Examples with Well1024a org.apache.commons.math3.random.Well1024a used on opensource projects

Example 11 with Well1024a

use of org.apache.commons.math3.random.Well1024a in project incubator-systemml by apache.

the class PoissonPRNGenerator method setup.

public void setup(double mean, long sd) {
    seed = sd;
    SynchronizedRandomGenerator srg = new SynchronizedRandomGenerator(new Well1024a());
    srg.setSeed(seed);
    _pdist = new PoissonDistribution(srg, _mean, PoissonDistribution.DEFAULT_EPSILON, PoissonDistribution.DEFAULT_MAX_ITERATIONS);
}

Example 12 with Well1024a

use of org.apache.commons.math3.random.Well1024a in project 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
 */
public static MatrixBlock randOperations(RandomMatrixGenerator rgen, long seed, int k) {
    MatrixBlock out = new MatrixBlock();
    Well1024a bigrand = null;
    // setup seeds and nnz per block
    if (!LibMatrixDatagen.isShortcutRandOperation(rgen._min, rgen._max, rgen._sparsity, rgen._pdf))
        bigrand = LibMatrixDatagen.setupSeedsForRand(seed);
    // generate rand data
    if (k > 1)
        out.randOperationsInPlace(rgen, bigrand, -1, k);
    else
        out.randOperationsInPlace(rgen, bigrand, -1);
    return out;
}

Example 13 with Well1024a

use of org.apache.commons.math3.random.Well1024a in project 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]);
        MRType 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 == MRType.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());
                for (long r = 0; r < Math.max(rlens[i], 1); r += brlens[i]) {
                    long curBlockRowSize = Math.min(brlens[i], (rlens[i] - r));
                    for (long c = 0; c < Math.max(clens[i], 1); 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(bigrand.nextLong());
                        pw.println(sb.toString());
                        sb.setLength(0);
                        numblocks++;
                    }
                }
            } finally {
                IOUtilFunctions.closeSilently(pw);
            }
            inputInfos[i] = InputInfo.TextCellInputInfo;
        } else if (mrtype == MRType.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 = UtilFunctions.getSeqLength(from, to, 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 14 with Well1024a

use of org.apache.commons.math3.random.Well1024a in project systemml by apache.

the class LibMatrixDatagen method setupSeedsForRand.

/**
 * A matrix of random numbers is generated by using multiple seeds, one for each
 * block. Such block-level seeds are produced via Well equidistributed long-period linear
 * generator (Well1024a). For a given seed, this function sets up the block-level seeds.
 *
 * This function is invoked from both CP (RandCPInstruction.processInstruction())
 * as well as MR (RandMR.java while setting up the Rand job).
 *
 * @param seed seed for random generator
 * @return Well1024a pseudo-random number generator
 */
public static Well1024a setupSeedsForRand(long seed) {
    long lSeed = (seed == DataGenOp.UNSPECIFIED_SEED ? DataGenOp.generateRandomSeed() : seed);
    LOG.trace("Setting up RandSeeds with initial seed = " + lSeed + ".");
    Random random = new Random(lSeed);
    Well1024a bigrand = new Well1024a();
    // random.setSeed(lSeed);
    int[] seeds = new int[32];
    for (int s = 0; s < seeds.length; s++) seeds[s] = random.nextInt();
    bigrand.setSeed(seeds);
    return bigrand;
}

Example 15 with Well1024a

use of org.apache.commons.math3.random.Well1024a in project systemml by apache.

the class RandSPInstruction method generateRandData.

private void generateRandData(SparkExecutionContext sec) {
    long lrows = sec.getScalarInput(rows).getLongValue();
    long lcols = sec.getScalarInput(cols).getLongValue();
    // step 1: generate pseudo-random seed (because not specified)
    // seed per invocation
    long lSeed = seed;
    if (lSeed == DataGenOp.UNSPECIFIED_SEED)
        lSeed = DataGenOp.generateRandomSeed();
    if (LOG.isTraceEnabled())
        LOG.trace("Process RandSPInstruction rand with seed = " + lSeed + ".");
    // step 2: potential in-memory rand operations if applicable
    if (isMemAvail(lrows, lcols, sparsity, minValue, maxValue) && DMLScript.rtplatform != RUNTIME_PLATFORM.SPARK) {
        RandomMatrixGenerator rgen = LibMatrixDatagen.createRandomMatrixGenerator(pdf, (int) lrows, (int) lcols, rowsInBlock, colsInBlock, sparsity, minValue, maxValue, pdfParams);
        MatrixBlock mb = MatrixBlock.randOperations(rgen, lSeed);
        sec.setMatrixOutput(output.getName(), mb, getExtendedOpcode());
        Statistics.decrementNoOfExecutedSPInst();
        return;
    }
    // step 3: seed generation
    JavaPairRDD<MatrixIndexes, Long> seedsRDD = null;
    Well1024a bigrand = LibMatrixDatagen.setupSeedsForRand(lSeed);
    double totalSize = OptimizerUtils.estimatePartitionedSizeExactSparsity(lrows, lcols, rowsInBlock, colsInBlock, // overestimate for on disk, ensures hdfs block per partition
    sparsity);
    double hdfsBlkSize = InfrastructureAnalyzer.getHDFSBlockSize();
    MatrixCharacteristics tmp = new MatrixCharacteristics(lrows, lcols, rowsInBlock, colsInBlock);
    long numBlocks = tmp.getNumBlocks();
    long numColBlocks = tmp.getNumColBlocks();
    // a) in-memory seed rdd construction
    if (numBlocks < INMEMORY_NUMBLOCKS_THRESHOLD) {
        ArrayList<Tuple2<MatrixIndexes, Long>> seeds = new ArrayList<>();
        for (long i = 0; i < numBlocks; i++) {
            long r = 1 + i / numColBlocks;
            long c = 1 + i % numColBlocks;
            MatrixIndexes indx = new MatrixIndexes(r, c);
            Long seedForBlock = bigrand.nextLong();
            seeds.add(new Tuple2<>(indx, seedForBlock));
        }
        // for load balancing: degree of parallelism such that ~128MB per partition
        int numPartitions = (int) Math.max(Math.min(totalSize / hdfsBlkSize, numBlocks), 1);
        // create seeds rdd
        seedsRDD = sec.getSparkContext().parallelizePairs(seeds, numPartitions);
    } else // b) file-based seed rdd construction (for robustness wrt large number of blocks)
    {
        Path path = new Path(LibMatrixDatagen.generateUniqueSeedPath(dir));
        PrintWriter pw = null;
        try {
            FileSystem fs = IOUtilFunctions.getFileSystem(path);
            pw = new PrintWriter(fs.create(path));
            StringBuilder sb = new StringBuilder();
            for (long i = 0; i < numBlocks; i++) {
                sb.append(1 + i / numColBlocks);
                sb.append(',');
                sb.append(1 + i % numColBlocks);
                sb.append(',');
                sb.append(bigrand.nextLong());
                pw.println(sb.toString());
                sb.setLength(0);
            }
        } catch (IOException ex) {
            throw new DMLRuntimeException(ex);
        } finally {
            IOUtilFunctions.closeSilently(pw);
        }
        // for load balancing: degree of parallelism such that ~128MB per partition
        int numPartitions = (int) Math.max(Math.min(totalSize / hdfsBlkSize, numBlocks), 1);
        // create seeds rdd
        seedsRDD = sec.getSparkContext().textFile(path.toString(), numPartitions).mapToPair(new ExtractSeedTuple());
    }
    // step 4: execute rand instruction over seed input
    JavaPairRDD<MatrixIndexes, MatrixBlock> out = seedsRDD.mapToPair(new GenerateRandomBlock(lrows, lcols, rowsInBlock, colsInBlock, sparsity, minValue, maxValue, pdf, pdfParams));
    // step 5: output handling
    MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(output.getName());
    if (!mcOut.dimsKnown(true)) {
        // note: we cannot compute the nnz from sparsity because this would not reflect the
        // actual number of non-zeros, except for extreme values of sparsity equals 0 or 1.
        long lnnz = (sparsity == 0 || sparsity == 1) ? (long) (sparsity * lrows * lcols) : -1;
        mcOut.set(lrows, lcols, rowsInBlock, colsInBlock, lnnz);
    }
    sec.setRDDHandleForVariable(output.getName(), out);
}