public SharedStateDiscreteSampler createSharedStateSampler()

in commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerCache.java [191:228]

22 lines of code
5 McCabe index (conditional complexity)


    public SharedStateDiscreteSampler createSharedStateSampler(UniformRandomProvider rng,
                                                               double mean) {
        // Ensure the same functionality as the PoissonSampler by
        // using a SmallMeanPoissonSampler under the switch point.
        if (mean < PoissonSampler.PIVOT) {
            return SmallMeanPoissonSampler.of(rng, mean);
        }
        if (mean > maxN) {
            // Outside the range of the cache.
            // This avoids extra parameter checks and handles the case when
            // the cache is empty or if Math.floor(mean) is not an integer.
            return LargeMeanPoissonSampler.of(rng, mean);
        }

        // Convert the mean into an integer.
        final int n = (int) Math.floor(mean);
        if (n < minN) {
            // Outside the lower range of the cache.
            return LargeMeanPoissonSampler.of(rng, mean);
        }

        // Look in the cache for a state that can be reused.
        // Note: The cache is offset by minN.
        final int index = n - minN;
        final LargeMeanPoissonSamplerState state = values[index];
        if (state == null) {
            // Create a sampler and store the state for reuse.
            // Do not worry about thread contention
            // as the state is effectively immutable.
            // If recomputed and replaced it will the same.
            final LargeMeanPoissonSampler sampler = new LargeMeanPoissonSampler(rng, mean);
            values[index] = sampler.getState();
            return sampler;
        }
        // Compute the remaining fraction of the mean
        final double lambdaFractional = mean - n;
        return new LargeMeanPoissonSampler(rng, state, lambdaFractional);
    }