Bootstrapping Stats in Python and JS

bs.js

var jStat = require('jStat').jStat;


function make_bs_samples(somearray, num_iter) {
    var all_samples = [];

    for (var ii = num_iter - 1; ii >= 0; ii--) {
        var this_sample = [];
        for (var jj = somearray.length - 1; jj >= 0; jj--) {
            var index = Math.floor(Math.random() * somearray.length);
            this_sample.push(somearray[index]);
        }
        all_samples.push(this_sample);
    }
    return all_samples;
};


function percentile_bootstrap(somearray, statfxn, num_iter, alpha) {
    var lower_percentile = alpha * 0.5;
    var upper_percentile = 1 - lower_percentile;
    var samples = make_bs_samples(somearray, num_iter);
    var stat_array = [];
    
    for (var ii = samples.length - 1; ii >= 0; ii--) {
        stat_array.push(statfxn(samples[ii]));
    }

    result = {
        'primary': statfxn(somearray),
        'lower': jStat.percentile(stat_array, lower_percentile),
        'upper': jStat.percentile(stat_array, upper_percentile),
    }
    return result;
};

bs.py

import numpy


def make_boot_index(num_elements, num_iter):
    return numpy.random.randint(low=0, high=num_elements, size=(num_iter, num_elements))

def percentile(somearray, statfxn, num_iter, alpha=0.05):
    index = make_boot_index(data.shape[0], num_iter)
    boot_stats = statfxn(somearray[index], axis=-1)

    # compute the `alpha/2` and `1-alpha/2` percentiles of `boot_stats`
    CI = numpy.percentile(boot_stats, [alpha*50, 100-(alpha*50)], axis=0)

    return statfxn(somearray), CI