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rng_trainer.html
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rng_trainer.html
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<!DOCTYPE html>
<!-- https://www.researchgate.net/publication/232494603_Can_People_Behave_Randomly_The_Role_of_Feedback -->
<meta charset="utf8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<style>
#buttons {
width: 100%;
display: flex;
}
#buttons button {
height: 20rem;
width: 100%;
margin: 2rem;
font-size: 3em;
}
button {
border-radius: 0;
border: 1px solid blue;
padding: 0.5rem;
}
</style>
<div id="buttons">
<button id="l">L</button>
<button id="r">R</button>
</div>
<div id="other-controls">
<div id="qty"></div>
<button id="restart">Restart</button>
</div>
<div id="seq"></div>
<script>
const error = e => { throw new Error(e) }
const floatField = {
mul: (a, b) => a * b,
add: (a, b) => a + b,
neg: a => -a,
inv: a => 1 / a,
zero: 0,
unity: 1
}
const gf2 = {
mul: (a, b) => a * b,
add: (a, b) => (a + b) % 2,
neg: a => a,
inv: a => a == 1 ? 1 : error("not invertible"),
zero: 0,
unity: 1
}
const evalPoly = (poly, x, field) => {
let a = field.zero
let b = field.unity
for (const coef of poly) {
a = field.add(field.mul(b, coef))
b = field.mul(b, x)
}
return a
}
const arrayOf = (n, x) => new Array(n).fill(x)
const xPowN = (n, field) => arrayOf(n, field.zero).concat([field.unity])
const polyField = field => {
const unity = [field.unity]
const zero = []
const add = (a, b) => {
const [ap, bp] = a.length > b.length ? [a, b] : [b, a]
return ap.map((aix, ix) => field.add(aix, bp[ix] ?? field.zero))
}
const mul = (a, b) => {
const out = arrayOf(a.length + b.length - 1, field.zero)
for (let i = 0; i < a.length; i++) {
for (let j = 0; j < b.length; j++) {
out[i + j] = field.add(out[i + j], field.mul(a[i], b[j]))
}
}
return out
}
const neg = a => a.map(field.neg)
return {
add,
mul,
neg,
unity,
zero,
inv: () => error("unimplemented")
}
}
// blatantly copied from Wikipedia https://en.wikipedia.org/wiki/Berlekamp%E2%80%93Massey_algorithm#Pseudocode
const berlekampMassey = (sequence, field) => {
const polys = polyField(field)
const N = sequence.length
let C = polys.unity
let B = polys.unity
let L = 0;
let m = 1;
let b = field.unity;
for (let n = 0; n < N; n++) {
let d = sequence[n]
for (let i = 1; i <= L; i++) {
d = field.add(d, field.mul(C[i], sequence[n - i]))
}
if (d == field.zero) {
m += 1
} else if (2 * L <= n) {
const T = C
C = polys.add(C, polys.neg(polys.mul(polys.mul([field.mul(d, field.inv(b))], xPowN(m, field)), B)))
L = n + 1 - L
B = T
b = d
m = 1
} else {
C = polys.add(C, polys.neg(polys.mul(polys.mul([field.mul(d, field.inv(b))], xPowN(m, field)), B)))
m += 1
}
}
return C
}
const polyToKey = p => p.join("")
const polyRecurrence = (polynomial, sequence) => gf2.mul(gf2.neg(gf2.inv(polynomial[0])), polynomial.slice(1).map((coef, ix) => gf2.mul(coef, sequence[sequence.length - 1 - ix])).reduce(gf2.add, gf2.zero))
const bmEnsemble = sequence => {
const seqlen = 10
const polys = new Map()
for (let i = 0; i < sequence.length; i++) {
const result = berlekampMassey(sequence.slice(i, i + seqlen), gf2)
polys.set(polyToKey(result), [1, 2, result])
}
for (let i = 0; i < sequence.length - 1; i++) {
const chunk = sequence.slice(0, i)
for (const [polystr, score] of polys.entries()) {
const poly = score[2]
if (chunk.length >= poly.length - 1) {
const prediction = polyRecurrence(poly, chunk)
if (prediction == sequence[i]) {
score[0] += 1
}
score[1] += 1
}
}
}
let max = 0
let pred = 0
for (const [polystr, score] of polys.entries()) {
const bits = score[0] - score[1] - polystr.length
//console.log(polystr, bits)
const weight = 2**bits
max += weight
pred += weight * polyRecurrence(score[2], sequence)
}
console.log("BM", pred / max)
return max > 0 ? pred / max > 0.5 : 0
}
const aaronsonPredictor = sequence => {
let k = 4
const m = new Map()
for (let i = 0; i < sequence.length - 1; i++) {
const slic = polyToKey(sequence.slice(Math.max(i - k + 1, 0), i + 1))
if (!m.get(slic)) m.set(slic, [0, 0])
const score = m.get(slic)
score[1] += 1
score[0] += sequence[i + 1]
}
var res
while (k) {
const slic = polyToKey(sequence.slice(-k))
if (res = m.get(slic)) {
const prob = res[0] / res[1]
console.log("AO", prob, slic)
return prob > 0.5
}
k -= 1
}
return 0
}
let correct = {
"aaronson": 0,
"bm": 0
}
var working = true
const FINALSEQLEN = 300
const tests = {
"RNG1": []
}
var seq = []
const push = val => {
if (working) {
seq.push(val)
qty.innerText = `${seq.length}/${FINALSEQLEN}`
if (seq.length > 0) {
correct.bm += bmEnsemble(seq.slice(0, seq.length - 1)) == seq[seq.length - 1] ? 1 : 0
correct.aaronson += aaronsonPredictor(seq.slice(0, seq.length - 1)) == seq[seq.length - 1] ? 1 : 0
}
if (seq.length === FINALSEQLEN) {
working = false
let accuracy = ""
for (const [name, count] of Object.entries(correct)) {
accuracy += `; ${name} ${count / FINALSEQLEN * 100}%`
}
qty.innerText = `Done${accuracy}`
console.log(correct)
}
}
}
restart.onclick = () => {
working = true
seq = []
}
l.onclick = () => push(0)
r.onclick = () => push(1)
window.onkeypress = ev => {
if (ev.key.toLowerCase() == "l" || ev.key == "1") {
push(0)
} else if (ev.key.toLowerCase() == "r" || ev.key == "2") {
push(1)
}
}
</script>