Example usage for java.lang Math log

Introduction

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

Prototype

@HotSpotIntrinsicCandidate
public static double log(double a) 

Source Link

Document

Returns the natural logarithm (base e) of a double value.

Usage

From source file:bide.math.NormalDistribution.java

/**
 * Normal distribution truncated at both side
 * //from  w w w  .j a  va2s.c  o m
 * @param x
 * @return
 */
public static double[] logPdfBT(double[] x, double m, double sd, double lLim, double uLim) {
    int n = x.length;
    if (n == 0) {
        double[] dnorm = { 0 };
        return dnorm;
    } else {
        double[] dnorm = new double[n];
        try {
            double limit = Math.log(NormalDistribution.cdf(uLim, m, sd) - NormalDistribution.cdf(lLim, m, sd));
            double d;
            for (int i = 0; i < n; i++) {
                d = x[i];
                if (d > lLim && d < uLim) {
                    dnorm[i] = NormalDistribution.logPdf(d, m, sd) - limit;
                } else {
                    dnorm[i] = 0;
                }
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
        return dnorm;
    }
}

---

From source file:com.insightml.utils.ui.UiUtils.java

public static String humanReadableByteCount(final long bytes, final boolean si) {
    final int unit = si ? 1000 : 1024;
    if (bytes < unit) {
        return bytes + " B";
    }/*from w w w .  j ava2s  .c  om*/
    final int exp = (int) (Math.log(bytes) / Math.log(unit));
    final String pre = (si ? "kMGTPE" : "KMGTPE").charAt(exp - 1) + (si ? "" : "i");
    return String.format("%.1f %sB", bytes / Math.pow(unit, exp), pre);
}

---

From source file:com.itemanalysis.psychometrics.polycor.PolychoricLogLikelihoodML.java

/**
 * Loglikelihood function to be minimized.
 *
 * Parameters are stored such that x[0] is the correlation
 * x[1:validNrow] are the row thresholds
 * x[validNrow:(1+validNrow+validNcol] are the column thresholds
 *
 * @param x/*from   www .  j a  va 2  s .c  o  m*/
 * @return
 */
public double value(double[] x) {
    double logLike = 0.0;
    if (x[0] > 1.0) {
        rho = maxcor;
    } else if (x[0] < -1.0) {
        rho = -maxcor;
    } else {
        rho = x[0];
    }

    for (int i = 0; i < validNrow; i++) {
        alpha[i] = x[i + 1];
    }
    alpha[validNrow] = 10;
    for (int i = 0; i < validNcol; i++) {
        beta[i] = x[i + 1 + validNrow];
    }
    beta[validNcol] = 10;

    double[][] Pd = new double[nrow][ncol];
    double[][] Pc = new double[nrow][ncol];

    for (int i = 0; i < nrow; i++) {
        for (int j = 0; j < ncol; j++) {
            Pd[i][j] = bvnorm.cumulativeProbability(alpha[i], beta[j], rho);
            Pc[i][j] = Pd[i][j];
            if (i > 0) {
                Pd[i][j] -= Pc[i - 1][j];
            }
            if (j > 0) {
                Pd[i][j] -= Pc[i][j - 1];
            }
            if (i > 0 && j > 0) {
                Pd[i][j] += Pc[i - 1][j - 1];
            }
            logLike += data[i][j] * Math.log(Pd[i][j]);
        }
    }
    return -logLike;
}

---

From source file:LogFormat.java

/**
 * Calculates the log of a given value./*from w ww . ja v a  2s. c o m*/
 *
 * @param value  the value.
 *
 * @return The log of the value.
 */
private double calculateLog(double value) {
    return Math.log(value) / this.baseLog;
}

---

From source file:com.datumbox.framework.core.statistics.distributions.ContinuousDistributions.java

/**
 * Calculates the probability from -INF to X under Student's Distribution
 * Ported to PHP from Javascript implementation found at http://www.math.ucla.edu/~tom/distributions/tDist.html
 * /* w  w w. j av  a2 s  .com*/
 * @param x
 * @param df
 * @return 
 */
public static double studentsCdf(double x, int df) {
    if (df <= 0) {
        throw new IllegalArgumentException("The degrees of freedom need to be positive.");
    }

    double A = df / 2.0;
    double S = A + 0.5;
    double Z = df / (df + x * x);
    double BT = Math.exp(logGamma(S) - logGamma(0.5) - logGamma(A) + A * Math.log(Z) + 0.5 * Math.log(1.0 - Z));
    double betacdf;
    if (Z < (A + 1.0) / (S + 2.0)) {
        betacdf = BT * betinc(Z, A, 0.5);
    } else {
        betacdf = 1 - BT * betinc(1.0 - Z, 0.5, A);
    }

    double tcdf;
    if (x < 0) {
        tcdf = betacdf / 2.0;
    } else {
        tcdf = 1.0 - betacdf / 2.0;
    }

    return tcdf;
}

---

From source file:com.opengamma.analytics.financial.model.finitedifference.applications.TwoStateMarkovChainDensity.java

private Function1D<Double, Double> getInitialCondition(final ForwardCurve forward, final double initialProb) {
    //using a log-normal distribution with a very small Standard deviation as a proxy for a Dirac delta
    return new Function1D<Double, Double>() {
        private final double _volRootTOffset = 0.01;

        @Override/*w w w  .j a v a  2  s .c  o  m*/
        public Double evaluate(final Double s) {
            if (s <= 0 || initialProb == 0) {
                return 0.0;
            }
            final double x = Math.log(s / forward.getSpot());
            final NormalDistribution dist = new NormalDistribution(0, _volRootTOffset);
            return initialProb * dist.getPDF(x) / s;
        }
    };
}

---

From source file:com.cloudera.oryx.kmeans.common.ClusterValidityStatistics.java

/**
 * Calculates the normalized variation-of-information for the contingency contingencyMatrix.
 *
 * @return the normalized variation-of-information for the contingency contingencyMatrix
 *//*from w  w w. jav  a 2 s .  c  om*/
private static double normVarInformation(RealMatrix contingencyMatrix, final double[] rowSums,
        final double[] colSums, final double n) {
    double den = n * (entropy(rowSums, n) + entropy(colSums, n));
    if (den == 0) {
        return Double.NaN;
    }

    double num = contingencyMatrix.walkInOptimizedOrder(new RealMatrixPreservingVisitor() {
        private double sum = 0.0;

        @Override
        public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) {
            sum = 0.0;
        }

        @Override
        public void visit(int row, int column, double value) {
            if (value > 0.0) {
                sum += value * (Math.log(value * n) - Math.log(rowSums[row]) - Math.log(colSums[column]));
            }
        }

        @Override
        public double end() {
            return sum;
        }
    });

    return 1.0 + 2.0 * (num / den);
}

---

From source file:com.datumbox.framework.statistics.distributions.ContinuousDistributions.java

/**
 * Calculates the probability from -INF to X under Student's Distribution
 * Ported to PHP from Javascript implementation found at http://www.math.ucla.edu/~tom/distributions/tDist.html
 * /*from w  w w.j  av  a 2 s .com*/
 * @param x
 * @param df
 * @return 
 * @throws IllegalArgumentException 
 */
public static double StudentsCdf(double x, int df) throws IllegalArgumentException {
    if (df <= 0) {
        throw new IllegalArgumentException();
    }

    double tcdf = 0.0;

    double A = df / 2.0;
    double S = A + 0.5;
    double Z = df / (df + x * x);
    double BT = Math.exp(LogGamma(S) - LogGamma(0.5) - LogGamma(A) + A * Math.log(Z) + 0.5 * Math.log(1.0 - Z));
    double betacdf = 0.0;
    if (Z < (A + 1.0) / (S + 2.0)) {
        betacdf = BT * Betinc(Z, A, 0.5);
    } else {
        betacdf = 1 - BT * Betinc(1.0 - Z, 0.5, A);
    }
    if (x < 0) {
        tcdf = betacdf / 2.0;
    } else {
        tcdf = 1.0 - betacdf / 2.0;
    }

    return tcdf;
}

---

From source file:change_point_detection.CpdDP.java

public int/*estimated change point*/ detectChange() throws Exception {
    int estimatedChangePoint = -1;
    int N = this.dynamicWindow.size();
    this.cushion = Math.max(100, (int) Math.floor(Math.pow(N, gamma)));
    //mean conf. should not fall below 0.3
    double preChangeMean, postChangeMean, wholeMean;
    wholeMean = calculateMean(0, N - 1);
    if ((N > (2 * this.cushion) && wholeMean <= 0.3) || this.dynamicWindow.size() > this.dim)
        return N - 1;
    double threshold = -Math.log(this.sensitivity);
    double w = 0;
    int kAtMaxW = -1;
    for (int k = this.cushion; k <= N - this.cushion; k++) {
        double skn = 0;
        int prevN = this.trackN[k];
        preChangeMean = prevN == -1 ? calculateMean(0, k - 1)
                : this.cusumElementArr[prevN][k].getPreChangeMean();
        postChangeMean = calculateMean(k, N - 1);
        if (postChangeMean <= (1 - this.secAlgMarSlack) * preChangeMean) {//signal from secondary
            if (prevN == -1) {
                //calculate from scratch
                /* estimate pre and post change parameters */
                double alphaPreChange = calcBetaDistAlpha(0, k - 1, preChangeMean);
                double betaPreChange = calculateBetaDistBeta(alphaPreChange, 0, k - 1, preChangeMean);
                double alphaPostChange = calcBetaDistAlpha(k, N - 1, postChangeMean);
                double betaPostChange = calculateBetaDistBeta(alphaPostChange, k, N - 1, postChangeMean);
                BetaDistributionImpl preBetaDist = new BetaDistributionImpl(alphaPreChange, betaPreChange);
                BetaDistributionImpl postBetaDist = new BetaDistributionImpl(alphaPostChange, betaPostChange);
                for (int i = k; i < N; i++) {
                    try {
                        skn += Math.log(postBetaDist.density(this.dynamicWindow.get(i).doubleValue())
                                / preBetaDist.density(this.dynamicWindow.get(i).doubleValue()));
                    } catch (Exception e) {
                        e.printStackTrace();
                        System.out.println("continuing...");
                        skn = 0;/*from www.  j  a va  2  s  . co m*/
                        break;
                    }
                }
                this.cusumElementArr[N - 1][k] = new CusumElement(preBetaDist, postBetaDist, preChangeMean,
                        skn);
            } else {//warning and calculate recursively
                double alphaPostChange2 = calcBetaDistAlpha(k, N - 1, postChangeMean);
                double betaPostChange2 = calculateBetaDistBeta(alphaPostChange2, k, N - 1, postChangeMean);
                BetaDistributionImpl postBetaDist2 = new BetaDistributionImpl(alphaPostChange2,
                        betaPostChange2);
                skn += this.cusumElementArr[prevN][k].getCusumScore();
                for (int i = prevN + 1; i < N; i++) {
                    try {
                        skn += Math.log(postBetaDist2.density(this.dynamicWindow.get(i).doubleValue())
                                / this.cusumElementArr[prevN][k].getPreChangeDist()
                                        .density(this.dynamicWindow.get(i).doubleValue()));
                    } catch (Exception e) {
                        e.printStackTrace();
                        System.out.println("continuing...");
                        skn = 0;
                        break;
                    }
                }
                this.cusumElementArr[N - 1][k] = new CusumElement(
                        this.cusumElementArr[prevN][k].getPreChangeDist(), postBetaDist2, preChangeMean, skn);
            }
            this.trackN[k] = N - 1;
        }
        if (skn > w) {
            w = skn;
            kAtMaxW = k;
        }
    }
    if (w >= threshold && kAtMaxW != -1) {
        System.out.println("\nChangePoint Found!");
        estimatedChangePoint = kAtMaxW;
        System.out.println("Estimated change point is " + estimatedChangePoint + ", detected at point: " + N);
    }
    //force change point if confidence falls down terribly
    if (estimatedChangePoint == -1 && N >= 100 && wholeMean < 0.3)
        estimatedChangePoint = N - 1;
    return estimatedChangePoint;
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• 18
• 19
• 20
• 21
• 22
• 23
• 24
• 25
• 26
• 27
• 28
• 29
• 30
• 31
• 32
• 33
• 34
• 35
• 36
• 37
• 38
• 39
• 40
• 41
• 42
• 43
• 44
• 45
• 46
• 47
• 48
• 49
• 50
• »