) {
        float[] rowData;
        if (v.isDense()) {
            rowData = ((IntFloatVector) v).getStorage().getValues();
        } else if (v.isSparse()) {
            rowData = new float[numCols];
            ObjectIterator<Int2FloatMap.Entry> iter = ((IntFloatVector) v).getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2FloatMap.Entry entry = iter.next();
                int j = entry.getIntKey();
                rowData[j] = entry.getFloatValue();
            }
        } else {
            // sorted
            rowData = new float[numCols];
            int[] idxs = ((IntFloatVector) v).getStorage().getIndices();
            float[] values = ((IntFloatVector) v).getStorage().getValues();
            int size = ((IntFloatVector) v).size();
            for (int k = 0; k < size; k++) {
                int j = idxs[k];
                rowData[j] = values[k];
            }
        }
        System.arraycopy(rowData, 0, data, i * numCols, numCols);
    } else if (v instanceof IntLongVector) {
        long[] rowData;
        if (v.isDense()) {
            rowData = ((IntLongVector) v).getStorage().getValues();
        } else if (v.isSparse()) {
            rowData = new long[numCols];
            ObjectIterator<Int2LongMap.Entry> iter = ((IntLongVector) v).getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2LongMap.Entry entry = iter.next();
                int j = entry.getIntKey();
                rowData[j] = entry.getLongValue();
            }
        } else {
            // sorted
            rowData = new long[numCols];
            int[] idxs = ((IntLongVector) v).getStorage().getIndices();
            long[] values = ((IntLongVector) v).getStorage().getValues();
            int size = ((IntLongVector) v).size();
            for (int k = 0; k < size; k++) {
                int j = idxs[k];
                rowData[j] = values[k];
            }
        }
        for (int j = 0; j < numCols; j++) {
            data[i * numCols + j] = rowData[j];
        }
    } else if (v instanceof IntIntVector) {
        int[] rowData;
        if (v.isDense()) {
            rowData = ((IntIntVector) v).getStorage().getValues();
        } else if (v.isSparse()) {
            rowData = new int[numCols];
            ObjectIterator<Int2IntMap.Entry> iter = ((IntIntVector) v).getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2IntMap.Entry entry = iter.next();
                int j = entry.getIntKey();
                rowData[j] = entry.getIntValue();
            }
        } else {
            // sorted
            rowData = new int[numCols];
            int[] idxs = ((IntIntVector) v).getStorage().getIndices();
            int[] values = ((IntIntVector) v).getStorage().getValues();
            int size = ((IntIntVector) v).size();
            for (int k = 0; k < size; k++) {
                int j = idxs[k];
                rowData[j] = values[k];
            }
        }
        for (int j = 0; j < numCols; j++) {
            data[i * numCols + j] = rowData[j];
        }
    } else if (v instanceof IntDummyVector) {
        int[] rowData = new int[numCols];
        int[] idxs = ((IntDummyVector) v).getIndices();
        int size = ((IntDummyVector) v).size();
        for (int k = 0; k < size; k++) {
            int j = idxs[k];
            rowData[j] = 1;
        }
        for (int j = 0; j < numCols; j++) {
            data[i * numCols + j] = rowData[j];
        }
    } else {
        throw new AngelException("The operation is not supported!");
    }
    return this;
}

Example 12 with IntDummyVector

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

the class MixedBinaryOutAllExecutor method apply.

private static Vector apply(CompIntIntVector v1, IntDummyVector v2, Binary op) {
    IntIntVector[] 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 IntIntSortedVectorStorage) {
                resParts[i] = new IntIntSparseVectorStorage(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;
        ((IntIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
    }
    IntIntVector[] res = new IntIntVector[parts.length];
    int i = 0;
    for (IntIntVector part : parts) {
        res[i] = new IntIntVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntIntVectorStorage) resParts[i]);
        i++;
    }
    return new CompIntIntVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}

Example 13 with IntDummyVector

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

the class MixedBinaryOutZAExecutor method apply.

private static Vector apply(CompIntIntVector v1, IntDummyVector v2, Binary op) {
    IntIntVector[] parts = v1.getPartitions();
    Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
    if (v1.size() > v2.size()) {
        int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
        int[] v2Indices = v2.getIndices();
        for (int i = 0; i < v2Indices.length; i++) {
            int idx = v2Indices[i];
            int pidx = (int) (idx / subDim);
            int subidx = idx % subDim;
            if (parts[pidx].hasKey(subidx)) {
                ((IntIntVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 1));
            }
        }
    } else {
        int base = 0;
        for (int i = 0; i < parts.length; i++) {
            IntIntVector part = parts[i];
            IntIntVectorStorage resPart = (IntIntVectorStorage) resParts[i];
            if (part.isDense()) {
                int[] partValues = part.getStorage().getValues();
                int[] resPartValues = resPart.getValues();
                for (int j = 0; j < partValues.length; j++) {
                    if (v2.hasKey(j + base)) {
                        resPartValues[j] = op.apply(partValues[j], v2.get(j + base));
                    }
                }
            } else if (part.isSparse()) {
                ObjectIterator<Int2IntMap.Entry> piter = part.getStorage().entryIterator();
                while (piter.hasNext()) {
                    Int2IntMap.Entry entry = piter.next();
                    int idx = entry.getIntKey();
                    if (v2.hasKey(idx + base)) {
                        resPart.set(idx, op.apply(entry.getIntValue(), v2.get(idx + base)));
                    }
                }
            } else {
                // sorted
                if (op.isKeepStorage()) {
                    int[] partIndices = part.getStorage().getIndices();
                    int[] partValues = part.getStorage().getValues();
                    int[] resPartIndices = resPart.getIndices();
                    int[] resPartValues = resPart.getValues();
                    for (int j = 0; j < partIndices.length; j++) {
                        int idx = partIndices[j];
                        if (v2.hasKey(idx + base)) {
                            resPartIndices[j] = idx;
                            resPartValues[j] = op.apply(partValues[j], v2.get(idx + base));
                        }
                    }
                } else {
                    int[] partIndices = part.getStorage().getIndices();
                    int[] partValues = part.getStorage().getValues();
                    for (int j = 0; j < partIndices.length; j++) {
                        int idx = partIndices[j];
                        if (v2.hasKey(idx + base)) {
                            resPart.set(idx, op.apply(partValues[j], v2.get(idx + base)));
                        }
                    }
                }
            }
            base += part.getDim();
        }
    }
    IntIntVector[] res = new IntIntVector[parts.length];
    int i = 0;
    for (IntIntVector part : parts) {
        res[i] = new IntIntVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntIntVectorStorage) resParts[i]);
        i++;
    }
    return new CompIntIntVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}

Example 14 with IntDummyVector

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

the class MixedBinaryOutNonZAExecutor 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++;
    }
    return new CompIntDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}

Example 15 with IntDummyVector

use of com.tencent.angel.ml.math2.vector.IntDummyVector 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);
}