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

Example 31 with IntDummyVector

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

the class BlasFloatMatrix method setCol.

public Matrix setCol(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[numRows];
            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[numRows];
            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];
            }
        }
        for (int j = 0; j < numRows; j++) {
            data[j * numCols + i] = rowData[j];
        }
    } else if (v instanceof IntLongVector) {
        long[] rowData;
        if (v.isDense()) {
            rowData = ((IntLongVector) v).getStorage().getValues();
        } else if (v.isSparse()) {
            rowData = new long[numRows];
            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[numRows];
            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 < numRows; j++) {
            data[j * numCols + i] = rowData[j];
        }
    } else if (v instanceof IntIntVector) {
        int[] rowData;
        if (v.isDense()) {
            rowData = ((IntIntVector) v).getStorage().getValues();
        } else if (v.isSparse()) {
            rowData = new int[numRows];
            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[numRows];
            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 < numRows; j++) {
            data[j * numCols + i] = rowData[j];
        }
    } else if (v instanceof IntDummyVector) {
        int[] rowData = new int[numRows];
        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 < numRows; j++) {
            data[j * numCols + i] = rowData[j];
        }
    } else {
        throw new AngelException("The operation is not supported!");
    }
    return this;
}

Example 32 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(CompIntLongVector v1, IntDummyVector v2, Binary op) {
    IntLongVector[] 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)) {
                ((IntLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 1));
            }
        }
    } else {
        int base = 0;
        for (int i = 0; i < parts.length; i++) {
            IntLongVector part = parts[i];
            IntLongVectorStorage resPart = (IntLongVectorStorage) resParts[i];
            if (part.isDense()) {
                long[] partValues = part.getStorage().getValues();
                long[] 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<Int2LongMap.Entry> piter = part.getStorage().entryIterator();
                while (piter.hasNext()) {
                    Int2LongMap.Entry entry = piter.next();
                    int idx = entry.getIntKey();
                    if (v2.hasKey(idx + base)) {
                        resPart.set(idx, op.apply(entry.getLongValue(), v2.get(idx + base)));
                    }
                }
            } else {
                // sorted
                if (op.isKeepStorage()) {
                    int[] partIndices = part.getStorage().getIndices();
                    long[] partValues = part.getStorage().getValues();
                    int[] resPartIndices = resPart.getIndices();
                    long[] 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();
                    long[] 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();
        }
    }
    IntLongVector[] res = new IntLongVector[parts.length];
    int i = 0;
    for (IntLongVector part : parts) {
        res[i] = new IntLongVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntLongVectorStorage) resParts[i]);
        i++;
    }
    return new CompIntLongVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}

Example 33 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(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++;
    }
    return new CompIntDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}

Example 34 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(CompIntLongVector v1, IntDummyVector v2, Binary op) {
    IntLongVector[] 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 IntLongSortedVectorStorage) {
                resParts[i] = new IntLongSparseVectorStorage(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;
        ((IntLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
    }
    IntLongVector[] res = new IntLongVector[parts.length];
    int i = 0;
    for (IntLongVector part : parts) {
        res[i] = new IntLongVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntLongVectorStorage) resParts[i]);
        i++;
    }
    return new CompIntLongVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}

Example 35 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(CompIntFloatVector v1, IntDummyVector v2, Binary op) {
    IntFloatVector[] 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 IntFloatSortedVectorStorage) {
                resParts[i] = new IntFloatSparseVectorStorage(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;
        ((IntFloatVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
    }
    IntFloatVector[] res = new IntFloatVector[parts.length];
    int i = 0;
    for (IntFloatVector part : parts) {
        res[i] = new IntFloatVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntFloatVectorStorage) resParts[i]);
        i++;
    }
    return new CompIntFloatVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}