Examples with AggregateOperator org.apache.sysml.runtime.matrix.operators.AggregateOperator used on opensource projects

Example 16 with AggregateOperator

use of org.apache.sysml.runtime.matrix.operators.AggregateOperator in project incubator-systemml by apache.

the class MatrixBlock method sumWithFn.

/**
 * Wrapper method for reduceall-sum of a matrix using the given
 * Kahan function for summation.
 *
 * @param kfunc A Kahan function object to use for summation.
 * @return Sum of the values in the matrix with the given
 *         function applied.
 */
private double sumWithFn(KahanFunction kfunc) {
    // construct operator
    CorrectionLocationType corrLoc = CorrectionLocationType.LASTCOLUMN;
    ReduceAll reduceAllObj = ReduceAll.getReduceAllFnObject();
    AggregateOperator aop = new AggregateOperator(0, kfunc, true, corrLoc);
    AggregateUnaryOperator auop = new AggregateUnaryOperator(aop, reduceAllObj);
    // execute operation
    MatrixBlock out = new MatrixBlock(1, 2, false);
    LibMatrixAgg.aggregateUnaryMatrix(this, out, auop);
    return out.quickGetValue(0, 0);
}

Example 17 with AggregateOperator

use of org.apache.sysml.runtime.matrix.operators.AggregateOperator in project incubator-systemml by apache.

the class MatrixBlock method min.

/**
 * Wrapper method for reduceall-min of a matrix.
 *
 * @return ?
 */
public double min() {
    // construct operator
    AggregateOperator aop = new AggregateOperator(Double.POSITIVE_INFINITY, Builtin.getBuiltinFnObject("min"));
    AggregateUnaryOperator auop = new AggregateUnaryOperator(aop, ReduceAll.getReduceAllFnObject());
    // execute operation
    MatrixBlock out = new MatrixBlock(1, 1, false);
    LibMatrixAgg.aggregateUnaryMatrix(this, out, auop);
    return out.quickGetValue(0, 0);
}

Example 18 with AggregateOperator

use of org.apache.sysml.runtime.matrix.operators.AggregateOperator in project incubator-systemml by apache.

the class GroupedAggMRCombiner method reduce.

@Override
public void reduce(TaggedMatrixIndexes key, Iterator<WeightedCell> values, OutputCollector<TaggedMatrixIndexes, WeightedCell> out, Reporter reporter) throws IOException {
    long start = System.currentTimeMillis();
    // get aggregate operator
    GroupedAggregateInstruction ins = grpaggInstructions.get(key.getTag());
    Operator op = ins.getOperator();
    boolean isPartialAgg = true;
    // combine iterator to single value
    try {
        if (// everything except sum
        op instanceof CMOperator) {
            if (((CMOperator) op).isPartialAggregateOperator()) {
                cmObj.reset();
                CM lcmFn = cmFn.get(key.getTag());
                // partial aggregate cm operator
                while (values.hasNext()) {
                    WeightedCell value = values.next();
                    lcmFn.execute(cmObj, value.getValue(), value.getWeight());
                }
                outCell.setValue(cmObj.getRequiredPartialResult(op));
                outCell.setWeight(cmObj.getWeight());
            } else // forward tuples to reducer
            {
                isPartialAgg = false;
                while (values.hasNext()) out.collect(key, values.next());
            }
        } else if (// sum
        op instanceof AggregateOperator) {
            AggregateOperator aggop = (AggregateOperator) op;
            if (aggop.correctionExists) {
                KahanObject buffer = new KahanObject(aggop.initialValue, 0);
                KahanPlus.getKahanPlusFnObject();
                // partial aggregate with correction
                while (values.hasNext()) {
                    WeightedCell value = values.next();
                    aggop.increOp.fn.execute(buffer, value.getValue() * value.getWeight());
                }
                outCell.setValue(buffer._sum);
                outCell.setWeight(1);
            } else // no correction
            {
                double v = aggop.initialValue;
                // partial aggregate without correction
                while (values.hasNext()) {
                    WeightedCell value = values.next();
                    v = aggop.increOp.fn.execute(v, value.getValue() * value.getWeight());
                }
                outCell.setValue(v);
                outCell.setWeight(1);
            }
        } else
            throw new IOException("Unsupported operator in instruction: " + ins);
    } catch (Exception ex) {
        throw new IOException(ex);
    }
    // collect the output (to reducer)
    if (isPartialAgg)
        out.collect(key, outCell);
    reporter.incrCounter(Counters.COMBINE_OR_REDUCE_TIME, System.currentTimeMillis() - start);
}

Example 19 with AggregateOperator

use of org.apache.sysml.runtime.matrix.operators.AggregateOperator in project incubator-systemml by apache.

the class GroupedAggMRReducer method reduce.

@Override
public void reduce(TaggedMatrixIndexes key, Iterator<WeightedCell> values, OutputCollector<MatrixIndexes, MatrixCell> out, Reporter report) throws IOException {
    commonSetup(report);
    // get operator
    GroupedAggregateInstruction ins = grpaggInstructions.get(key.getTag());
    Operator op = ins.getOperator();
    try {
        if (// all, but sum
        op instanceof CMOperator) {
            cmObj.reset();
            CM lcmFn = cmFn.get(key.getTag());
            while (values.hasNext()) {
                WeightedCell value = values.next();
                lcmFn.execute(cmObj, value.getValue(), value.getWeight());
            }
            outCell.setValue(cmObj.getRequiredResult(op));
        } else if (// sum
        op instanceof AggregateOperator) {
            AggregateOperator aggop = (AggregateOperator) op;
            if (aggop.correctionExists) {
                KahanObject buffer = new KahanObject(aggop.initialValue, 0);
                while (values.hasNext()) {
                    WeightedCell value = values.next();
                    aggop.increOp.fn.execute(buffer, value.getValue() * value.getWeight());
                }
                outCell.setValue(buffer._sum);
            } else {
                double v = aggop.initialValue;
                while (values.hasNext()) {
                    WeightedCell value = values.next();
                    v = aggop.increOp.fn.execute(v, value.getValue() * value.getWeight());
                }
                outCell.setValue(v);
            }
        } else
            throw new IOException("Unsupported operator in instruction: " + ins);
    } catch (Exception ex) {
        throw new IOException(ex);
    }
    outIndex.setIndexes(key.getBaseObject());
    cachedValues.reset();
    cachedValues.set(key.getTag(), outIndex, outCell);
    processReducerInstructions();
    // output the final result matrices
    outputResultsFromCachedValues(report);
}

Example 20 with AggregateOperator

use of org.apache.sysml.runtime.matrix.operators.AggregateOperator in project incubator-systemml by apache.

the class ParVectorMatrixMultTest method runMatrixVectorMultTest.

private static void runMatrixVectorMultTest(SparsityType sptype, ValueType vtype, boolean compress) {
    try {
        // prepare sparsity for input data
        double sparsity = -1;
        switch(sptype) {
            case DENSE:
                sparsity = sparsity1;
                break;
            case SPARSE:
                sparsity = sparsity2;
                break;
            case EMPTY:
                sparsity = sparsity3;
                break;
        }
        // generate input data
        double min = (vtype == ValueType.CONST) ? 10 : -10;
        double[][] input = TestUtils.generateTestMatrix(rows, cols, min, 10, sparsity, 7);
        if (vtype == ValueType.RAND_ROUND_OLE || vtype == ValueType.RAND_ROUND_DDC) {
            CompressedMatrixBlock.ALLOW_DDC_ENCODING = (vtype == ValueType.RAND_ROUND_DDC);
            input = TestUtils.round(input);
        }
        MatrixBlock mb = DataConverter.convertToMatrixBlock(input);
        MatrixBlock vector = DataConverter.convertToMatrixBlock(TestUtils.generateTestMatrix(1, rows, 1, 1, 1.0, 3));
        // compress given matrix block
        CompressedMatrixBlock cmb = new CompressedMatrixBlock(mb);
        if (compress)
            cmb.compress();
        // matrix-vector uncompressed
        AggregateOperator aop = new AggregateOperator(0, Plus.getPlusFnObject());
        AggregateBinaryOperator abop = new AggregateBinaryOperator(Multiply.getMultiplyFnObject(), aop, InfrastructureAnalyzer.getLocalParallelism());
        MatrixBlock ret1 = vector.aggregateBinaryOperations(vector, mb, new MatrixBlock(), abop);
        // matrix-vector compressed
        MatrixBlock ret2 = cmb.aggregateBinaryOperations(vector, cmb, new MatrixBlock(), abop);
        // compare result with input
        double[][] d1 = DataConverter.convertToDoubleMatrix(ret1);
        double[][] d2 = DataConverter.convertToDoubleMatrix(ret2);
        TestUtils.compareMatrices(d1, d2, 1, cols, 0.0000001);
    } catch (Exception ex) {
        throw new RuntimeException(ex);
    } finally {
        CompressedMatrixBlock.ALLOW_DDC_ENCODING = true;
    }
}