Commit aee12142 authored by Mike Bostock's avatar Mike Bostock

Restore Math.random on teardown.

parent e44ae3c0
......@@ -54,7 +54,7 @@
"smash": "~0.0.12",
"uglify-js": "2.4.0",
"vows": "0.7.0",
"seedrandom": "0.1.6"
"seedrandom": "2.3.1"
},
"scripts": {
"test": "node_modules/.bin/vows"
......
var vows = require("vows"),
load = require("../load"),
assert = require("../assert"),
seedrandom = require('seedrandom');
seedrandom = require("seedrandom");
var suite = vows.describe("d3.random");
/**
* Testing a random number generator is a bit more complicated than testing
* deterministic code, so we use different techniques.
*
* If the RNG is correct, each test in this suite will pass with probability
* at least P. The tests have been designed so that P ≥ 98%. Specific values
* of P are given above each case. We use the seedrandom module to get around
* this non-deterministic aspect -- so it is safe to assume that if the tests
* fail, then d3's RNG is broken.
*
* More on RNG testing here:
* @see http://www.johndcook.com/Beautiful_Testing_ch10.pdf
*/
// Overwrites Math.random to a seeded random function.
// (by default Math.random is seeded with current time)
Math.seedrandom('a random seed.');
// Testing a random number generator is a bit more complicated than testing
// deterministic code, so we use different techniques.
//
// If the RNG is correct, each test in this suite will pass with probability
// at least P. The tests have been designed so that P ≥ 98%. Specific values
// of P are given above each case. We use the seedrandom module to get around
// this non-deterministic aspect -- so it is safe to assume that if the tests
// fail, then d3's RNG is broken.
//
// See also: http://www.johndcook.com/Beautiful_Testing_ch10.pdf
suite.addBatch({
"random": {
topic: load("math/random").expression("d3.random"),
"normal": {
"topic": function(random) { return random.normal(-43289, 38.8); },
// P = 98%
"has normal distribution" : KSTest(normalCDF(-43289, 38.8))
},
"logNormal": {
// Use reasonable values for mean here because random.logNormal() grows
// exponentially with the mean.
"topic": function(random) { return random.logNormal(10, 2.5); },
// P = 98%
"has log-normal distribution" : KSTest(logNormalCDF(10, 2.5))
},
"irwinHall": {
"topic": function(random) { return random.irwinHall(10); },
// P = 98%
"has Irwin-Hall distribution" : KSTest(irwinHallCDF(10))
"(using seedrandom)": {
topic: function(random) {
_random = Math.random;
Math.seedrandom("a random seed.");
return random;
},
"normal": {
"topic": function(random) { return random.normal(-43289, 38.8); },
"has normal distribution": KSTest(normalCDF(-43289, 38.8))
},
"logNormal": {
"topic": function(random) { return random.logNormal(10, 2.5); },
"has log-normal distribution": KSTest(logNormalCDF(10, 2.5))
},
"irwinHall": {
"topic": function(random) { return random.irwinHall(10); },
"has Irwin-Hall distribution": KSTest(irwinHallCDF(10))
},
teardown: function() {
Math.random = _random;
}
}
}
});
......@@ -59,9 +54,7 @@ suite.addBatch({
*
* @param cdf function(x) { returns CDF of the distribution evaluated at x }
* @param n number of sample points. Higher n = better evaluation, slower test.
* @return function(rng) {
* // asserts that rng produces values fitting the distribution
* }
* @return a function that asserts the rng produces values fitting the distribution
*/
function KSTest(cdf, n) {
return function(rng) {
......@@ -85,15 +78,15 @@ function KSTest(cdf, n) {
}
}
// Logistic approximation to normal CDF around N(mean, stddev).
function normalCDF(mean, stddev) {
// Logistic approximation to normal CDF around N(mean, stddev).
return function(x) {
return 1 / (1 + Math.exp(-0.07056 * Math.pow((x-mean)/stddev, 3) - 1.5976 * (x-mean)/stddev));
}
}
// See http://en.wikipedia.org/wiki/Log-normal_distribution#Similar_distributions
function logNormalCDF(mean, stddev) {
// @see http://en.wikipedia.org/wiki/Log-normal_distribution#Similar_distributions
var exponent = Math.PI / (stddev * Math.sqrt(3));
var numerator = Math.exp(mean);
return function(x) {
......@@ -111,15 +104,9 @@ function irwinHallCDF(n) {
binoms.push(binom(n, k));
}
// @see CDF at http://en.wikipedia.org/wiki/Irwin%E2%80%93Hall_distribution
// See CDF at http://en.wikipedia.org/wiki/Irwin–Hall_distribution
return function(x) {
var t = 0;
// What d3 calls Irwin-Hill distribution is actually a Bates distribution:
// the Irwin-Hall distribution divided by n. So we multiply the Bates
// distribution's x-value by n to get the Irwin-Hall CDF at x.
x *= n;
for (var k = 0; k < x; k++) {
t += Math.pow(-1, k % 2) * binoms[k] * Math.pow(x - k, n);
}
......
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