Examples of java.util.Random.nextGaussian()

java.util.Random.nextGaussian()
een -1.0 and 1.0 v2=2 * nextDouble() - 1; // between -1.0 and 1.0 s=v1 * v1 + v2 * v2; } while (s >= 1 || s == 0); double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s); nextNextGaussian = v2 * multiplier; haveNextNextGaussian = true; return v1 * multiplier; } }} This uses the polar method of G. E. P. Box, M. E. Muller, and G. Marsaglia, as described by Donald E. Knuth in The Art of Computer Programming, Volume 3: Seminumerical Algorithms, section 3.4.1, subsection C, algorithm P. Note that it generates two independent values at the cost of only one call to {@code StrictMath.log}and one call to {@code StrictMath.sqrt}. @return the next pseudorandom, Gaussian ("normally") distributed{@code double} value with mean {@code 0.0} andstandard deviation {@code 1.0} from this random numbergenerator's sequence

        FastMultiByteArrayOutputStream out = new FastMultiByteArrayOutputStream(4096);
        DataOutputStream data = new DataOutputStream(out);
        Random rand = new Random(3333);
        for(int times = 0; times < 3; times++) {
            for(int i = 0; i < 10000; i++) {
                data.writeDouble(rand.nextGaussian());
            }
            probe(out.toByteArray_clear());
        }
    }

View Full Code Here

        FastMultiByteArrayOutputStream out = new FastMultiByteArrayOutputStream(4096);
        DataOutputStream data = new DataOutputStream(out);
        Random rand = new Random(3333);
        for(int times = 0; times < 3; times++) {
            for(int i = 0; i < 10000; i++) {
                data.writeDouble(rand.nextGaussian());
            }
            probe(out.toByteArray_clear());
        }
    }

View Full Code Here

      for (int j = 0; j < 10; j++) {
        input.put(String.valueOf(i) + String.valueOf(j), String.valueOf(i));
      }
      JSONEvent e = new JSONEvent();
      e.setHeaders(input);
      e.setBody(String.valueOf(rand.nextGaussian()).getBytes(encoding));
      events.add(e);
    }
    Gson gson = new Gson();
    String json = gson.toJson(events, listType);
    StringEntity input = new StringEntity(json);

View Full Code Here

      for (int j = 0; j < 10; j++) {
        input.put(String.valueOf(i) + String.valueOf(j), String.valueOf(i));
      }
      JSONEvent e = new JSONEvent();
      e.setHeaders(input);
      e.setBody(String.valueOf(rand.nextGaussian()).getBytes(encoding));
      events.add(e);
    }
    Gson gson = new Gson();
    String json = gson.toJson(events, listType);
    StringEntity input = new StringEntity(json);

View Full Code Here

        input.put(String.valueOf(i) + String.valueOf(j), String.valueOf(i));
      }
      input.put("MsgNum", String.valueOf(i));
      JSONEvent e = new JSONEvent();
      e.setHeaders(input);
      e.setBody(String.valueOf(rand.nextGaussian()).getBytes("UTF-8"));
      events.add(e);
    }
    Gson gson = new Gson();
    String json = gson.toJson(events, listType);
    HttpsURLConnection httpsURLConnection = null;

View Full Code Here

            input.put(String.valueOf(i) + String.valueOf(j), String.valueOf(i));
        }
        input.put("MsgNum", String.valueOf(i));
        JSONEvent e = new JSONEvent();
        e.setHeaders(input);
        e.setBody(String.valueOf(rand.nextGaussian()).getBytes("UTF-8"));
        events.add(e);
    }
    Gson gson = new Gson();
    String json = gson.toJson(events, listType);
    HttpURLConnection httpURLConnection = null;

View Full Code Here

    ArrayList<double[]> train = new ArrayList<double[]>();
    // 1. generate positive train samples
    for (int i = 0; i < nbPosTrain; i++) {
      double[] t = new double[dimension];
      for (int x = 0; x < dimension; x++) {
        t[x] = ran.nextGaussian();
      }


      train.add(t);
    }
    System.out.println("Samples generated");

View Full Code Here

    ArrayList<double[]> test = new ArrayList<double[]>();
    // 4. generate positive test samples
    for (int i = 0; i < nbPosTest; i++) {
      double[] t = new double[dimension];
      for (int x = 0; x < dimension; x++) {
        t[x] = ran.nextGaussian();
      }


      test.add(t);
    }

View Full Code Here

      for(int i = 0 ; i < nbPosTrain; i++)
      {
        double[] t = new double[dimension];
        for(int x = 0 ; x < dimension; x++)
        {
          t[x] = posstart + ran.nextGaussian();
        }


        train.add(new TrainingSample<double[]>(t, 1));
      }
      //2. generate negative train samples

View Full Code Here

      for(int i = 0 ; i < nbNegTrain; i++)
      {
        double[] t = new double[dimension];
        for(int x = 0 ; x < dimension; x++)
        {
          t[x] = negstart + ran.nextGaussian();
        }


        train.add(new TrainingSample<double[]>(t, -1));
      }

View Full Code Here

0 1 2 3 4 5 6 7 8 9

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.