Example usage for org.apache.commons.math3.stat.descriptive DescriptiveStatistics DescriptiveStatistics

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.descriptive DescriptiveStatistics DescriptiveStatistics.

Prototype

public DescriptiveStatistics() 

Source Link

Document

Construct a DescriptiveStatistics instance with an infinite window

Usage

From source file:com.insightml.evaluation.functions.MeanAbsoluteError.java

@Override
public DescriptiveStatistics acrossLabels(
        final List<? extends Predictions<? extends Number, ? extends Number>>[] predictions) {
    final DescriptiveStatistics stats = new DescriptiveStatistics();
    for (final List<? extends Predictions<? extends Number, ? extends Number>> predz : predictions) {
        for (final Predictions<? extends Number, ? extends Number> preds : predz) {
            final Number[] pred = preds.getPredictions();
            final Number[] exp = preds.getExpected();
            for (int i = 0; i < pred.length; ++i) {
                if (exp[i] != null) {
                    stats.addValue(instance(pred[i], exp[i], preds.getSample(i)));
                }//w  w w  . jav a  2 s  . c om
            }
        }
    }
    return stats;
}

---

From source file:algorithms.quality.JndRegionSize.java

@Override
public double getQuality(Colormap2D colormap) {
    JndRegionComputer computer = new JndRegionComputer(colormap, sampling, 3.0);

    DescriptiveStatistics stats = new DescriptiveStatistics();

    for (Point2D center : computer.getPoints()) {
        List<Point2D> poly = computer.getRegion(center);
        double area = computeArea(poly, center);

        stats.addValue(area);//w ww  .ja  v a2  s .c  om
    }

    // TODO: find a better scaling factor
    return stats.getVariance() * 10000000.d;
}

---

From source file:algorithms.quality.AttentionQuality.java

@Override
public double getQuality(Colormap2D colormap) {
    // max L + max c (which is the same as a or b)
    double normFac = Math.sqrt(100 * 100 + 150 * 150);

    DescriptiveStatistics stats = new DescriptiveStatistics();

    for (Point2D pt : sampling.getPoints()) {
        Color color = colormap.getColor(pt.getX(), pt.getY());
        double[] lch = new CIELABLch().fromColor(color);
        double attention = Math.sqrt(lch[0] * lch[0] + lch[1] * lch[1]) / normFac;

        stats.addValue(attention);//  w w  w .  ja v a 2 s . c  o  m
    }

    return stats.getVariance();
}

---

From source file:com.andrewkroh.common.math.PoissonProcessTester.java

@Before
public void setup() {
    stats = new DescriptiveStatistics();
    process = new PoissonProcess(DEFAULT_MEAN);
}

---

From source file:mase.me.MEGenerationalStat.java

@Override
public void postBreedingStatistics(EvolutionState state) {
    super.postBreedingStatistics(state);
    MESubpopulation pop = (MESubpopulation) state.population.subpops[0];
    DescriptiveStatistics fit = new DescriptiveStatistics();
    for (Individual ind : pop.map.values()) {
        fit.addValue(((ExpandedFitness) ind.fitness).getFitnessScore());
    }/* w ww . j  av a2s.  c o m*/
    state.output.println(state.generation + " " + pop.map.keySet().size() + " " + pop.map.size() + " "
            + fit.getMin() + " " + fit.getMean() + " " + fit.getMax() + " " + pop.newInRepo, log);
    state.output.message("Repertoire size: " + pop.map.keySet().size() + " | New: " + pop.newInRepo
            + " | Avg. fitness: " + new DecimalFormat("0.0000").format(fit.getMean()));
}

---

From source file:com.linkedin.pinot.perf.ForwardIndexReaderBenchmark.java

public static void singleValuedReadBenchMarkV1(File file, int numDocs, int columnSizeInBits) throws Exception {
    boolean signed = false;
    boolean isMmap = false;
    PinotDataBuffer heapBuffer = PinotDataBuffer.fromFile(file, ReadMode.heap, FileChannel.MapMode.READ_ONLY,
            "benchmark");
    BaseSingleColumnSingleValueReader reader = new com.linkedin.pinot.core.io.reader.impl.v1.FixedBitSingleValueReader(
            heapBuffer, numDocs, columnSizeInBits, signed);
    // sequential read
    long start, end;
    DescriptiveStatistics stats = new DescriptiveStatistics();
    for (int run = 0; run < MAX_RUNS; run++) {
        start = System.currentTimeMillis();
        for (int i = 0; i < numDocs; i++) {
            int value = reader.getInt(i);
        }/*from ww  w . j  av  a 2s  .co m*/
        end = System.currentTimeMillis();
        stats.addValue(end - start);
    }
    System.out.println(" v1 sequential read stats for " + file.getName());
    System.out.println(stats.toString().replaceAll("\n", ", ") + " raw:" + Arrays.toString(stats.getValues()));
    reader.close();
    heapBuffer.close();
}

---

From source file:com.intuit.tank.persistence.databases.BucketDataItemTest.java

/**
 * Run the int getPeriod() method test.//from   www.j a  va2 s .c  om
 * 
 * @throws Exception
 * 
 * @generatedBy CodePro at 9/10/14 10:32 AM
 */
@Test
public void testGetPeriod_1() throws Exception {
    BucketDataItem fixture = new BucketDataItem(1, new Date(), new DescriptiveStatistics());

    int result = fixture.getPeriod();

    assertEquals(1, result);
}

---

From source file:mase.app.playground.PlaygroundSDBCStandardizer.java

@Override
public void preInitializationStatistics(EvolutionState state) {
    super.preInitializationStatistics(state);

    MasonSimulationProblem prob = (MasonSimulationProblem) state.evaluator.p_problem;
    PlaygroundSDBCRaw fun = null;
    for (EvaluationFunction ef : prob.getEvalFunctions()) {
        if (ef instanceof PlaygroundSDBCRaw) {
            fun = (PlaygroundSDBCRaw) ef;
            break;
        }//from  ww w  .  j  a va  2s.com
    }

    if (fun == null) {
        state.output.warning("PlaygroundSDBCRaw evaluation function not found. Standardizer not run.");
        return;
    }

    DescriptiveStatistics[] ds = null;
    for (int i = 0; i < 1000; i++) {
        Playground pl = (Playground) prob.getSimState(new HomogeneousGroupController(null), i);
        pl.start();
        double[] s = fun.state(pl);

        if (ds == null) {
            ds = new DescriptiveStatistics[s.length];
            for (int j = 0; j < s.length; j++) {
                ds[j] = new DescriptiveStatistics();
            }
        }

        for (int j = 0; j < s.length; j++) {
            ds[j].addValue(s[j]);
        }
    }

    double[] means = new double[ds.length];
    double[] sds = new double[ds.length];
    for (int i = 0; i < ds.length; i++) {
        means[i] = ds[i].getMean();
        sds[i] = ds[i].getStandardDeviation();
        state.output.message("Feature " + i + ": Mean: " + ds[i].getMean() + " SD: "
                + ds[i].getStandardDeviation() + " Min: " + ds[i].getMin() + " Max: " + ds[i].getMax());
    }

    fun.setStandardizationScores(means, sds);

}

---

From source file:mase.spec.SpecialisationStats.java

@Override
public void postPreBreedingExchangeStatistics(EvolutionState state) {
    super.postPreBreedingExchangeStatistics(state);
    SpecialisationExchanger exc = (SpecialisationExchanger) state.exchanger;
    state.output.print(state.generation + " " + exc.metaPops.size(), log);

    // metapop size (min, mean, max)
    DescriptiveStatistics ds = new DescriptiveStatistics();
    for (MetaPopulation mp : exc.metaPops) {
        ds.addValue(mp.populations.size());
    }/*from ww w . j a v  a  2  s  . c om*/
    state.output.print(" " + ds.getMin() + " " + ds.getMean() + " " + ds.getMax(), log);

    // metapop dispersion (min, mean, max)
    ds.clear();
    for (MetaPopulation mp : exc.metaPops) {
        double dispersion = 0;
        for (Integer i : mp.populations) {
            for (Integer j : mp.populations) {
                dispersion += exc.distanceMatrix[i][j];
            }
        }
        ds.addValue(dispersion / (mp.populations.size() * mp.populations.size()));
    }
    state.output.print(" " + ds.getMin() + " " + ds.getMean() + " " + ds.getMax(), log);

    // total number of merges and splits
    int count = 0;
    for (MetaPopulation mp : exc.metaPops) {
        count += mp.waitingIndividuals.size();
    }
    state.output.print(" " + count + " " + exc.splits, log);

    for (int i = 0; i < exc.prototypeSubs.length; i++) {
        // MetaPop to which they belong
        MetaPopulation pop = null;
        for (int m = 0; m < exc.metaPops.size(); m++) {
            if (exc.metaPops.get(m).populations.contains(i)) {
                pop = exc.metaPops.get(m);
                state.output.print(" " + m, log);
            }
        }

        // Population dispersion
        state.output.print(" " + exc.originalMatrix[i][i], log);

        // Normalised distance to internal pops -- include itself -- 1
        ds.clear();
        for (Integer p : pop.populations) {
            ds.addValue(exc.distanceMatrix[i][p]);
        }
        state.output.print(" " + ds.getMin() + " " + ds.getMean() + " " + ds.getMax(), log);

        // Normalised distance to external pops
        ds.clear();
        for (MetaPopulation mp : exc.metaPops) {
            if (mp != pop) {
                for (Integer p : mp.populations) {
                    ds.addValue(exc.distanceMatrix[i][p]);
                }
            }
        }
        if (ds.getN() == 0) {
            ds.addValue(1);
        }
        state.output.print(" " + ds.getMin() + " " + ds.getMean() + " " + ds.getMax(), log);
    }

    String str = "";
    for (MetaPopulation mp : exc.metaPops) {
        str += mp + " ; ";
    }
    state.output.message(str);

    /*for(double[] m : exc.distanceMatrix) {
     state.output.message(Arrays.toString(m));
     }*/
    // representatives
    /*MetaEvaluator me = (MetaEvaluator) state.evaluator;
     MultiPopCoevolutionaryEvaluator2 baseEval = (MultiPopCoevolutionaryEvaluator2) me.getBaseEvaluator();
     Individual[][] elites = baseEval.getEliteIndividuals();
     ds.clear();
     for(MetaPopulation mp : exc.metaPops) {
     HashSet<Individual> inds = new HashSet<Individual>();
     for(Integer p : mp.populations) {
     inds.add(elites[p][0]);
     }
     ds.addValue(inds.size() / (double) mp.populations.size());
     }
     state.output.print(" " + ds.getMin() + " " + ds.getMean() + " " + ds.getMax(), log);*/
    state.output.println("", log);
}

---

From source file:com.intuit.tank.vm.common.util.ReportUtilCpTest.java

/**
 * Run the String[] getSummaryData(String,DescriptiveStatistics) method test.
 * /*from w  w w. j a  v a 2  s  .  c  o  m*/
 * @throws Exception
 * 
 * @generatedBy CodePro at 9/3/14 3:41 PM
 */
@Test
public void testGetSummaryData_1() throws Exception {
    String key = "";
    DescriptiveStatistics stats = new DescriptiveStatistics();

    String[] result = ReportUtil.getSummaryData(key, stats);

    assertNotNull(result);
    assertEquals(23, result.length);
    assertEquals("", result[0]);
    assertEquals("0", result[1]);
    assertEquals("", result[2]);
    assertEquals("", result[3]);
    assertEquals("", result[4]);
    assertEquals("", result[5]);
    assertEquals("", result[6]);
    assertEquals("", result[7]);
    assertEquals("", result[8]);
    assertEquals("", result[9]);
    assertEquals("", result[10]);
    assertEquals("", result[11]);
    assertEquals("", result[12]);
    assertEquals("", result[13]);
    assertEquals("", result[14]);
    assertEquals("", result[15]);
    assertEquals("", result[16]);
    assertEquals("", result[17]);
    assertEquals("", result[18]);
    assertEquals("", result[19]);
    assertEquals("", result[20]);
    assertEquals(null, result[21]);
    assertEquals(null, result[22]);
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• »