webppl/src/dists/logisticNormal.js

Probabilistic programming for the web

'use strict';
var ad = require('../ad');
var base = require('./base');
var types = require('../types');
var util = require('../util');
var numeric = require('../math/numeric');
var diagCovGaussian = require('./diagCovGaussian');
var TensorGaussian = require('./tensorGaussian').TensorGaussian;
var LogisticNormal = base.makeDistributionType({
    name: 'LogisticNormal',
    desc: 'A distribution over probability vectors obtained by transforming a random variable ' + 'drawn from ``DiagCovGaussian({mu: mu, sigma: sigma})``. If ``mu`` and ``sigma`` have length ``d`` ' + 'then the distribution is over probability vectors of length ``d+1``.',
    params: [
        {
            name: 'mu',
            desc: 'mean',
            type: types.unboundedVector
        },
        {
            name: 'sigma',
            desc: 'standard deviations',
            type: types.positiveVector
        }
    ],
    wikipedia: 'Logit-normal_distribution#Multivariate_generalization',
    mixins: [
        base.continuousSupport,
        base.noHMC
    ],
    constructor: function () {
        var _mu = ad.value(this.params.mu);
        var _sigma = ad.value(this.params.sigma);
        if (!util.tensorEqDim0(_mu, _sigma)) {
            throw new Error(this.meta.name + ': mu and sigma should have the same length.');
        }
    },
    sample: function () {
        return numeric.squishToProbSimplex(diagCovGaussian.sample(ad.value(this.params.mu), ad.value(this.params.sigma)));
    },
    score: function (val) {
        var mu = this.params.mu;
        var sigma = this.params.sigma;
        var _mu = ad.value(mu);
        var _val = ad.value(val);
        if (!util.isVector(_val) || _val.dims[0] - 1 !== _mu.dims[0]) {
            return -Infinity;
        }
        var d = _mu.dims[0];
        var u = ad.tensor.reshape(ad.tensor.range(val, 0, d), [
            d,
            1
        ]);
        var u_last = ad.tensor.get(val, d);
        var inv = ad.tensor.log(ad.tensor.div(u, u_last));
        var normScore = diagCovGaussian.score(mu, sigma, inv);
        return ad.scalar.sub(normScore, ad.tensor.sumreduce(ad.tensor.log(val)));
    },
    base: function () {
        var dims = ad.value(this.params.mu).dims;
        return new TensorGaussian({
            mu: 0,
            sigma: 1,
            dims: dims
        });
    },
    transform: function (x) {
        var mu = this.params.mu;
        var sigma = this.params.sigma;
        return numeric.squishToProbSimplex(ad.tensor.add(ad.tensor.mul(sigma, x), mu));
    }
});
module.exports = { LogisticNormal: LogisticNormal };