webppl/src/dists/marginal.js

Probabilistic programming for the web

'use strict';
var ad = require('../ad');
var _ = require('lodash');
var assert = require('assert');
var util = require('../util');
var base = require('./base');
var Marginal = base.makeDistributionType({
    name: 'Marginal',
    nodoc: true,
    nohelper: true,
    params: [{ name: 'dist' }],
    mixins: [base.finiteSupport],
    constructor: function () {
        var norm = _.reduce(this.params.dist, function (acc, obj) {
            return ad.scalar.add(acc, obj.prob);
        }, 0);
        assert.ok(ad.scalar.lt(ad.scalar.abs(ad.scalar.sub(1, norm)), 1e-8), 'Expected marginal distribution to be normalized.');
        this.supp = _.map(this.params.dist, function (obj) {
            return obj.val;
        });
        this.getDist = function () {
            return this.params.dist;
        };
    },
    sample: function () {
        var x = util.random();
        var dist = this.params.dist;
        var probAccum = 0;
        for (var i in dist) {
            if (dist.hasOwnProperty(i)) {
                probAccum = ad.scalar.add(probAccum, dist[i].prob);
                if (ad.scalar.lt(x, probAccum)) {
                    return dist[i].val;
                }
            }
        }
        return this.params.dist[i].val;
    },
    score: function (val) {
        var obj = this.params.dist[util.serialize(val)];
        return obj ? ad.scalar.log(obj.prob) : ad.scalar.neg(Infinity);
    },
    support: function () {
        return this.supp;
    },
    print: function () {
        return print(this.params.dist);
    }
});
function print(dist) {
    return 'Marginal:\n' + _.map(dist, function (obj, val) {
        return [
            val,
            obj.prob
        ];
    }).sort(function (a, b) {
        return b[1] - a[1];
    }).map(function (pair) {
        return '    ' + pair[0] + ' : ' + pair[1];
    }).join('\n');
}
module.exports = {
    Marginal: Marginal,
    print: print
};