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:com.github.cambierr.lorawanpacket.semtech.Stat.java

public Stat(JSONObject _json) throws MalformedPacketException {

    /**// w  w w  .j  a  v  a 2s. c om
     * time
     */
    if (!_json.has("time")) {
        throw new MalformedPacketException("missing time");
    } else {
        time = _json.getString("time");
    }

    /**
     * lati
     */
    if (!_json.has("lati")) {
        lati = Double.MAX_VALUE;
    } else {
        lati = _json.getDouble("lati");
    }

    /**
     * longi
     */
    if (!_json.has("longi")) {
        lati = Double.MAX_VALUE;
    } else {
        longi = _json.getDouble("longi");
    }

    /**
     * alti
     */
    if (!_json.has("alti")) {
        alti = Integer.MAX_VALUE;
    } else {
        alti = _json.getInt("alti");
    }

    /**
     * rxnb
     */
    if (!_json.has("rxnb")) {
        rxnb = Integer.MAX_VALUE;
    } else {
        rxnb = _json.getInt("rxnb");
    }

    /**
     * rxok
     */
    if (!_json.has("rxok")) {
        rxok = Integer.MAX_VALUE;
    } else {
        rxok = _json.getInt("rxok");
    }

    /**
     * rxfw
     */
    if (!_json.has("rxfw")) {
        rxfw = Integer.MAX_VALUE;
    } else {
        rxfw = _json.getInt("rxfw");
    }

    /**
     * ackr
     */
    if (!_json.has("ackr")) {
        ackr = Integer.MAX_VALUE;
    } else {
        ackr = _json.getInt("ackr");
    }

    /**
     * dwnb
     */
    if (!_json.has("dwnb")) {
        dwnb = Integer.MAX_VALUE;
    } else {
        dwnb = _json.getInt("dwnb");
    }

    /**
     * txnb
     */
    if (!_json.has("txnb")) {
        txnb = Integer.MAX_VALUE;
    } else {
        txnb = _json.getInt("txnb");
    }

}

---

From source file:edu.ucsf.valelab.saim.calculations.SaimFunctionWithBounds.java

/**
 * Calculates the field intensity at a given angle, given the parameters
 * A, B, and height./* w w  w .j a va 2  s  .  c om*/
 * @param x - angle in radians
 * @param parameters - array of 3 values:
 *    A - scaling parameter
 *    B - offset parameter, accounting for background
 *    h - height in nm
 * @return - Calculated field intensity
 */
@Override
public double value(double x, double... parameters) {
    if (parameters.length != 3)
        throw new DimensionMismatchException(parameters.length, 3);
    sd_.A_ = parameters[0];
    sd_.B_ = parameters[1];
    angle_ = x;
    if (parameters[0] < 0 || parameters[1] < 0 || parameters[2] < 0) {
        return Double.MAX_VALUE;
    }
    return value(parameters[2]);
}

---

From source file:classif.kmeans.KMeansSymbolicSequence.java

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

    // pickup centers
    int[] selected = randGen.nextPermutation(data.size(), nbClusters);
    for (int i = 0; i < selected.length; i++) {
        centers[i] = data.get(selected[i]);
    }//  w  w  w.j  a  va2  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<Sequence>();
        }
        // 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].distance(data.get(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 < nbClusters; j++) {
            if (affectation[j].size() == 0) {
                centers[j] = null;
            } else {
                centers[j] = Sequences.mean(affectation[j].toArray(new Sequence[0]));
            }
        }
    }
}

---

From source file:net.sf.dsp4j.octave.packages.signal_1_2_0.Cheb2Ord.java

private void calcCheb2Ord(double[] Wp, double[] Ws, double Rp, double Rs) {
    double T = 2;

    // returned frequency is the same as the input frequency
    Wc = Arrays.copyOf(Ws, Ws.length);

    // warp the target frequencies according to the bilinear transform
    for (int i = 0; i < Wp.length; i++) {
        Ws[i] = 2.0 / T * Math.tan(Math.PI * Ws[i] / T);
        Wp[i] = 2.0 / T * Math.tan(Math.PI * Wp[i] / T);
    }//from   w w w.j a  va  2s  . c  o m
    double Wa;

    if (Wp[0] < Ws[0]) {
        // low pass
        if (Wp.length == 1) {
            Wa = Wp[0] / Ws[0];
        } else {
            // band reject
            throw new RuntimeException("band reject is not implement yet.");
        }
    } else {
        // if high pass, reverse the sense of the test
        if (Wp.length == 1) {
            Wa = Ws[0] / Wp[0];
        } else {
            // band pass 
            Wa = Double.MAX_VALUE;
            for (int i = 0; i < Wp.length; i++) {
                Wa = Math.min(Wa, Math.abs((Math.pow(Wp[i], 2) - Ws[0] * Ws[1]) / (Wp[i] * (Ws[0] - Ws[1]))));
            }
        }
    }

    // compute minimum n which satisfies all band edge conditions
    final double stop_atten = Math.pow(10, Math.abs(Rs) / 10.0);
    final double pass_atten = Math.pow(10, Math.abs(Rp) / 10.0);
    n = (int) Math.ceil(
            FastMath.acosh(Math.sqrt((stop_atten - 1.0) / (pass_atten - 1.0))) / FastMath.acosh(1.0 / Wa));

}

---

From source file:net.sf.dsp4j.octave.packages.signal_1_2_0.Cheb1Ord.java

private void calcCheb1Ord(double[] Wp, double[] Ws, double Rp, double Rs) {

    double T = 2;

    // returned frequency is the same as the input frequency
    Wc = Arrays.copyOf(Wp, Wp.length);

    // warp the target frequencies according to the bilinear transform
    for (int i = 0; i < Wp.length; i++) {
        Ws[i] = 2.0 / T * Math.tan(Math.PI * Ws[i] / T);
        Wp[i] = 2.0 / T * Math.tan(Math.PI * Wp[i] / T);
    }//from w ww.j a va 2  s.co m
    double Wa;
    if (Wp[0] < Ws[0]) // low pass
    {
        if (Wp.length == 1) {
            Wa = Ws[0] / Wp[0];
        } else {
            // band reject
            throw new RuntimeException("band reject is not implement yet.");
        }
    } else {
        // if high pass, reverse the sense of the test
        if (Wp.length == 1) {
            Wa = Wp[0] / Ws[0];
        } else {
            // band pass
            Wa = Double.MAX_VALUE;
            for (int i = 0; i < Wp.length; i++) {
                Wa = Math.min(Wa, Math.abs((Math.pow(Ws[i], 2) - Wp[0] * Wp[1]) / (Ws[i] * (Wp[0] - Wp[1]))));
            }
        }
    }

    // compute minimum n which satisfies all band edge conditions
    final double stop_atten = Math.pow(10, Math.abs(Rs) / 10.0);
    final double pass_atten = Math.pow(10, Math.abs(Rp) / 10.0);
    n = (int) Math
            .ceil(FastMath.acosh(Math.sqrt((stop_atten - 1.0) / (pass_atten - 1.0))) / FastMath.acosh(Wa));

}

---

From source file:eu.vital.orchestrator.rest.EvaluationRESTService.java

@POST
@Path("/latest")
public Response executeLatestScenario(JsonNode input) throws Exception {
    String dmsUrl = "https://local.vital-iot.eu:8443/vital-core-dms";

    // 1. Get List of sensors from DMS observing AvailableBikes
    Client dmsClient = ClientBuilder.newClient();
    WebTarget dmsTarget = dmsClient.target(dmsUrl).path("querySensor").queryParam("encodeKeys", "false");
    ObjectNode sensorQuery = objectMapper.createObjectNode();
    sensorQuery.put("http://purl\\u002eoclc\\u002eorg/NET/ssnx/ssn#observes.@type",
            "http://vital-iot.eu/ontology/ns/Speed");
    ArrayNode sensorList = dmsTarget.request(MediaType.APPLICATION_JSON_TYPE).post(Entity.json(sensorQuery),
            ArrayNode.class);

    // 2. Find the nearest sensor
    double minDistance = Double.MAX_VALUE;
    JsonNode nearestSensor = null;/*from w w w . j  a va 2s. c  om*/
    for (int i = 0; i < sensorList.size(); i++) {
        JsonNode sensor = sensorList.get(i);

        // Calculate Distance:
        double tmp = distance(input.get("lat").asDouble(), input.get("lng").asDouble(),
                sensor.get("hasLastKnownLocation").get("geo:lat").asDouble(),
                sensor.get("hasLastKnownLocation").has("geo:long")
                        ? sensor.get("hasLastKnownLocation").get("geo:long").asDouble()
                        : sensor.get("hasLastKnownLocation").get("geo:lon").asDouble());
        if (tmp < minDistance) {
            minDistance = tmp;
            nearestSensor = sensor;
        }
    }

    // 3. Find the System of the Sensor
    dmsTarget = dmsClient.target(dmsUrl).path("querySystem").queryParam("encodeKeys", "false");
    ObjectNode systemQuery = objectMapper.createObjectNode();
    systemQuery.put("http://vital-iot\\u002eeu/ontology/ns/managesSensor.@id",
            nearestSensor.get("id").asText());
    ArrayNode systemList = dmsTarget.request(MediaType.APPLICATION_JSON_TYPE).post(Entity.json(systemQuery),
            ArrayNode.class);
    JsonNode system = systemList.get(0);

    // 4. Find the Observation Service of the System
    dmsTarget = dmsClient.target(dmsUrl).path("queryService").queryParam("encodeKeys", "false");

    ObjectNode serviceAndQuery = objectMapper.createObjectNode();
    ArrayNode serviceAndParameters = objectMapper.createArrayNode();
    serviceAndQuery.put("$and", serviceAndParameters);

    ObjectNode serviceIdQuery = objectMapper.createObjectNode();
    ObjectNode serviceInQuery = objectMapper.createObjectNode();
    serviceInQuery.put("$in", system.get("services"));
    serviceIdQuery.put("@id", serviceInQuery);
    serviceAndParameters.add(serviceIdQuery);

    ObjectNode serviceTypeQuery = objectMapper.createObjectNode();
    serviceTypeQuery.put("@type", "http://vital-iot.eu/ontology/ns/ObservationService");
    serviceAndParameters.add(serviceTypeQuery);

    ArrayNode serviceList = dmsTarget.request(MediaType.APPLICATION_JSON_TYPE)
            .post(Entity.json(serviceAndQuery), ArrayNode.class);
    JsonNode observationService = serviceList.get(0);

    // 5. Call GetObservation operation of the service
    String operationUrl = observationService.get("operations").get("hrest:hasAddress").asText();
    Client systemClient = ClientBuilder.newClient();
    WebTarget systemTarget = systemClient.target(operationUrl);
    ObjectNode operationInput = objectMapper.createObjectNode();
    ArrayNode sensorsArray = objectMapper.createArrayNode();
    sensorsArray.add(nearestSensor.get("id").asText());
    operationInput.put("sensor", sensorsArray);
    operationInput.put("property", "http://vital-iot.eu/ontology/ns/Speed");

    ArrayNode observationList = systemTarget.request(MediaType.APPLICATION_JSON_TYPE)
            .post(Entity.json(operationInput), ArrayNode.class);
    JsonNode latestObservation = observationList.get(0);

    // 6. Parse Result and return response
    ObjectNode result = objectMapper.createObjectNode();
    result.put("measurementValue",
            latestObservation.get("ssn:observationResult").get("ssn:hasValue").get("value"));
    result.put("measurementDate", latestObservation.get("ssn:observationResultTime").get("time:inXSDDateTime"));

    return Response.ok(result).build();
}

---

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

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

        @Override/*from   ww  w . j av  a 2 s . 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;
        }
    });
}

---

From source file:classif.pukmeans.KMeansCachedSymbolicSequence.java

public void cluster() {

    Sequence[] initialCenters = new Sequence[nbClusters];
    affectation = new ArrayList[nbClusters];

    // init/*from w ww. j ava2  s  .c  o m*/
    for (int k = 0; k < affectation.length; k++) {
        affectation[k] = new ArrayList<Integer>();
    }

    // pickup centers
    int[] selected = randGen.nextPermutation(data.size(), nbClusters);
    for (int i = 0; i < selected.length; i++) {
        initialCenters[i] = data.get(selected[i]);
    }
    wcss = 0.0;
    // first affectation
    for (int j = 0; j < data.size(); j++) {

        double minDist = Double.MAX_VALUE;
        int bestK = -1;
        // for each cluster k
        for (int k = 0; k < initialCenters.length; k++) {
            // distance between cluster k and data point j
            double currentDist = initialCenters[k].distance(data.get(j));
            if (currentDist < minDist) {
                bestK = k;
                minDist = currentDist;
            }
        }
        wcss += minDist * minDist;
        // affect data point j to cluster affected to j
        affectation[bestK].add(j);
    }

    // for each iteration i
    for (int i = 0; i < 15; i++) {

        ArrayList<Integer>[] newAffectation = new ArrayList[nbClusters];
        // init
        for (int k = 0; k < newAffectation.length; k++) {
            newAffectation[k] = new ArrayList<Integer>();
        }
        wcss = 0.0;
        // reassign element to cluster
        for (int j = 0; j < data.size(); j++) {
            int bestK = -1;
            double bestDist = Double.POSITIVE_INFINITY;
            // for each cluster k
            for (int k = 0; k < nbClusters; k++) {
                if (affectation[k].size() == 0)
                    continue;
                double distToK = 0.0;
                for (Integer elIndex : affectation[k]) {
                    double tmpDist = distances[j][elIndex];
                    distToK += tmpDist;//TODO squared??
                }
                distToK /= affectation[k].size();

                if (distToK < bestDist) {
                    bestDist = distToK;
                    bestK = k;
                }

            }
            wcss += bestDist * bestDist;

            newAffectation[bestK].add(j);
        }

        affectation = newAffectation;

    }

    //find prototypes for classifier
    centers = new Sequence[nbClusters];
    for (int k = 0; k < nbClusters; k++) {
        if (affectation[k].size() == 0) {
            centers[k] = null;
        } else {
            int medoidIndex = Sequences.medoidIndex(affectation[k], distances);
            Sequence medoid = data.get(medoidIndex);

            Sequence[] sequenceTab = new Sequence[affectation[k].size()];
            for (int i = 0; i < sequenceTab.length; i++) {
                sequenceTab[i] = data.get(affectation[k].get(i));
            }
            centers[k] = Sequences.meanWithMedoid(medoid, sequenceTab);
        }
    }

}

---

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

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

        @Override//from  w w w.j av a 2  s.  c  o m
        public void update(Entry _entry) {
            if (_entry.getValue() < min) {
                min = _entry.getValue();
            }
        }

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

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

---

From source file:com.cidre.algorithms.CidreMath.java

public static double min(double[][] a) {
    double min = Double.MAX_VALUE;
    for (int i = 0; i < a.length; i++) {
        for (int j = 0; j < a[i].length; j++) {
            if (a[i][j] < min) {
                min = a[i][j];/*w  w  w  . ja  v a  2  s .co m*/
            }
        }
    }
    return min;
}

---

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