import mir.test: shouldApprox; 0.hypergeometricCDF(7, 4, 3).shouldApprox == 0.02857143; 1.hypergeometricCDF(7, 4, 3).shouldApprox == 0.3714286; 2.hypergeometricCDF(7, 4, 3).shouldApprox == 0.8857143; 3.hypergeometricCDF(7, 4, 3).shouldApprox == 1.0; // can also provide a template argument to change output type static assert(is(typeof(hypergeometricCDF!float(3, 7, 4, 3)) == float));
Alternate algorithms
import mir.test: shouldApprox; // Can approximate hypergeometric with binomial distribution 20.hypergeometricCDF!"approxBinomial"(750_000, 250_000, 50).shouldApprox(1e-2) == 0.8740839; // Can approximate hypergeometric with poisson distribution 8.hypergeometricCDF!"approxPoisson"(100_000, 100, 5_000).shouldApprox(1e-2) == 0.9370063; // Can approximate hypergeometric with normal distribution 3_750.hypergeometricCDF!"approxNormal"(10_000, 7_500, 5_000).shouldApprox(2e-2) == 0.5092122; // Can approximate hypergeometric with normal distribution 3_750.hypergeometricCDF!"approxNormalContinuityCorrection"(10_000, 7_500, 5_000).shouldApprox(1e-2) == 0.5092122;
Computes the hypergeometric cumulative distribution function (CDF).
Additional algorithms may be provided for calculating CDF that allow trading off time and accuracy. If approxPoisson is provided, PoissonAlgo.gamma is assumed.
Setting hypergeometricAlgo = HypergeometricAlgo.direct results in direct summation being used, which can result in significant slowdowns for large values of k.