Package org.apache.mahout.common

Examples of org.apache.mahout.common.RandomWrapper

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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();
     
      DatasetSplit split = new DatasetSplit(rng.getSeed(), threshold);

      // read the training set
      split.storeJobParameters(conf);
      long datasetSize = 100;
      RndLineRecordReader rndReader = new RndLineRecordReader(new MockReader(datasetSize), conf);
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  public void testStoreJobParameters() {
    int n = 20;

    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);

      JobConf conf = new JobConf();
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  @Test
  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);

      // read the training set
      split.storeJobParameters(conf);
      long datasetSize = 100;
      RndLineRecordReader rndReader = new RndLineRecordReader(new MockReader(datasetSize), conf);
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  @Test
  public void testStoreJobParameters() {
    int n = 20;

    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);

      Configuration conf = new Configuration();
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    this.preventOverfitting = preventOverfitting;
    this.randomNoise = randomNoise;
  }

  protected void prepareTraining() throws TasteException {
    RandomWrapper random = RandomUtils.getRandom();
    userVectors = new double[dataModel.getNumUsers()][numFeatures];
    itemVectors = new double[dataModel.getNumItems()][numFeatures];

    double globalAverage = getAveragePreference();
    for (int userIndex = 0; userIndex < userVectors.length; userIndex++) {
      userVectors[userIndex][0] = globalAverage;
      userVectors[userIndex][USER_BIAS_INDEX] = 0; // will store user bias
      userVectors[userIndex][ITEM_BIAS_INDEX] = 1; // corresponding item feature contains item bias
      for (int feature = FEATURE_OFFSET; feature < numFeatures; feature++) {
        userVectors[userIndex][feature] = random.nextGaussian() * randomNoise;
      }
    }
    for (int itemIndex = 0; itemIndex < itemVectors.length; itemIndex++) {
      itemVectors[itemIndex][0] = 1; // corresponding user feature contains global average
      itemVectors[itemIndex][USER_BIAS_INDEX] = 1; // corresponding user feature contains user bias
      itemVectors[itemIndex][ITEM_BIAS_INDEX] = 0; // will store item bias
      for (int feature = FEATURE_OFFSET; feature < numFeatures; feature++) {
        itemVectors[itemIndex][feature] = random.nextGaussian() * randomNoise;
      }
    }

    cachePreferences();
    shufflePreferences();
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      }
    }
  }

  protected void shufflePreferences() {
    RandomWrapper random = RandomUtils.getRandom();
    /* Durstenfeld shuffle */
    for (int currentPos = cachedUserIDs.length - 1; currentPos > 0; currentPos--) {
      int swapPos = random.nextInt(currentPos + 1);
      swapCachedPreferences(currentPos, swapPos);
    }
  }
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    this.numThreads = numThreads;
  }

  protected void initialize() throws TasteException {
    RandomWrapper random = RandomUtils.getRandom();
    userVectors = new double[dataModel.getNumUsers()][rank];
    itemVectors = new double[dataModel.getNumItems()][rank];

    double globalAverage = getAveragePreference();
    for (int userIndex = 0; userIndex < userVectors.length; userIndex++) {
      userVectors[userIndex][0] = globalAverage;
      userVectors[userIndex][USER_BIAS_INDEX] = 0; // will store user bias
      userVectors[userIndex][ITEM_BIAS_INDEX] = 1; // corresponding item feature contains item bias
      for (int feature = FEATURE_OFFSET; feature < rank; feature++) {
        userVectors[userIndex][feature] = random.nextGaussian() * NOISE;
      }
    }
    for (int itemIndex = 0; itemIndex < itemVectors.length; itemIndex++) {
      itemVectors[itemIndex][0] = 1; // corresponding user feature contains global average
      itemVectors[itemIndex][USER_BIAS_INDEX] = 1; // corresponding user feature contains user bias
      itemVectors[itemIndex][ITEM_BIAS_INDEX] = 0; // will store item bias
      for (int feature = FEATURE_OFFSET; feature < rank; feature++) {
        itemVectors[itemIndex][feature] = random.nextGaussian() * NOISE;
      }
    }
  }
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        deviationDump.flush();
    }

    @Test
    public void testUniform() {
        RandomWrapper gen = RandomUtils.getRandom();
        for (int i = 0; i < 5; i++) {
            runTest(new Uniform(0, 1, gen), 100,
//                    new double[]{0.0001, 0.001, 0.01, 0.1, 0.5, 0.9, 0.99, 0.999, 0.9999},
                    new double[]{0.001, 0.01, 0.1, 0.5, 0.9, 0.99, 0.999},
                    "uniform", true);
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    public void testGamma() {
        // this Gamma distribution is very heavily skewed.  The 0.1%-ile is 6.07e-30 while
        // the median is 0.006 and the 99.9th %-ile is 33.6 while the mean is 1.
        // this severe skew means that we have to have positional accuracy that
        // varies by over 11 orders of magnitude.
        RandomWrapper gen = RandomUtils.getRandom();
        for (int i = 0; i < 5; i++) {
            runTest(new Gamma(0.1, 0.1, gen), 100,
//                    new double[]{6.0730483624079e-30, 6.0730483624079e-20, 6.0730483627432e-10, 5.9339110446023e-03,
//                            2.6615455373884e+00, 1.5884778179295e+01, 3.3636770117188e+01},
                    new double[]{0.001, 0.01, 0.1, 0.5, 0.9, 0.99, 0.999},
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    @Test
    public void testNarrowNormal() {
        // this mixture of a uniform and normal distribution has a very narrow peak which is centered
        // near the median.  Our system should be scale invariant and work well regardless.
        final RandomWrapper gen = RandomUtils.getRandom();
        AbstractContinousDistribution mix = new AbstractContinousDistribution() {
            AbstractContinousDistribution normal = new Normal(0, 1e-5, gen);
            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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Related Classes of org.apache.mahout.common.RandomWrapper

  • org.apache.commons.math3.random.MersenneTwister
  • org.apache.mahout.cf.taste.impl.recommender.svd.ParallelSGDFactorizer
  • org.apache.mahout.cf.taste.impl.recommender.svd.RatingSGDFactorizer
  • org.apache.mahout.classifier.mlp.NeuralNetwork
  • org.apache.mahout.ga.watchmaker.cd.hadoop.DatasetSplitTest
  • org.apache.mahout.math.stats.TDigestTest
  • org.uncommons.maths.random.MersenneTwisterRNG
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