Discrete probability distribution sampling in JavaScript

sample.js

// MIT License
//
// (c) 2018 Brannon Dorsey <brannon@brannondorsey.com>
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in all
// copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.

// draw a discrete sample (index) from a probability distribution (an array of probabilities)
// probs will be rescaled to sum to 1.0 if the values do not already
function sample(probs) {
    const sum = probs.reduce((a, b) => a + b, 0)
    if (sum <= 0) throw Error('probs must sum to a value greater than zero')
    const normalized = probs.map(prob => prob / sum)
    const sample = Math.random()
    let total = 0
    for (let i = 0; i < normalized.length; i++) {
        total += normalized[i]
        if (sample < total) return i
    }
}

test.js

const counts = {}
for (let i = 0; i < 1000000; i++) {
    let index = sample([0.2, 0.2, 0.8, 0.8])
    if (!counts.hasOwnProperty(index)) counts[index] = 0
    counts[index]++
}

const numTrials = Object.values(counts).reduce((num, total) => num + total, 0)
for (key in counts) {
    console.log(`${key}: ${counts[key] / numTrials}`)
}