Example usage for java.util Arrays fill

Introduction

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

Prototype

public static void fill(Object[] a, Object val) 

Source Link

Document

Assigns the specified Object reference to each element of the specified array of Objects.

Usage

From source file:com.antsdb.saltedfish.sql.vdm.KeyUtil.java

public static void add(byte[] bytes, byte n) {
    int overflow = 0;
    for (int i = bytes.length - 1; i >= 0; i--) {
        int v = (bytes[i] & 0xff) + n + overflow;
        bytes[i] = (byte) v;
        overflow = v >>> 8;/*from   w ww  . j  a v a 2  s . c o m*/
        if (overflow == 0) {
            break;
        }
        n = 0;
    }
    if (overflow > 0) {
        Arrays.fill(bytes, (byte) 0xff);
    } else if (overflow < 0) {
        Arrays.fill(bytes, (byte) 0);
    }
}

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From source file:es.udc.gii.common.eaf.benchmark.real_param.ShekelFamilyObjectiveFunction.java

@Override
public double[][] getOptimum(int dim) {

    double[][] optimums = new double[1][];
    double[] optimum = new double[4];

    Arrays.fill(optimum, 4.0 / 5.0 - 1.0);

    optimums[0] = optimum;//from   ww  w  .  j a va  2s  . c o m
    return optimums;

}

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From source file:gda.analysis.numerical.straightline.StraightLineFit.java

public static Results fitInt(List<Object> data, long[] dims, double[] x) {

    Object object = data.get(0);/*from   w  w w  . j  a  v a2s.c  om*/
    if (!object.getClass().isArray()) {
        throw new IllegalArgumentException("fitInt can only accept arrays");
    }
    int numLines = ArrayUtils.getLength(object);
    int pointsPerLine = x.length;
    if (data.size() != pointsPerLine)
        throw new IllegalArgumentException("data.size() != pointsPerLine");

    for (int i = 0; i < pointsPerLine; i++) {
        if (ArrayUtils.getLength(data.get(i)) != numLines)
            throw new IllegalArgumentException("data.get(i).length != numLines");

    }

    double[] slopes = new double[numLines];
    double[] offsets = new double[numLines];
    short[] fitoks = new short[numLines];
    Arrays.fill(fitoks, (short) 0);
    if (pointsPerLine > 2) {
        double xAverage = getXAverage(x);
        double x1 = getX(x, xAverage);
        double[] y = new double[pointsPerLine];

        Arrays.fill(fitoks, (short) 1);
        for (int line = 0; line < numLines; line++) {
            for (int point = 0; point < pointsPerLine; point++) {
                y[point] = Array.getDouble(data.get(point), line);
            }
            Result fit2 = fit2(y, x, xAverage, x1);
            slopes[line] = fit2.getSlope();
            offsets[line] = fit2.getOffset();
        }
    } else if (pointsPerLine == 2) {

        double[] y = new double[pointsPerLine];
        Arrays.fill(fitoks, (short) 1);
        for (int line = 0; line < numLines; line++) {
            for (int point = 0; point < pointsPerLine; point++) {
                y[point] = Array.getDouble(data.get(point), line);
            }

            slopes[line] = (y[1] - y[0]) / (x[1] - x[0]);
            offsets[line] = y[1] - slopes[line] * x[1];
        }
    }
    return new Results(offsets, slopes, dims, fitoks);
}

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From source file:com.bconomy.autobit.Encryption.java

private static byte[] charsToBytes(char[] chars) {
    CharBuffer charBuffer = CharBuffer.wrap(chars);
    ByteBuffer byteBuffer = Charset.forName("UTF-8").encode(charBuffer);
    byte[] bytes = Arrays.copyOfRange(byteBuffer.array(), byteBuffer.position(), byteBuffer.limit());
    Arrays.fill(charBuffer.array(), '\u0000'); // clear sensitive data
    Arrays.fill(byteBuffer.array(), (byte) 0); // clear sensitive data
    Arrays.fill(chars, '\u0000'); // clear sensitive data
    return bytes;
}

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From source file:net.myrrix.common.collection.FastByIDMap.java

/**
 * Creates a new  whose capacity can accommodate the given number of entries without
 * rehash.//from   w  w w  .j  a  v a 2s . c  o m
 *
 * @param size desired capacity
 * @throws IllegalArgumentException if size is less than 0, maxSize is less than 1
 *  or at least half of {@link RandomUtils#MAX_INT_SMALLER_TWIN_PRIME}, or
 *  loadFactor is less than 1
 */
public FastByIDMap(int size) {
    Preconditions.checkArgument(size >= 0, "size must be at least 0");
    Preconditions.checkArgument(size < MAX_SIZE, "size must be less than " + MAX_SIZE);
    int hashSize = RandomUtils.nextTwinPrime((int) (DEFAULT_LOAD_FACTOR * size) + 1);
    keys = new long[hashSize];
    Arrays.fill(keys, NULL);

    @SuppressWarnings("unchecked")
    V[] theValues = (V[]) new Object[hashSize];
    values = theValues;
}

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From source file:com.cloudera.oryx.common.collection.LongSet.java

/**
 * Creates a new {@code LongSet} with the given initial capacity.
 *
 * @param initialCapacity initial capacity of set
 *//*from   w w  w . j  a v  a 2  s  . com*/
public LongSet(int initialCapacity) {
    Preconditions.checkArgument(initialCapacity >= 0, "initialCapacity must be at least 0");
    Preconditions.checkArgument(initialCapacity < MAX_SIZE, "initialCapacity must be less than %d", MAX_SIZE);
    int hashSize = RandomUtils.nextTwinPrime((int) (LOAD_FACTOR * initialCapacity) + 1);
    keys = new long[hashSize];
    Arrays.fill(keys, NULL);
}

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From source file:net.femtoparsec.jnlmin.GradientEvaluator.java

/**
 * Evaluate the model and compute its gradient. If the model cannot compute its gradient, an approximation is obtained with forward differences.
 * If the model can compute its gradient, this call is equivalent to :
 *
 * <pre>/*w  w w.  j  a va 2s. c o  m*/
 * model.evaluate(parameter, true, resultBuffer)
 * return 1
 * </pre>
 * @param parameters the parameters at which the model must be evaluated.
 * @param resultBuffer the result buffer that will contain the value and the gradient of the model at the given parameters.
 * @param computationBuffer a buffer used for computation.
 * @return the number of times the model has been evaluated
 */
public int evaluateWithGradient(double[] parameters, ModelResult resultBuffer, ModelResult computationBuffer) {
    int nbEvaluations;
    if (model.hasDerivative()) {
        model.evaluate(parameters, true, resultBuffer);
        nbEvaluations = 1;
    } else {
        model.evaluate(parameters, false, resultBuffer);

        final double[] gradient = resultBuffer.getGradient();

        Arrays.fill(gradient, 0);

        double increment, backup;
        double invIncrement;
        int subIndex;

        for (int i = 0; i < nbFitted; i++) {
            subIndex = indexes[i];
            backup = parameters[subIndex];

            increment = this.epsilon * Math.abs(backup);
            if (increment <= 0) {
                increment = this.epsilon;
            }
            invIncrement = 1. / increment;

            parameters[subIndex] += increment;
            model.evaluate(parameters, false, computationBuffer);
            parameters[subIndex] = backup;
            gradient[subIndex] = (computationBuffer.getValue() - resultBuffer.getValue()) * invIncrement;
        }

        nbEvaluations = 1 + nbFitted;
    }

    return nbEvaluations;
}

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From source file:bookkeepr.xmlable.DatabaseManager.java

public DatabaseManager() {

    Arrays.fill(nextId, 0x1L);
    for (int i = 0; i < latestIds.length; i++) {
        Arrays.fill(latestIds[i], 0x0L);
    }/* www.j av  a  2 s. co  m*/
    for (int i = 0; i < listeners.length; i++) {
        listeners[i] = new ArrayList();
    }
}

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From source file:com.qtplaf.library.trading.data.indicators.GaussianSmoother.java

/**
 * Calculates the indicator data at the given index, for the list of indicator sources.
 * <p>//from   w w  w. ja v a  2  s.  c o  m
 * This indicator already calculated data is passed as a parameter because some indicators may need previous
 * calculated values or use them to improve calculation performance.
 * 
 * @param index The data index.
 * @param indicatorSources The list of indicator sources.
 * @param indicatorData This indicator already calculated data.
 * @return The result data.
 */
@Override
public Data calculate(int index, List<IndicatorSource> indicatorSources, DataList indicatorData) {

    // If index < 0 do nothing.
    if (index < 0) {
        return null;
    }

    // The unique data list and the index of the data.
    int period = getIndicatorInfo().getParameter("PERIOD").getValue().getInteger();

    // If index < period, calculate the mean from scratch.
    if (index < period) {
        return getSource(index, indicatorSources);
    }

    // The Gaussian curve fitter and the Gaussian function.
    GaussianCurveFitter fitter = GaussianCurveFitter.create();
    Gaussian.Parametric function = new Gaussian.Parametric();

    // For every indicator source, build the list of observation points.
    int numIndexes = getNumIndexes();
    double[] values = new double[numIndexes];
    Arrays.fill(values, 0);
    int valueIndex = 0;
    for (IndicatorSource source : indicatorSources) {
        DataList dataList = source.getDataList();
        List<Integer> indexes = source.getIndexes();
        for (Integer dataIndex : indexes) {

            // The list of observations.
            WeightedObservedPoints obs = new WeightedObservedPoints();
            int startIndex = index - period + 1;
            int endIndex = index;
            int x = 0;
            for (int i = startIndex; i <= endIndex; i++) {
                double y = dataList.get(i).getValue(dataIndex);
                obs.add(x, y);
                x++;
            }

            // Reduce last x to get the last coordinate applied.
            x--;

            // The parameters to apply to the function.
            double[] params = fitter.fit(obs.toList());

            // The value.
            values[valueIndex] = function.value(x, params);
            valueIndex++;
        }
    }

    Data data = new Data();
    data.setData(values);
    data.setTime(indicatorSources.get(0).getDataList().get(index).getTime());
    return data;
}

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From source file:bide.core.par.TunePar.java

private void setType(String type) {

    int typeIndex = -1;
    if (type.equalsIgnoreCase("normal")) {
        typeIndex = 0;//ww w.j av  a 2s.  c  o  m
    } else if (type.equalsIgnoreCase("normalbig")) {
        typeIndex = 1;
    } else if (type.equalsIgnoreCase("scale")) {
        typeIndex = 2;
    }

    tuneType = new int[noTunePar];
    Arrays.fill(tuneType, typeIndex);

}

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