Example usage for java.lang Math round

Introduction

In this page you can find the example usage for java.lang Math round.

Prototype

public static long round(double a) 

Source Link

Document

Returns the closest long to the argument, with ties rounding to positive infinity.

Usage

From source file:Main.java

public static void getDisplayDpi(Context ctx) {
    DisplayMetrics dm = new DisplayMetrics();
    WindowManager wm = (WindowManager) ctx.getSystemService(Context.WINDOW_SERVICE);
    wm.getDefaultDisplay().getMetrics(dm);
    double x = Math.pow(dm.widthPixels / dm.xdpi, 2);
    double y = Math.pow(dm.heightPixels / dm.ydpi, 2);
    double screenInches = Math.sqrt(x + y);
    int screenInch = (int) Math.round(screenInches);
    Log.d("screeninch", String.valueOf(screenInch));
    int dapi = dm.densityDpi;
    Log.d("dapi", String.valueOf(dapi));
    try {/*from   ww w  .j  a va 2  s .  c  o  m*/
        switch (dm.densityDpi) {

        case DisplayMetrics.DENSITY_LOW:
            UI_DENSITY = 120;
            if (screenInch <= 7) {
                UI_SIZE = 4;

            } else {
                UI_SIZE = 10;
            }
            break;
        case DisplayMetrics.DENSITY_MEDIUM:
            UI_DENSITY = 160;
            if (screenInch <= 7) {
                UI_SIZE = 4;
            } else {
                UI_SIZE = 10;
            }
            break;
        case DisplayMetrics.DENSITY_HIGH:
            UI_DENSITY = 240;
            if (screenInch <= 7) {
                UI_SIZE = 4;
            } else {
                UI_SIZE = 10;
            }
            break;

        default:
            break;
        }
    } catch (Exception e) {
        // Caught exception here
    }
}

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From source file:com.opengamma.analytics.math.statistics.descriptive.robust.TrimmedMeanCalculator.java

@Override
public Double evaluate(final double[] x) {
    Validate.notNull(x, "x was null");
    final int length = x.length;
    Validate.isTrue(length > 0, "x was empty");
    final int value = (int) Math.round(length * _gamma);
    final double[] copy = Arrays.copyOf(x, length);
    Arrays.sort(copy);/* w  ww.jav  a2 s.co  m*/
    final double[] trimmed = new double[length - 2 * value];
    for (int i = 0; i < trimmed.length; i++) {
        trimmed[i] = x[i + value];
    }
    return MEAN_CALCULATOR.evaluate(trimmed);
}

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From source file:geogebra.kernel.statistics.AlgoInverseBinomial.java

protected final void compute() {

    if (input[0].isDefined() && input[1].isDefined() && input[2].isDefined()) {
        int param = (int) Math.round(a.getDouble());
        double param2 = b.getDouble();
        double val = c.getDouble();
        try {/* w w w  .  ja v a  2  s.  c o  m*/
            BinomialDistribution dist = getBinomialDistribution(param, param2);

            double result = dist.inverseCumulativeProbability(val);

            // eg InversePascal[1,1,1] returns  2147483647 
            if (result > param)
                num.setValue(param);
            else
                num.setValue(result + 1);

        } catch (Exception e) {
            num.setUndefined();
        }
    } else
        num.setUndefined();
}

---

From source file:com.apress.prospringintegration.corespring.aop.PurchaseOrderProcessorImpl.java

public Receipt processPurchaseOrder(PurchaseOrder order) {
    order.setProcessedTime(Calendar.getInstance().getTime());
    Receipt receipt = new Receipt();
    receipt.setPurchaseAmt(order.getItemCost());
    receipt.setAuthcode(Math.round(Math.random() * 2000000));

    return receipt;
}

---

From source file:geogebra.kernel.statistics.AlgoInverseHyperGeometric.java

protected final void compute() {

    if (input[0].isDefined() && input[1].isDefined() && input[2].isDefined()) {
        int param = (int) Math.round(a.getDouble());
        int param2 = (int) Math.round(b.getDouble());
        int param3 = (int) Math.round(c.getDouble());
        double val = d.getDouble();
        try {/*from   ww  w.  j av a2s .  c o  m*/
            HypergeometricDistribution dist = getHypergeometricDistribution(param, param2, param3);
            num.setValue(dist.inverseCumulativeProbability(val) + 1); // P(T <= val)

        } catch (Exception e) {
            num.setUndefined();
        }
    } else
        num.setUndefined();
}

---

From source file:Main.java

@Override
public void start(Stage stage) {
    Scene scene = new Scene(new Group());
    stage.setTitle("Sample");
    stage.setWidth(300);// w w w  .j a v a  2s .c o  m
    stage.setHeight(190);

    VBox vbox = new VBox();
    vbox.setLayoutX(20);
    vbox.setLayoutY(20);

    final String content = "Lorem ipsum";
    final Text text = new Text(10, 20, "");

    final Animation animation = new Transition() {
        {
            setCycleDuration(Duration.millis(2000));
        }

        protected void interpolate(double frac) {
            final int length = content.length();
            final int n = Math.round(length * (float) frac);
            text.setText(content.substring(0, n));
        }

    };
    animation.play();

    vbox.getChildren().add(text);
    vbox.setSpacing(10);
    ((Group) scene.getRoot()).getChildren().add(vbox);

    stage.setScene(scene);
    stage.show();
}

---

From source file:Main.java

@Override
public void start(Stage stage) {
    Scene scene = new Scene(new Group());
    stage.setTitle("Sample");
    stage.setWidth(300);/*from  w w  w  .j  a va  2  s  . c o m*/
    stage.setHeight(190);

    VBox vbox = new VBox();
    vbox.setLayoutX(20);
    vbox.setLayoutY(20);

    final String content = "Lorem ipsum";
    final Text text = new Text(10, 20, "");

    final Animation animation = new Transition() {
        {
            setCycleDuration(Duration.millis(2000));
        }

        protected void interpolate(double frac) {
            final int length = content.length();
            final int n = Math.round(length * (float) frac);
            text.setText(content.substring(0, n));
        }

    };

    animation.play();

    vbox.getChildren().add(text);
    vbox.setSpacing(10);
    ((Group) scene.getRoot()).getChildren().add(vbox);

    stage.setScene(scene);
    stage.show();
}

---

From source file:com.apress.prospringintegration.corespring.aop.RandomItemGenerator.java

public Item getRandomItem() {
    Item item = new Item();
    item.setCost((float) Math.random() * 250);
    item.setDescription("random item");

    int inum = (int) Math.round(Math.random() * (itemNames.length - 1));
    item.setId(inum);/* w ww  . ja  v a 2s .  c  o m*/
    item.setName(itemNames[inum]);

    return item;
}

---

From source file:knop.psfj.utils.MathUtils.java

/**
 * Round./*ww w  .j  a  va2 s.  c  om*/
 *
 * @param nb2round the nb2round
 * @param nbOfDigits the nb of digits
 * @return the double
 */
public static double round(double nb2round, int nbOfDigits) {
    return Math.round(nb2round * Math.pow(10, nbOfDigits)) / Math.pow(10, nbOfDigits);
}

---

From source file:endrov.nucAutoJH.FitGaussian.java

private static double[] fitGaussian2D_(EvPixels p, double sigmaInit, final double midxInit,
        final double midyInit) {
    //sigma00, sigma01, sigma11, mu_x, mu_y, c 

    p = p.getReadOnly(EvPixelsType.DOUBLE);
    final double[] arrPixels = p.getArrayDouble();
    final int w = p.getWidth();
    final int h = p.getHeight();

    int extent = (int) Math.round(3 * sigmaInit);
    extent = Math.max(extent, 2);

    final int sx = Math.max(0, (int) (midxInit - extent));
    final int ex = Math.min(w, (int) (midxInit + extent + 1)); //+1 to the right?
    final int sy = Math.max(0, (int) (midyInit - extent));
    final int ey = Math.min(h, (int) (midyInit + extent + 1));

    double minIntensity = Double.MAX_VALUE;
    double maxIntensity = Double.MIN_VALUE;
    for (int y = sy; y < ey; y++) {
        int base = y * w;
        double dy2 = y - midyInit;
        dy2 = dy2 * dy2;/*from ww w  .  ja va 2s . co  m*/
        for (int x = sx; x < ex; x++) {
            double dx2 = x - midxInit;
            dx2 = dx2 * dx2;
            double t = arrPixels[base + x];
            //if(dx2+dy2<=extent*extent)
            {
                if (t < minIntensity)
                    minIntensity = t;
                if (t > maxIntensity)
                    maxIntensity = t;
            }
        }
    }

    //double[] weights=new double[]{1};
    double[] startPoint = new double[] { sigmaInit, 0, sigmaInit, midxInit, midyInit, minIntensity,
            maxIntensity - minIntensity };
    //double[] output=new double[startPoint.length];

    try {
        MultivariateRealFunction func = new MultivariateRealFunction() {
            //      opt.optimize(

            public double value(double[] arg) throws FunctionEvaluationException, IllegalArgumentException {
                double sigma00 = arg[0];
                double sigma01 = arg[1];
                double sigma11 = arg[2];
                double mu0 = arg[3];
                double mu1 = arg[4];
                double C = arg[5];
                double D = arg[6];

                double sumError = 0;

                Matrix2d sigma = new Matrix2d(sigma00, sigma01, sigma01, sigma11);
                Matrix2d sigmaInv = new Matrix2d();
                sigma.invert(sigmaInv);
                double sigmaDet = sigma.determinant();
                double front = 1.0 / (2 * Math.PI * Math.sqrt(sigmaDet));
                //System.out.println("front: "+front);
                //System.out.println("sigma inv "+sigmaInv);

                if (mu0 < sx || mu0 > ex)
                    sumError += 1000000;
                if (mu1 < sy || mu1 > ey)
                    sumError += 1000000;
                if (sigma00 < 1)
                    sumError += 1000000;
                //if(sigma01<0)               sumError+=1000000;
                if (sigma11 < 1)
                    sumError += 1000000;
                if (D <= 0)
                    sumError += 1000000;

                for (int y = sy; y < ey; y++) {
                    int base = y * w;
                    double dy2 = y - midyInit;
                    dy2 = dy2 * dy2;
                    for (int x = sx; x < ex; x++) {
                        double dx2 = x - midxInit;
                        dx2 = dx2 * dx2;
                        double thisReal = arrPixels[base + x];
                        //                  if(dx2+dy2<=extent*extent)
                        {
                            //               DoubleMatrix2D sigma=new DenseDoubleMatrix2D(new double[][]{{sigma00,sigma01},{sigma01,sigma11}});
                            //double sigmaDet=sigma00*sigma11-sigma01*sigma01;

                            double dx0 = x - mu0;
                            double dx1 = y - mu1;

                            //http://en.wikipedia.org/wiki/Multivariate_normal_distribution

                            Vector2d vX = new Vector2d(dx0, dx1);
                            Vector2d op = new Vector2d(vX);
                            sigmaInv.transform(op);
                            double upper = -0.5 * op.dot(vX);
                            double exp = Math.exp(upper);

                            //System.out.println("front "+front+" "+exp+" C "+C+" thisreal"+thisReal+" upper "+upper);

                            if (upper > -0.4)
                                exp = 1;
                            else
                                exp = Math.max(0, 1 + upper + 0.4);

                            /*
                            if(exp<0.7)
                               exp=0;
                            else
                               exp=1;
                            */

                            double thisExpected = D * front * exp + C;
                            double diff = thisExpected - thisReal;
                            sumError += diff * diff;

                        }
                    }
                }

                //System.out.println(sigma00+"\t"+sigma01+"\t"+sigma11+"\tC"+C+"\tmu "+mu0+","+mu1+"\terr "+sumError);
                return sumError;
                //            return new double[]{sumError};
            }

        };

        NelderMead opt = new NelderMead();
        //LevenbergMarquardtOptimizer opt=new LevenbergMarquardtOptimizer();
        opt.setMaxIterations(10000);
        RealPointValuePair pair = opt.optimize(func, GoalType.MINIMIZE, startPoint);

        int numit = opt.getIterations();
        System.out.println("#it " + numit);
        System.out.println("err " + func.value(pair.getPointRef()));
        return pair.getPointRef();

        //         for(int i=0;i<startPoint.length;i++)
        //         System.out.println("i: "+i+"  "+output[i]);
        //, output, weights, startPoint);
    }
    /*
    catch (MaxIterationsExceededException e)
       {
       System.out.println("max it reached");
               
       }*/
    catch (Exception e) {
        e.printStackTrace();
    }

    //Maybe this is a bad point?
    System.out.println("max it reached");
    return startPoint;
    //      return output;
}

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