70 lines
1.8 KiB
JavaScript
70 lines
1.8 KiB
JavaScript
// Imports
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import stdlib_normal from '@stdlib/random/base/normal/lib/main.js';
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import stdlib_lognormal from '@stdlib/random/base/lognormal/lib/main.js';
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// Functions
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const DEFAULT_N = 10**6
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function generator(f) {
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let g = (a, b, n = DEFAULT_N) => {
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let result = new Array(n)
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for (let i = 0; i < n; i++) {
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result[i] = f(a, b, n)
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}
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return result
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}
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return g
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}
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const sum = xs => xs.reduce((acc, x) => acc + x, 0)
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const normal = generator(stdlib_normal)
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const lognormal = generator(stdlib_lognormal)
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const to = (low, high, n = DEFAULT_N) => {
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let normal95confidencePoint = 1.6448536269514722
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let logLow = Math.log(low)
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let logHigh = Math.log(high)
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let meanlog = (logLow + logHigh) / 2
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let sdlog = (logHigh - logLow) / (2.0 * normal95confidencePoint)
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return lognormal(meanlog, sdlog, n)
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}
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const mixture = (dists_array, weights_array, n = DEFAULT_N) => {
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let normalized_weights = weights_array.map(w => w / sum(weights_array))
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let cummulative_sums = Array(normalized_weights.length)
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normalized_weights.reduce((acc, x, i) => {
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cummulative_sums[i] = acc + x
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return cummulative_sums[i]
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}, 0)
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const helper_probs = [...new Array(n)].map(_ => Math.random())
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const results = helper_probs.map(p => {
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let match_index = cummulative_sums.findIndex(x => x > p)
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if(match_index == -1){
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console.log("Error: This should never happen.")
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}
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let target_loc = match_index // == -1 ? 0 : match_index
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let target_samples = dists_array[target_loc]
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return target_samples[Math.floor(Math.random() * target_samples.length)];
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})
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return(results)
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}
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// Example
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let p_a = 0.8
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let p_b = 0.5
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let p_c = p_a * p_b
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let dists = [
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[0], [1], to(1, 3),
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to(2, 10)
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]
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let weights = [
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(1 - p_c),
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p_c / 2,
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p_c / 4,
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p_c / 4
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]
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let result = mixture(dists, weights)
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let mean_result = sum(result)/result.length
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console.log(mean_result)
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