Example usage for java.util BitSet BitSet

Introduction

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

Prototype

public BitSet() 

Source Link

Document

Creates a new bit set.

Usage

From source file:de.upb.wdqa.wdvd.revisiontags.TagDownloaderRevisionData.java

/**
 * Converts the tags to a memory efficient byte[] representation
 *///from  www .  ja va  2  s.  c  o m
private byte[] tagsToBytes(Set<DbTag> tags) {
    BitSet bitSet = new BitSet();

    if (tags != null) {
        for (DbTag tag : tags) {
            bitSet.set(tag.getTagId());
        }
    }

    return bitSet.toByteArray();
}

---

From source file:mastodon.algorithms.FlipPenaltyAlgorithm.java

protected void initialize() {
    stub = "Flip Penalty";

    //      Random.setSeed(4443245);

    pruningFreq = new HashMap<Integer, Integer>();
    for (int i = 0; i < bts.getTaxaCount(); i++) {
        pruningFreq.put(i, 0);/*from w w w  .j a  va  2  s  .  c om*/
    }

    maxScorePruning = new HashMap<BitSet, double[]>();
    currPruning = new BitSet();
    prevPruning = new BitSet();
    prevScore = new double[] { 0, 0 };

    maxScore = prevScore.clone();
    maxScorePruning.put(prevPruning, maxScore);

    iterationCounter = 0;
}

---

From source file:mastodon.algorithms.SALinearAlgorithm.java

protected void initialize() {
    if (minPrunedSpeciesCount == maxPrunedSpeciesCount) {
        stub = "SA Cons.";
    } else {/*from  w w w  .  j a  v a 2s .  c o  m*/
        stub = "SA Lin.";
    }

    pruningFreq = new HashMap<Integer, Integer>();
    for (int i = 0; i < bts.getTaxaCount(); i++) {
        pruningFreq.put(i, 0);
    }

    currTemp = initTemp;

    currPrunedSpeciesCount = minPrunedSpeciesCount;
    maxScorePruning = new HashMap<BitSet, double[]>();
    currPruning = new BitSet();

    for (int i = 0; i < currPrunedSpeciesCount; i++) {
        int choice = 0;
        do {
            choice = (int) (Random.nextDouble() * bts.getTaxaCount());
        } while (currPruning.get(choice));
        currPruning.set(choice);
    }
    prevPruning = (BitSet) currPruning.clone();

    prevScore = bts.pruneFast(currPruning);
    bts.unPrune();
    maxScore = prevScore.clone();
    maxScorePruning.put(prevPruning, maxScore);

    double mean = 1.0; //needed when pruning 1 taxon (can't have a mean of 0 in PoissonDistribution())
    if (currPrunedSpeciesCount > 1) {
        mean = 0.5 * (currPrunedSpeciesCount - 1);
    }
    pd = new PoissonDistribution(mean);

    setupIterations();
    coolingRate = Math.pow(finalTemp / initTemp, 1.0 / stepIterations[currPrunedSpeciesCount - 1]);

    iterationCounter = 0;
}

---

From source file:com.opengamma.analytics.math.minimization.NonLinearTransformFunctionTest.java

@Test
public void testNullTransform() {
    BitSet fixed = new BitSet();
    fixed.set(0);/*from   w ww.  j  av  a  2s .  com*/
    DoubleMatrix1D start = new DoubleMatrix1D(new double[] { Math.PI / 4, 1 });
    UncoupledParameterTransforms transforms = new UncoupledParameterTransforms(start, NULL_TRANSFORMS, fixed);
    NonLinearTransformFunction transFunc = new NonLinearTransformFunction(FUNCTION, JACOBIAN, transforms);
    Function1D<DoubleMatrix1D, DoubleMatrix1D> func = transFunc.getFittingFunction();
    Function1D<DoubleMatrix1D, DoubleMatrix2D> jacFunc = transFunc.getFittingJacobian();

    DoubleMatrix1D x = new DoubleMatrix1D(new double[] { 0.5 });
    final double rootHalf = Math.sqrt(0.5);
    DoubleMatrix1D y = func.evaluate(x);
    assertEquals(3, y.getNumberOfElements());
    assertEquals(rootHalf * Math.cos(0.5), y.getEntry(0), 1e-9);
    assertEquals(rootHalf * Math.sin(0.5), y.getEntry(1), 1e-9);
    assertEquals(rootHalf, y.getEntry(2), 1e-9);

    DoubleMatrix2D jac = jacFunc.evaluate(x);
    assertEquals(3, jac.getNumberOfRows());
    assertEquals(1, jac.getNumberOfColumns());
    assertEquals(-rootHalf * Math.sin(0.5), jac.getEntry(0, 0), 1e-9);
    assertEquals(rootHalf * Math.cos(0.5), jac.getEntry(1, 0), 1e-9);
    assertEquals(0, jac.getEntry(2, 0), 1e-9);
}

---

From source file:solidstack.io.DiskBuffer.java

/**
 * Constructs a disk map with the given block size.
 *
 * @param filePrefix The prefix of the temporary file.
 * @param blockSize The block size for the data written to the temporary file.
 *///from   w w  w .  j  a v a 2  s .com
public DiskBuffer(String filePrefix, int blockSize) {
    try {
        this.tempFile = File.createTempFile(filePrefix, null);
        this.blockSize = blockSize;
        this.store = new RandomAccessFile(this.tempFile, "rw");
        this.usedBlocks = new BitSet();
        this.emptyBlockStart = 0;
    } catch (IOException e) {
        throw new FatalIOException(e);
    }
}

---

From source file:sf.net.experimaestro.manager.plans.ProductReference.java

@Override
public Operator prepare(Map<Operator, Operator> map, OperatorMap opMap) {
    // --- Loop over the cartesian product of the inputs
    Plan.OperatorIterable inputValues[] = new Plan.OperatorIterable[parents.size()];
    {/*from   w w  w . j  a  v  a 2 s . com*/

        int index = 0;
        for (Operator input : parents) {
            inputValues[index] = new Plan.OperatorIterable(Arrays.asList(input), map, opMap);
            index++;
        }
    }

    // Create a new operator
    Operator inputOperators[] = new Operator[inputValues.length];
    BitSet[] joins = new BitSet[inputOperators.length];

    // Process union of operators
    for (int i = inputValues.length; --i >= 0;) {
        Plan.OperatorIterable values = inputValues[i];
        Union union = new Union();
        for (Operator operator : values) {
            union.addParent(operator);
        }

        if (union.getParents().size() == 1)
            inputOperators[i] = union.getParent(0);
        else
            inputOperators[i] = union;

        joins[i] = new BitSet();
        opMap.add(inputOperators[i]);

    }

    // TODO: build a simplified graph of operators:
    // for each input, we only have LCAs (organized in a hierarchy)

    // Process this simplified graph step-wise, by merging

    // Find LCAs and store them in a map operator ID -> inputs
    for (int i = 0; i < inputOperators.length - 1; i++) {
        for (int j = i + 1; j < inputOperators.length; j++) {
            ArrayList<Operator> lca = opMap.findLCAs(inputOperators[i], inputOperators[j]);
            for (Operator operator : lca) {
                int key = opMap.get(operator);
                joins[i].set(key);
                joins[j].set(key);
            }
        }
    }

    // Build the lattice of operators
    Lattice lattice = new Lattice(opMap);
    for (int i = 0; i < joins.length; i++) {
        lattice.add(joins[i], inputOperators[i]);
    }
    MergeResult merge = lattice.merge();

    // Build the trie structure for product/joins

    int[] mapping = new int[inputOperators.length];
    for (int i = 0; i < parents.size(); i++) {
        mapping[i] = merge.map.get(inputOperators[i]);
    }

    ReorderNodes reorder = new ReorderNodes(mapping);
    reorder.addParent(merge.operator);

    return reorder;
}

---

From source file:mastodon.algorithms.MHBisectionAlgorithm.java

protected void choosePruningCount() {
    if (iterationCounter % stepIterations == 0) {
        System.out.println(currPrunedSpeciesCount);
        System.out.println(maxScore[0] + " " + maxScore[1]);

        if (iterationCounter > 0) {
            if (maxScore[0] < minMapScore) {
                kLeft = currPrunedSpeciesCount;
            } else {
                kRight = currPrunedSpeciesCount;
            }/*from   w w w. j a v a  2s.c  o  m*/
            currPrunedSpeciesCount = (int) ((kRight + kLeft) / 2);
        }

        maxScore = new double[2];
        maxScorePruning = new HashMap<BitSet, double[]>();
        currPruning = new BitSet();

        for (int i = 0; i < currPrunedSpeciesCount; i++) {
            int choice = 0;
            do {
                choice = (int) (Random.nextDouble() * bts.getTaxaCount());
            } while (currPruning.get(choice));
            currPruning.set(choice);
        }
        prevPruning = (BitSet) currPruning.clone();
        prevScore = bts.pruneFast(currPruning);
        bts.unPrune();

        maxScorePruning.put(prevPruning, prevScore);

        double mean = 1.0; //needed when pruning 1 taxon (can't have a mean of 0 in PoissonDistribution())
        if (currPrunedSpeciesCount > 1) {
            mean = 0.5 * (currPrunedSpeciesCount - 1);
        }
        pd = new PoissonDistribution(mean);
    }
}

---

From source file:ca.phon.ipa.features.FeatureSet.java

/** 
 * Create a new instance of a feature set
 * /*w  w  w  .  ja  v a2 s  .co  m*/
 */
public FeatureSet() {
    this.features = new BitSet();
    this.ipaChar = '\u0000';
}

---

From source file:ca.phon.ipa.features.FeatureSet.java

public FeatureSet(Set<String> features) {
    this.features = new BitSet();
    this.ipaChar = '\u0000';
    for (String f : features) {
        addFeature(f);/*from   ww w  .  j  a v a 2  s. c om*/
    }
}

---

From source file:mastodon.algorithms.SABisectionAlgorithm.java

protected void choosePruningCount() {
    if (iterationCounter % stepIterations == 0) {
        System.out.println(currPrunedSpeciesCount);
        System.out.println(maxScore[0] + " " + maxScore[1]);

        if (iterationCounter > 0) {
            if (maxScore[0] < minMapScore) {
                kLeft = currPrunedSpeciesCount;
            } else {
                kRight = currPrunedSpeciesCount;
            }/*from   w  w w .  j  a v a2  s.c  o m*/
            currPrunedSpeciesCount = (int) ((kRight + kLeft) / 2);
        }

        maxScore = new double[2];
        maxScorePruning = new HashMap<BitSet, double[]>();
        currPruning = new BitSet();

        for (int i = 0; i < currPrunedSpeciesCount; i++) {
            int choice = 0;
            do {
                choice = (int) (Random.nextDouble() * bts.getTaxaCount());
            } while (currPruning.get(choice));
            currPruning.set(choice);
        }
        prevPruning = (BitSet) currPruning.clone();
        prevScore = bts.pruneFast(currPruning);
        bts.unPrune();

        maxScorePruning.put(prevPruning, prevScore);

        double mean = 1.0; //needed when pruning 1 taxon (can't have a mean of 0 in PoissonDistribution())
        if (currPrunedSpeciesCount > 1) {
            mean = 0.5 * (currPrunedSpeciesCount - 1);
        }
        pd = new PoissonDistribution(mean);
        currTemp = initTemp;
    }

    currTemp *= coolingRate;
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• »