Examples of org.apache.commons.math3.stat.descriptive.moment.Mean

Package org.apache.commons.math3.stat.descriptive.moment

Examples of org.apache.commons.math3.stat.descriptive.moment.Mean

org.apache.commons.math3.stat.descriptive.moment.Mean
Computes the arithmetic mean of a set of values. Uses the definitional formula:

mean = sum(x_i) / n

where n is the number of observations.

When {@link #increment(double)} is used to add data incrementally from astream of (unstored) values, the value of the statistic that {@link #getResult()} returns is computed using the following recursiveupdating algorithm:
1. Initialize m = the first value
2. For each additional value, update using
  m = m + (new value - m) / (number of observations)
If {@link #evaluate(double[])} is used to compute the mean of an arrayof stored values, a two-pass, corrected algorithm is used, starting with the definitional formula computed using the array of stored values and then correcting this by adding the mean deviation of the data values from the arithmetic mean. See, e.g. "Comparison of Several Algorithms for Computing Sample Means and Variances," Robert F. Ling, Journal of the American Statistical Association, Vol. 69, No. 348 (Dec., 1974), pp. 859-866.

Returns Double.NaN if the dataset is empty.
Note that this implementation is not synchronized. If multiple threads access an instance of this class concurrently, and at least one of the threads invokes the increment() or clear() method, it must be synchronized externally.

  public void testLSH() {
    System.setProperty("model.lsh.sampleRatio", "0.1");
    System.setProperty("model.lsh.numHashes", "20");
    RandomGenerator random = RandomManager.getRandom();


    Mean avgPercentTopRecsConsidered = new Mean();
    Mean avgNDCG = new Mean();
    Mean avgPercentAllItemsConsidered= new Mean();


    for (int iteration = 0; iteration < ITERATIONS; iteration++) {


      FastByIDMap<float[]> Y = new FastByIDMap<float[]>();
      for (int i = 0; i < NUM_ITEMS; i++) {
        Y.put(i, RandomUtils.randomUnitVector(NUM_FEATURES, random));
      }
      float[] userVec = RandomUtils.randomUnitVector(NUM_FEATURES, random);


      double[] results = doTestRandomVecs(Y, userVec);
      double percentTopRecsConsidered = results[0];
      double ndcg = results[1];
      double percentAllItemsConsidered = results[2];


      log.info("Considered {}% of all candidates, {} nDCG, got {}% recommendations correct",
               100 * percentAllItemsConsidered,
               ndcg,
               100 * percentTopRecsConsidered);


      avgPercentTopRecsConsidered.increment(percentTopRecsConsidered);
      avgNDCG.increment(ndcg);
      avgPercentAllItemsConsidered.increment(percentAllItemsConsidered);
    }


    log.info("{}", avgPercentTopRecsConsidered.getResult());
    log.info("{}", avgNDCG.getResult());
    log.info("{}", avgPercentAllItemsConsidered.getResult());


    assertTrue(avgPercentTopRecsConsidered.getResult() > 0.55);
    assertTrue(avgNDCG.getResult() > 0.55);
    assertTrue(avgPercentAllItemsConsidered.getResult() < 0.075);
  }

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            dest.varianceImpl = new Variance(dest.secondMoment);
        } else {
            dest.varianceImpl = source.varianceImpl.copy();
        }
        if (source.meanImpl instanceof Mean) {
            dest.meanImpl = new Mean(dest.secondMoment);
        } else {
            dest.meanImpl = source.meanImpl.copy();
        }
        if (source.getGeoMeanImpl() instanceof GeometricMean) {
            dest.geoMeanImpl = new GeometricMean((SumOfLogs) dest.sumLogImpl);

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     * @throws  MathIllegalArgumentException if the arrays lengths do not match or
     * there is insufficient data
     */
    public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
        throws MathIllegalArgumentException {
        Mean mean = new Mean();
        double result = 0d;
        int length = xArray.length;
        if (length != yArray.length) {
            throw new MathIllegalArgumentException(
                  LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, length, yArray.length);
        } else if (length < 2) {
            throw new MathIllegalArgumentException(
                  LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE, length, 2);
        } else {
            double xMean = mean.evaluate(xArray);
            double yMean = mean.evaluate(yArray);
            for (int i = 0; i < length; i++) {
                double xDev = xArray[i] - xMean;
                double yDev = yArray[i] - yMean;
                result += (xDev * yDev - result) / (i + 1);
            }

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            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }


        covarianceImpl =
            new VectorialCovariance(k, isCovarianceBiasCorrected);

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            double[] values = dstats.getValues();
            for (int j = 0; j < values.length; j++) {
                sstats.addValue(values[j]);
            }
            TestUtils.assertEquals(dstats.getMean(), sstats.getMean(), tol);
            TestUtils.assertEquals(new Mean().evaluate(values), dstats.getMean(), tol);
            TestUtils.assertEquals(dstats.getMax(), sstats.getMax(), tol);
            TestUtils.assertEquals(new Max().evaluate(values), dstats.getMax(), tol);
            TestUtils.assertEquals(dstats.getGeometricMean(), sstats.getGeometricMean(), tol);
            TestUtils.assertEquals(new GeometricMean().evaluate(values), dstats.getGeometricMean(), tol);
            TestUtils.assertEquals(dstats.getMin(), sstats.getMin(), tol);

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        u.addValue(new double[] { 3, 4 });
        Assert.assertEquals(4, u.getMean()[0], 1E-14);
        Assert.assertEquals(6, u.getMean()[1], 1E-14);
        u.clear();
        u.setMeanImpl(new StorelessUnivariateStatistic[] {
                        new Mean(), new Mean()
                      }); // OK after clear
        u.addValue(new double[] { 1, 2 });
        u.addValue(new double[] { 3, 4 });
        Assert.assertEquals(2, u.getMean()[0], 1E-14);
        Assert.assertEquals(3, u.getMean()[1], 1E-14);

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        u.clear();
        u.addValue(1);
        u.addValue(2);
        Assert.assertEquals(3, u.getMean(), 1E-14);
        u.clear();
        u.setMeanImpl(new Mean()); // OK after clear
    }

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    @Test
    public void testOverrideMeanWithMathClass() {
        double[] scores = {1, 2, 3, 4};
        SummaryStatistics stats = new SummaryStatistics();
        stats.setMeanImpl(new Mean()); 
        for(double i : scores) {
          stats.addValue(i);
        }
        Assert.assertEquals((new Mean()).evaluate(scores),stats.getMean(), 0); 
    }

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        }
        return this.geoMean;
    }
    private Mean _getMean() {
        if (this.mean == null) {
            this.mean = new Mean(this._getSecondMoment());
        }
        return this.mean;
    }

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  public void testRecommendLoad() throws Exception {
    // Since latency is more important, and local machine will also be busy handling requests,
    // use few concurrent workers, like 1:
    int workers = 1;
    AtomicLong count = new AtomicLong();
    Mean meanReqTimeMS = new Mean();
    long start = System.currentTimeMillis();


    List<Callable<Void>> tasks = new ArrayList<>(workers);
    for (int i = 0; i < workers; i++) {
      tasks.add(new LoadCallable(Integer.toString(i), meanReqTimeMS, count, start));
    }


    ExecutorService executor = Executors.newFixedThreadPool(workers);
    try {
      executor.invokeAll(tasks);
    } finally {
      executor.shutdown();
    }


    int totalRequests = workers * REQS_PER_WORKER;
    log(totalRequests, meanReqTimeMS, start);


    Assert.assertTrue(meanReqTimeMS.getResult() < 300.0);
  }

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Related Classes of org.apache.commons.math3.stat.descriptive.moment.Mean

org.apache.commons.math3.analysis.differentiation.DerivativeStructure

org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

org.apache.commons.math3.analysis.function.HarmonicOscillator

org.apache.commons.math3.analysis.function.Sin

org.apache.commons.math3.analysis.function.Sinc

org.apache.commons.math3.analysis.MultivariateFunction

org.apache.commons.math3.analysis.QuinticFunction

org.apache.commons.math3.analysis.solvers.BrentSolver

org.apache.commons.math3.analysis.UnivariateFunction

org.apache.commons.math3.complex.Complex

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