Example usage for java.lang Double POSITIVE_INFINITY

Introduction

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

Prototype

double POSITIVE_INFINITY

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Document

A constant holding the positive infinity of type double .

Usage

From source file:org.jfree.data.RangeTest.java

/**
 * Tests the constrain() method for various values.
 *//*from  w ww .  j  ava  2s. c  om*/
@Test
public void testConstrain() {
    Range r1 = new Range(0.0, 1.0);

    double d = r1.constrain(0.5);
    assertEquals(0.5, d, 0.0000001);

    d = r1.constrain(0.0);
    assertEquals(0.0, d, 0.0000001);

    d = r1.constrain(1.0);
    assertEquals(1.0, d, 0.0000001);

    d = r1.constrain(-1.0);
    assertEquals(0.0, d, 0.0000001);

    d = r1.constrain(2.0);
    assertEquals(1.0, d, 0.0000001);

    d = r1.constrain(Double.POSITIVE_INFINITY);
    assertEquals(1.0, d, 0.0000001);

    d = r1.constrain(Double.NEGATIVE_INFINITY);
    assertEquals(0.0, d, 0.0000001);

    d = r1.constrain(Double.NaN);
    assertTrue(Double.isNaN(d));
}

---

From source file:ch.epfl.leb.sass.models.fluorophores.commands.internal.FluorophoreReceiverIT.java

/**
 * Test of generateFluorophoresRandom2D method, of class FluorophoreReceiver.
 *///from   w ww  .j  av  a 2  s. c o m
@Test
public void testGenerateFluorophoresRandom2D() {
    GenerateFluorophoresRandom2D.Builder fluorBuilder = new GenerateFluorophoresRandom2D.Builder();
    fluorBuilder.numFluors(25); // Number of fluorophores

    // Create the set of fluorophores.
    fluorBuilder.camera(camera).psfBuilder(psfBuilder).fluorDynamics(fluorDynamics).illumination(illumination);
    FluorophoreCommand fluorCommand = fluorBuilder.build();
    List<Fluorophore> fluorophores = fluorCommand.generateFluorophores();
    assertEquals(25, fluorophores.size());

    double minX = Double.POSITIVE_INFINITY;
    double maxX = Double.NEGATIVE_INFINITY;
    double minY = Double.POSITIVE_INFINITY;
    double maxY = Double.NEGATIVE_INFINITY;
    for (Fluorophore f : fluorophores) {
        if (f.getX() < minX)
            minX = f.getX();

        if (f.getX() > maxX)
            maxX = f.getX();

        if (f.getY() < minY)
            minY = f.getY();

        if (f.getY() > maxY)
            maxY = f.getY();
    }

    assertTrue(maxX <= 32);
    assertTrue(minX >= 0.0);
    assertTrue(maxY <= 32);
    assertTrue(minY >= 0.0);
}

---

From source file:com.rapidminer.gui.plotter.RangeablePlotterAdapter.java

@Override
public List<ParameterType> getAdditionalParameterKeys(InputPort inputPort) {
    List<ParameterType> types = super.getAdditionalParameterKeys(inputPort);

    boolean inputDeliversAttributes = false;
    if (inputPort != null) {
        MetaData metaData = inputPort.getMetaData();
        if (metaData != null && (metaData instanceof ExampleSetMetaData || metaData instanceof ModelMetaData)) {
            inputDeliversAttributes = true;
        }//w  w w  . ja  va 2s.co  m
    }

    if (inputDeliversAttributes) {
        types.add(new ParameterTypeList(PARAMETER_PREFIX_RANGE_LIST,
                "Defines the ranges for the given attribute",
                new ParameterTypeAttribute(PARAMETER_DIMENSION_NAME,
                        "This is the name of the dimension, the range should be applied onto.", inputPort),
                new ParameterTypeTupel(PARAMETER_PREFIX_RANGE, "Defines the range of the corresponding axis.",
                        new ParameterTypeDouble(PARAMETER_PREFIX_RANGE_MIN,
                                "Defines the lower bound of the axis.", Double.NEGATIVE_INFINITY,
                                Double.POSITIVE_INFINITY),
                        new ParameterTypeDouble(PARAMETER_PREFIX_RANGE_MAX,
                                "Defines the upper bound of the axis.", Double.NEGATIVE_INFINITY,
                                Double.POSITIVE_INFINITY))));
    } else {
        types.add(new ParameterTypeList(PARAMETER_PREFIX_RANGE_LIST,
                "Defines the ranges for the given attribute",
                new ParameterTypeString(PARAMETER_DIMENSION_NAME,
                        "This is the name of the dimension, the range should be applied onto."),
                new ParameterTypeTupel(PARAMETER_PREFIX_RANGE, "Defines the range of the corresponding axis.",
                        new ParameterTypeDouble(PARAMETER_PREFIX_RANGE_MIN,
                                "Defines the lower bound of the axis.", Double.NEGATIVE_INFINITY,
                                Double.POSITIVE_INFINITY),
                        new ParameterTypeDouble(PARAMETER_PREFIX_RANGE_MAX,
                                "Defines the upper bound of the axis.", Double.NEGATIVE_INFINITY,
                                Double.POSITIVE_INFINITY))));

    }
    return types;
}

---

From source file:visualizer.graph.scalar.DistanceScalar.java

public Scalar scalarFromDistanceMarix(String filename, Vertex vertex) throws IOException {
    DistanceMatrix dmat = new DistanceMatrix(filename);
    List<String> filenames = dmat.getIds();

    //defining the index
    int index = -1;
    for (int i = 0; i < filenames.size(); i++) {
        if (filenames.get(i).equals(vertex.getUrl())) {
            index = i;/*w  w w .  ja v  a2s . com*/
            break;
        }
    }

    if (index == -1) {
        throw new IOException("Query data instance not found on the points file.");
    }

    //creating the scalar values
    double[] scalar = new double[dmat.getElementCount()];

    double min = Double.POSITIVE_INFINITY;
    for (int i = 0; i < dmat.getElementCount(); i++) {
        scalar[i] = dmat.getDistance(i, index);

        if (min > scalar[i] && i != index) {
            min = scalar[i];
        }
    }

    scalar[index] = min;

    return this.createScalar(scalar, filenames, vertex);
}

---

From source file:Tsne.java

/**
 * Convert data to probability/* ww  w. ja  v  a2 s .  c  o m*/
 * co-occurrences (aka calculating the kernel)
 * @param d the data to convert
 * @param u the perplexity of the model
 * @return the probabilities of co-occurrence
 */
public INDArray computeGaussianPerplexity(final INDArray d, double u) {
    int n = d.rows();
    final INDArray p = zeros(n, n);
    final INDArray beta = ones(n, 1);
    final double logU = Math.log(u);

    log.info("Calculating probabilities of data similarities..");
    for (int i = 0; i < n; i++) {
        if (i % 500 == 0 && i > 0)
            log.info("Handled " + i + " records");

        double betaMin = Double.NEGATIVE_INFINITY;
        double betaMax = Double.POSITIVE_INFINITY;
        NDArrayIndex[] range = new NDArrayIndex[] {
                NDArrayIndex.concat(NDArrayIndex.interval(0, i), NDArrayIndex.interval(i + 1, d.columns())) };

        INDArray row = d.slice(i).get(range);
        Pair<INDArray, INDArray> pair = hBeta(row, beta.getDouble(i));
        INDArray hDiff = pair.getFirst().sub(logU);
        int tries = 0;

        //while hdiff > tolerance
        while (BooleanIndexing.and(abs(hDiff), Conditions.greaterThan(tolerance)) && tries < 50) {
            //if hdiff > 0
            if (BooleanIndexing.and(hDiff, Conditions.greaterThan(0))) {
                if (Double.isInfinite(betaMax))
                    beta.putScalar(i, beta.getDouble(i) * 2.0);
                else
                    beta.putScalar(i, (beta.getDouble(i) + betaMax) / 2.0);
                betaMin = beta.getDouble(i);
            } else {
                if (Double.isInfinite(betaMin))
                    beta.putScalar(i, beta.getDouble(i) / 2.0);
                else
                    beta.putScalar(i, (beta.getDouble(i) + betaMin) / 2.0);
                betaMax = beta.getDouble(i);
            }

            pair = hBeta(row, beta.getDouble(i));
            hDiff = pair.getFirst().subi(logU);
            tries++;
        }

        p.slice(i).put(range, pair.getSecond());

    }

    //dont need data in memory after
    log.info("Mean value of sigma " + sqrt(beta.rdiv(1)).mean(Integer.MAX_VALUE));
    BooleanIndexing.applyWhere(p, Conditions.isNan(), new Value(realMin));

    //set 0 along the diagonal
    INDArray permute = p.transpose();

    INDArray pOut = p.add(permute);

    pOut.divi(pOut.sum(Integer.MAX_VALUE));
    BooleanIndexing.applyWhere(pOut, Conditions.lessThan(Nd4j.EPS_THRESHOLD), new Value(Nd4j.EPS_THRESHOLD));
    //ensure no nans
    return pOut;

}

---

From source file:org.matsim.pt.analysis.RouteTimeDiagram.java

public void createGraph(final String filename, final TransitRoute route) {

    HashMap<Id, Integer> stopIndex = new HashMap<Id, Integer>();
    int idx = 0;//w  ww .jav a  2  s.  c  o m
    for (TransitRouteStop stop : route.getStops()) {
        stopIndex.put(stop.getStopFacility().getId(), idx);
        idx++;
    }

    HashSet<Id> vehicles = new HashSet<Id>();
    for (Departure dep : route.getDepartures().values()) {
        vehicles.add(dep.getVehicleId());
    }

    XYSeriesCollection dataset = new XYSeriesCollection();
    int numSeries = 0;
    double earliestTime = Double.POSITIVE_INFINITY;
    double latestTime = Double.NEGATIVE_INFINITY;

    for (Map.Entry<Id, List<Tuple<Id, Double>>> entry : this.positions.entrySet()) {
        if (vehicles.contains(entry.getKey())) {
            XYSeries series = new XYSeries("t", false, true);
            for (Tuple<Id, Double> pos : entry.getValue()) {
                Integer stopIdx = stopIndex.get(pos.getFirst());
                if (stopIdx != null) {
                    double time = pos.getSecond().doubleValue();
                    series.add(stopIdx.intValue(), time);
                    if (time < earliestTime) {
                        earliestTime = time;
                    }
                    if (time > latestTime) {
                        latestTime = time;
                    }
                }
            }
            dataset.addSeries(series);
            numSeries++;

        }
    }

    JFreeChart c = ChartFactory.createXYLineChart("Route-Time Diagram, Route = " + route.getId(), "stops",
            "time", dataset, PlotOrientation.VERTICAL, false, // legend?
            false, // tooltips?
            false // URLs?
    );
    c.setBackgroundPaint(new Color(1.0f, 1.0f, 1.0f, 1.0f));

    XYPlot p = (XYPlot) c.getPlot();

    p.getRangeAxis().setInverted(true);
    p.getRangeAxis().setRange(earliestTime, latestTime);
    XYItemRenderer renderer = p.getRenderer();
    for (int i = 0; i < numSeries; i++) {
        renderer.setSeriesPaint(i, Color.black);
    }

    try {
        ChartUtilities.saveChartAsPNG(new File(filename), c, 1024, 768, null, true, 9);
    } catch (IOException e) {
        e.printStackTrace();
    }
}

---

From source file:lu.lippmann.cdb.ext.hydviga.data.StationsDataProvider.java

public String findNearestStation(final String stationName, final Collection<String> attrNames) {
    if (getCoordinates(stationName) == null) {
        throw new IllegalArgumentException(stationName + " not in coordinates map!");
    }//  w w w  . j av  a  2 s  .c om

    final double[] coords = this.coordinatesMap.get(stationName);

    double mindist = Double.POSITIVE_INFINITY;
    String nearestStation = null;
    for (final String k : attrNames) {
        if (k.equals(stationName))
            continue;
        if (!this.coordinatesMap.containsKey(k))
            throw new IllegalStateException(k + " not in coordinates map!");

        final double dist = MathsUtil.distance(coords, this.coordinatesMap.get(k));
        if (dist < 0000.1d)
            continue;
        if (dist < mindist) {
            mindist = dist;
            nearestStation = k;
        }
    }
    return nearestStation;
}

---

From source file:org.gvsig.symbology.fmap.symbols.BarChart3DSymbol.java

protected Plot getOutlinePlot() {
    outlinePlot = null; // <- delete this
    if (outlinePlot == null) {
        CategoryPlot myOutlinePlot;/*from   w ww  . jav  a 2  s.  c o m*/
        DefaultCategoryDataset dataset = new DefaultCategoryDataset();

        dataset.addValue(23D, "Series 1", "London");
        // dataset.addValue(14D, "Series 1", "New York");
        // dataset.addValue(14D, "Series 1", "Istanbul");
        // dataset.addValue(14D, "Series 1", "Cairo");
        dataset.addValue(13D, "Series 2", "London");
        // dataset.addValue(19D, "Series 2", "New York");
        // dataset.addValue(19D, "Series 2", "Istanbul");
        // dataset.addValue(19D, "Series 2", "Cairo");
        dataset.addValue(7D, "Series 3", "London");
        // dataset.addValue(9D, "Series 3", "New York");
        // dataset.addValue(9D, "Series 3", "Istanbul");
        // dataset.addValue(9D, "Series 3", "Cairo");

        CategoryAxis categoryAxis = new CategoryAxis3D(categoryAxisLabel);
        ValueAxis valueAxis = new NumberAxis3D(valueAxisLabel);

        BarRenderer3D renderer = new BarRenderer3D();
        myOutlinePlot = new CategoryPlot(dataset, categoryAxis, valueAxis, renderer);
        double min = Double.POSITIVE_INFINITY;
        double max = Double.NEGATIVE_INFINITY;
        // for (int i = 0; values != null && i < values.length; i++) {
        // min = Math.min(min, values[i]);
        // max = Math.min(max, values[i]);
        // dataset.addValue(values[i], rowKeys[i], columnKeys[i]);
        // }
        myOutlinePlot.setDataset(dataset);
        myOutlinePlot.setOrientation(vertical ? PlotOrientation.VERTICAL : PlotOrientation.HORIZONTAL);
        outlinePlot = myOutlinePlot;

    }
    return outlinePlot;
}

---

From source file:com.analog.lyric.dimple.solvers.core.parameterizedMessages.MultivariateNormalParameters.java

/**
 * Multivariate normal with specified number of dimensions, zero mean, and infinite covariance.
 * @param dimensions a positive number//from ww  w.j a  v  a2  s.  com
 * @since 0.08
 */
public MultivariateNormalParameters(int dimensions) {
    this(new double[dimensions], arrayOf(dimensions, Double.POSITIVE_INFINITY));
}

---

From source file:com.cloudera.oryx.kmeans.computation.cluster.KSketchIndex.java

public Distance getDistance(RealVector vec, int id, boolean approx) {
    double distance = Double.POSITIVE_INFINITY;
    int closestPoint = -1;
    if (approx) {
        if (updated) {
            rebuildIndices();//from   w  ww .  j a v  a2  s.c  o  m
        }

        BitSet q = index(vec);
        List<BitSet> index = indices.get(id);
        SortedSet<Idx> lookup = Sets.newTreeSet();
        for (int j = 0; j < index.size(); j++) {
            Idx idx = new Idx(hammingDistance(q, index.get(j)), j);
            if (lookup.size() < projectionSamples) {
                lookup.add(idx);
            } else if (idx.compareTo(lookup.last()) < 0) {
                lookup.add(idx);
                lookup.remove(lookup.last());
            }
        }

        List<RealVector> p = points.get(id);
        List<Double> lsq = lengthSquared.get(id);
        for (Idx idx : lookup) {
            double lenSq = lsq.get(idx.getIndex());
            double length = vec.getNorm();
            double d = length * length + lenSq - 2 * vec.dotProduct(p.get(idx.getIndex()));
            if (d < distance) {
                distance = d;
                closestPoint = idx.getIndex();
            }
        }
    } else { // More expensive exact computation
        List<RealVector> px = points.get(id);
        List<Double> lsq = lengthSquared.get(id);
        for (int j = 0; j < px.size(); j++) {
            RealVector p = px.get(j);
            double lenSq = lsq.get(j);
            double length = vec.getNorm();
            double d = length * length + lenSq - 2 * vec.dotProduct(p);
            if (d < distance) {
                distance = d;
                closestPoint = j;
            }
        }
    }

    return new Distance(distance, closestPoint);
}

---

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