Example usage for java.lang Double MAX_VALUE

Introduction

In this page you can find the example usage for java.lang Double MAX_VALUE.

Prototype

double MAX_VALUE

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Document

A constant holding the largest positive finite value of type double , (2-2^-52)·2¹⁰²³.

Usage

From source file:org.hbird.business.navigation.processors.orekit.RangeCalculator.java

void calculateMinAndMax(SpacecraftState startState, AbsoluteDate endDate, TopocentricFrame locationOnEarth,
        Frame inertialFrame, double calculationStep, ExtendedContactParameterRange range)
        throws OrekitException {
    double minRange = Double.MAX_VALUE;
    double maxRange = 0;
    SpacecraftState current = startState.shiftedBy(0.0D); // make a copy
    while (current.getDate().compareTo(endDate) < 0) {
        double currentRange = calculateRang(current, locationOnEarth, inertialFrame);
        maxRange = Math.max(maxRange, currentRange);
        minRange = Math.min(minRange, currentRange);
        current = current.shiftedBy(calculationStep);
    }/*from  w w w. j  a  va 2 s .c o m*/
    range.setMax(maxRange);
    range.setMin(minRange);
}

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From source file:com.github.lynxdb.server.core.aggregators.MimMax.java

@Override
public TimeSerie downsample(TimeSerie _serie, long _period) {
    return doDownsampling(_serie, _period, new Aggregator.Reducer() {
        double max;

        @Override//ww w.  java  2s.  c o  m
        public void update(Entry _entry) {
            if (_entry.getValue() > max) {
                max = _entry.getValue();
            }
        }

        @Override
        public double result() {
            return max;
        }

        @Override
        public void reset() {
            max = -Double.MAX_VALUE;
        }
    });
}

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From source file:es.udc.gii.common.eaf.plugin.multiobjective.crowding.ObjectiveSpaceCrowding.java

@Override
public void calculate(List<NSGA2Individual> list) {

    /* Reset all crowding distances. */
    resetCrowdingDistance(list);/*  www  .j a  v a 2 s .  co  m*/

    if (list == null || list.isEmpty()) {
        return;
    }

    /* Comparator. */
    MinimizingObjectiveComparator<NSGA2Individual> comparator = new MinimizingObjectiveComparator<NSGA2Individual>();

    /* Number of objectives. */
    int nObjectives = list.get(0).getObjectives().size();

    /* For each objective. */
    for (int obj = 0; obj < nObjectives; obj++) {

        /* Set the objective to consider. */
        comparator.setObjectiveIndex(obj);

        /* Sort individuals considering the objective above. */
        try {
            Collections.sort(list, comparator);
        } catch (IllegalArgumentException ex) {
            System.out.println(list.get(0).getClass().getSimpleName());
            ex.printStackTrace();
        }
        /* Individuals on the boundaries have maximal crowding distance. */
        NSGA2Individual firstInd = list.get(0);
        firstInd.setCrowdingDistance(Double.MAX_VALUE);

        NSGA2Individual lastInd = list.get(list.size() - 1);
        lastInd.setCrowdingDistance(Double.MAX_VALUE);

        double minValue = firstInd.getObjectives().get(obj);
        double maxValue = lastInd.getObjectives().get(obj);

        /* If there are diferent values, i.e. if there is some distance
         * between individuals in objective space.
         */
        if (minValue != maxValue) {

            /* Calculate the increase of crowding distance 
            for each individual. */
            for (int i = 1; i < list.size() - 1; i++) {
                NSGA2Individual i0 = list.get(i);
                NSGA2Individual i1 = list.get(i - 1);
                NSGA2Individual i2 = list.get(i + 1);

                double delta = (i2.getObjectives().get(obj) - i1.getObjectives().get(obj))
                        / (maxValue - minValue);

                i0.increaseCrowdingDistance(delta);
            }
        }
    }

    for (int i = 1; i < list.size() - 1; i++) {
        NSGA2Individual ind = list.get(i);
        double cd = ind.getCrowdingDistance() / (double) nObjectives;
        ind.setCrowdingDistance(cd);
    }
}

---

From source file:Statistics.java

public void calc() {
    if (dirty) {/*from   w w  w  .  j  ava2 s . co  m*/
        double n = (double) data.size();
        max = Double.MIN_VALUE;
        min = Double.MAX_VALUE;
        sum = 0.0;
        variation = 0.0;
        for (double d : data) {
            sum += d;
            min = Math.min(d, min);
            max = Math.max(d, max);
        }
        average = sum / n;
        for (double d : data) {
            variation += Math.pow(d - average, 2.0);
        }
        variance = variation / n;

        // calculate median
        List<Double> copy = new ArrayList<Double>(data);
        Collections.sort(copy);
        if (copy.size() == 1) {
            median = copy.get(0);
        } else if ((copy.size() % 2) == 0) {
            median = copy.get(copy.size() / 2);
        } else {
            double v1 = copy.get(copy.size() / 2);
            double v2 = copy.get((copy.size() / 2) + 1);
            median = (v1 + v2) / 2.0;
        }
    }
    dirty = false;
}

---

From source file:io.sidecar.notification.NotificationRule.java

public NotificationRule(UUID ruleId, String name, String description, UUID appId, UUID userId, String stream,
        String key, double min, double max) {

    this.ruleId = checkNotNull(ruleId);

    checkArgument(StringUtils.isNotBlank(name) && name.length() <= 100);
    this.name = name.trim();

    checkArgument(StringUtils.isBlank(description) || description.length() <= 140);
    this.description = (description == null) ? "" : description.trim();

    this.appId = checkNotNull(appId);

    this.userId = checkNotNull(userId);

    Preconditions.checkArgument(ModelUtils.isValidStreamId(stream));
    this.stream = checkNotNull(stream);

    checkArgument(ModelUtils.isValidReadingKey(key));
    this.key = key;

    this.min = (min == Double.NEGATIVE_INFINITY || min == Double.NaN) ? Double.MIN_VALUE : min;
    this.max = (max == Double.POSITIVE_INFINITY || max == Double.NaN) ? Double.MAX_VALUE : max;
}

---

From source file:inflor.core.utils.PlotUtils.java

public static AbstractTransform createDefaultTransform(TransformType selectedType) {

    AbstractTransform newTransform = null;

    if (selectedType == TransformType.LINEAR || selectedType == TransformType.BOUNDARY) {
        newTransform = new BoundDisplayTransform(Double.MAX_VALUE, Double.MAX_VALUE);
    } else if (selectedType == TransformType.LOGARITHMIC) {
        newTransform = new LogrithmicTransform(1, 1000000);
    } else if (selectedType == TransformType.LOGICLE) {
        newTransform = new LogicleTransform();
    } else {/*w  w w. j  a  va  2 s  . co  m*/
        // noop
    }
    return newTransform;
}

---

From source file:com.basetechnology.s0.agentserver.field.MoneyField.java

public static Field fromJson(SymbolTable symbolTable, JSONObject fieldJson) {
    String type = fieldJson.optString("type");
    if (type == null || !type.equals("money"))
        return null;
    String name = fieldJson.has("name") ? fieldJson.optString("name") : null;
    String label = fieldJson.has("label") ? fieldJson.optString("label") : null;
    String description = fieldJson.has("description") ? fieldJson.optString("description") : null;
    double defaultValue = fieldJson.has("default_value") ? fieldJson.optDouble("default_value") : 0;
    double minValue = fieldJson.has("min_value") ? fieldJson.optDouble("min_value") : Double.MIN_VALUE;
    double maxValue = fieldJson.has("max_value") ? fieldJson.optDouble("max_value") : Double.MAX_VALUE;
    int nominalWidth = fieldJson.has("nominal_width") ? fieldJson.optInt("nominal_width") : 0;
    String compute = fieldJson.has("compute") ? fieldJson.optString("compute") : null;
    return new MoneyField(symbolTable, name, label, description, defaultValue, minValue, maxValue, nominalWidth,
            compute);//from   ww w  .  ja v  a2s.  c o  m
}

---

From source file:be.makercafe.apps.makerbench.editors.XMLEditor.java

public XMLEditor(String tabText, Path path) {
    super(tabText);

    this.caCodeArea = new CodeArea("");
    this.caCodeArea.setEditable(true);
    this.caCodeArea.setParagraphGraphicFactory(LineNumberFactory.get(caCodeArea));
    this.caCodeArea.setPrefSize(Double.MAX_VALUE, Double.MAX_VALUE);
    this.caCodeArea.getStylesheets().add(this.getClass().getResource("xml-highlighting.css").toExternalForm());
    this.caCodeArea.textProperty().addListener((obs, oldText, newText) -> {
        this.caCodeArea.setStyleSpans(0, computeHighlighting(newText));
    });//from w  w w  .j  a v a2 s.com
    addContextMenu(this.caCodeArea);

    try {
        this.caCodeArea.replaceText(FileUtils.readFileToString(path.toFile()));
    } catch (IOException ex) {
        Logger.getLogger(this.getClass().getName()).log(Level.SEVERE, "Error reading file.", ex);
    }

    BorderPane rootPane = new BorderPane();

    toolBar = createToolBar();
    rootPane.setTop(toolBar);
    rootPane.setCenter(caCodeArea);
    this.getTab().setContent(rootPane);

}

---

From source file:classif.Majority.KMeansSymbolicSequence.java

public void cluster() {
    centers = new ClassedSequence[nbClusters];
    affectation = new ArrayList[nbClusters];

    // pickup centers
    int[] selected = randGen.nextPermutation(data.size(), nbClusters);
    for (int i = 0; i < selected.length; i++) {
        centers[i] = new ClassedSequence(data.get(selected[i]).sequence, data.get(selected[i]).classValue);
    }//from   w  w w .j  a v  a  2  s .c  o m

    // for each iteration i
    for (int i = 0; i < 15; i++) {
        // init
        for (int k = 0; k < affectation.length; k++) {
            affectation[k] = new ArrayList<ClassedSequence>();
        }
        // for each data point j
        for (int j = 0; j < data.size(); j++) {

            double minDist = Double.MAX_VALUE;
            // for each cluster k
            for (int k = 0; k < centers.length; k++) {
                // distance between cluster k and data point j
                double currentDist = centers[k].sequence.distance(data.get(j).sequence);
                if (currentDist < minDist) {
                    clusterMap[j] = k;
                    minDist = currentDist;
                }
            }

            // affect data point j to cluster affected to j
            affectation[clusterMap[j]].add(data.get(j));
        }

        // redefine
        for (int j = 0; j < nbClusters; j++) {
            if (affectation[j].size() == 0) {
                centers[j] = null;
            } else {
                ArrayList<Sequence> copyaffectation = new ArrayList<Sequence>();
                for (ClassedSequence eachClassedSequence : affectation[j]) {
                    copyaffectation.add(eachClassedSequence.sequence);
                }
                centers[j].sequence = Sequences.mean(copyaffectation.toArray(new Sequence[0]));
                centers[j].classValue = MajorityClass(affectation[j]);
            }
        }
    }
}

---

From source file:classif.kmedoid.KMedoidsSymbolicSequence.java

public void cluster() {

    // pickup centers
    int nbSelected = Math.min(data.size(), nbCluster);
    indexMedoids = randGen.nextPermutation(data.size(), nbSelected);
    nbCluster = nbSelected;//from  w ww . j  av a 2s .com

    double[][] distances = new double[data.size()][data.size()];
    for (int i = 0; i < distances.length; i++) {
        for (int j = i + 1; j < distances[i].length; j++) {
            distances[i][j] = data.get(i).distance(data.get(j));
            distances[j][i] = distances[i][j];
        }
    }

    ArrayList<Sequence>[] affectation = new ArrayList[nbCluster];
    // init
    for (int i = 0; i < affectation.length; i++) {
        affectation[i] = new ArrayList<Sequence>();
    }

    boolean changed = true;
    // for each iteration i
    for (int i = 0; i < 150 && changed; i++) {
        changed = false;
        // System.out.println(i);
        // for each data point j
        for (int j = 0; j < data.size(); j++) {

            double minDist = Double.MAX_VALUE;
            // for each cluster k
            for (int k = 0; k < indexMedoids.length; k++) {
                if (indexMedoids[k] == -1) {// nothing in cluster k
                    continue;
                }
                // distance between cluster k and data point j
                double currentDist = distances[indexMedoids[k]][j];
                if (currentDist < minDist) {
                    clusterMap[j] = k;
                    minDist = currentDist;
                }
            }

            // affect data point j to cluster affected to j
            affectation[clusterMap[j]].add(data.get(j));
        }

        // redefine
        for (int j = 0; j < nbCluster; j++) {
            int tmpIndex = Sequences.medoidIndex(affectation[j]);
            if (tmpIndex != indexMedoids[j]) {
                indexMedoids[j] = tmpIndex;
                changed = true;
            }
        }

        // reset affect
        for (int k = 0; k < affectation.length; k++) {
            affectation[k] = new ArrayList<Sequence>();
        }
    }

}

---

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