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mbostockmbostock
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Restore Math.random on teardown.
1 parent e44ae3c commit aee1214

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Lines changed: 37 additions & 50 deletions

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‎package.json‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -54,7 +54,7 @@
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"smash": "~0.0.12",
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"uglify-js": "2.4.0",
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"vows": "0.7.0",
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"seedrandom": "0.1.6"
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"seedrandom": "2.3.1"
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},
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"scripts": {
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"test": "node_modules/.bin/vows"

‎test/math/random-test.js‎

Lines changed: 36 additions & 49 deletions
Original file line numberDiff line numberDiff line change
@@ -1,50 +1,45 @@
11
var vows = require("vows"),
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load = require("../load"),
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assert = require("../assert"),
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seedrandom = require('seedrandom');
4+
seedrandom = require("seedrandom");
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var suite = vows.describe("d3.random");
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8-
/**
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* Testing a random number generator is a bit more complicated than testing
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* deterministic code, so we use different techniques.
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*
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* If the RNG is correct, each test in this suite will pass with probability
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* at least P. The tests have been designed so that P ≥ 98%. Specific values
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* of P are given above each case. We use the seedrandom module to get around
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* this non-deterministic aspect -- so it is safe to assume that if the tests
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* fail, then d3's RNG is broken.
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*
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* More on RNG testing here:
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* @see http://www.johndcook.com/Beautiful_Testing_ch10.pdf
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*/
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// Overwrites Math.random to a seeded random function.
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// (by default Math.random is seeded with current time)
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Math.seedrandom('a random seed.');
8+
// Testing a random number generator is a bit more complicated than testing
9+
// deterministic code, so we use different techniques.
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//
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// If the RNG is correct, each test in this suite will pass with probability
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// at least P. The tests have been designed so that P ≥ 98%. Specific values
13+
// of P are given above each case. We use the seedrandom module to get around
14+
// this non-deterministic aspect -- so it is safe to assume that if the tests
15+
// fail, then d3's RNG is broken.
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//
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// See also: http://www.johndcook.com/Beautiful_Testing_ch10.pdf
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suite.addBatch({
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"random": {
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topic: load("math/random").expression("d3.random"),
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"normal": {
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"topic": function(random) { return random.normal(-43289, 38.8); },
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// P = 98%
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"has normal distribution" : KSTest(normalCDF(-43289, 38.8))
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},
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"logNormal": {
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// Use reasonable values for mean here because random.logNormal() grows
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// exponentially with the mean.
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"topic": function(random) { return random.logNormal(10, 2.5); },
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// P = 98%
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"has log-normal distribution" : KSTest(logNormalCDF(10, 2.5))
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},
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"irwinHall": {
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"topic": function(random) { return random.irwinHall(10); },
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// P = 98%
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"has Irwin-Hall distribution" : KSTest(irwinHallCDF(10))
22+
"(using seedrandom)": {
23+
topic: function(random) {
24+
_random = Math.random;
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Math.seedrandom("a random seed.");
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return random;
27+
},
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"normal": {
29+
"topic": function(random) { return random.normal(-43289, 38.8); },
30+
"has normal distribution": KSTest(normalCDF(-43289, 38.8))
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},
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"logNormal": {
33+
"topic": function(random) { return random.logNormal(10, 2.5); },
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"has log-normal distribution": KSTest(logNormalCDF(10, 2.5))
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},
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"irwinHall": {
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"topic": function(random) { return random.irwinHall(10); },
38+
"has Irwin-Hall distribution": KSTest(irwinHallCDF(10))
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},
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teardown: function() {
41+
Math.random = _random;
42+
}
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}
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}
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});
@@ -59,9 +54,7 @@ suite.addBatch({
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*
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* @param cdf function(x) { returns CDF of the distribution evaluated at x }
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* @param n number of sample points. Higher n = better evaluation, slower test.
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* @return function(rng) {
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* // asserts that rng produces values fitting the distribution
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* }
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* @return a function that asserts the rng produces values fitting the distribution
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*/
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function KSTest(cdf, n) {
6760
return function(rng) {
@@ -85,15 +78,15 @@ function KSTest(cdf, n) {
8578
}
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}
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81+
// Logistic approximation to normal CDF around N(mean, stddev).
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function normalCDF(mean, stddev) {
89-
// Logistic approximation to normal CDF around N(mean, stddev).
9083
return function(x) {
9184
return 1 / (1 + Math.exp(-0.07056 * Math.pow((x-mean)/stddev, 3) - 1.5976 * (x-mean)/stddev));
9285
}
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}
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88+
// See http://en.wikipedia.org/wiki/Log-normal_distribution#Similar_distributions
9589
function logNormalCDF(mean, stddev) {
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// @see http://en.wikipedia.org/wiki/Log-normal_distribution#Similar_distributions
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var exponent = Math.PI / (stddev * Math.sqrt(3));
9891
var numerator = Math.exp(mean);
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return function(x) {
@@ -111,15 +104,9 @@ function irwinHallCDF(n) {
111104
binoms.push(binom(n, k));
112105
}
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114-
// @see CDF at http://en.wikipedia.org/wiki/Irwin%E2%80%93Hall_distribution
107+
// See CDF at http://en.wikipedia.org/wiki/Irwin–Hall_distribution
115108
return function(x) {
116109
var t = 0;
117-
118-
// What d3 calls Irwin-Hill distribution is actually a Bates distribution:
119-
// the Irwin-Hall distribution divided by n. So we multiply the Bates
120-
// distribution's x-value by n to get the Irwin-Hall CDF at x.
121-
x *= n;
122-
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for (var k = 0; k < x; k++) {
124111
t += Math.pow(-1, k % 2) * binoms[k] * Math.pow(x - k, n);
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}

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