Examples with LongStream java.util.stream.LongStream used on opensource projects

Example 6 with LongStream

use of java.util.stream.LongStream in project jdk8u_jdk by JetBrains.

the class SplittableRandomTest method longsDataProvider.

@DataProvider(name = "longs")
public static Object[][] longsDataProvider() {
    List<Object[]> data = new ArrayList<>();
    // Function to create a stream using a RandomBoxedSpliterator
    Function<Function<SplittableRandom, Long>, LongStream> rbsf = sf -> StreamSupport.stream(new RandomBoxedSpliterator<>(new SplittableRandom(), 0, SIZE, sf), false).mapToLong(i -> i);
    // Unbounded
    data.add(new Object[] { TestData.Factory.ofLongSupplier(String.format("new SplittableRandom().longs().limit(%d)", SIZE), () -> new SplittableRandom().longs().limit(SIZE)), randomAsserter(SIZE, Long.MAX_VALUE, 0L) });
    data.add(new Object[] { TestData.Factory.ofLongSupplier(String.format("new SplittableRandom().longs(%d)", SIZE), () -> new SplittableRandom().longs(SIZE)), randomAsserter(SIZE, Long.MAX_VALUE, 0L) });
    data.add(new Object[] { TestData.Factory.ofLongSupplier(String.format("new RandomBoxedSpliterator(0, %d, sr -> sr.nextLong())", SIZE), () -> rbsf.apply(sr -> sr.nextLong())), randomAsserter(SIZE, Long.MAX_VALUE, 0L) });
    for (int b : BOUNDS) {
        for (int o : ORIGINS) {
            final long origin = o;
            final long bound = b;
            data.add(new Object[] { TestData.Factory.ofLongSupplier(String.format("new SplittableRandom().longs(%d, %d).limit(%d)", origin, bound, SIZE), () -> new SplittableRandom().longs(origin, bound).limit(SIZE)), randomAsserter(SIZE, origin, bound) });
            data.add(new Object[] { TestData.Factory.ofLongSupplier(String.format("new SplittableRandom().longs(%d, %d, %d)", SIZE, origin, bound), () -> new SplittableRandom().longs(SIZE, origin, bound)), randomAsserter(SIZE, origin, bound) });
            if (origin == 0) {
                data.add(new Object[] { TestData.Factory.ofLongSupplier(String.format("new RandomBoxedSpliterator(0, %d, sr -> sr.nextLong(%d))", SIZE, bound), () -> rbsf.apply(sr -> sr.nextLong(bound))), randomAsserter(SIZE, origin, bound) });
            }
            data.add(new Object[] { TestData.Factory.ofLongSupplier(String.format("new RandomBoxedSpliterator(0, %d, sr -> sr.nextLong(%d, %d))", SIZE, origin, bound), () -> rbsf.apply(sr -> sr.nextLong(origin, bound))), randomAsserter(SIZE, origin, bound) });
        }
    }
    return data.toArray(new Object[0][]);
}

Example 7 with LongStream

use of java.util.stream.LongStream in project jdk8u_jdk by JetBrains.

the class ConcatTest method assertLongConcat.

private void assertLongConcat(Stream<Integer> s1, Stream<Integer> s2, boolean parallel, boolean ordered) {
    LongStream result = LongStream.concat(s1.mapToLong(Integer::longValue), s2.mapToLong(Integer::longValue));
    assertEquals(result.isParallel(), parallel);
    assertConcatContent(result.spliterator(), ordered, expected.stream().mapToLong(Integer::longValue).spliterator());
}

Example 8 with LongStream

use of java.util.stream.LongStream in project incubator-systemml by apache.

the class DataGenMR method runJob.

/**
	 * <p>Starts a Rand MapReduce job which will produce one or more random objects.</p>
	 * 
	 * @param inst MR job instruction
	 * @param dataGenInstructions array of data gen instructions
	 * @param instructionsInMapper instructions in mapper
	 * @param aggInstructionsInReducer aggregate instructions in reducer
	 * @param otherInstructionsInReducer other instructions in reducer
	 * @param numReducers number of reducers
	 * @param replication file replication
	 * @param resultIndexes result indexes for each random object
	 * @param dimsUnknownFilePrefix file path prefix when dimensions unknown
	 * @param outputs output file for each random object
	 * @param outputInfos output information for each random object
	 * @return matrix characteristics for each random object
	 * @throws Exception if Exception occurs
	 */
public static JobReturn runJob(MRJobInstruction inst, String[] dataGenInstructions, String instructionsInMapper, String aggInstructionsInReducer, String otherInstructionsInReducer, int numReducers, int replication, byte[] resultIndexes, String dimsUnknownFilePrefix, String[] outputs, OutputInfo[] outputInfos) throws Exception {
    JobConf job = new JobConf(DataGenMR.class);
    job.setJobName("DataGen-MR");
    //whether use block representation or cell representation
    MRJobConfiguration.setMatrixValueClass(job, true);
    byte[] realIndexes = new byte[dataGenInstructions.length];
    for (byte b = 0; b < realIndexes.length; b++) realIndexes[b] = b;
    String[] inputs = new String[dataGenInstructions.length];
    InputInfo[] inputInfos = new InputInfo[dataGenInstructions.length];
    long[] rlens = new long[dataGenInstructions.length];
    long[] clens = new long[dataGenInstructions.length];
    int[] brlens = new int[dataGenInstructions.length];
    int[] bclens = new int[dataGenInstructions.length];
    FileSystem fs = FileSystem.get(job);
    String dataGenInsStr = "";
    int numblocks = 0;
    int maxbrlen = -1, maxbclen = -1;
    double maxsparsity = -1;
    for (int i = 0; i < dataGenInstructions.length; i++) {
        dataGenInsStr = dataGenInsStr + Lop.INSTRUCTION_DELIMITOR + dataGenInstructions[i];
        MRInstruction mrins = MRInstructionParser.parseSingleInstruction(dataGenInstructions[i]);
        MRINSTRUCTION_TYPE mrtype = mrins.getMRInstructionType();
        DataGenMRInstruction genInst = (DataGenMRInstruction) mrins;
        rlens[i] = genInst.getRows();
        clens[i] = genInst.getCols();
        brlens[i] = genInst.getRowsInBlock();
        bclens[i] = genInst.getColsInBlock();
        maxbrlen = Math.max(maxbrlen, brlens[i]);
        maxbclen = Math.max(maxbclen, bclens[i]);
        if (mrtype == MRINSTRUCTION_TYPE.Rand) {
            RandInstruction randInst = (RandInstruction) mrins;
            inputs[i] = LibMatrixDatagen.generateUniqueSeedPath(genInst.getBaseDir());
            maxsparsity = Math.max(maxsparsity, randInst.getSparsity());
            PrintWriter pw = null;
            try {
                pw = new PrintWriter(fs.create(new Path(inputs[i])));
                //for obj reuse and preventing repeated buffer re-allocations
                StringBuilder sb = new StringBuilder();
                //seed generation
                Well1024a bigrand = LibMatrixDatagen.setupSeedsForRand(randInst.getSeed());
                LongStream nnz = LibMatrixDatagen.computeNNZperBlock(rlens[i], clens[i], brlens[i], bclens[i], randInst.getSparsity());
                PrimitiveIterator.OfLong nnzIter = nnz.iterator();
                for (long r = 0; r < rlens[i]; r += brlens[i]) {
                    long curBlockRowSize = Math.min(brlens[i], (rlens[i] - r));
                    for (long c = 0; c < clens[i]; c += bclens[i]) {
                        long curBlockColSize = Math.min(bclens[i], (clens[i] - c));
                        sb.append((r / brlens[i]) + 1);
                        sb.append(',');
                        sb.append((c / bclens[i]) + 1);
                        sb.append(',');
                        sb.append(curBlockRowSize);
                        sb.append(',');
                        sb.append(curBlockColSize);
                        sb.append(',');
                        sb.append(nnzIter.nextLong());
                        sb.append(',');
                        sb.append(bigrand.nextLong());
                        pw.println(sb.toString());
                        sb.setLength(0);
                        numblocks++;
                    }
                }
            } finally {
                IOUtilFunctions.closeSilently(pw);
            }
            inputInfos[i] = InputInfo.TextCellInputInfo;
        } else if (mrtype == MRINSTRUCTION_TYPE.Seq) {
            SeqInstruction seqInst = (SeqInstruction) mrins;
            inputs[i] = genInst.getBaseDir() + System.currentTimeMillis() + ".seqinput";
            //always dense
            maxsparsity = 1.0;
            double from = seqInst.fromValue;
            double to = seqInst.toValue;
            double incr = seqInst.incrValue;
            //handle default 1 to -1 for special case of from>to
            incr = LibMatrixDatagen.updateSeqIncr(from, to, incr);
            // Correctness checks on (from, to, incr)
            boolean neg = (from > to);
            if (incr == 0)
                throw new DMLRuntimeException("Invalid value for \"increment\" in seq().");
            if (neg != (incr < 0))
                throw new DMLRuntimeException("Wrong sign for the increment in a call to seq()");
            // Compute the number of rows in the sequence
            long numrows = 1 + (long) Math.floor((to - from) / incr);
            if (rlens[i] > 0) {
                if (numrows != rlens[i])
                    throw new DMLRuntimeException("Unexpected error while processing sequence instruction. Expected number of rows does not match given number: " + rlens[i] + " != " + numrows);
            } else {
                rlens[i] = numrows;
            }
            if (clens[i] > 0 && clens[i] != 1)
                throw new DMLRuntimeException("Unexpected error while processing sequence instruction. Number of columns (" + clens[i] + ") must be equal to 1.");
            else
                clens[i] = 1;
            PrintWriter pw = null;
            try {
                pw = new PrintWriter(fs.create(new Path(inputs[i])));
                StringBuilder sb = new StringBuilder();
                double temp = from;
                double block_from, block_to;
                for (long r = 0; r < rlens[i]; r += brlens[i]) {
                    long curBlockRowSize = Math.min(brlens[i], (rlens[i] - r));
                    // block (bid_i,bid_j) generates a sequence from the interval [block_from, block_to] (inclusive of both end points of the interval) 
                    long bid_i = ((r / brlens[i]) + 1);
                    long bid_j = 1;
                    block_from = temp;
                    block_to = temp + (curBlockRowSize - 1) * incr;
                    // next block starts from here
                    temp = block_to + incr;
                    sb.append(bid_i);
                    sb.append(',');
                    sb.append(bid_j);
                    sb.append(',');
                    sb.append(block_from);
                    sb.append(',');
                    sb.append(block_to);
                    sb.append(',');
                    sb.append(incr);
                    pw.println(sb.toString());
                    sb.setLength(0);
                    numblocks++;
                }
            } finally {
                IOUtilFunctions.closeSilently(pw);
            }
            inputInfos[i] = InputInfo.TextCellInputInfo;
        } else {
            throw new DMLRuntimeException("Unexpected Data Generation Instruction Type: " + mrtype);
        }
    }
    //remove the first ","
    dataGenInsStr = dataGenInsStr.substring(1);
    RunningJob runjob;
    MatrixCharacteristics[] stats;
    try {
        //set up the block size
        MRJobConfiguration.setBlocksSizes(job, realIndexes, brlens, bclens);
        //set up the input files and their format information
        MRJobConfiguration.setUpMultipleInputs(job, realIndexes, inputs, inputInfos, brlens, bclens, false, ConvertTarget.BLOCK);
        //set up the dimensions of input matrices
        MRJobConfiguration.setMatricesDimensions(job, realIndexes, rlens, clens);
        MRJobConfiguration.setDimsUnknownFilePrefix(job, dimsUnknownFilePrefix);
        //set up the block size
        MRJobConfiguration.setBlocksSizes(job, realIndexes, brlens, bclens);
        //set up the rand Instructions
        MRJobConfiguration.setRandInstructions(job, dataGenInsStr);
        //set up unary instructions that will perform in the mapper
        MRJobConfiguration.setInstructionsInMapper(job, instructionsInMapper);
        //set up the aggregate instructions that will happen in the combiner and reducer
        MRJobConfiguration.setAggregateInstructions(job, aggInstructionsInReducer);
        //set up the instructions that will happen in the reducer, after the aggregation instrucions
        MRJobConfiguration.setInstructionsInReducer(job, otherInstructionsInReducer);
        //set up the replication factor for the results
        job.setInt(MRConfigurationNames.DFS_REPLICATION, replication);
        //set up map/reduce memory configurations (if in AM context)
        DMLConfig config = ConfigurationManager.getDMLConfig();
        DMLAppMasterUtils.setupMRJobRemoteMaxMemory(job, config);
        //set up custom map/reduce configurations 
        MRJobConfiguration.setupCustomMRConfigurations(job, config);
        //determine degree of parallelism (nmappers: 1<=n<=capacity)
        //TODO use maxsparsity whenever we have a way of generating sparse rand data
        int capacity = InfrastructureAnalyzer.getRemoteParallelMapTasks();
        long dfsblocksize = InfrastructureAnalyzer.getHDFSBlockSize();
        //correction max number of mappers on yarn clusters
        if (InfrastructureAnalyzer.isYarnEnabled())
            capacity = (int) Math.max(capacity, YarnClusterAnalyzer.getNumCores());
        int nmapers = Math.max(Math.min((int) (8 * maxbrlen * maxbclen * (long) numblocks / dfsblocksize), capacity), 1);
        job.setNumMapTasks(nmapers);
        //set up what matrices are needed to pass from the mapper to reducer
        HashSet<Byte> mapoutputIndexes = MRJobConfiguration.setUpOutputIndexesForMapper(job, realIndexes, dataGenInsStr, instructionsInMapper, null, aggInstructionsInReducer, otherInstructionsInReducer, resultIndexes);
        MatrixChar_N_ReducerGroups ret = MRJobConfiguration.computeMatrixCharacteristics(job, realIndexes, dataGenInsStr, instructionsInMapper, null, aggInstructionsInReducer, null, otherInstructionsInReducer, resultIndexes, mapoutputIndexes, false);
        stats = ret.stats;
        //set up the number of reducers
        MRJobConfiguration.setNumReducers(job, ret.numReducerGroups, numReducers);
        // print the complete MRJob instruction
        if (LOG.isTraceEnabled())
            inst.printCompleteMRJobInstruction(stats);
        // Update resultDimsUnknown based on computed "stats"
        byte[] resultDimsUnknown = new byte[resultIndexes.length];
        for (int i = 0; i < resultIndexes.length; i++) {
            if (stats[i].getRows() == -1 || stats[i].getCols() == -1) {
                resultDimsUnknown[i] = (byte) 1;
            } else {
                resultDimsUnknown[i] = (byte) 0;
            }
        }
        boolean mayContainCtable = instructionsInMapper.contains("ctabletransform") || instructionsInMapper.contains("groupedagg");
        //set up the multiple output files, and their format information
        MRJobConfiguration.setUpMultipleOutputs(job, resultIndexes, resultDimsUnknown, outputs, outputInfos, true, mayContainCtable);
        // configure mapper and the mapper output key value pairs
        job.setMapperClass(DataGenMapper.class);
        if (numReducers == 0) {
            job.setMapOutputKeyClass(Writable.class);
            job.setMapOutputValueClass(Writable.class);
        } else {
            job.setMapOutputKeyClass(MatrixIndexes.class);
            job.setMapOutputValueClass(TaggedMatrixBlock.class);
        }
        //set up combiner
        if (numReducers != 0 && aggInstructionsInReducer != null && !aggInstructionsInReducer.isEmpty())
            job.setCombinerClass(GMRCombiner.class);
        //configure reducer
        job.setReducerClass(GMRReducer.class);
        //job.setReducerClass(PassThroughReducer.class);
        // By default, the job executes in "cluster" mode.
        // Determine if we can optimize and run it in "local" mode.
        MatrixCharacteristics[] inputStats = new MatrixCharacteristics[inputs.length];
        for (int i = 0; i < inputs.length; i++) {
            inputStats[i] = new MatrixCharacteristics(rlens[i], clens[i], brlens[i], bclens[i]);
        }
        //set unique working dir
        MRJobConfiguration.setUniqueWorkingDir(job);
        runjob = JobClient.runJob(job);
        /* Process different counters */
        Group group = runjob.getCounters().getGroup(MRJobConfiguration.NUM_NONZERO_CELLS);
        for (int i = 0; i < resultIndexes.length; i++) {
            // number of non-zeros
            stats[i].setNonZeros(group.getCounter(Integer.toString(i)));
        }
        String dir = dimsUnknownFilePrefix + "/" + runjob.getID().toString() + "_dimsFile";
        stats = MapReduceTool.processDimsFiles(dir, stats);
        MapReduceTool.deleteFileIfExistOnHDFS(dir);
    } finally {
        for (String input : inputs) MapReduceTool.deleteFileIfExistOnHDFS(new Path(input), job);
    }
    return new JobReturn(stats, outputInfos, runjob.isSuccessful());
}

Example 9 with LongStream

use of java.util.stream.LongStream in project incubator-systemml by apache.

the class MatrixBlock method randOperations.

/**
	 * Function to generate the random matrix with specified dimensions and block dimensions.
	 * 
	 * @param rgen random matrix generator
	 * @param seed seed value
	 * @param k ?
	 * @return matrix block
	 * @throws DMLRuntimeException if DMLRuntimeException occurs
	 */
public static MatrixBlock randOperations(RandomMatrixGenerator rgen, long seed, int k) throws DMLRuntimeException {
    MatrixBlock out = new MatrixBlock();
    Well1024a bigrand = null;
    LongStream nnzInBlock = null;
    //setup seeds and nnz per block
    if (!LibMatrixDatagen.isShortcutRandOperation(rgen._min, rgen._max, rgen._sparsity, rgen._pdf)) {
        bigrand = LibMatrixDatagen.setupSeedsForRand(seed);
        nnzInBlock = LibMatrixDatagen.computeNNZperBlock(rgen._rows, rgen._cols, rgen._rowsPerBlock, rgen._colsPerBlock, rgen._sparsity);
    }
    //generate rand data
    if (k > 1)
        out.randOperationsInPlace(rgen, nnzInBlock, bigrand, -1, k);
    else
        out.randOperationsInPlace(rgen, nnzInBlock, bigrand, -1);
    return out;
}