62 lines
2.1 KiB
JavaScript
62 lines
2.1 KiB
JavaScript
|
var vows = require('vows');
|
||
|
|
var assert = require('assert');
|
||
|
|
var suite = vows.describe('jStat.distribution');
|
||
|
|
|
||
|
|
require('../env.js');
|
||
|
|
|
||
|
|
var tol = 0.0000001;
|
||
|
|
|
||
|
|
suite.addBatch({
|
||
|
|
'poisson pdf': {
|
||
|
|
'topic': function() {
|
||
|
|
return jStat;
|
||
|
|
},
|
||
|
|
'check pdf calculation': function(jStat) {
|
||
|
|
var pdf = jStat.poisson.pdf;
|
||
|
|
|
||
|
|
assert.epsilon(tol, pdf(1, 1), Math.exp(-1));
|
||
|
|
assert.epsilon(tol, pdf(2, 1), Math.exp(-1) / 2);
|
||
|
|
|
||
|
|
assert.epsilon(tol, pdf(1, 3), 3 * Math.exp(-3));
|
||
|
|
assert.epsilon(tol, pdf(1, 1.5), 1.5 * Math.exp(- 1.5));
|
||
|
|
|
||
|
|
//Negative test cases outside of support
|
||
|
|
assert.epsilon(tol, pdf(1.4, 1), 0);
|
||
|
|
assert.epsilon(tol, pdf(1, -2), 0);
|
||
|
|
},
|
||
|
|
'check cdf calculation': function(jStat) {
|
||
|
|
var curriedCdf = function(k) { return jStat.poisson.cdf(k, 1); };
|
||
|
|
|
||
|
|
var pdfValues = [Math.exp(-1), Math.exp(-1), Math.exp(-1) / 2, Math.exp(-1) / 6, Math.exp(-1) / 24];
|
||
|
|
|
||
|
|
assert.epsilon(tol, curriedCdf(0.5), pdfValues[0]);
|
||
|
|
assert.epsilon(tol, curriedCdf(3.5), pdfValues[0] + pdfValues[1] + pdfValues[2] + pdfValues[3]);
|
||
|
|
|
||
|
|
assert.epsilon(tol, curriedCdf(-1), 0);
|
||
|
|
assert.epsilon(tol, curriedCdf(2), pdfValues[0] + pdfValues[1] + pdfValues[2]);
|
||
|
|
},
|
||
|
|
'mean': function(jStat) {
|
||
|
|
var mean = jStat.poisson.mean;
|
||
|
|
|
||
|
|
assert.epsilon(tol, mean(1), 1);
|
||
|
|
assert.epsilon(tol, mean(3.5), 3.5);
|
||
|
|
},
|
||
|
|
'check sample': function(jStat) {
|
||
|
|
var samplingTol = 10;
|
||
|
|
var lambdaToTest = [10, 100, 1000, 10000, 100000];
|
||
|
|
var sampleSize = 10000;
|
||
|
|
for(var i in lambdaToTest) {
|
||
|
|
var lambda = lambdaToTest[i];
|
||
|
|
var sum = 0;
|
||
|
|
for(let i = 0; i < sampleSize; i++) {
|
||
|
|
sum += jStat.poisson.sample(lambda);
|
||
|
|
}
|
||
|
|
var mean = sum/sampleSize;
|
||
|
|
assert.epsilon(samplingTol, mean, lambda);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
});
|
||
|
|
|
||
|
|
suite.export(module);
|