List of usage examples for java.util Random nextGaussian
public synchronized double nextGaussian()
From source file:MainClass.java
public static void main(String args[]) { Random generator = new Random(); for (int i = 0; i < 10; i++) { System.out.println(generator.nextGaussian()); }/*from w w w .ja v a 2 s .co m*/ }
From source file:Main.java
public static void main(String args[]) { Random randomno = new Random(); // check next Gaussian value System.out.println("Next Gaussian value: " + randomno.nextGaussian()); }
From source file:Random3.java
public static void main(String[] argv) { // java.util.Random methods are non-static, do need to construct Math //+/*from w w w . ja va 2 s. c om*/ Random r = new Random(); for (int i = 0; i < 10; i++) System.out.println("A gaussian random double is " + r.nextGaussian()); //- }
From source file:RandDemo.java
public static void main(String args[]) { Random r = new Random(); double val; double sum = 0; int bell[] = new int[10]; for (int i = 0; i < 100; i++) { val = r.nextGaussian(); sum += val; double t = -2; for (int x = 0; x < 10; x++, t += 0.5) if (val < t) { bell[x]++;/* w ww. ja v a 2s . c o m*/ break; } } System.out.println("Average of values: " + (sum / 100)); for (int i = 0; i < 10; i++) { for (int x = bell[i]; x > 0; x--) System.out.print("*"); System.out.println(); } }
From source file:poisondog.demo.StringDemo.java
public static void main(String[] args) { // String temp=" command para1 para2 "; // System.out.println(temp.replaceAll("^\\s*\\S*\\s*", "").replaceAll("\\s*$", "") + "]]"); Queue<Double> queue = new CircularFifoQueue<Double>(1000); List<Double> all = new LinkedList<Double>(); List<Double> list = new ArrayList<Double>(); double result = 0; double sum = 0; Random r = new Random(); for (int i = 0; i < 10000; i++) { double g = r.nextGaussian() * 100 - 5; sum += g;// w ww.j ava 2s . c om if (g > 0) result++; if (i > 5000 && getRate(queue) < getRate(all) * .94) list.add(g); all.add(g); queue.add(g); // if (queue.size() > 1000) // queue.remove(); } // System.out.println(list.size()); int count = 0; for (Double d : list) { if (d > 0) count++; } System.out.println("Origin Rate: " + (double) result / all.size()); System.out.println("Origin Sum: " + sum); System.out.println("Upgrade Rate: " + (double) count / list.size()); System.out.println("Upgrade Count: " + list.size()); System.out.println("Upgrade Sum: " + getSum(list)); System.out.println("Queue Count: " + queue.size()); }
From source file:Random4.java
public static void main(String[] argv) throws IOException { // java.util.Random methods are non-static, do need to construct Math Random r = new Random(); PrintWriter file1 = new PrintWriter(new FileWriter("file1")); PrintWriter file2 = new PrintWriter(new FileWriter("file2")); for (int i = 0; i < 10000; i++) { file1.println(r.nextDouble());/*from ww w . ja v a 2s. c o m*/ file2.println(r.nextGaussian()); } file1.close(); file2.close(); }
From source file:state.FindOutliers.java
public static void main(String[] args) throws Exception { final double threshold = args.length == 0 ? 2.0 : Double.parseDouble(args[0]); Topology t = new Topology("StandardDeviationFilter"); final Random rand = new Random(); // Produce a stream of random double values with a normal // distribution, mean 0.0 and standard deviation 1. TStream<Double> values = t.limitedSource(new Supplier<Double>() { private static final long serialVersionUID = 1L; @Override/*from w w w. ja va 2 s.co m*/ public Double get() { return rand.nextGaussian(); } }, 100000); /* * Filters the values based on calculating the mean and standard * deviation from the incoming data. In this case only outliers are * present in the output stream outliers. A outlier is defined as one * more than (threshold*standard deviation) from the mean. * * This demonstrates an anonymous functional logic class that is * stateful. The two fields mean and sd maintain their values across * multiple invocations of the test method, that is for multiple tuples. * * Note both Mean & StandardDeviation classes are serializable. */ TStream<Double> outliers = values.filter(new Predicate<Double>() { private static final long serialVersionUID = 1L; private final Mean mean = new Mean(); private final StandardDeviation sd = new StandardDeviation(); @Override public boolean test(Double tuple) { mean.increment(tuple); sd.increment(tuple); double multpleSd = threshold * sd.getResult(); double absMean = Math.abs(mean.getResult()); double absTuple = Math.abs(tuple); return absTuple > absMean + multpleSd; } }); outliers.print(); StreamsContextFactory.getEmbedded().submit(t).get(); }
From source file:lambertmrev.LambertMRev.java
/** * @param args the command line arguments *//*from w w w . jav a 2 s .c o m*/ public static void main(String[] args) { // Want to test the Lambert class so you can specify the number of revs for which to compute //System.out.print("this is the frames tutorial \n"); try { Frame inertialFrame = FramesFactory.getEME2000(); TimeScale utc = TimeScalesFactory.getTAI(); AbsoluteDate initialDate = new AbsoluteDate(2004, 01, 01, 23, 30, 00.000, utc); double mu = 3.986004415e+14; double a = 24396159; // semi major axis in meters double e = 0.72831215; // eccentricity double i = Math.toRadians(7); // inclination double omega = Math.toRadians(180); // perigee argument double raan = Math.toRadians(261); // right ascension of ascending node double lM = 0; // mean anomaly Orbit initialOrbit = new KeplerianOrbit(a, e, i, omega, raan, lM, PositionAngle.MEAN, inertialFrame, initialDate, mu); //KeplerianPropagator kepler = new KeplerianPropagator(initialOrbit); // set geocentric positions Vector3D r1 = new Vector3D(-6.88999e3, 3.92763e4, 2.67053e3); Vector3D r2 = new Vector3D(-3.41458e4, 2.05328e4, 3.44315e3); Vector3D r1_site = new Vector3D(4.72599e3, 1.26633e3, 4.07799e3); Vector3D r2_site = new Vector3D(4.70819e3, 1.33099e3, 4.07799e3); // get the topocentric positions Vector3D top1 = Transform.geo2radec(r1.scalarMultiply(1000), r1_site.scalarMultiply(1000)); Vector3D top2 = Transform.geo2radec(r2.scalarMultiply(1000), r2_site.scalarMultiply(1000)); // time of flight in seconds double tof = 3 * 3600; // propagate to 0 and tof Lambert test = new Lambert(); boolean cw = false; int multi_revs = 1; RealMatrix v1_mat; Random randomGenerator = new Random(); PrintWriter out_a = new PrintWriter("out_java_a.txt"); PrintWriter out_e = new PrintWriter("out_java_e.txt"); PrintWriter out_rho1 = new PrintWriter("out_java_rho1.txt"); PrintWriter out_rho2 = new PrintWriter("out_java_rho2.txt"); // start the loop double A, Ecc, rho1, rho2, tof_hyp; long time1 = System.nanoTime(); for (int ll = 0; ll < 1e6; ll++) { rho1 = top1.getZ() / 1000 + 1e-3 * randomGenerator.nextGaussian() * top1.getZ() / 1000; rho2 = top2.getZ() / 1000 + 1e-3 * randomGenerator.nextGaussian() * top2.getZ() / 1000; //tof_hyp = FastMath.abs(tof + 0.1*3600 * randomGenerator.nextGaussian()); // from topo to geo Vector3D r1_hyp = Transform.radec2geo(top1.getX(), top1.getY(), rho1, r1_site); Vector3D r2_hyp = Transform.radec2geo(top2.getX(), top2.getY(), rho2, r2_site); // System.out.println(r1_hyp.scalarMultiply(1000).getNorm()); // System.out.println(r2_hyp.scalarMultiply(1000).getNorm()); // System.out.println(tof/3600); test.lambert_problem(r1_hyp.scalarMultiply(1000), r2_hyp.scalarMultiply(1000), tof, mu, cw, multi_revs); v1_mat = test.get_v1(); Vector3D v1 = new Vector3D(v1_mat.getEntry(0, 0), v1_mat.getEntry(0, 1), v1_mat.getEntry(0, 2)); // System.out.println(v1); PVCoordinates rv1 = new PVCoordinates(r1_hyp.scalarMultiply(1000), v1); Orbit orbit_out = new KeplerianOrbit(rv1, inertialFrame, initialDate, mu); A = orbit_out.getA(); Ecc = orbit_out.getE(); // System.out.println(ll + " - " +A); out_a.println(A); out_e.println(Ecc); out_rho1.println(rho1); out_rho2.println(rho2); } long time2 = System.nanoTime(); long timeTaken = time2 - time1; out_a.close(); out_e.close(); out_rho1.close(); out_rho2.close(); System.out.println("Time taken " + timeTaken / 1000 / 1000 + " milli secs"); // get the truth test.lambert_problem(r1.scalarMultiply(1000), r2.scalarMultiply(1000), tof, mu, cw, multi_revs); v1_mat = test.get_v1(); Vector3D v1 = new Vector3D(v1_mat.getEntry(0, 0), v1_mat.getEntry(0, 1), v1_mat.getEntry(0, 2)); PVCoordinates rv1 = new PVCoordinates(r1.scalarMultiply(1000), v1); Orbit orbit_out = new KeplerianOrbit(rv1, inertialFrame, initialDate, mu); //System.out.println(orbit_out.getA()); } catch (FileNotFoundException ex) { Logger.getLogger(LambertMRev.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:uk.ac.babraham.BamQC.Graphs.ScatterGraph.java
public static void main(String[] args) { SwingUtilities.invokeLater(new Runnable() { @Override// w w w. j av a 2 s . com public void run() { Random r = new Random(); int sampleSize = 1000; double[] data = new double[sampleSize]; double[] xCategories = new double[sampleSize]; String[] toolTipsLabels = new String[sampleSize]; for (int i = 0; i < sampleSize; i++) { data[i] = Math.log((r.nextGaussian() * 1.5 + 10) * i + 50); xCategories[i] = Math.log(i + 50); toolTipsLabels[i] = String.valueOf(i); } String xLabel = "xLabel"; String yLabel = "yLabel"; //String yLabel = null; String graphTitle = "Graph Title"; JFrame frame = new JFrame(); ScatterGraph scatterGraph = new ScatterGraph(data, xCategories, toolTipsLabels, xLabel, yLabel, graphTitle); frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); frame.setSize(500, 500); frame.add(scatterGraph); frame.setLocationRelativeTo(null); frame.setVisible(true); } }); }
From source file:plugin.trackmate.tram.trackanalyzer.TrAM.java
/** * Unit test//w ww.j a va 2 s . c om */ public static void main(final String[] args) { final int numTracks = 500; final int numDiscontinuousTracks = 10; final int numTimePoints = 50; final int discontinuitySafeZoneWidth = 5; final String[] features = { "x", "y", "f1", "f2", "f3" }; final double[] featureFluctuationScales = { 1, 1, 5, 10, 0.1 }; final double relativeDiscontinuityScale = 9; final int numKnots = 6; // populate with Gaussian noise. Add discontinuities for the first ones final Random rnd = new Random(); final List<Map<String, double[]>> allTrackData = new ArrayList<>(); for (int i = 0; i < numTracks; ++i) { final Map<String, double[]> featureData = new HashMap<>(features.length); allTrackData.add(featureData); final boolean isDiscontinuous = i < numDiscontinuousTracks; final int discontinuityTimePoint = discontinuitySafeZoneWidth + rnd.nextInt(numTimePoints - 2 * discontinuitySafeZoneWidth); for (int j = 0; j < features.length; ++j) { final double[] data = new double[numTimePoints]; for (int k = 0; k < numTimePoints; ++k) data[k] = featureFluctuationScales[j] * rnd.nextGaussian(); // if discontinuous add a big jump if (isDiscontinuous) data[discontinuityTimePoint] += relativeDiscontinuityScale * featureFluctuationScales[j] * rnd.nextGaussian(); featureData.put(features[j], data); // save it } } final Map<String, Double> mad = computeMedianAbsoluteDifferences(allTrackData); System.out.println(mad); // sanity check final Map<String, String[]> euclidians = new HashMap<>(); euclidians.put("XY", new String[] { "x", "y" }); final TrAM tram = new TrAM(mad, numKnots, 0.5); final double[] tramValues = new double[numTracks]; for (int trackNumber = 0; trackNumber < numTracks; ++trackNumber) { tramValues[trackNumber] = tram.computeTrAM(allTrackData.get(trackNumber), euclidians); // print out some of the discontinuous and an equal number of // continuous track TrAM values if (trackNumber < 2 * numDiscontinuousTracks) System.out.println(trackNumber + " " + tramValues[trackNumber]); } }