Example usage for java.util Arrays binarySearch

Introduction

In this page you can find the example usage for java.util Arrays binarySearch.

Prototype

public static int binarySearch(Object[] a, Object key) 

Source Link

Document

Searches the specified array for the specified object using the binary search algorithm.

Usage

From source file:Main.java

/**
 * Returns the child nodes of the passed node where the type of the child node is in the passed array of type codes
 * @param node The node to return the child nodes of
 * @param types The short codes for the node types
 * @return a [possibly empty] list of nodes
 *///from  ww  w.j a  va  2  s. com
public static List<Node> getChildNodes(final Node node, short... types) {
    if (node == null)
        return Collections.emptyList();
    if (types == null || types.length == 0)
        return Collections.emptyList();
    final List<Node> nodes = new ArrayList<Node>();
    for (Node n : nodeListToList(node.getChildNodes())) {
        if (Arrays.binarySearch(types, n.getNodeType()) >= 0) {
            nodes.add(n);
        }
    }
    return nodes;
}

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From source file:gedi.util.math.function.StepFunction.java

public double applyAsDouble(double x) {
    int index = Arrays.binarySearch(this.x, x);
    double fx = 0;

    if (index < -1) {
        // "x" is between "abscissa[-index-2]" and "abscissa[-index-1]".
        fx = y[-index - 2];/*from   w w  w . j av a2 s .  c  o m*/
    } else if (index >= 0) {
        // "x" is exactly "abscissa[index]".
        fx = y[index];
    } else {
        // Otherwise, "x" is smaller than the first value in "abscissa"
        // (hence the returned value should be "ordinate[0]").
        fx = y[0];
    }

    return fx;
}

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From source file:gedi.util.math.function.PiecewiseLinearFunction.java

public double applyAsDouble(double x) {
    int index = Arrays.binarySearch(this.x, x);
    double fx = 0;

    if (index < -1) {
        // "x" is between "abscissa[-index-2]" and "abscissa[-index-1]".
        if (-index - 1 == this.x.length)
            fx = y[-index - 2];//w w w .  j  av a  2s.  c  om
        else {
            double frac = (x - this.x[-index - 2]) / (this.x[-index - 1] - this.x[-index - 2]);
            fx = (1 - frac) * y[-index - 2] + frac * y[-index - 1];
        }
    } else if (index >= 0) {
        // "x" is exactly "abscissa[index]".
        fx = y[index];
    } else {
        // Otherwise, "x" is smaller than the first value in "abscissa"
        // (hence the returned value should be "ordinate[0]").
        fx = y[0];
    }

    return fx;
}

---

From source file:com.actelion.research.spiritcore.util.StatUtils.java

/**
 * Calculate Kruskal-Walis (http://en.wikipedia.org/wiki/Kruskal%E2%80%93Wallis_one-way_analysis_of_variance)
 * @param values/*from www .j  a  v  a2  s  .c  o m*/
 * @return
 */
public static double getKruskalWallis(List<double[]> values) {

    //Assign ranks
    assert values.size() > 1;

    List<Double> allDoubles = new ArrayList<>();
    for (double[] a : values) {
        assert a.length > 0;
        for (double d : a) {
            allDoubles.add(d);
        }
    }
    int N = allDoubles.size();
    Collections.sort(allDoubles);
    double[] allDoublesArray = new double[allDoubles.size()];
    for (int i = 0; i < allDoubles.size(); i++)
        allDoublesArray[i] = allDoubles.get(i);
    List<double[]> ranks = new ArrayList<double[]>();
    for (double[] a : values) {
        double[] rankArray = new double[a.length];
        ranks.add(rankArray);
        for (int i = 0; i < a.length; i++) {
            int r = Arrays.binarySearch(allDoublesArray, a[i]);
            assert r >= 0;
            int r1 = r, r2 = r;
            while (r1 > 0 && allDoublesArray[r1 - 1] == a[i])
                r1--;
            while (r2 < allDoublesArray.length - 1 && allDoublesArray[r2 + 1] == a[i])
                r2++;
            rankArray[i] = (r1 + r2) / 2.0 + 1;
        }
    }

    //Calculate rank average per group
    List<Double> rankSum = new ArrayList<Double>();
    for (double[] a : ranks) {
        double sum = 0;
        for (double d : a)
            sum += d;
        rankSum.add(sum);
    }

    double sum = 0;
    for (int i = 0; i < ranks.size(); i++) {
        sum += rankSum.get(i) * rankSum.get(i) / ranks.get(i).length;
    }
    double H = 12.0 / (N * (N + 1)) * sum - 3 * (N + 1);

    ChiSquaredDistribution chi = new ChiSquaredDistribution(values.size() - 1);
    double K = 1 - chi.cumulativeProbability(H);

    return K;
}

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From source file:com.l2jfree.gameserver.model.skills.conditions.ConditionTargetRaceId.java

@Override
public boolean testImpl(Env env) {
    if (!(env.target instanceof L2Npc))
        return false;

    return Arrays.binarySearch(_raceIds, ((L2Npc) env.target).getTemplate().getRace().ordinal()) >= 0;
}

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From source file:com.l2jfree.gameserver.model.skills.conditions.ConditionPlayerClassIdRestriction.java

@Override
public boolean testImpl(Env env) {
    if (!(env.player instanceof L2Player))
        return false;

    return Arrays.binarySearch(_classIds, ((L2Player) env.player).getClassId().getId()) >= 0;
}

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From source file:com.l2jfree.gameserver.model.skills.conditions.ConditionTargetClassIdRestriction.java

@Override
public boolean testImpl(Env env) {
    if (!(env.target instanceof L2Player))
        return false;

    return Arrays.binarySearch(_classIds, ((L2Player) env.target).getClassId().getId()) >= 0;
}

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From source file:com.trenako.validation.ISOCountryValidator.java

@Override
public boolean isValid(String value, ConstraintValidatorContext context) {
    if (StringUtils.isBlank(value)) {
        return true;
    }/*from   w w  w  . j a v  a2 s. co m*/
    return Arrays.binarySearch(Locale.getISOCountries(), value.toUpperCase()) >= 0;
}

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From source file:com.trenako.validation.ISOLanguageValidator.java

@Override
public boolean isValid(String value, ConstraintValidatorContext context) {
    if (StringUtils.isBlank(value)) {
        return true;
    }/* ww  w.  j a va 2  s.co m*/
    return Arrays.binarySearch(Locale.getISOLanguages(), value.toLowerCase()) >= 0;
}

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From source file:org.drugepi.hdps.ZBiasCalculator.java

public static void scoreVariables(List<HdpsVariable> variableList) {
    // copy variables list
    List<HdpsVariable> expSortVariableList = new ArrayList<HdpsVariable>();
    List<HdpsVariable> outcomeSortVariableList = new ArrayList<HdpsVariable>();

    for (HdpsVariable var : variableList) {
        var.zBiasScore = 0;

        if ((var.expAssocRankingVariable != HdpsVariable.INVALID)
                && (var.outcomeAssocRankingVariable != HdpsVariable.INVALID)) {
            expSortVariableList.add(var);
            outcomeSortVariableList.add(var);
        }/*from   www.j a v  a 2  s .c om*/
    }

    // sort variables by exposure association (strongest first) 
    Collections.sort(expSortVariableList, new HdpsVariableReverseExposureAssociationComparator());

    // sort variables by outcome association (weakest first) 
    Collections.sort(outcomeSortVariableList, new HdpsVariableReverseOutcomeAssociationComparator());
    Collections.reverse(outcomeSortVariableList);

    // create an array of outcome strengths
    double[] outcomeStrengths = new double[outcomeSortVariableList.size()];
    for (int i = 0; i < outcomeStrengths.length; i++)
        outcomeStrengths[i] = outcomeSortVariableList.get(i).outcomeAssocRankingVariable;

    // array that will store breaks between deciles
    // find the median of outcome strength 
    Percentile pctile = new Percentile();

    // Find quintiles 1 through 5 of outcome weakness
    // AMONG the weakest half of the variables.
    // List is sorted strongest first, so the weakest variables 
    // will be at the end
    // quintile 1 = weakest 
    // don't use startsOfQuintile[0]

    double median = pctile.evaluate(outcomeStrengths, 50.0);
    int searchCeiling = Arrays.binarySearch(outcomeStrengths, median);
    if (searchCeiling < 0)
        searchCeiling = -(searchCeiling + 1);

    int startsOfQuintile[] = new int[7];

    for (int quintile = 1; quintile <= 5; quintile++) {
        // find the probability that *begins* this quintile
        double p = (quintile - 1) * 20;
        if (p > 0) {
            double quintileStartP = pctile.evaluate(outcomeStrengths, 0, searchCeiling, (quintile - 1) * 20);

            startsOfQuintile[quintile] = Arrays.binarySearch(outcomeStrengths, quintileStartP);
            if (startsOfQuintile[quintile] < 0)
                startsOfQuintile[quintile] = -(startsOfQuintile[quintile] + 1);
        } else
            startsOfQuintile[quintile] = 0;
    }
    startsOfQuintile[6] = searchCeiling;

    // score the variables, BUT make quintile 5 the weakest
    for (int quintile = 1; quintile <= 5; quintile++) {
        for (int i = startsOfQuintile[quintile]; i < startsOfQuintile[quintile + 1]; i++) {
            HdpsVariable v = outcomeSortVariableList.get(i);
            v.zBiasScore = 6 - quintile;
        }
    }

    //      for (HdpsVariable v: outcomeSortVariableList) {
    //         System.out.printf("%s    %1.4f    %d\n", v.varName, v.outcomeAssocRankingVariable, v.zBiasScore);
    //      }
}

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