Package org.apache.mahout.common

Examples of org.apache.mahout.common.RandomWrapper.nextDouble()

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  public void testTrainingTestingSets() throws IOException {
    int n = 20;

    for (int nloop = 0; nloop < n; nloop++) {
      RandomWrapper rng = (RandomWrapper) RandomUtils.getRandom();
      double threshold = rng.nextDouble();

      JobConf conf = new JobConf();
      Set<Long> dataset = new HashSet<Long>();
      LongWritable key = new LongWritable();
      Text value = new Text();
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    for (int nloop = 0; nloop < n; nloop++) {
      RandomWrapper rng = (RandomWrapper) RandomUtils.getRandom();

      long seed = rng.getSeed();
      double threshold = rng.nextDouble();
      boolean training = rng.nextBoolean();

      DatasetSplit split = new DatasetSplit(seed, threshold);
      split.setTraining(training);
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  public void testTrainingTestingSets() throws Exception {
    int n = 20;

    for (int nloop = 0; nloop < n; nloop++) {
      RandomWrapper rng = (RandomWrapper) RandomUtils.getRandom();
      double threshold = rng.nextDouble();

      Configuration conf = new Configuration();
      Collection<Long> dataset = new HashSet<Long>();

      DatasetSplit split = new DatasetSplit(rng.getSeed(), threshold);
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    for (int nloop = 0; nloop < n; nloop++) {
      RandomWrapper rng = (RandomWrapper) RandomUtils.getRandom();

      long seed = rng.getSeed();
      double threshold = rng.nextDouble();
      boolean training = rng.nextBoolean();

      DatasetSplit split = new DatasetSplit(seed, threshold);
      split.setTraining(training);
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            AbstractContinousDistribution uniform = new Uniform(-1, 1, gen);

            @Override
            public double nextDouble() {
                double x;
                if (gen.nextDouble() < 0.5) {
                    x = uniform.nextDouble();
                } else {
                    x = normal.nextDouble();
                }
                return x;
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        // 5% of samples will be 0 or 1.0.  10% for each of the values 0.1 through 0.9
        AbstractContinousDistribution mix = new AbstractContinousDistribution() {
            @Override
            public double nextDouble() {
                return Math.rint(gen.nextDouble() * 10) / 10.0;
            }
        };

        TDigest dist = new TDigest((double) 1000);
        long t0 = System.nanoTime();
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        for (int k = 0; k < 40; k++) {
            for (int size : new int[]{10, 100, 1000, 10000}) {
                for (double compression : new double[]{2, 5, 10, 20, 50, 100, 200, 500, 1000}) {
                    TDigest dist = new TDigest(compression);
                    for (int i = 0; i < size * 1000; i++) {
                        dist.add(gen.nextDouble());
                    }
                    System.out.printf("%d\t%d\t%.0f\t%d\t%d\n", k, size, compression, dist.smallByteSize(), dist.byteSize());
                }
            }
        }
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        System.out.printf("pass\tcompression\tq\terror\tsize\n");
        // change to 50 passes for better graphs
        for (int k = 0; k < 3; k++) {
            List<Double> data = Lists.newArrayList();
            for (int i = 0; i < 100000; i++) {
                data.add(gen.nextDouble());
            }
            Collections.sort(data);

            for (double compression : new double[]{2, 5, 10, 20, 50, 100, 200, 500, 1000}) {
                TDigest dist = new TDigest(compression);
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            for (int i = 0; i < parts; i++) {
                subs.add(new TDigest(50).recordAllData());
            }

            for (int i = 0; i < 100000; i++) {
                double x = gen.nextDouble();
                data.add(x);
                dist.add(x);
                subs.get(i % parts).add(x);
            }
            dist.compress();
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      // initialize weights
      final RandomWrapper rnd = RandomUtils.getRandom();
      weightMatrix.assign(new DoubleFunction() {
        @Override
        public double apply(double value) {
          return rnd.nextDouble() - 0.5;
        }
      });
      this.weightMatrixList.add(weightMatrix);
      this.prevWeightUpdatesList.add(new DenseMatrix(row, sizePrevLayer));
      this.squashingFunctionList.add(squashingFunctionName);
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