Example usage for java.util Random nextGaussian

Introduction

In this page you can find the example usage for java.util Random nextGaussian.

Prototype

public synchronized double nextGaussian() 

Source Link

Document

Returns the next pseudorandom, Gaussian ("normally") distributed double value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence.

Usage

From source file:dataGen.DataGen.java

/**
 * Creates anti-correlated Test-Data with the specified Seed
 * The resulting Data is stored in the resources Folder.
 * @param dimensions/*from w w  w .  jav  a2 s. com*/
 *       The dimension count of the resulting Data
 * @param rowCount
 *       How many data tuples should be created?
 * @param gaussianData
 *       Should we create gaussian distributed Data?
 *       if false: uniform distributed Data will get created.
 * @param std
 *       standard deviation value
 * @param center
 *       center of the distribution
 * @param saveData
 *       should the Data get Saved?
 * @param seed
 *       the seed for the Random Generations
 * @throws IOException
 *       If Stream to a File couldn't be written/closed 
 */
public static CustomRealMatrix genAntiCorrData(int dimensions, int rowCount, boolean gaussianData, float std,
        float center, boolean saveData, long seed) throws IOException {
    File file = null;
    // Generate the Export Files
    if (gaussianData) {
        logger.info("Generating gaussian anti-correlated Data with " + rowCount + " Tuples in " + dimensions
                + " dimensions");
        if (saveData)
            file = new File("src/main/resources/anticorr-gaussian-" + rowCount + "-" + dimensions + ".dat");
    } else {
        logger.info("Generating uniform anti-correlated Data with " + rowCount + " Tuples in " + dimensions
                + "dimensions");
        if (saveData)
            file = new File("src/main/resources/anticorr-normal-" + rowCount + "-" + dimensions + ".dat");
    }

    Random gen = new Random(seed);
    //PearsonsCorrelation corr = new PearsonsCorrelation();
    //RealMatrix corrMatrix = null;

    // Files should be created OS/Language independent
    DecimalFormatSymbols dfs = DecimalFormatSymbols.getInstance();
    dfs.setDecimalSeparator('.');
    NumberFormat nf = new DecimalFormat("0.000000000", dfs);

    // Create a good Start-Matrix: a (dimension x dimension) matrix which fulfills the Anti-Correlation Condition
    CustomRealMatrix values = new CustomRealMatrix(rowCount, dimensions);
    int validTupleCount = 0;

    // Create the remaining Tuples
    while (validTupleCount < rowCount) {
        boolean minimalEntryFound = false;
        double entry = 0d;
        for (int j = 0; j < dimensions; j++) {
            if (gaussianData)
                entry = std * gen.nextGaussian() + center;
            else
                entry = std * gen.nextDouble() + center;

            if (!minimalEntryFound && entry < center)
                minimalEntryFound = true;
            else if (minimalEntryFound) {
                while (entry < center) {
                    if (gaussianData)
                        entry = std * gen.nextGaussian() + center;
                    else
                        entry = std * gen.nextDouble() + center;
                }
            }
            values.setEntry(validTupleCount, j, entry);
        }
        validTupleCount = validTupleCount + 1;
    }
    logger.info(values.getRowDimension() + " entries generated");
    if (saveData) {
        Writer fw = new FileWriter(file);
        saveData(values, fw, nf);
    }

    return values;
}

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From source file:eu.amidst.core.exponentialfamily.EF_InverseGamma.java

/**
 * {@inheritDoc}/*from   w w  w  .ja v a2 s .  c om*/
 */
@Override
public EF_UnivariateDistribution randomInitialization(Random random) {
    double alpha = random.nextGaussian() * 2 + 1;
    double beta = random.nextDouble() * 1 + 0.1;

    this.getNaturalParameters().set(0, -alpha - 1);
    this.getNaturalParameters().set(1, -beta);
    this.fixNumericalInstability();
    this.updateMomentFromNaturalParameters();

    return this;
}

---

From source file:org.apache.edgent.samples.utils.sensor.PeriodicRandomSensor.java

/**
 * Create a periodic sensor stream with readings from {@link Random#nextGaussian()}.
 * @param t the topology to add the sensor stream to
 * @param periodMsec how frequently to generate a reading
 * @return the sensor value stream/*from ww w .j  a  v a  2  s .c o m*/
 */
public TStream<Pair<Long, Double>> newGaussian(Topology t, long periodMsec) {
    Random r = newRandom();
    return t.poll(() -> new Pair<Long, Double>(System.currentTimeMillis(), r.nextGaussian()), periodMsec,
            TimeUnit.MILLISECONDS);

}

---

From source file:org.powertac.producer.fossil.SteamPlantTest.java

@Test
public void dataGenerateOutputGraph() throws IOException {
    double adjustmentSpeed = 10000;
    double variance = 2000;
    double time = (200000 - 500000) / (signum(200000 - 500000) * adjustmentSpeed);

    Curve output = new Curve();
    output.add(0, 500000);/*www  .  ja v a 2 s  . co  m*/
    output.add(time, 200000);
    output.add(time + 10, 200000);
    Random r = new Random();
    new File("data/").mkdir();
    PrintWriter fw = new PrintWriter("data/dataFossilOutput.txt");
    for (int i = 0; i < 60; i++) {
        fw.printf("%d,%f%n", i, output.value(i - 5) + r.nextGaussian() * variance);
    }
    fw.close();
}

---

From source file:org.powertac.producer.fossil.SteamPlantTest.java

@Test
public void dataGenerateOutputGraph2() throws IOException {
    double adjustmentSpeed = 4500;
    double variance = 1000;
    double time = (600000 - 780000) / (signum(600000 - 780000) * adjustmentSpeed);

    Curve output = new Curve();
    output.add(0, 780000);// ww w.  j  a v  a 2s  . c  om
    output.add(time / 2, (signum(600000 - 780000) * adjustmentSpeed) * time / 2 + 780000);
    output.add(time, 600000);
    Random r = new Random();
    new File("data/").mkdir();
    PrintWriter fw = new PrintWriter("data/fossil.txt");
    for (int i = 0; i <= 80; i++) {
        fw.printf(Locale.ENGLISH, "%d,%f%n", i, output.value(i - 20) + r.nextGaussian() * variance);
    }
    fw.close();
}

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From source file:com.facebook.presto.operator.aggregation.AbstractTestApproximateAggregationFunction.java

@Test
public void testCorrectnessOnGaussianData() throws Exception {
    int originalDataSize = 10000;
    Random distribution = new Random(0);
    List<Number> list = new ArrayList<>();
    for (int i = 0; i < originalDataSize; i++) {
        list.add(distribution.nextGaussian() * 100);
    }//from  www.  ja  va 2  s.  c  o m

    testCorrectnessOfErrorFunction(list);
}

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From source file:dev.il.spiderficationsample.DemoMapActivity.java

private void addDemoMarkersAround(GoogleMap map, LatLng center) {
    MarkerOptions options = new MarkerOptions();
    Random r = new Random();
    for (int k = 0; k < 20; k++) {
        map.addMarker(//from ww  w  .  j  a  v  a2  s  . c om
                options.title("Place " + k)
                        .position(new LatLng(center.latitude + r.nextGaussian() * 0.002,
                                center.longitude + r.nextGaussian() * 0.002))
                        .clusterGroup(ClusterGroup.FIRST_USER));
    }
}

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From source file:ml.shifu.shifu.core.binning.EqualPopulationBinningTest.java

@Test
public void tesGussiantBinning() {
    Random rd = new Random(System.currentTimeMillis());

    EqualPopulationBinning binning = new EqualPopulationBinning(10);
    for (int i = 0; i < 10000; i++) {
        binning.addData(Double.toString(rd.nextGaussian() % 1000));
    }/*  w w w .j av a2  s  .c  om*/

    System.out.println(binning.getDataBin());
}

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From source file:eu.amidst.core.inference.ImportanceSamplingExperiments.java

private static Assignment randomEvidence(long seed, double evidenceRatio, BayesianNetwork bn,
        Variable varInterest) throws UnsupportedOperationException {

    if (evidenceRatio <= 0 || evidenceRatio >= 1) {
        throw new UnsupportedOperationException("Error: invalid ratio");
    }/*from   w  ww.j  a v a2s. c  o  m*/

    int numVariables = bn.getVariables().getNumberOfVars();

    Random random = new Random(seed); //1823716125
    int numVarEvidence = (int) Math.ceil(numVariables * evidenceRatio); // Evidence on 20% of variables
    //numVarEvidence = 0;
    //List<Variable> varEvidence = new ArrayList<>(numVarEvidence);
    double[] evidence = new double[numVarEvidence];
    Variable aux;
    HashMapAssignment assignment = new HashMapAssignment(2);

    int[] indexesEvidence = new int[numVarEvidence + 1];
    indexesEvidence[0] = varInterest.getVarID();
    //if (Main.VERBOSE) System.out.println(variable.getVarID());

    if (Main.VERBOSE)
        System.out.println("Evidence:");
    for (int k = 0; k < numVarEvidence; k++) {
        int varIndex = -1;
        do {
            varIndex = random.nextInt(bn.getNumberOfVars());
            //if (Main.VERBOSE) System.out.println(varIndex);
            aux = bn.getVariables().getVariableById(varIndex);

            double thisEvidence;
            if (aux.isMultinomial()) {
                thisEvidence = random.nextInt(aux.getNumberOfStates());
            } else {
                thisEvidence = random.nextGaussian();
            }
            evidence[k] = thisEvidence;

        } while (ArrayUtils.contains(indexesEvidence, varIndex));

        indexesEvidence[k + 1] = varIndex;
        //if (Main.VERBOSE) System.out.println(Arrays.toString(indexesEvidence));
        if (Main.VERBOSE)
            System.out.println("Variable " + aux.getName() + " = " + evidence[k]);

        assignment.setValue(aux, evidence[k]);
    }
    if (Main.VERBOSE)
        System.out.println();

    return assignment;
}

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From source file:ml.shifu.shifu.core.binning.EqualPopulationBinningTest.java

@Test
public void testObjectSeri() {
    Random rd = new Random(System.currentTimeMillis());

    EqualPopulationBinning binning = new EqualPopulationBinning(10);
    for (int i = 0; i < 10000; i++) {
        binning.addData(Double.toString(rd.nextGaussian() % 1000));
    }//w  w w .j a  v  a 2  s.  c  o  m

    String binningStr = binning.objToString();
    String originalBinningData = binning.getDataBin().toString();

    ModelConfig modelConfig = new ModelConfig();
    modelConfig.getStats().setBinningMethod(BinningMethod.EqualPositive);

    ColumnConfig columnConfig = new ColumnConfig();
    columnConfig.setColumnType(ColumnType.N);

    AbstractBinning<?> otherBinning = AbstractBinning.constructBinningFromStr(modelConfig, columnConfig,
            binningStr);
    String newBinningData = otherBinning.getDataBin().toString();

    Assert.assertEquals(originalBinningData, newBinningData);
}

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