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

Introduction

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

Prototype

public double getMin() 

Source Link

Document

Returns the minimum of the available values

Usage

From source file:mase.stat.FitnessStat.java

/**
 * Prints out the statistics, but does not end with a println -- this lets
 * overriding methods print additional statistics on the same line
 *//*from   ww w.j  a v  a 2  s .  c o  m*/
@Override
public void postEvaluationStatistics(final EvolutionState state) {
    super.postEvaluationStatistics(state);

    int subpops = state.population.subpops.length; // number of supopulations
    DescriptiveStatistics[] fitness = new DescriptiveStatistics[subpops];
    for (int i = 0; i < subpops; i++) {
        fitness[i] = new DescriptiveStatistics();
    }
    int evals = state.evaluator.p_problem instanceof MaseProblem
            ? ((MaseProblem) state.evaluator.p_problem).getTotalEvaluations()
            : 0;

    // gather per-subpopulation statistics
    for (int x = 0; x < subpops; x++) {
        for (int y = 0; y < state.population.subpops[x].individuals.length; y++) {
            if (state.population.subpops[x].individuals[y].evaluated) {// he's got a valid fitness
                // update fitness
                double f = ((ExpandedFitness) state.population.subpops[x].individuals[y].fitness)
                        .getFitnessScore();
                bestSoFar[x] = Math.max(bestSoFar[x], f);
                absoluteBest = Math.max(absoluteBest, f);
                fitness[x].addValue(f);
            }
        }
        // print out fitness information
        if (doSubpops) {
            state.output.println(state.generation + " " + evals + " " + x + " " + fitness[x].getN() + " "
                    + fitness[x].getMin() + " " + fitness[x].getMean() + " " + fitness[x].getMax() + " "
                    + bestSoFar[x], statisticslog);
        }
    }

    // Now gather global statistics
    DescriptiveStatistics global = new DescriptiveStatistics();
    for (DescriptiveStatistics ds : fitness) {
        for (double v : ds.getValues()) {
            global.addValue(v);
        }
    }

    state.output.println(state.generation + " " + evals + " NA " + global.getN() + " " + global.getMin() + " "
            + global.getMean() + " " + global.getMax() + " " + absoluteBest, statisticslog);
}

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From source file:com.tascape.reactor.report.SuiteResultView.java

private void processMetrics() {
    Map<String, Map<String, Object>> tm = new HashMap<>();
    this.caseMetrics.forEach(row -> {
        String key = row.get(CaseResultMetric.METRIC_GROUP) + "." + row.get(CaseResultMetric.METRIC_NAME);
        Map<String, Object> r = tm.get(key);
        if (r == null) {
            tm.put(key, row);/*  w  w w .  j  a va2  s .  c  o m*/
            List<Double> values = new ArrayList<>();
            values.add((double) row.get(CaseResultMetric.METRIC_VALUE));
            row.put("values", values);
        } else {
            @SuppressWarnings("unchecked")
            List<Double> values = (List<Double>) r.get("values");
            values.add((double) row.get(CaseResultMetric.METRIC_VALUE));
        }
    });

    tm.values().stream().forEach(row -> {
        @SuppressWarnings("unchecked")
        List<Double> values = (List<Double>) row.get("values");
        if (values.size() > 1) {
            DescriptiveStatistics stats = new DescriptiveStatistics();
            values.forEach(v -> stats.addValue(v));
            row.put("max", stats.getMax());
            row.put("min", stats.getMin());
            row.put("mean", stats.getMean());
            row.put("size", values.size());
        }
    });

    this.caseMetrics = new ArrayList<>(tm.values());
}

---

From source file:io.yields.math.framework.DomainTest.java

@Explore(name = "Test Variable Distribution", dataProvider = DataProviders.FixedMersenneTwisterDataProvider.class)
@Exploration(name = "Test Function", context = FunctionExplorerContext.class, group = "domain")
public void testVariableDistribution(Explorer<Double> explorer) {

    assertThat(explorer.all().count()).isEqualTo(explorer.valid().count());
    assertThat(explorer.valid().count()).isEqualTo(explorer.valid().count());
    assertThat(explorer.invalid().count()).isEqualTo(0);
    assertThat(explorer.propertyError().count()).isEqualTo(0);

    DescriptiveStatistics stats = new DescriptiveStatistics();
    explorer.all().forEach(result -> stats.addValue(result.getFunctionOutcome().orElse(0d)));

    assertThat(stats.getMean()).isEqualTo(0, delta(0.1));
    assertThat(stats.getMax()).isEqualTo(1, delta(0.1));
    assertThat(stats.getMin()).isEqualTo(-1, delta(0.1));
}

---

From source file:io.yields.math.framework.DomainTest.java

@Explore(name = "Test Variable Distribution with multiple properties", dataProvider = DataProviders.FixedMersenneTwisterDataProvider.class)
@Exploration(name = "Test Function", context = FunctionExplorerMultiplePropertiesContext.class, group = "domain")
public void testVariableDistributionMultipleProperties(Explorer<Double> explorer) {

    assertThat(explorer.all().count()).isEqualTo(explorer.valid().count());
    assertThat(explorer.valid().count()).isEqualTo(explorer.valid().count());
    assertThat(explorer.invalid().count()).isEqualTo(0);
    assertThat(explorer.propertyError().count()).isEqualTo(0);

    DescriptiveStatistics stats = new DescriptiveStatistics();
    explorer.all().forEach(result -> stats.addValue(result.getFunctionOutcome().orElse(0d)));

    assertThat(stats.getMean()).isEqualTo(0, delta(0.1));
    assertThat(stats.getMax()).isEqualTo(1, delta(0.1));
    assertThat(stats.getMin()).isEqualTo(-1, delta(0.1));
}

---

From source file:io.yields.math.framework.DomainTest.java

@Explore(name = "Multiple Explorations", dataProvider = DataProviders.FixedMersenneTwisterDataProvider.class)
@Exploration(name = "Test Function", context = FunctionExplorerContext.class, group = "domain")
@Exploration(name = "Test Function 2", context = Function2ExplorerContext.class, group = "domain")
public void testMultipleExplorations(List<Explorer<Double>> explorers) {

    for (Explorer<Double> explorer : explorers) {

        assertThat(explorer.all().count()).isEqualTo(explorer.valid().count());
        assertThat(explorer.invalid().count()).isEqualTo(0);
        assertThat(explorer.propertyError().count()).isEqualTo(0);

        DescriptiveStatistics stats = new DescriptiveStatistics();
        explorer.all().forEach(result -> stats.addValue(result.getFunctionOutcome().orElse(0d)));

        assertThat(stats.getMean()).isEqualTo(0, delta(0.1));
        assertThat(stats.getMax()).isEqualTo(1, delta(0.1));
        assertThat(stats.getMin()).isEqualTo(-1, delta(0.1));

    }//ww  w  .  j a  va2 s  .com

    // compare 2 explorers
    Explorer<Double> firstExplorer = explorers.get(0);
    Explorer<Double> secondExplorer = explorers.get(1);

    List<PropertyVerifications<Double>> resultsOfFirstExplorer = firstExplorer.all()
            .collect(Collectors.toList());
    List<PropertyVerifications<Double>> resultsOfSecondExplorer = secondExplorer.all()
            .collect(Collectors.toList());

    for (int i = 0; i < resultsOfFirstExplorer.size(); i++) {
        assertThat(resultsOfFirstExplorer.get(i).getFunctionOutcome().orElse(0d))
                .isEqualTo(resultsOfSecondExplorer.get(i).getFunctionOutcome().orElse(0d), delta(2d));
    }

}

---

From source file:com.duy.pascal.interperter.libraries.math.MathLib.java

@PascalMethod(description = "")
public double MinValue(double... arr) {
    DescriptiveStatistics descriptiveStatistics1 = new DescriptiveStatistics(arr);
    return descriptiveStatistics1.getMin();
}

---

From source file:com.duy.pascal.interperter.libraries.math.MathLib.java

@PascalMethod(description = "")
public int MinIntValue(int... arr) {
    double[] copy = new double[arr.length];
    for (int i = 0; i < arr.length; i++) {
        copy[i] = arr[i];// w w  w  . j  a  v  a2s.  c  om
    }
    DescriptiveStatistics descriptiveStatistics1 = new DescriptiveStatistics(copy);
    return (int) descriptiveStatistics1.getMin();
}

---

From source file:com.linuxbox.enkive.statistics.consolidation.AbstractConsolidator.java

/**
 * Builds a map that cooresponds to the consolidation methods
 * /*  ww  w  . ja va 2s  .com*/
 * @param method
 *            - the method to use
 * @param exampleData
 *            - an example data object (for type consistancy after
 *            consolidation)
 * @param statsMaker
 *            - the pre-populated DescriptiveStatstistics object to pull
 *            stats from
 * @param statData
 *            - the map to populate with consolidated data
 */
public void methodMapBuilder(String method, DescriptiveStatistics statsMaker, Map<String, Object> statData) {
    if (method.equals(CONSOLIDATION_SUM)) {
        statData.put(method, statsMaker.getSum());
    } else if (method.equals(CONSOLIDATION_MAX)) {
        statData.put(method, statsMaker.getMax());
    } else if (method.equals(CONSOLIDATION_MIN)) {
        statData.put(method, statsMaker.getMin());
    } else if (method.equals(CONSOLIDATION_AVG)) {
        statData.put(method, statsMaker.getMean());
    }
}

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From source file:org.sakaiproject.gradebookng.tool.panels.SettingsGradingSchemaPanel.java

/**
 * Calculates the min grade for the course
 * /* ww w .  j av  a2s  . c  o m*/
 * @return String min grade
 */
private String getMin(DescriptiveStatistics stats) {
    return this.total > 0 ? String.format("%.2f", stats.getMin()) : "-";
}

---

From source file:com.fpuna.preproceso.PreprocesoTS.java

private static TrainingSetFeature calculoFeaturesMagnitud(List<Registro> muestras, String activity) {

    TrainingSetFeature Feature = new TrainingSetFeature();
    DescriptiveStatistics stats_m = new DescriptiveStatistics();

    double[] fft_m;
    double[] AR_4;

    muestras = Util.calcMagnitud(muestras);

    for (int i = 0; i < muestras.size(); i++) {
        stats_m.addValue(muestras.get(i).getM_1());
    }/*from   w  w w  . jav  a  2  s. co m*/

    //********* FFT *********
    //fft_m = Util.transform(stats_m.getValues());
    fft_m = FFTMixedRadix.fftPowerSpectrum(stats_m.getValues());

    //******************* Calculos Magnitud *******************//
    //mean(s) - Arithmetic mean
    System.out.print(stats_m.getMean() + ",");
    Feature.setMeanX((float) stats_m.getMean());

    //std(s) - Standard deviation
    System.out.print(stats_m.getStandardDeviation() + ",");
    Feature.setStdX((float) stats_m.getStandardDeviation());

    //mad(s) - Median absolute deviation
    //
    //max(s) - Largest values in array
    System.out.print(stats_m.getMax() + ",");
    Feature.setMaxX((float) stats_m.getMax());

    //min(s) - Smallest value in array
    System.out.print(stats_m.getMin() + ",");
    Feature.setMinX((float) stats_m.getMin());

    //skewness(s) - Frequency signal Skewness
    System.out.print(stats_m.getSkewness() + ",");
    Feature.setSkewnessX((float) stats_m.getSkewness());

    //kurtosis(s) - Frequency signal Kurtosis
    System.out.print(stats_m.getKurtosis() + ",");
    Feature.setKurtosisX((float) stats_m.getKurtosis());

    //energy(s) - Average sum of the squares
    System.out.print(stats_m.getSumsq() / stats_m.getN() + ",");
    Feature.setEnergyX((float) (stats_m.getSumsq() / stats_m.getN()));

    //entropy(s) - Signal Entropy
    System.out.print(Util.calculateShannonEntropy(fft_m) + ",");
    Feature.setEntropyX(Util.calculateShannonEntropy(fft_m).floatValue());

    //iqr (s) Interquartile range
    System.out.print(stats_m.getPercentile(75) - stats_m.getPercentile(25) + ",");
    Feature.setIqrX((float) (stats_m.getPercentile(75) - stats_m.getPercentile(25)));

    try {
        //autoregression (s) -4th order Burg Autoregression coefficients
        AR_4 = AutoRegression.calculateARCoefficients(stats_m.getValues(), 4, true);
        System.out.print(AR_4[0] + ",");
        System.out.print(AR_4[1] + ",");
        System.out.print(AR_4[2] + ",");
        System.out.print(AR_4[3] + ",");
        Feature.setArX1((float) AR_4[0]);
        Feature.setArX2((float) AR_4[1]);
        Feature.setArX3((float) AR_4[2]);
        Feature.setArX4((float) AR_4[3]);
    } catch (Exception ex) {
        Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex);
    }
    //meanFreq(s) - Frequency signal weighted average
    System.out.print(Util.meanFreq(fft_m, stats_m.getValues()) + ",");
    Feature.setMeanFreqx((float) Util.meanFreq(fft_m, stats_m.getValues()));

    //******************* Actividad *******************/
    System.out.print(activity);
    System.out.print("\n");
    Feature.setEtiqueta(activity);

    return Feature;
}

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