Example usage for java.lang Math ceil

Introduction

In this page you can find the example usage for java.lang Math ceil.

Prototype

public static double ceil(double a)

Source Link

Document

Returns the smallest (closest to negative infinity) double value that is greater than or equal to the argument and is equal to a mathematical integer.

Usage

From source file:com.baifendian.swordfish.common.utils.DateUtils.java

/**
 * ?//  w w  w  .  ja v  a  2  s  .  c om
 *
 * @param d1
 * @param d2
 * @return
 */
public static long diffMin(Date d1, Date d2) {
    return (long) Math.ceil(diffSec(d1, d2) / 60.0);
}

From source file:Util.java

public static double fix(double x) {
    int sign = sign(x);
    double y = 0;
    if (sign == -1) {
        y = Math.ceil(x);
    } else if (sign == 1) {
        y = Math.floor(x);/* www  .j  ava2s.  c  o m*/
    }
    return y;
}

From source file:bb.mcmc.analysis.ConvergeStatUtils.java

protected static boolean[] thinWindow(boolean[] array, int kthin) {
    if (kthin == 1) {
        return array;
    } else {/*from  w  w w .  j a v a 2s .  c  om*/

        final int count = (int) Math.ceil(array.length / (double) kthin);
        final boolean[] newArray = new boolean[count];

        for (int i = 0; i < newArray.length; i++) {
            newArray[i] = array[kthin * i];
        }
        return newArray;
    }

}

From source file:de.perdian.apps.dashboard.mvc.modules.jira.JiraController.java

@RequestMapping(value = "/jira/burndown/")
@ResponseBody/*from   w w  w.j  a va  2s.  c om*/
JiraControllerResponse doBurndown(@ModelAttribute JiraControllerRequest controllerRequest) {

    BurndownDto burndownDto = this.getJiraService().loadBurndown(controllerRequest.getRapidViewId(),
            controllerRequest.getBurndownBasis(), controllerRequest.getStoryPointFieldName());

    // We want to have twenty lines at maximum, so let's adjust the steps
    // accordingly
    double maxSteps = 20;
    double incrementPerStep = burndownDto.getStoryPointsTotal() / maxSteps;
    int increment = (int) Math.max(1, Math.ceil(incrementPerStep));
    TickUnits rangeTickUnits = new TickUnits();
    for (int i = 0; i < burndownDto.getStoryPointsTotal(); i += increment) {
        rangeTickUnits.add(new NumberTickUnit(i + 1));
    }

    TickUnits domainTickUnits = new TickUnits();
    domainTickUnits.add(new NumberTickUnit(burndownDto.getDays().size() + 1));

    ChartCreator chartCreator = new ChartCreator(controllerRequest);
    chartCreator.setTitle(controllerRequest.getTitle());
    chartCreator.setDomainTickUnits(domainTickUnits);
    chartCreator.setRangeTickUnits(rangeTickUnits);
    chartCreator
            .addStrokeDefinition(new ChartStrokeDefinition(new BasicStroke(7f), chartCreator.getColor(), null));
    chartCreator.addStrokeDefinition(new ChartStrokeDefinition(null, null, false));
    chartCreator.addStrokeDefinition(new ChartStrokeDefinition(new BasicStroke(1.0f, BasicStroke.CAP_ROUND,
            BasicStroke.JOIN_ROUND, 1.0f, new float[] { 2.0f, 6.0f }, 0.0f), chartCreator.getColor(), null));
    chartCreator.setRangeAxisLabel(
            "worklog".equalsIgnoreCase(controllerRequest.getBurndownBasis()) ? "Worklog (Hours)"
                    : "Storypoints");

    JiraControllerResponse controllerResponse = new JiraControllerResponse();
    controllerResponse.setBurndown(burndownDto);
    controllerResponse.setImageContent(chartCreator.createChartAsImageContent(burndownDto.toDataset()));
    return controllerResponse;

}

From source file:edu.txstate.dmlab.clusteringwiki.cluster.KMeansClusterer.java

/**
 * Set number of clusters based on document collection to be clustered
 * Very simple approach - try to get an average of 10 results per cluster
 *//*from   www  . ja  v  a  2 s. c o m*/
public void setNumClusters() {
    if (documentsToCluster != null) {
        int n = documentsToCluster.length;
        numClusters = (int) Math.ceil(Math.sqrt(n));
    }
}

From source file:edu.asu.ca.kaushik.algorithms.twostage.TwoStageColoring.java

@Override
protected int partialArraySize(int t, int k, int v) {
    double vpowt = Math.pow(v, t);
    double n1 = Math.ceil(
            Math.log(CombinatoricsUtils.binomialCoefficientDouble(k, t) * vpowt * Math.log(vpowt / (vpowt - 1)))
                    / Math.log(vpowt / (vpowt - 1)));
    double denom = Math.log(1 - (1 / vpowt));
    double n = (Math.log(this.times) + n1 * denom) / denom;
    return (int) Math.ceil(n);
}

From source file:com.github.obiteaaron.common.data.FixPageImpl.java

@Override
public int getTotalPages() {
    return getSize() == 0 ? 1 : (int) Math.ceil((double) total / (double) getSize());
}

From source file:com.linkedin.drelephant.math.Statistics.java

/**
 * The percentile method returns the least value from the given list which has at least given percentile.
 * @param values The list of values to find the percentile from
 * @param percentile The percentile/*from w  w  w. j  ava 2  s.c  o m*/
 * @return The least value from the list with at least the given percentile
 */
public static long percentile(List<Long> values, int percentile) {

    if (values.size() == 0) {
        throw new IllegalArgumentException("Percentile of empty list is not defined.");
    }

    if (percentile > 100 || percentile < 0) {
        throw new IllegalArgumentException("Percentile has to be between 0-100");
    }

    if (percentile == 0) {
        return 0;
    }

    Collections.sort(values);

    // Use Nearest Rank method.
    // https://en.wikipedia.org/wiki/Percentile#The_Nearest_Rank_method
    int position = (int) Math.ceil(values.size() * percentile / 100.0);

    // should never happen.
    if (position == 0) {
        return values.get(position);
    }

    // position is always one greater than index. Return value at the proper index
    return values.get(position - 1);
}

From source file:gr.eap.LSHDB.HammingKey.java

public int getLc() {
    double p = 1 - (t * 1.0) / (this.size * 1.0);
    p = Math.pow(p, k);/*www .j a  v a 2  s.c  o m*/
    double exp = (L * p);
    double std = Math.sqrt(exp * (1 - p));
    int C = (int) Math.round(exp - std);

    double x = (Math.sqrt(Math.log(delta) * Math.log(delta) - 2 * C * Math.log(delta)) - Math.log(delta) + C)
            / p;
    int Lc = (int) Math.ceil(x);
    double b = Lc * p;
    if (C > b)
        System.out.println("does not apply C > np.");
    BinomialDistribution bd1 = new BinomialDistribution(L, p);
    for (int l = L; l < L * 2; l++) {
        bd1 = new BinomialDistribution(l, p);
        double result = bd1.cumulativeProbability(C - 1);
        if (result < delta) {
            Lc = l;
            break;
        }
    }
    System.out.println("Lc reduced to=" + Lc);
    return Lc;
}

From source file:edu.asu.ca.kaushik.algorithms.twostage.TwoStageOnlineGreedy.java

@Override
protected int partialArraySize(int t, int k, int v) {
    //Side effect; bad style
    this.second = new ListCA(t, k, v);
    this.t = t;// w ww .j a  v a  2  s.co m
    this.k = k;
    this.v = v;

    // function proper
    double vpowt = Math.pow(v, t);
    double n1 = Math.ceil(
            Math.log(CombinatoricsUtils.binomialCoefficientDouble(k, t) * vpowt * Math.log(vpowt / (vpowt - 1)))
                    / Math.log(vpowt / (vpowt - 1)));
    double denom = Math.log(1 - (1 / vpowt));
    double n = (Math.log(this.times) + n1 * denom) / denom;
    return (int) Math.ceil(n);
}