ParticleFilter.prototype.resampleParticles = function() {
// Residual resampling following Liu 2008; p. 72, section 3.4.4
var m = this.particles.length;
var W = webpplUtil.logsumexp(_.map(this.particles, function(p) {
return p.weight;
}));
var avgW = W - Math.log(m);
if (avgW == -Infinity) { // debugging: check if NaN
if (this.strict) {
throw 'Error! All particles -Infinity';
}
} else {
// Compute list of retained particles
var retainedParticles = [];
var newExpWeights = [];
_.each(
this.particles,
function(particle) {
var w = Math.exp(particle.weight - avgW);
var nRetained = Math.floor(w);
newExpWeights.push(w - nRetained);
for (var i = 0; i < nRetained; i++) {
retainedParticles.push(copyParticle(particle));
}
});
// Compute new particles
var numNewParticles = m - retainedParticles.length;
var newParticles = [];
var j;
for (var i = 0; i < numNewParticles; i++) {
j = erp.multinomialSample(newExpWeights);
newParticles.push(copyParticle(this.particles[j]));
}
// Particles after update: Retained + new particles
this.particles = newParticles.concat(retainedParticles);
}
// Reset all weights
_.each(this.particles, function(particle) {
particle.weight = avgW;
});
};
ParticleFilter.prototype.retrainAis = function() {
var self = this;
var W = webpplUtil.logsumexp(_.map(this.particles, function(p) {
return p.weight;
}));
for (var i = 0; i < this.aisRetTypes.length; ++i) {
var retType = this.aisRetTypes[i];
var oldParams = this.aisParams[i] || expfam.defaultParameters([[], retType]);
var samps = this.aisTrainingSamples[i].map(function(s) {
var whichParticle = s[0];
var val = s[1];
var weight = self.particles.length * Math.exp(self.particles[whichParticle].weight - W);
return [weight, retType.sufStat(val)];
});
// var newParams = expfam.mle(retType, samps, oldParams);
var newParams = {
base: optNp(retType, samps, retType.defaultNatParam),
weights: _.times(expfam.featuresDim(retType), function() { return []; })
};
console.log('newParams', newParams);
this.aisParams[i] = newParams;
}
};