public int sample()

in commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.java [236:299]
51 lines of code
13 McCabe index (conditional complexity)

    public int sample() {
        // This will never be null. It may be a no-op delegate that returns zero.
        final int y2 = smallMeanPoissonSampler.sample();

        double x;
        double y;
        double v;
        int a;
        double t;
        double qr;
        double qa;
        while (true) {
            // Step 1:
            final double u = rng.nextDouble();
            if (u <= p1) {
                // Step 2:
                final double n = gaussian.sample();
                x = n * sqrtLambdaPlusHalfDelta - 0.5d;
                if (x > delta || x < -lambda) {
                    continue;
                }
                y = x < 0 ? Math.floor(x) : Math.ceil(x);
                final double e = exponential.sample();
                v = -e - 0.5 * n * n + c1;
            } else {
                // Step 3:
                if (u > p1 + p2) {
                    y = lambda;
                    break;
                }
                x = delta + (twolpd / delta) * exponential.sample();
                y = Math.ceil(x);
                v = -exponential.sample() - delta * (x + 1) / twolpd;
            }
            // The Squeeze Principle
            // Step 4.1:
            a = x < 0 ? 1 : 0;
            t = y * (y + 1) / (2 * lambda);
            // Step 4.2
            if (v < -t && a == 0) {
                y = lambda + y;
                break;
            }
            // Step 4.3:
            qr = t * ((2 * y + 1) / (6 * lambda) - 1);
            qa = qr - (t * t) / (3 * (lambda + a * (y + 1)));
            // Step 4.4:
            if (v < qa) {
                y = lambda + y;
                break;
            }
            // Step 4.5:
            if (v > qr) {
                continue;
            }
            // Step 4.6:
            if (v < y * logLambda - getFactorialLog((int) (y + lambda)) + logLambdaFactorial) {
                y = lambda + y;
                break;
            }
        }

        return (int) Math.min(y2 + (long) y, Integer.MAX_VALUE);
    }