Published
Edited
Oct 7, 2019
Importers
viewof mccTree = html`<input type=file accept="text/*">`
tree =Files.text(mccTree).then(t=>figtreeModule.Tree.parseNexus(t)[0])
population =startingPopulation(1000)
log ={
population;
return[];
}
function* nextGeneration(population,log){
const best = mostFitindividual(population)
const bf = best.fitness;
const newPopulation = [best,...compose(mutator(0.15,0.1),replicate,sampleWeightedArray(population.length-1)(population.map(i=>i.fitness)))(population)]
const logPoint = getLog(newPopulation);
yield logPoint;
log.push(logPoint)
if(ess(log.map(l=>l.meanCrossOvers))>1.5||log.length<100){
console.log(ess(log.map(l=>l.meanFitness)));
yield* nextGeneration(newPopulation,log);
}
}
mutator=(mutationRate,recombinationRate)=>(population)=>{
return population.map(i=>{
if(Math.random()<mutationRate){
i.mutate()
}
if(Math.random()<recombinationRate){
i = recombine(i,sampleWeightedArray(1)(population.map(i=>i.fitness))(population)[0])
}
return i
})
}
function ess(samples){
let auto =1;
let lag = 1;
let sum = 0;
while(auto>0.05&&lag<samples.length/2){
auto= autocorrelation(lag)(samples)
lag+=1;
sum+= auto;
}
return samples.length/(1+2*sum);
}
autocorrelation=(k)=>(samples)=>{
const samplePairs = []
for(let i=0;i<samples.length-k;i++){
samplePairs.push({x0:samples[i],x1:samples[i+k]})
}
return Math.abs(correlation(samplePairs));
}
function correlation(samplePairs){
const firstMean = d3.mean(samplePairs,d=>d.x0);
const secondMean = d3.mean(samplePairs,d=>d.x1);
const firstStd = d3.deviation(samplePairs,d=>d.x0);
const secondstd = d3.deviation(samplePairs,d=>d.x1);
const standardizedPairs = samplePairs.map(s=>({x0:(s.x0-firstMean)/firstStd,x1:(s.x1-secondMean)/secondstd}));
return standardizedPairs.map(s=>s.x0*s.x1).reduce((acc,el)=>acc+el)/(samplePairs.length-1)
}
function replicate(population){
return population.map(i=>i.replicate());
}
class Individual {
constructor(genome){
this._genome= genome;
this.transitions = transitions;
this.bottom = transitions.map(t=>t.weight*transitions.length).reduce((acc,curr)=>acc+curr,0)*10;
}
replicate(){
return new Individual(this.genome);
}
get fitness(){
const crossovers = this.crossOvers
return this.bottom - 10*this.crossOvers
}
get crossOvers(){
let crossOvers = 0;
for(const transition of this.transitions){
crossOvers+=Math.abs(this._genome.indexOf(transition.source)- this._genome.indexOf(transition.target))*transition.weight
}
return crossOvers;
}
get genome(){
return this._genome.slice();
}
mutate(){
//pick two locations at random and switch
const swaps = [Math.round(Math.random()*(this._genome.length-1)),Math.round(Math.random()*(this._genome.length-1))].sort((a,b)=>a-b);
if(swaps[0]!==swaps[1]){
this._genome = [...this._genome.slice(0,swaps[0]),this._genome[swaps[1]],...this._genome.slice(swaps[0]+1,swaps[1]),this._genome[swaps[0]],...this._genome.slice(swaps[1]+1)]
}
}
}
compose = (...fns) => input => fns.reduceRight((mem, fn) => fn(mem), input)
figtreeModule=import("https://cdn.jsdelivr.net/gh/rambaut/figtree.js@total-refactor/dist/figtree.esm.js")
d3 = require("d3")
import {errorLog} from "@mootari/tracking-and-displaying-errors"
errorLog()
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
