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:eu.crisis_economics.abm.algorithms.statistics.TestFloorInterpolator.java

/**
  * Test whether an instance of {@link FloorInterpolator} interpolates
  * a simple discrete input series as expected. This unit test operates
  * as follows:<br><br>//from   ww w .  j  ava2  s.  c  om
  * 
  * {@code (a)}
  *    A short discrete subsequence of the function {@code f(T) = T**2}
  *    is generated;<br>
  * {@code (b)}
  *    An instance of {@link FloorInterpolator} is used to create a
  *   {@link UnivariateFunction}, {@code U}, from this sequence;<br>
  * {@code (c)}
  *    {@code U} is sampled repeatedly for a number of points in the domain
  *    of the input sequence. The results of this operation are compared
  *    to correct, expected outputs.
  */
@Test
public void testFloorInterpolatorOutput() {
    final double[] x = new double[] { 2., 3., 4., 5., 6., }, y = new double[] { 4., 9., 16., 25., 36., };
    final double[] expectedResults = { -Double.MAX_VALUE, 4.000000000, Double.MIN_VALUE, 4.000000000,
            1.000000000, 4.000000000, 1.111111111, 4.000000000, 1.222222222, 4.000000000, 1.333333333,
            4.000000000, 1.444444444, 4.000000000, 1.555555556, 4.000000000, 1.666666667, 4.000000000,
            1.777777778, 4.000000000, 1.888888889, 4.000000000, 2.000000000, 4.000000000, 2.111111111,
            4.000000000, 2.222222222, 4.000000000, 2.333333333, 4.000000000, 2.444444444, 4.000000000,
            2.555555556, 4.000000000, 2.666666667, 4.000000000, 2.777777778, 4.000000000, 2.888888889,
            4.000000000, 3.000000000, 9.000000000, 3.111111111, 9.000000000, 3.222222222, 9.000000000,
            3.333333333, 9.000000000, 3.444444444, 9.000000000, 3.555555556, 9.000000000, 3.666666667,
            9.000000000, 3.777777778, 9.000000000, 3.888888889, 9.000000000, 4.000000000, 16.00000000,
            4.111111111, 16.00000000, 4.222222222, 16.00000000, 4.333333333, 16.00000000, 4.444444444,
            16.00000000, 4.555555556, 16.00000000, 4.666666667, 16.00000000, 4.777777778, 16.00000000,
            4.888888889, 16.00000000, 5.000000000, 25.00000000, 5.111111111, 25.00000000, 5.222222222,
            25.00000000, 5.333333333, 25.00000000, 5.444444444, 25.00000000, 5.555555556, 25.00000000,
            5.666666667, 25.00000000, 5.777777778, 25.00000000, 5.888888889, 25.00000000, 6.000000000,
            36.00000000, 6.111111111, 36.00000000, 6.222222222, 36.00000000, 6.333333333, 36.00000000,
            6.444444444, 36.00000000, 6.555555556, 36.00000000, 6.666666667, 36.00000000, 6.777777778,
            36.00000000, 6.888888889, 36.00000000, 7.000000000, 36.00000000, 7.111111111, 36.00000000,
            7.222222222, 36.00000000, 7.333333333, 36.00000000, 7.444444444, 36.00000000, 7.555555556,
            36.00000000, 7.666666667, 36.00000000, 7.777777778, 36.00000000, 7.888888889, 36.00000000,
            8.000000000, 36.00000000, 8.111111111, 36.00000000, 8.222222222, 36.00000000, 8.333333333,
            36.00000000, 8.444444444, 36.00000000, 8.555555556, 36.00000000, 8.666666667, 36.00000000,
            8.777777778, 36.00000000, 8.888888889, 36.00000000, 9.000000000, 36.00000000, 9.111111111,
            36.00000000, 9.222222222, 36.00000000, 9.333333333, 36.00000000, 9.444444444, 36.00000000,
            9.555555556, 36.00000000, 9.666666667, 36.00000000, 9.777777778, 36.00000000, 9.888888889,
            36.00000000, 10.00000000, 36.00000000, 10.11111111, 36.00000000, 10.22222222, 36.00000000,
            10.33333333, 36.00000000, 10.44444444, 36.00000000, 10.55555556, 36.00000000, 10.66666667,
            36.00000000, 10.77777778, 36.00000000, 10.88888889, 36.00000000, 11.00000000, 36.00000000,
            11.11111111, 36.00000000, 11.22222222, 36.00000000, 11.33333333, 36.00000000, 11.44444444,
            36.00000000, 11.55555556, 36.00000000, 11.66666667, 36.00000000, 11.77777778, 36.00000000,
            11.88888889, 36.00000000, 12.00000000, 36.00000000, Double.MAX_VALUE, 36.00000000 };
    final UnivariateInterpolator interpolator = new FloorInterpolator();
    final UnivariateFunction f = interpolator.interpolate(x, y);
    for (int i = 0; i < expectedResults.length; i += 2) {
        final double t = expectedResults[i], f_t_Observed = f.value(t), f_t_Expected = expectedResults[i + 1];
        System.out.printf("t: %16.10g observed: %16.10g expected: %16.10g\n", t, f_t_Observed, f_t_Expected);
        Assert.assertEquals(f_t_Observed, f_t_Expected, 1.e-12);
    }
}

---

From source file:com.opensymphony.xwork2.conversion.impl.StringConverterTest.java

public void testDoubleToStringConversionPL() throws Exception {
    // given/*from w ww.j a  v  a  2  s.  co  m*/
    StringConverter converter = new StringConverter();
    Map<String, Object> context = new HashMap<>();
    context.put(ActionContext.LOCALE, new Locale("pl", "PL"));

    // when has max fraction digits
    Object value = converter.convertValue(context, null, null, null, Double.MIN_VALUE, null);

    // then does not lose fraction digits
    assertEquals("0," + StringUtils.repeat('0', 323) + "49", value);

    // when has max integer digits
    value = converter.convertValue(context, null, null, null, Double.MAX_VALUE, null);

    // then does not lose integer digits
    assertEquals("17976931348623157" + StringUtils.repeat('0', 292), value);

    // when cannot be represented exactly with a finite binary number
    value = converter.convertValue(context, null, null, null, 0.1d, null);

    // then produce the shortest decimal representation that can unambiguously identify the true value of the floating-point number
    assertEquals("0,1", value);
}

---

From source file:edu.oregonstate.eecs.mcplan.ml.GaussianMixtureModel.java

/**
 * @param args/*from w  w  w .j a v a  2  s . c  o m*/
 */
public static void main(final String[] args) {
    final RandomGenerator rng = new MersenneTwister(42);
    final ArrayList<double[]> data = new ArrayList<double[]>();

    // This data displays some problems with singular covariance estimates,
    // perhaps due to "multicollinearity" in the data.
    //      for( int x = -1; x <= 1; ++x ) {
    //         for( int y = -1; y <= 1; ++y ) {
    //            data.add( new double[] { x, y } );
    //            data.add( new double[] { x + 10, y + 10} );
    //            data.add( new double[] { x + 20, y + 20} );
    //            data.add( new double[] { x + 30, y + 30} );
    //         }
    //      }

    final int nsamples = 1000;
    final double[][] mu = new double[][] { new double[] { 0, 0 }, new double[] { 5, 0 }, new double[] { 0, 5 },
            new double[] { 5, 5 } };
    final double[][] Sigma = new double[][] { new double[] { 1, 0 }, new double[] { 0, 1 } };
    final MultivariateNormalDistribution[] p = new MultivariateNormalDistribution[4];
    for (int i = 0; i < 4; ++i) {
        p[i] = new MultivariateNormalDistribution(rng, mu[i], Sigma);
    }
    for (int i = 0; i < nsamples; ++i) {
        final int c = rng.nextInt(4);
        final double[] x = p[c].sample();
        data.add(x);
    }

    // Perturb data
    //      for( final double[] x : data ) {
    //         for( int i = 0; i < x.length; ++i ) {
    //            final double r = rng.nextGaussian() / 1.0;
    //            x[i] += r;
    //         }
    //      }

    double best_bic = Double.MAX_VALUE;
    int best_k = 0;
    for (int k = 1; k <= 6; ++k) {
        System.out.println("*** k = " + k);
        final GaussianMixtureModel gmm = new GaussianMixtureModel(k, data.toArray(new double[data.size()][]),
                10e-5, rng);

        gmm.run();
        for (int i = 0; i < gmm.mu().length; ++i) {
            System.out.println("Center " + i + ": " + gmm.mu()[i]);
        }

        final double bic = ScoreFunctions.bic(data.size(), gmm.nparameters(), gmm.logLikelihood());
        System.out.println("BIC = " + bic);
        System.out.println("ll = " + gmm.logLikelihood());
        gmm.debug();
        if (bic < best_bic) {
            best_bic = bic;
            best_k = k;
        }
    }
    System.out.println("Best model: k = " + best_k);
}

---

From source file:net.openhft.smoothie.MathDecisions.java

private static void printFootprints(int min, int max, int refSize) {
    System.out.println("Footprints:");
    for (int i = min; i <= max; i++) {
        System.out.println("average entries/segment: " + i + " " + " double: " + footprint(i, refSize, 1)
                + " quad: " + footprint(i, refSize, 2));
    }//  w  ww .  j a va  2s  . c  o m
    System.out.println("Footprint if size not specified:");
    double minF = Double.MAX_VALUE, maxF = Double.MIN_VALUE;
    int minE = 0, maxE = 0;
    int maxCap = cap(max, refSize);
    for (int i = maxCap / 2; i <= maxCap; i++) {
        double f = footprint(i, refSize, 1, maxCap);
        if (f < minF) {
            minF = f;
            minE = i;
        }
        if (f > maxF) {
            maxF = f;
            maxE = i;
        }
    }
    System.out.println("Best case: " + refSize + ": " + minE + " " + minF);
    System.out.println("Worst case: " + refSize + ": " + maxE + " " + maxF);
}

---

From source file:edu.cuny.qc.speech.AuToBI.core.Aggregation.java

/**
 * Constructs a new Aggregation/*from   w  w w  . ja  v  a  2 s. c  o m*/
 */
public Aggregation() {
    this.label = "";
    this.min = Double.MAX_VALUE;
    this.max = -Double.MAX_VALUE;
    this.sum = 0.0;
    this.ssq = 0.0;
    this.n = 0;
}

---

From source file:com.opengamma.analytics.math.minimization.BrentMinimizer1D.java

/**
 * {@inheritDoc}//from  w  w w .j  av  a2 s .c  om
 */
@Override
public Double minimize(final Function1D<Double, Double> function, final Double startPosition) {
    Validate.notNull(function, "function");
    final UnivariateRealFunction commonsFunction = CommonsMathWrapper.wrapUnivariate(function);
    try {
        return OPTIMIZER.optimize(commonsFunction, MINIMIZE, -Double.MAX_VALUE, Double.MAX_VALUE,
                startPosition);
    } catch (final FunctionEvaluationException e) {
        throw new MathException(e);
    } catch (final org.apache.commons.math.ConvergenceException e) {
        throw new MathException(e);
    }
}

---

From source file:gov.va.isaac.gui.listview.operations.ParentReplace.java

@Override
public void init(ObservableList<SimpleDisplayConcept> conceptList) {
    super.init(conceptList);
    root_.add(new Label("Replace: "), 0, 0);

    replaceOptions_ = new ComboBox<>();
    replaceOptions_.setMaxWidth(Double.MAX_VALUE);
    replaceOptions_.setPromptText("-Populate the Concepts List-");
    root_.add(ErrorMarkerUtils.setupErrorMarker(replaceOptions_, replaceOptionsInvalidString_), 1, 0);
    ComboBoxSetupTool.setupComboBox(replaceOptions_);

    root_.add(new Label("With Parent: "), 0, 1);
    withConcept_ = new ConceptNode(null, true);
    root_.add(withConcept_.getNode(), 1, 1);

    GridPane.setHgrow(withConcept_.getNode(), Priority.ALWAYS);
    FxUtils.preventColCollapse(root_, 0);
    initActionListeners();//from   w w  w. jav a2  s  .  c  om
    replaceOptions_.getItems().addAll(conceptList);
}

---

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

public static Field fromJson(SymbolTable symbolTable, JSONObject fieldJson) {
    String type = fieldJson.optString("type");
    if (type == null || !type.equals("float"))
        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 FloatField(symbolTable, name, label, description, defaultValue, minValue, maxValue, nominalWidth,
            compute);//from   ww  w.j  a  v  a  2s  .c om
}

---

From source file:edu.utexas.cs.tactex.subscriptionspredictors.LWRCustOldAppache.java

/**
 * @param candidateEval/*  w  ww. j  a  va2  s  . c  o m*/
 * @param e2n
 * @return
 */
@Override
public Double predictNumSubs(double candidateEval, TreeMap<Double, Double> e2n, CustomerInfo customer,
        int timeslot) {
    // tree map guarantees that keys are unique
    // so we are suppose to be able to run LWR
    // if there are at least 3 entries (even 2)

    // LWR, run n-fold cross validation with different bandwidth

    double min = e2n.firstKey();
    double max = e2n.lastKey();
    ArrayRealVector xVec = createNormalizedXVector(e2n.keySet(), min, max);
    ArrayRealVector yVec = createYVector(e2n.values());

    double bestTau = Double.MAX_VALUE;
    double bestMSE = Double.MAX_VALUE;

    ArrayList<Double> candidateTaus = new ArrayList<Double>();
    //candidateTaus.add(0.025 * SQUEEZE);
    candidateTaus.add(0.05);// * SQUEEZE);
    candidateTaus.add(0.1);// * SQUEEZE);
    candidateTaus.add(0.2);// * SQUEEZE);
    candidateTaus.add(0.3);// * SQUEEZE);
    candidateTaus.add(0.4);// * SQUEEZE);
    candidateTaus.add(0.5);// * SQUEEZE);
    candidateTaus.add(0.6);// * SQUEEZE);
    candidateTaus.add(0.7);// * SQUEEZE);
    candidateTaus.add(0.8);// * SQUEEZE);
    candidateTaus.add(0.9);// * SQUEEZE);
    candidateTaus.add(1.0);// * SQUEEZE);
    for (Double tau : candidateTaus) {
        Double mse = CrossValidationError(tau, xVec, yVec);
        if (null == mse) {
            log.error(" cp cross-validation failed, return null");
            return null;
        }
        if (mse < bestMSE) {
            bestMSE = mse;
            bestTau = tau;
        }
    }
    log.info(" cp LWR bestTau " + bestTau);
    double x0 = candidateEval;
    Double prediction = LWRPredict(xVec, yVec, normalizeX(x0, min, max), bestTau);
    if (null == prediction) {
        log.error("LWR passed CV but cannot predict on new point. falling back to interpolateOrNN()");
        log.error("e2n: " + e2n.toString());
        log.error("candidateEval " + candidateEval);
        return null;
    }
    // cast to int, and cannot be negative
    return Math.max(0, (double) (int) (double) prediction);
}

---

From source file:com.jkoolcloud.tnt4j.streams.utils.DoubleRange.java

/**
 * Makes range object using values parsed from {@code rangeStr}.
 * <p>// w  w  w.j av  a2 s . co m
 * If {@code rangeStr} has missing range bound values, default ones are set: lower
 * {@code positive ? 0 : -Double.MAX_VALUE}, upper {@code Double.MAX_VALUE}.
 * <p>
 * Range separator symbol is '{@value com.jkoolcloud.tnt4j.streams.utils.Range#RANGE_SEPARATOR}'.
 *
 * @param rangeStr
 *            range definition string to parse
 * @param positive
 *            {@code true} means range has only positive values, {@code} - range can have negative values.
 * @return double range parsed from range definition string
 * @throws Exception
 *             if range string can't be parsed
 * @see Range#parseRange(String, Pattern)
 */
public static DoubleRange getRange(String rangeStr, boolean positive) throws Exception {
    String[] rangeTokens = parseRange(rangeStr, positive ? D_PATTERN_POSITIVE : D_PATTERN);

    double from = NumberUtils.toDouble(rangeTokens[0], positive ? 0 : -Double.MAX_VALUE);
    double to = NumberUtils.toDouble(rangeTokens[1], Double.MAX_VALUE);

    return new DoubleRange(from, to);
}

---

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