Data Visualization and Data Science consultant.
Currently at UFRJ/PPGI Master's program in Computer Science (Data Visualization and Machine Learning).
Published
Edited
Jan 30, 2019
3 forks
2 stars
function bbVar(pontos, i, entropia, erro=1e-6, passos=40) {
function bbVarRec(variancia, limiteInf, limiteSup) {
const probabilidades = calcPi(pontos, i, variancia),
entropiaAtual = calcEntropia(probabilidades);
if (passos-- <= 1 || Math.abs(entropiaAtual - entropia) <= erro)
return probabilidades;
if (entropiaAtual > entropia)
return bbVarRec((limiteInf+variancia)/2, limiteInf, variancia);
else
return bbVarRec((variancia+limiteSup)/2, variancia, limiteSup);
}
const MINVAR = 0.01,
MAXVAR = 50;
return bbVarRec((MINVAR+MAXVAR)/2, MINVAR, MAXVAR);
}
function ajustaVariancias(pontos, entropia, erro, passos) {
let tamanho = pontos.length,
p = [];
for (let i = 0; i < tamanho; i++)
p = p.concat(bbVar(pontos, i, entropia, erro, passos))
var total = p.reduce((soma, atual)=>soma+atual,0)
return p.map((x)=>x/total);
}
pontosMapa = {
if (iteracao >= T)
yield yDisplay.map((p, i)=>(p.y==undefined?{x:Y[i][0],
nome:p.nome,
Cluster:p.Cluster}:
{x:Y[i][0],
y:Y[i][1],
nome:p.nome,
Cluster:p.Cluster}));
else {
// Adaptado de: https://github.com/karpathy/tsnejs
var N = Y.length;
var dim = Y[0].length;
var grad = gradiente(Y, pFinal, iteracao);
var sign = ((p)=>p<0?-1:p>0?1:p);
mutable custoRun = custo(Y, pFinal);
mutable iteracao++;
// perform gradient step
for(var i=0;i<N;i++) {
for(var d=0;d<dim;d++) {
var gid = grad[i][d];
var sid = ystep[i][d];
var gainid = gains[i][d];
// compute gain update
var newgain = sign(gid) === sign(sid) ? gainid * 0.8 : gainid + 0.2;
if(gainid < 0.01) gainid = 0.01; // clamp
gains[i][d] = newgain; // store for next turn
// compute momentum step direction
var momval = valores_momentum[iteracao < gatilhoMomentum]; // ? 0.5 : 0.8;
var newsid = momval * sid - eta * newgain * grad[i][d];
ystep[i][d] = newsid; // remember the step we took
// step!
Y[i][d] += newsid;
}
}
yield yDisplay.map((p, i)=>(p.y==undefined?{x:Y[i][0],
nome:p.nome,
Cluster:p.Cluster}:
{x:Y[i][0],
y:Y[i][1],
nome:p.nome,
Cluster:p.Cluster}));
}
}
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