More work on tests for random functions
This commit is contained in:
Jesse Schwartzentruber
parent
e927fac479
commit
446dcdf1d9
|
|
@ -263,11 +263,205 @@ QUnit.test("random.bool() distribution", function(assert) {
|
|||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
/*
|
||||
XXX
|
||||
pick(obj)
|
||||
chance(limit)
|
||||
*/
|
||||
QUnit.test("random.pick() cases", function(assert) {
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let i = 0; i < 100; ++i) {
|
||||
let tmp = Math.random();
|
||||
assert.equal(tmp, random.pick(tmp));
|
||||
}
|
||||
for (let i = 0; i < 100; ++i) {
|
||||
let tmp = (Math.random() * 100) >>> 0;
|
||||
assert.equal(tmp, random.pick(tmp));
|
||||
}
|
||||
for (let i = 0; i < 100; ++i) {
|
||||
let tmp = Math.random() + "";
|
||||
assert.equal(tmp, random.pick(tmp));
|
||||
}
|
||||
for (let i = 0; i < 100; ++i) {
|
||||
let tmp = Math.random();
|
||||
assert.equal(tmp, random.pick([tmp]));
|
||||
}
|
||||
for (let i = 0; i < 100; ++i) {
|
||||
let tmp = Math.random();
|
||||
assert.equal(tmp, random.pick(function(){ return tmp; }));
|
||||
}
|
||||
for (let i = 0; i < 100; ++i) {
|
||||
let tmp = Math.random();
|
||||
assert.equal(tmp, random.pick(function(){ return [tmp]; }));
|
||||
}
|
||||
});
|
||||
|
||||
QUnit.test("random.pick() with equal distribution", function(assert) {
|
||||
const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(3);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let tmp = random.pick([0, [1, 1], function(){ return 2; }]);
|
||||
if (tmp < 0) throw "random.pick() < lower bound";
|
||||
if (tmp >= bins.length) throw "random.pick() > upper bound";
|
||||
++bins[tmp];
|
||||
}
|
||||
let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 2)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.pick() with unequal distribution", function(assert) {
|
||||
const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(3);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let tmp = random.pick([[0, 1], [1], function(){ return [2]; }]);
|
||||
if (tmp < 0) throw "random.pick() < lower bound";
|
||||
if (tmp >= bins.length) throw "random.pick() > upper bound";
|
||||
++bins[tmp];
|
||||
}
|
||||
let xsq = Math.pow(bins[0] - N / 6, 2) / (N / 6) + Math.pow(bins[1] - N / 2, 2) / (N / 2) + Math.pow(bins[2] - N / 3, 2) / (N / 3);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 2)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.chance(2) distribution", function(assert) {
|
||||
const N = 1e4, TRIES = 3, XSQ = 3.84; // quantile of chi-square dist. k=1, p=.05
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(2);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let tmp = random.chance(2);
|
||||
if (tmp === true)
|
||||
tmp = 1;
|
||||
else if (tmp === false)
|
||||
tmp = 0;
|
||||
else
|
||||
assert.ok(false, "unexpected random.chance() result: " + tmp);
|
||||
++bins[tmp];
|
||||
}
|
||||
let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 1)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.chance(undefined) distribution", function(assert) {
|
||||
const N = 1e4, TRIES = 3, XSQ = 3.84; // quantile of chi-square dist. k=1, p=.05
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(2);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let tmp = random.chance();
|
||||
if (tmp === true)
|
||||
tmp = 1;
|
||||
else if (tmp === false)
|
||||
tmp = 0;
|
||||
else
|
||||
assert.ok(false, "unexpected random.chance() result: " + tmp);
|
||||
++bins[tmp];
|
||||
}
|
||||
let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 1)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.chance(3) distribution", function(assert) {
|
||||
const N = 1e4, TRIES = 3, XSQ = 3.84; // quantile of chi-square dist. k=1, p=.05
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(2);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let tmp = random.chance(3);
|
||||
if (tmp === true)
|
||||
tmp = 0;
|
||||
else if (tmp === false)
|
||||
tmp = 1;
|
||||
else
|
||||
assert.ok(false, "unexpected random.chance() result: " + tmp);
|
||||
++bins[tmp];
|
||||
}
|
||||
let xsq = Math.pow(bins[0] - (N / 3), 2) / (N / 3) + Math.pow(bins[1] - (2 * N / 3), 2) / (2 * N / 3);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 1)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.chance(1000) distribution", function(assert) {
|
||||
const N = 1e6, TRIES = 3, XSQ = 3.84; // quantile of chi-square dist. k=1, p=.05
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(2);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let tmp = random.chance(1000);
|
||||
if (tmp === true)
|
||||
tmp = 0;
|
||||
else if (tmp === false)
|
||||
tmp = 1;
|
||||
else
|
||||
assert.ok(false, "unexpected random.chance() result: " + tmp);
|
||||
++bins[tmp];
|
||||
}
|
||||
let xsq = Math.pow(bins[0] - (N / 1000), 2) / (N / 1000) + Math.pow(bins[1] - (999 * N / 1000), 2) / (999 * N / 1000);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 1)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.choose() with equal distribution", function(assert) {
|
||||
const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
|
||||
|
|
@ -319,13 +513,265 @@ QUnit.test("random.choose() with unequal distribution", function(assert) {
|
|||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.choose() with unequal distribution and pick", function(assert) {
|
||||
const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(3);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let tmp = random.choose([[1, 0], [2, [1, 2]], [1, function(){ return 2; }]]);
|
||||
if (tmp >= bins.length) throw "random.choose() > upper bound";
|
||||
++bins[tmp];
|
||||
}
|
||||
let xsq = Math.pow(bins[0] - N / 4, 2) / (N / 4) + Math.pow(bins[1] - N / 4, 2) / (N / 4) + Math.pow(bins[2] - N / 2, 2) / (N / 2);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 2)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.choose(flat) with unequal distribution", function(assert) {
|
||||
const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(3);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let tmp = random.choose([[1, 0], [2, 1], [1, 2]], true);
|
||||
if (tmp >= bins.length) throw "random.choose() > upper bound";
|
||||
++bins[tmp];
|
||||
}
|
||||
let xsq = Math.pow(bins[0] - N / 4, 2) / (N / 4) + Math.pow(bins[1] - N / 2, 2) / (N / 2) + Math.pow(bins[2] - N / 4, 2) / (N / 4);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 2)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.choose(flat) equal distribution with types not picked", function(assert) {
|
||||
const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
|
||||
const v1 = 1, v2 = [12], v3 = function(){};
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(3);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let tmp = random.choose([[1, v1], [1, v2], [1, v3]], true);
|
||||
if (tmp === v1)
|
||||
tmp = 0;
|
||||
else if (tmp === v2)
|
||||
tmp = 1;
|
||||
else if (tmp === v3)
|
||||
tmp = 2;
|
||||
else
|
||||
assert.ok(false, "unexpected random.choose() result: " + tmp);
|
||||
++bins[tmp];
|
||||
}
|
||||
let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 2)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.weighted() with equal distribution", function(assert) {
|
||||
const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(3);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let tmp = random.item(random.weighted([{w: 1, v: 0}, {w: 1, v: 1}, {w: 1, v: 2}]));
|
||||
if (tmp >= bins.length) throw "random.weighted() > upper bound";
|
||||
++bins[tmp];
|
||||
}
|
||||
let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 2)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.weighted() with unequal distribution", function(assert) {
|
||||
const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(3);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let tmp = random.item(random.weighted([{w: 1, v: 0}, {w: 2, v: 1}, {w: 1, v: 2}]));
|
||||
if (tmp >= bins.length) throw "random.weighted() > upper bound";
|
||||
++bins[tmp];
|
||||
}
|
||||
let xsq = Math.pow(bins[0] - N / 4, 2) / (N / 4) + Math.pow(bins[1] - N / 2, 2) / (N / 2) + Math.pow(bins[2] - N / 4, 2) / (N / 4);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 2)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.weighted() equal distribution with types not picked", function(assert) {
|
||||
const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
|
||||
const v1 = 1, v2 = [12], v3 = function(){};
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(3);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let tmp = random.item(random.weighted([{w: 1, v: v1}, {w: 1, v: v2}, {w: 1, v: v3}]));
|
||||
if (tmp === v1)
|
||||
tmp = 0;
|
||||
else if (tmp === v2)
|
||||
tmp = 1;
|
||||
else if (tmp === v3)
|
||||
tmp = 2;
|
||||
else
|
||||
assert.ok(false, "unexpected random.weighted() result: " + tmp);
|
||||
++bins[tmp];
|
||||
}
|
||||
let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 2)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.use() distribution", function(assert) {
|
||||
const N = 1e4, TRIES = 3, XSQ = 3.84; // quantile of chi-square dist. k=1, p=.05
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(2);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let rnd = Math.random(), use = random.use(rnd);
|
||||
if (use === rnd)
|
||||
use = 1;
|
||||
else if (use === "")
|
||||
use = 0;
|
||||
else
|
||||
assert.ok(false, "unexpected random.use() result: " + use);
|
||||
++bins[use];
|
||||
}
|
||||
let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 1)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.shuffle() distribution", function(assert) {
|
||||
const N = 1e4, M = 10, TRIES = 3, XSQ = 123.23; // quantile of chi-square dist. k=M*M-1, p=.05
|
||||
// XXX: shouldn't k be M! ?
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(M * M);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let array = [];
|
||||
for (let j = 0; j < M; ++j)
|
||||
array.push(j);
|
||||
random.shuffle(array);
|
||||
for (let j = 0; j < M; ++j)
|
||||
++bins[j * M + array[j]];
|
||||
}
|
||||
let xsq = bins.reduce(function(a, v){ let e = N / M; return a + Math.pow(v - e, 2) / e; }, 0);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 99)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
QUnit.test("random.shuffled() distribution", function(assert) {
|
||||
const N = 1e4, M = 10, TRIES = 3, XSQ = 123.23; // quantile of chi-square dist. k=M*M-1, p=.05
|
||||
// XXX: shouldn't k be M! ?
|
||||
let tries = [];
|
||||
random.init(Math.random() * 0x100000000);
|
||||
for (let attempt = 0; attempt < TRIES; ++attempt) {
|
||||
let bins = new Uint32Array(M * M);
|
||||
let array_ref = [];
|
||||
for (let j = 0; j < M; ++j)
|
||||
array_ref.push(j);
|
||||
for (let i = 0; i < N; ++i) {
|
||||
let array = random.shuffled(array_ref);
|
||||
for (let j = 0; j < M; ++j) {
|
||||
++bins[j * M + array[j]];
|
||||
if (array_ref[j] !== j)
|
||||
throw "array modified";
|
||||
}
|
||||
}
|
||||
let xsq = bins.reduce(function(a, v){ let e = N / M; return a + Math.pow(v - e, 2) / e; }, 0);
|
||||
/*
|
||||
* XSQ = scipy.stats.chi2.isf(.05, 99)
|
||||
* if xsq > XSQ, the result is biased at 95% significance
|
||||
*/
|
||||
if (xsq < XSQ) {
|
||||
assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
|
||||
return;
|
||||
}
|
||||
tries.push(xsq);
|
||||
}
|
||||
assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
|
||||
});
|
||||
|
||||
/*
|
||||
XXX
|
||||
choose(list, flat=true)
|
||||
weighted(wa)
|
||||
use(obj)
|
||||
shuffle(arr)
|
||||
shuffled(arr)
|
||||
subset(list, limit)
|
||||
pop(arr)
|
||||
*/
|
||||
|
|
|
|||
Loading…
Reference in New Issue