webppl/src/dists/index.js

Probabilistic programming for the web

'use strict';
var ad = require('../ad');
var _ = require('lodash');
var util = require('../util');
var base = require('./base');
var distributions = _.chain([
    [
        'Bernoulli',
        require('./bernoulli')
    ],
    [
        'Beta',
        require('./beta')
    ],
    [
        'Binomial',
        require('./binomial')
    ],
    [
        'Categorical',
        require('./categorical')
    ],
    [
        'Cauchy',
        require('./cauchy')
    ],
    [
        'Delta',
        require('./delta')
    ],
    [
        'DiagCovGaussian',
        require('./diagCovGaussian')
    ],
    [
        'Dirichlet',
        require('./dirichlet')
    ],
    [
        'Discrete',
        require('./discrete')
    ],
    [
        'Exponential',
        require('./exponential')
    ],
    [
        'Gamma',
        require('./gamma')
    ],
    [
        'Gaussian',
        require('./gaussian')
    ],
    [
        'ImproperUniform',
        require('./improperUniform')
    ],
    [
        'IspNormal',
        require('./ispNormal')
    ],
    [
        'KDE',
        require('./kde')
    ],
    [
        'Laplace',
        require('./laplace')
    ],
    [
        'LogisticNormal',
        require('./logisticNormal')
    ],
    [
        'LogitNormal',
        require('./logitNormal')
    ],
    [
        'LogNormal',
        require('./logNormal')
    ],
    [
        'Marginal',
        require('./marginal')
    ],
    [
        'Mixture',
        require('./mixture')
    ],
    [
        'Multinomial',
        require('./multinomial')
    ],
    [
        'MultivariateBernoulli',
        require('./multivariateBernoulli')
    ],
    [
        'MultivariateGaussian',
        require('./multivariateGaussian')
    ],
    [
        'Poisson',
        require('./poisson')
    ],
    [
        'RandomInteger',
        require('./randomInteger')
    ],
    [
        'SampleBasedMarginal',
        require('./sampleBasedMarginal')
    ],
    [
        'TensorGaussian',
        require('./tensorGaussian')
    ],
    [
        'TensorLaplace',
        require('./tensorLaplace')
    ],
    [
        'Uniform',
        require('./uniform')
    ]
]).fromPairs().mapValues(function (module, name) {
    return module[name];
}).value();
function metadata() {
    return _.chain(distributions).toPairs().sortBy(function (pair) {
        return pair[0];
    }).map(function (pair) {
        return pair[1];
    }).map(function (dist) {
        return dist.prototype.meta;
    }).value();
}
var wpplFns = _.chain(distributions).mapValues(function (ctor) {
    return function (s, k, a, params) {
        if (arguments.length > 4) {
            throw new Error('Too many arguments. Distributions take at most one argument.');
        }
        return k(s, new ctor(params));
    };
}).mapKeys(function (ctor, name) {
    return 'make' + name;
}).value();
var serialize = function (dist) {
    return util.serialize(dist);
};
var deserialize = function (JSONString) {
    var obj = util.deserialize(JSONString);
    if (!obj.probs || !obj.support) {
        throw new Error('Cannot deserialize a non-distribution JSON object: ' + JSONString);
    }
    return new distributions.Categorical({
        ps: obj.probs,
        vs: obj.support
    });
};
module.exports = _.assign({
    metadata: metadata,
    isDist: base.isDist,
    clone: base.clone,
    serialize: serialize,
    deserialize: deserialize
}, distributions, wpplFns);