Example usage for java.lang Double NaN

Introduction

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

Prototype

double NaN

To view the source code for java.lang Double NaN.

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Document

A constant holding a Not-a-Number (NaN) value of type double .

Usage

From source file:com.cti.vpx.controls.graph.utilities.ui.graphs.waterfallGraph.ArithmeticMean.java

public void clear() {
    m1 = Double.NaN;
    n = 0;
    dev = Double.NaN;
    nDev = Double.NaN;
}

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From source file:com.ironiacorp.statistics.r.type.GenericAnovaResult.java

public Double getMainEffectP(String factorName) {
    if (mainEffects.get(factorName) == null) {
        return Double.NaN;
    }/*from   ww w .  j  av a  2 s .co m*/
    return mainEffects.get(factorName).getPValue();
}

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From source file:org.jfree.data.statistics.Statistics.java

/**
 * Returns the mean of an array of numbers.
 *
 * @param values  the values ({@code null} not permitted).
 * @param includeNullAndNaN  a flag that controls whether or not
 *     {@code null} and {@code Double.NaN} values are included
 *     in the calculation (if either is present in the array, the result is
 *     {@link Double#NaN})./*from  w w  w.  j  a va2 s. c o  m*/
 *
 * @return The mean.
 *
 * @since 1.0.3
 */
public static double calculateMean(Number[] values, boolean includeNullAndNaN) {

    ParamChecks.nullNotPermitted(values, "values");
    double sum = 0.0;
    double current;
    int counter = 0;
    for (int i = 0; i < values.length; i++) {
        // treat nulls the same as NaNs
        if (values[i] != null) {
            current = values[i].doubleValue();
        } else {
            current = Double.NaN;
        }
        // calculate the sum and count
        if (includeNullAndNaN || !Double.isNaN(current)) {
            sum = sum + current;
            counter++;
        }
    }
    double result = (sum / counter);
    return result;
}

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From source file:br.unicamp.ic.recod.gpsi.applications.gpsiTimeSeriesCreator.java

@Override
public void run() throws Exception {
    String datasetsPath = Paths.get(super.datasetPath).getParent().getParent().toString();
    System.out.println(datasetsPath);

    HashMap<Byte, double[][]> indicesMap = new HashMap<>();
    ArrayList<double[]> entities;
    double[][] series;

    int daysCount = 0, i, dayIndex;

    gpsiRawDataset rawDataset;/*from   www . j a va2s  .  c om*/
    gpsiMLDataset dataset;

    VectorialMean vmean;
    Mean mean = new Mean();

    File[] days, years = new File(datasetsPath).listFiles(File::isDirectory);
    Arrays.sort(years);

    for (File year : years)
        daysCount += 365 + (Integer.parseInt(year.getName()) % 4 == 0 ? 1 : 0);

    for (Byte label : classLabels) {
        series = new double[daysCount][descriptors.length];
        for (double[] row : series)
            Arrays.fill(row, Double.NaN);
        indicesMap.put(label, series);
    }

    daysCount = 0;
    for (File year : years) {
        days = new File(year.getAbsolutePath()).listFiles(File::isDirectory);
        Arrays.sort(days);
        for (File day : days) {
            dayIndex = daysCount + Integer.parseInt(day.getName()) - 1;
            rawDataset = datasetReader.readDataset(day.getAbsolutePath() + "/", null, 0.0);
            rawDataset.assignFolds(new byte[] { 0 }, null, null);
            for (i = 0; i < descriptors.length; i++) {
                dataset = new gpsiMLDataset(descriptors[i]);
                dataset.loadTrainingSet(rawDataset.getTrainingEntities(), true);
                for (Byte label : classLabels) {

                    if (!dataset.getTrainingEntities().containsKey(label)) {
                        indicesMap.get(label)[dayIndex][i] = Double.NaN;
                        continue;
                    }
                    vmean = new VectorialMean(dataset.getDimensionality());
                    entities = (ArrayList<double[]>) dataset.getTrainingEntities().get(label);
                    for (double[] v : entities)
                        vmean.increment(v);
                    indicesMap.get(label)[dayIndex][i] = mean.evaluate(vmean.getResult());
                }
            }
        }
        daysCount += 365 + (Integer.parseInt(year.getName()) % 4 == 0 ? 1 : 0);
    }

    for (Byte label : classLabels)
        stream.register(new gpsiDoubleCsvIOElement(indicesMap.get(label), names, "series/" + label + ".csv"));

}

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From source file:com.whizzosoftware.hobson.rest.v1.resource.device.DeviceTelemetryResource.java

/**
 * @api {get} /api/v1/users/:userId/hubs/:hubId/plugins/:pluginId/devices/:deviceId/telemetry Get device variable telemetry
 * @apiVersion 0.1.8// w  w  w .  ja  v  a 2 s .co m
 * @apiName GetDeviceTelemetry
 * @apiDescription Retrieves telemetry for a specific device variable.
 * @apiGroup Devices
 * @apiSuccessExample {json} Success Response:
 * {
 *   "tempF": {
 *     "1408390215763": 72.0
 *   }
 * },
 * {
 *   "targetTempF": {
 *     "1408390215763": 73.0
 *   }
 * }
 */
@Override
protected Representation get() {
    HobsonRestContext ctx = HobsonRestContext.createContext(this, getRequest());
    String pluginId = getAttribute("pluginId");
    String deviceId = getAttribute("deviceId");
    long endTime = System.currentTimeMillis() / 1000; // TODO: should be pulled from request
    TelemetryInterval interval = TelemetryInterval.HOURS_24; // TODO: should be pulled from request

    Map<String, Collection<TemporalValue>> telemetry = deviceManager.getDeviceTelemetry(ctx.getUserId(),
            ctx.getHubId(), pluginId, deviceId, endTime, interval);

    JSONObject results = new JSONObject();

    for (String varName : telemetry.keySet()) {
        Collection<TemporalValue> varTm = telemetry.get(varName);

        JSONObject seriesJSON = new JSONObject();
        results.put(varName, seriesJSON);

        for (TemporalValue value : varTm) {
            Double d = (Double) value.getValue();
            if (d != null && !d.equals(Double.NaN)) {
                seriesJSON.put(Long.toString(value.getTime()), d);
            }
        }
    }

    return new JsonRepresentation(results);
}

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From source file:edu.cudenver.bios.power.glmm.GLMMTestPillaiBartlett.java

/**
 * Calculate the denominator degrees of freedom for the PBT, based on
 * whether the null or alternative hypothesis is assumed true.  
 * /*www.  ja  v a2 s  . c o  m*/
 * @param type distribution type
 * @return denominator degrees of freedom
 * @throws IllegalArgumentException
 */
@Override
public double getDenominatorDF(DistributionType type) {
    // a = #rows in between subject contrast matrix, C
    double a = C.getRowDimension();
    // b = #columns in within subject contrast matrix
    double b = U.getColumnDimension();
    // minimum of a and b dimensions
    double s = (a < b) ? a : b;

    double df = Double.NaN;
    if (fMethod == FApproximation.PILLAI_ONE_MOMENT || fMethod == FApproximation.PILLAI_ONE_MOMENT_OMEGA_MULT) {
        df = s * ((totalN - rank) - b + s);
    } else {
        double mu1 = a * b / (totalN - rank + a);
        double factor1 = (totalN - rank + a - b) / (totalN - rank + a - 1);
        double factor2 = (totalN - rank) / (totalN - rank + a + 2);
        double variance = 2 * a * b * factor1 * factor2 / ((totalN - rank + a) * (totalN - rank + a));
        double mu2 = variance + mu1 * mu1;
        double m1 = mu1 / s;
        double m2 = mu2 / (s * s);
        double denom = m2 - m1 * m1;
        df = 2 * (m1 - m2) * (1 - m1) / denom;
    }

    return df;
}

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From source file:geogebra.common.kernel.algos.AlgoRootNewton.java

public final double calcRoot(Function fun, double start) {
    double root = Double.NaN;
    if (rootFinderBrent == null)
        rootFinderBrent = new BrentSolver(Kernel.STANDARD_PRECISION);

    // try Brent method with borders close to start value
    try {//  w  w w .j a  v  a 2s . com

        // arbitrary (used to depend on screen width)
        double step = 1;

        root = rootFinderBrent.solve(MAX_ITERATIONS, new RealRootAdapter(fun), start - step, start + step,
                start);
        if (checkRoot(fun, root)) {
            // System.out.println("1. Brent worked: " + root);
            return root;
        }
    } catch (Exception e) {
        root = Double.NaN;
    }

    // try Brent method on valid interval around start
    double[] borders = getDomain(fun, start);
    try {
        root = rootFinderBrent.solve(MAX_ITERATIONS, new RealRootAdapter(fun), borders[0], borders[1], start);
        if (checkRoot(fun, root)) {
            // System.out.println("2. Brent worked: " + root);
            return root;
        }
    } catch (Exception e) {
        root = Double.NaN;
    }

    // try Newton's method
    RealRootDerivFunction derivFun = fun.getRealRootDerivFunction();
    if (derivFun != null) {
        // check if fun(start) is defined
        double eval = fun.evaluate(start);
        if (Double.isNaN(eval) || Double.isInfinite(eval)) {
            // shift left border slightly right
            borders[0] = 0.9 * borders[0] + 0.1 * borders[1];
            start = (borders[0] + borders[1]) / 2;
        }

        if (rootFinderNewton == null) {
            rootFinderNewton = new NewtonSolver();
        }

        try {
            root = rootFinderNewton.solve(MAX_ITERATIONS, new RealRootDerivAdapter(derivFun), borders[0],
                    borders[1], start);
            if (checkRoot(fun, root)) {
                // System.out.println("Newton worked: " + root);
                return root;
            }
        } catch (Exception e) {
            root = Double.NaN;
        }
    }

    // neither Brent nor Newton worked
    return Double.NaN;
}

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From source file:org.jfree.data.xy.AbstractXYDataset.java

/**
 * Returns the y-value (as a double primitive) for an item within a series.
 *
 * @param series  the series index (zero-based).
 * @param item  the item index (zero-based).
 *
 * @return The value.//from  w  ww  . j a v a2  s.c om
 */
public double getYValue(int series, int item) {
    double result = Double.NaN;
    Number y = getY(series, item);
    if (y != null) {
        result = y.doubleValue();
    }
    return result;
}

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From source file:controller.ViewPackageController.java

@RequestMapping(value = "/pickPackage/{id}", method = RequestMethod.GET)
public String pickPackage(ModelMap mm, Authentication authen, @PathVariable(value = "id") int id) {
    try {//w  ww.j  a  va2  s.  c o  m
        List<Distributor> listDis = disModel.getAll();
        Packages pack = packModel.getByID(id);
        mm.put("listDistributor", listDis);
        Customer cus = cusModel.find(authen.getName(), "username", false).get(0);
        mm.addAttribute("requirement", new Requirement(null, cus, null, Double.NaN));
        mm.put("type", pack.getType());
        mm.put("packid", pack.getPackageId());
    } catch (Exception ex) {
        ex.printStackTrace();
    }
    return "pickPackage";

}

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From source file:com.itemanalysis.psychometrics.polycor.Covariance.java

public Double value(boolean unbiased) {
    if (fixedValue)
        return covariance;
    if (N < 1)
        return Double.NaN;
    if (unbiased) {
        return covarianceNumerator / (N - 1.0);
    } else {/*from  ww w.  j av  a2 s  .c o m*/
        return covarianceNumerator / N;
    }
}

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