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86 lines
2.4 KiB
JavaScript
86 lines
2.4 KiB
JavaScript
// test import
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var assert = require('assert'),
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math = require('../../../lib/index.js'),
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approx = require('../../../tools/approx.js');
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math.import({
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myvalue: 42,
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hello: function (name) {
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return 'hello, ' + name + '!';
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}
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});
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assert.equal(math.myvalue * 2, 84);
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assert.equal(math.hello('user'), 'hello, user!');
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// test whether not overwriting existing functions by default
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math.import({myvalue: 10});
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approx.equal(math.myvalue, 42);
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// test whether overwritten when forced
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math.import({myvalue: 10}, {override: true});
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approx.equal(math.myvalue, 10);
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var parser = math.parser();
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parser.eval('myvalue + 10'); // 52
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parser.eval('hello("user")'); // 'hello, user!'
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// test wrapping
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var getSize = function (array) {
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return array.length;
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};
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math.import({
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getSizeWrapped: getSize
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});
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math.import({
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getSizeNotWrapped: getSize
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}, {
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wrap: false
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});
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assert.equal(math.getSizeWrapped([1,2,3]), 3);
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assert.equal(math.getSizeWrapped(math.matrix([1,2,3])), 3);
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assert.equal(math.getSizeNotWrapped([1,2,3]), 3);
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assert.equal(math.getSizeNotWrapped(math.matrix([1,2,3])), undefined);
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// extend math.js with numbers.js
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// examples copied from https://github.com/sjkaliski/numbers.js/blob/master/examples/statistic.js
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math.import('numbers');
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assert.equal(math.fibonacci(7), 13);
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// Consider a data representing total follower count of a
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// variety of users.
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var followers = math.matrix([100, 50, 1000, 39, 283, 634, 3, 6123]);
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// We can generate a report of summary statistics
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// which includes the mean, 1st and 3rd quartiles,
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// and standard deviation.
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var report = math.report(followers);
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approx.deepEqual(report, {
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mean: 1029,
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firstQuartile: 44.5,
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median: 191.5,
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thirdQuartile: 817,
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standardDev: 1953.0897316815733
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});
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// Maybe we decide to become a bit more curious about
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// trends in follower count, so we start conjecturing about
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// our ability to "predict" trends.
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// Let's consider the number of tweets those users have.
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var tweets = math.matrix([100, 10, 400, 5, 123, 24, 302, 2000]);
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// Let's calculate the correlation.
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var correlation = math.correlation(tweets, followers);
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approx.equal(correlation, 0.98054753183666);
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// Now let's create a linear regression.
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var linReg = math.linearRegression(tweets, followers);
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// linReg is actually a function we can use to map tweets
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// onto followers. We'll see that around 1422 followers
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// are expected if a user tweets 500 times.
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var estFollowers = linReg(500);
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approx.equal(estFollowers, 1422.431464053916);
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