Example usage for java.util Random nextDouble

Introduction

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

Prototype

public double nextDouble() 

Source Link

Document

Returns the next pseudorandom, uniformly distributed double value between 0.0 and 1.0 from this random number generator's sequence.

Usage

From source file:de.tum.bgu.msm.syntheticPopulationGenerator.kagawa.SyntheticPopJP.java

private static int obtainDriverLicense(int gender, int age, TableDataSet prob) {
    //assign if the person holds a driver license based on the probabilities obtained from MiD data
    int license = 0;
    int finish = 0;
    int row = 1;//from w  ww. jav a 2s. c om
    int threshold = 0;
    if (age > 18) {
        while (finish == 0 & row < prob.getRowCount()) {
            if (age > prob.getValueAt(row, "ageLimit")) {
                row++;
            } else {
                finish = 1;
            }
        }
        if (finish == 0) {
            row = prob.getRowCount();
        }
        if (gender == 0) {
            threshold = (int) prob.getValueAt(row, "male");
        } else {
            threshold = (int) prob.getValueAt(row, "female");
        }
        Random rn = new Random();
        if (rn.nextDouble() * 100 < threshold) {
            license = 1;
        }
    } //if they are younger than 18, they don't hold driver license
    return license;
}

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From source file:com.ibm.crail.tools.CrailBenchmark.java

void readRandom(String filename, int size, int loop, boolean buffered) throws Exception {
    System.out.println("readRandom, filename " + filename + ", size " + size + ", loop " + loop + ", buffered "
            + buffered);// w w  w . ja v a  2s .c  om

    CrailBuffer buf = null;
    if (size == CrailConstants.BUFFER_SIZE) {
        buf = fs.allocateBuffer();
    } else if (size < CrailConstants.BUFFER_SIZE) {
        CrailBuffer _buf = fs.allocateBuffer();
        _buf.clear().limit(size);
        buf = _buf.slice();
    } else {
        buf = OffHeapBuffer.wrap(ByteBuffer.allocateDirect(size));
    }

    //warmup
    ConcurrentLinkedQueue<CrailBuffer> bufferQueue = new ConcurrentLinkedQueue<CrailBuffer>();
    bufferQueue.add(buf);
    warmUp(filename, warmup, bufferQueue);

    //benchmark
    System.out.println("starting benchmark...");
    fs.getStatistics().reset();
    CrailFile file = fs.lookup(filename).get().asFile();
    CrailBufferedInputStream bufferedStream = file.getBufferedInputStream(file.getCapacity());
    CrailInputStream directStream = file.getDirectInputStream(file.getCapacity());

    double sumbytes = 0;
    double ops = 0;
    long _range = file.getCapacity() - ((long) buf.capacity());
    double range = (double) _range;
    Random random = new Random();

    long start = System.currentTimeMillis();
    while (ops < loop) {
        if (buffered) {
            buf.clear();
            double _offset = range * random.nextDouble();
            long offset = (long) _offset;
            directStream.seek(offset);
            double ret = (double) directStream.read(buf).get().getLen();
            if (ret > 0) {
                sumbytes = sumbytes + ret;
                ops = ops + 1.0;
            } else {
                break;
            }
        } else {
            buf.clear();
            double _offset = range * random.nextDouble();
            long offset = (long) _offset;
            bufferedStream.seek(offset);
            double ret = (double) bufferedStream.read(buf.getByteBuffer());
            if (ret > 0) {
                sumbytes = sumbytes + ret;
                ops = ops + 1.0;
            } else {
                break;
            }
        }
    }
    long end = System.currentTimeMillis();
    double executionTime = ((double) (end - start)) / 1000.0;
    double throughput = 0.0;
    double latency = 0.0;
    double sumbits = sumbytes * 8.0;
    if (executionTime > 0) {
        throughput = sumbits / executionTime / 1000.0 / 1000.0;
        latency = 1000000.0 * executionTime / ops;
    }
    bufferedStream.close();
    directStream.close();

    System.out.println("execution time " + executionTime);
    System.out.println("ops " + ops);
    System.out.println("sumbytes " + sumbytes);
    System.out.println("throughput " + throughput);
    System.out.println("latency " + latency);

    fs.getStatistics().print("close");
}

---

From source file:de.tum.bgu.msm.syntheticPopulationGenerator.kagawa.SyntheticPopJP.java

public static int[] select(double[] probabilities, int[] id, int sumProb) {
    // select item based on probabilities (for zero-based float array)
    int[] results = new int[2];
    Random rand = new Random();
    double selPos = sumProb * rand.nextDouble();
    double sum = 0;
    for (int i = 0; i < probabilities.length; i++) {
        sum += probabilities[i];/*from  w w w .  jav  a 2 s .c o m*/
        if (sum > selPos) {
            //return i;
            results[0] = id[i];
            results[1] = i;
            return results;
        }
    }
    results[0] = id[probabilities.length - 1];
    results[1] = probabilities.length - 1;
    return results;
}

---

From source file:de.tum.bgu.msm.syntheticPopulationGenerator.kagawa.SyntheticPopJP.java

public static int[] select(double[] probabilities, int[] id, double sumProb) {
    // select item based on probabilities (for zero-based float array)
    int[] results = new int[2];
    Random rand = new Random();
    double selPos = sumProb * rand.nextDouble();
    double sum = 0;
    for (int i = 0; i < probabilities.length; i++) {
        sum += probabilities[i];//  w w w . j a  v a2 s .  c om
        if (sum > selPos) {
            //return i;
            results[0] = id[i];
            results[1] = i;
            return results;
        }
    }
    results[0] = id[probabilities.length - 1];
    results[1] = probabilities.length - 1;
    return results;
}

---

From source file:de.tum.bgu.msm.syntheticPopulationGenerator.kagawa.SyntheticPopJP.java

public static int[] select(double[] probabilities, int length, int[] id, double sumProb) {
    //select item based on probabilities and return the name
    //probabilities and name have more items than the required (max number of required items is set on "length")

    int[] results = new int[2];
    Random rand = new Random();
    double selPos = sumProb * rand.nextDouble();
    double sum = 0;
    for (int i = 0; i < probabilities.length; i++) {
        sum += probabilities[i];/*from  w w w.j  av  a  2 s  .  co  m*/
        if (sum > selPos) {
            //return i;
            results[0] = id[i];
            results[1] = i;
            return results;
        }
    }
    results[0] = id[probabilities.length - 1];
    results[1] = probabilities.length - 1;
    return results;
}

---

From source file:de.tum.bgu.msm.syntheticPopulationGenerator.kagawa.SyntheticPopJP.java

public static int[] select(double[] probabilities, int[] id) {
    // select item based on probabilities (for zero-based float array)
    double sumProb = 0;
    int[] results = new int[2];
    for (double val : probabilities)
        sumProb += val;
    Random rand = new Random();
    double selPos = sumProb * rand.nextDouble();
    double sum = 0;
    for (int i = 0; i < probabilities.length; i++) {
        sum += probabilities[i];/*from ww  w .j  av a  2s  .c  om*/
        if (sum > selPos) {
            //return i;
            results[0] = id[i];
            results[1] = i;
            return results;
        }
    }
    results[0] = id[probabilities.length - 1];
    results[1] = probabilities.length - 1;
    return results;
}

---

From source file:de.tum.bgu.msm.syntheticPopulationGenerator.kagawa.SyntheticPopJP.java

public static int[] select(double[] probabilities, int length, int[] id) {
    //select item based on probabilities and return the name
    //probabilities and name have more items than the required (max number of required items is set on "length")
    double sumProb = 0;
    int[] results = new int[2];
    for (int i = 0; i < length; i++) {
        sumProb += probabilities[i];/*from w ww . ja v a 2  s  .  co m*/
    }
    Random rand = new Random();
    double selPos = sumProb * rand.nextDouble();
    double sum = 0;
    for (int i = 0; i < probabilities.length; i++) {
        sum += probabilities[i];
        if (sum > selPos) {
            //return i;
            results[0] = id[i];
            results[1] = i;
            return results;
        }
    }
    results[0] = id[probabilities.length - 1];
    results[1] = probabilities.length - 1;
    return results;
}

---

From source file:de.tum.bgu.msm.syntheticPopulationGenerator.kagawa.SyntheticPopJP.java

private static double translateIncome(int incomeClass, double[] incomeThresholds, GammaDistributionImpl q)
        throws MathException, MathException {
    //provide the income value for each person give the income class.
    //income follows a gamma distribution that was calibrated using the microdata. Income thresholds are calculated for the stiches
    double income;
    int finish = 0;
    double low = 0;
    double high = 1;
    if (incomeClass == 90) {
        income = 0; // kein Einkommen
        /*        } else if (incomeClass == 50) {
                    income = 0; // Selbstndige/r Landwirt/in in der Hauptttigkeit
                } else if (incomeClass == 99) {
                    income = -1; //keine Angabe*/
    } else {/*from  w  ww  . j  a  v  a  2 s  . c  o  m*/
        if (incomeClass == 1) {
            low = 0;
            high = incomeThresholds[0];
        } else if (incomeClass == 50) { // Selbstndige/r Landwirt/in in der Hauptttigkeit
            low = 0; //give them a random income following the distribution
            high = 1;
        } else if (incomeClass == 99) { //keine Angabe
            low = 0; //give them a random income following the distribution
            high = 1;
        } else if (incomeClass == incomeThresholds.length + 1) {
            low = incomeThresholds[incomeThresholds.length - 1];
            high = 1;
        } else {
            int i = 2;
            while (finish == 0) {
                if (incomeClass > i) {
                    i++;
                } else {
                    finish = 1;
                    low = incomeThresholds[i - 2];
                    high = incomeThresholds[i - 1];
                }
            }
        }
        Random rnd = new Random();
        double cummulativeProb = rnd.nextDouble() * (high - low) + low;
        income = q.inverseCumulativeProbability(cummulativeProb);
    }
    return income;
}

---

From source file:com.ibm.bi.dml.test.utils.TestUtils.java

/**
 * <p>//from  w w w  . j  a v  a 2s. c  o m
 * Generates a test matrix with the specified parameters as a two
 * dimensional array.
 * </p>
 * <p>
 * Set seed to -1 to use the current time as seed.
 * </p>
 * 
 * @param rows
 *            number of rows
 * @param cols
 *            number of columns
 * @param min
 *            minimum value
 * @param max
 *            maximum value
 * @param sparsity
 *            sparsity
 * @param seed
 *            seed
 * @return random matrix
 */
public static double[][] generateTestMatrix(int rows, int cols, double min, double max, double sparsity,
        long seed) {
    double[][] matrix = new double[rows][cols];
    Random random;
    if (seed == -1)
        random = TestUtils.random;
    else
        random = new Random(seed);

    for (int i = 0; i < rows; i++) {
        for (int j = 0; j < cols; j++) {
            if (random.nextDouble() > sparsity)
                continue;
            matrix[i][j] = (random.nextDouble() * (max - min) + min);
            // System.out.print(matrix[i][j] + "(" + i + "," + j + ")");
        }
        // System.out.println();
    }

    return matrix;
}

---

From source file:org.apache.hadoop.hbase.master.TestRegionStateOnMasterFailure.java

/**
 * Writes some data to the table.//from   ww w. j a  v a 2  s . c o  m
 * @throws InterruptedException
 */
private void fillTable() throws IOException, InterruptedException {
    Random rand = new Random(19387129L);
    HTable table = testUtil.createTable(TABLE_NAME, FAMILIES);
    for (int iStoreFile = 0; iStoreFile < 4; ++iStoreFile) {
        for (int iRow = 0; iRow < 100; ++iRow) {
            final byte[] row = Bytes.toBytes("row" + iRow);
            Put put = new Put(row);
            Delete del = new Delete(row);
            for (int iCol = 0; iCol < 10; ++iCol) {
                final byte[] cf = rand.nextBoolean() ? CF1 : CF2;
                final long ts = rand.nextInt();
                final byte[] qual = Bytes.toBytes("col" + iCol);
                if (rand.nextBoolean()) {
                    final byte[] value = Bytes
                            .toBytes("value_for_row_" + iRow + "_cf_" + Bytes.toStringBinary(cf) + "_col_"
                                    + iCol + "_ts_" + ts + "_random_" + rand.nextLong());
                    put.add(cf, qual, ts, value);
                } else if (rand.nextDouble() < 0.8) {
                    del.deleteColumn(cf, qual, ts);
                } else {
                    del.deleteColumns(cf, qual, ts);
                }
            }
            table.put(put);
            table.delete(del);
            table.flushCommits();
        }
    }
    testUtil.waitUntilAllRegionsAssigned(1);
}

---

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