Example usage for java.lang Math exp

Introduction

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

Prototype

@HotSpotIntrinsicCandidate
public static double exp(double a) 

Source Link

Document

Returns Euler's number e raised to the power of a double value.

Usage

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 . j a v a2s  .  c o  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;
}

---

From source file:endrov.typeLineageAutoNucJH.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.MAX_VALUE;
    for (int y = sy; y < ey; y++) {
        int base = y * w;
        double dy2 = y - midyInit;
        dy2 = dy2 * dy2;/*from w  w  w.j ava2 s . c o  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;
}

---

From source file:eagle.security.userprofile.impl.UserProfileAnomalyKDEEvaluator.java

@Override
public List<MLCallbackResult> detect(final String user, final String algorithm,
        UserActivityAggModel userActivity, UserProfileKDEModel aModel) {
    List<MLCallbackResult> mlPredictionOutputList = new ArrayList<MLCallbackResult>();
    RealMatrix inputData = userActivity.matrix();

    double[] probabilityEstimation = new double[inputData.getRowDimension()];
    for (int i = 0; i < probabilityEstimation.length; i++)
        probabilityEstimation[i] = 1.0;// ww w.j a  v a 2s .co m

    boolean[][] anomalyFeature = new boolean[inputData.getRowDimension()][inputData.getColumnDimension()];

    for (int i = 0; i < anomalyFeature.length; i++) {
        for (int j = 0; j < anomalyFeature[i].length; j++) {
            anomalyFeature[i][j] = false;
        }
    }

    if (aModel == null) {
        LOG.info("No model available for this uer, returning");
        return null;
    }

    Map<String, String> context = new HashMap<String, String>() {
        {
            put(UserProfileConstants.USER_TAG, user);
            put(UserProfileConstants.ALGORITHM_TAG, algorithm);
        }
    };

    for (int i = 0; i < inputData.getRowDimension(); i++) {
        List<String> cmds = JavaConversions.seqAsJavaList(userActivity.cmdTypes());
        if (inputData.getColumnDimension() != cmds.size()) {
            LOG.error("Test data is not with same dimension as training, aborting...");
            return null;
        } else {

            UserCommandStatistics[] listStats = aModel.statistics();

            for (int j = 0; j < inputData.getColumnDimension(); j++) {
                //                    LOG.info("mean for j=" + j + " is:" + listStats[j].getMean());
                //                    LOG.info("stddev for j=" + j + " is:" + listStats[j].getStddev());
                if (listStats[j].isLowVariant()) {
                    //                        LOG.info(listStats[j].getCommandName() + " is low variant for user: " + user);
                    if (inputData.getEntry(i, j) > listStats[j].getMean()) {
                        probabilityEstimation[i] *= Double.NEGATIVE_INFINITY;
                        anomalyFeature[i][j] = true;
                    }
                } else {
                    double stddev = listStats[j].getStddev();
                    //LOG.info("stddev: " + stddev);
                    double mean = listStats[j].getMean();
                    //LOG.info("mean: " + mean);
                    double sqrt2PI = Math.sqrt(2.0 * Math.PI);
                    //LOG.info("sqrt2PI: " + sqrt2PI);
                    double denominatorFirstPart = sqrt2PI * stddev;
                    //LOG.info("denominatorFirstPart: " + denominatorFirstPart);
                    double squareMeanNormal = Math.pow((inputData.getEntry(i, j) - mean), 2);
                    //LOG.info("squareMeanNormal: " + squareMeanNormal);
                    double twoPowStandardDev = Math.pow(stddev, 2);
                    //LOG.info("twoPowStandardDev: " + twoPowStandardDev);
                    double twoTimesTwoPowStandardDev = 2.0 * twoPowStandardDev;
                    //LOG.info("twoTimesTwoPowStandardDev: " + twoTimesTwoPowStandardDev);

                    double tempVal = ((1.00 / denominatorFirstPart)
                            * (Math.exp(-(squareMeanNormal / twoTimesTwoPowStandardDev))));
                    probabilityEstimation[i] *= tempVal;
                    //LOG.info("probabilityEstimation: " + probabilityEstimation[i]);
                    if ((inputData.getEntry(i, j) - mean) > 2 * stddev)
                        anomalyFeature[i][j] = true;
                }
            }

        }
    }

    for (int i = 0; i < probabilityEstimation.length; i++) {
        MLCallbackResult callBackResult = new MLCallbackResult();
        callBackResult.setContext(context);
        //LOG.info("probability estimation for data @" + i + " is: " + probabilityEstimation[i]);
        if (probabilityEstimation[i] < aModel.maxProbabilityEstimate()) {
            callBackResult.setAnomaly(true);
            for (int col = 0; col < anomalyFeature[i].length; col++) {
                //LOG.info("feature anomaly? " + (featureVals[col] == true));
                if (anomalyFeature[i][col] == true) {
                    callBackResult.setFeature(aModel.statistics()[col].getCommandName());
                }
            }
        } else {
            callBackResult.setAnomaly(false);
        }

        callBackResult.setTimestamp(userActivity.timestamp());
        List<String> datapoints = new ArrayList<String>();
        double[] rowVals = userActivity.matrix().getRow(i);
        for (double rowVal : rowVals)
            datapoints.add(rowVal + "");
        callBackResult.setDatapoints(datapoints);
        callBackResult.setId(user);
        callBackResult.setAlgorithm(UserProfileConstants.KDE_ALGORITHM);
        mlPredictionOutputList.add(callBackResult);
    }
    return mlPredictionOutputList;
}

---

From source file:eu.amidst.core.utils.Utils.java

/**
 * Normalizes an array of doubles./* ww w. j a  v a  2  s .  co  m*/
 * @param vals an {@code array} of {@code double}.
 * @return a normalized array of doubles.
 */
public static double[] logs2probs(double[] vals) {
    double max = vals[Utils.maxIndex(vals)];
    double[] normalizedVals = new double[vals.length];
    for (int i = 0; i < vals.length; i++) {
        normalizedVals[i] = Math.exp(vals[i] + max);
    }
    return Utils.normalize(normalizedVals);
}

---

From source file:com.opengamma.analytics.financial.model.option.pricing.fourier.HestonCharacteristicExponent.java

@Override
public ComplexNumber getValue(ComplexNumber u, double t) {
    // that u = 0 gives zero is true for any characteristic function, that u = -i gives zero is because this is already mean corrected
    if (u.getReal() == 0.0 && (u.getImaginary() == 0.0 || u.getImaginary() == -1.0)) {
        return ZERO;
    }/*from   www.  j  a  v a 2s  . com*/

    //non-stochastic vol limit
    if (_omega == 0.0 || mod(multiply(multiply(_omega / _kappa, u), add(I, u))) < 1e-6) {
        final ComplexNumber z = multiply(u, add(I, u));
        if (_kappa * t < 1e-6) {
            return multiply(-_vol0 / 2 * t, z);
        }
        final double var = _theta * t + (_vol0 - _theta) * (1 - Math.exp(-_kappa * t)) / _kappa;
        return multiply(-var / 2, z);
    }

    final ComplexNumber c = getC(u, t);
    final ComplexNumber dv0 = multiply(_vol0, getD(u, t));
    return add(c, dv0);
}

---

From source file:fiji.plugin.trackmate.action.brownianmotion.WalkerMethodEstimator.java

private double probMSD(double msd, double k, double r) {

    double pmsd = 0;
    double thetaFactor = (2 * kB * temp * frameduration) / (3 * Math.PI * visk);
    double theta = thetaFactor / r;
    pmsd = (logK(k) + (k - 1) * (logK(k) + Math.log(msd)) + (-k * msd / theta))
            - (k * Math.log(theta) + logGammaK(k));
    //pmsd = (Math.log(k)+(k-1)*(Math.log(k)+Math.log(msd)) + (-k*msd/theta) ) - (k*Math.log(theta) + Gamma.logGamma(k)); 
    pmsd = Math.exp(pmsd);
    return pmsd;//  ww w  .java  2s.com

}

---

From source file:com.bmwcarit.barefoot.matcher.MatcherTest.java

private void assertCandidate(Tuple<MatcherCandidate, Double> candidate, Point sample) {
    Polyline polyline = map.get(candidate.one().point().edge().id()).geometry();
    double f = spatial.intercept(polyline, sample);
    Point i = spatial.interpolate(polyline, f);
    double l = spatial.distance(i, sample);
    double sig2 = Math.pow(5d, 2);
    double sqrt_2pi_sig2 = Math.sqrt(2d * Math.PI * sig2);
    double p = 1 / sqrt_2pi_sig2 * Math.exp((-1) * l / (2 * sig2));

    assertEquals(f, candidate.one().point().fraction(), 10E-6);
    assertEquals(p, candidate.two(), 10E-6);
}

---

From source file:fingerprints.helper.BloomFilter.java

/**
 * Calculate the probability of a false positive given the specified number of inserted elements.
 *
 * Calculates the approximate probability of the contains() method returning true for an object that had not
 * previously been inserted into the bloom filter. This is known as the "false positive probability".
 *
 * @return The estimated false positive rate
 *//*w ww.  j av a2  s. c  om*/
public double getExpectedFalsePositiveProbability() {
    // (1 - e^(-k * n / m)) ^ k
    return Math.pow((1 - Math.exp(-k * (double) expectedPatterns / (double) bitSetSize)), k);
}

---

From source file:com.opengamma.analytics.financial.model.option.pricing.analytic.JarrowRuddSkewnessKurtosisModel.java

private double getQ3(final double s, final double k, final double sigmaT, final double t, final double r,
        final double a, final double d2) {
    final double da = a * (d2 - sigmaT) / (k * sigmaT);
    final double df = Math.exp(-r * t);
    return -Math.pow(s * df, 3) * Math.pow(Math.exp(sigmaT * sigmaT - 1), 1.5) * df * da / 6.;
}

---

From source file:ch.epfl.leb.sass.models.illuminations.internal.SquareUniformIllumination.java

/**
 * Returns the irradiance in the sample at the point (x, y, z).
 * // w ww . j  a  v a  2  s . c  o m
 * @param x The x-position in the sample.
 * @param y The y-position in the sample.
 * @param z The z-position in the sample.
 */
@Override
public double getIrradiance(double x, double y, double z) {
    // Compute the absorption, if any.
    double abs = Math.exp(-4 * Math.PI * electricField.getRefractiveIndex().getN(x, y, z).getImaginary() * z
            / electricField.getWavelength());
    double irrad = this.power * abs / width / height;

    // TODO: Change this to an exception!
    assert (irrad != Double.NaN);
    return irrad;
}

---

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