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import epsilon from "./epsilon.js";
/** * The [Binomial Distribution](http://en.wikipedia.org/wiki/Binomial_distribution) is the discrete probability * distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields * success with probability `probability`. Such a success/failure experiment is also called a Bernoulli experiment or * Bernoulli trial; when trials = 1, the Binomial Distribution is a Bernoulli Distribution. * * @param {number} trials number of trials to simulate * @param {number} probability * @returns {number[]} output */function binomialDistribution(trials, probability) /*: ?number[] */ {    // Check that `p` is a valid probability (0 ≤ p ≤ 1),    // that `n` is an integer, strictly positive.    if (probability < 0 || probability > 1 || trials <= 0 || trials % 1 !== 0) {        return undefined;    }
    // We initialize `x`, the random variable, and `accumulator`, an accumulator    // for the cumulative distribution function to 0. `distribution_functions`    // is the object we'll return with the `probability_of_x` and the    // `cumulativeProbability_of_x`, as well as the calculated mean &    // variance. We iterate until the `cumulativeProbability_of_x` is    // within `epsilon` of 1.0.    let x = 0;    let cumulativeProbability = 0;    const cells = [];    let binomialCoefficient = 1;
    // This algorithm iterates through each potential outcome,    // until the `cumulativeProbability` is very close to 1, at    // which point we've defined the vast majority of outcomes    do {        // a [probability mass function](https://en.wikipedia.org/wiki/Probability_mass_function)        cells[x] =            binomialCoefficient *            Math.pow(probability, x) *            Math.pow(1 - probability, trials - x);        cumulativeProbability += cells[x];        x++;        binomialCoefficient = (binomialCoefficient * (trials - x + 1)) / x;        // when the cumulativeProbability is nearly 1, we've calculated        // the useful range of this distribution    } while (cumulativeProbability < 1 - epsilon);
    return cells;}
export default binomialDistribution;