2b:null 2c:["$","title",null,{"children":"t-SNE | Yin Shanyang | Observable"}] 31:T552,chart = { const data = nodes.map((node, index) => ({ ...node, position: positions[index] })) const minX = positions.map((d) => d[0]).reduce((a, b) => Math.min(a, b), Infinity) const maxX = positions.map((d) => d[0]).reduce((a, b) => Math.max(a, b), -Infinity) const minY = positions.map((d) => d[1]).reduce((a, b) => Math.min(a, b), Infinity) const maxY = positions.map((d) => d[1]).reduce((a, b) => Math.max(a, b), -Infinity) const x = d3.scaleLinear().domain([minX, maxX]).range([margin, width - margin]) const y = d3.scaleLinear().domain([minY, maxY]).range([margin, height - margin]) const svg = d3.select(DOM.svg(width, height)) .style('width', '100%') .style('height', 'auto') // const node = svg.append('g') // .selectAll('circle').data(data) // .enter().append('circle') // .attr('r', 5) // .attr('cx', (d) => x(d.position[0])) // .attr('cy', (d) => y(d.position[1])) const node = svg.append('g') .attr('transform', 'translate(-16, -16)') .selectAll('image').data(data) .enter().append('svg:image') .attr('xlink:href', (d) => d.image) .attr('width', 32) .attr('height', 32) .attr('x', (d) => x(d.position[0])) .attr('y', (d) => y(d.position[1])) .style('opacity', (d) => isSelected(d) ? 1 : 0.1) return svg.node() }32:T2d0c,tSNE = { // create main global object var tsnejs = tsnejs || { REVISION: 'ALPHA' }; (function(global) { "use strict"; // utility function var assert = function(condition, message) { if (!condition) { throw message || "Assertion failed"; } } // syntax sugar var getopt = function(opt, field, defaultval) { if(opt.hasOwnProperty(field)) { return opt[field]; } else { return defaultval; } } // return 0 mean unit standard deviation random number var return_v = false; var v_val = 0.0; var gaussRandom = function() { if(return_v) { return_v = false; return v_val; } var u = 2*Math.random()-1; var v = 2*Math.random()-1; var r = u*u + v*v; if(r == 0 || r > 1) return gaussRandom(); var c = Math.sqrt(-2*Math.log(r)/r); v_val = v*c; // cache this for next function call for efficiency return_v = true; return u*c; } // return random normal number var randn = function(mu, std){ return mu+gaussRandom()*std; } // utilitity that creates contiguous vector of zeros of size n var zeros = function(n) { if(typeof(n)==='undefined' || isNaN(n)) { return []; } if(typeof ArrayBuffer === 'undefined') { // lacking browser support var arr = new Array(n); for(var i=0;i 1e-7) Hhere -= pj * Math.log(pj); } // adjust beta based on result if(Hhere > Htarget) { // entropy was too high (distribution too diffuse) // so we need to increase the precision for more peaky distribution betamin = beta; // move up the bounds if(betamax === Infinity) { beta = beta * 2; } else { beta = (beta + betamax) / 2; } } else { // converse case. make distrubtion less peaky betamax = beta; if(betamin === -Infinity) { beta = beta / 2; } else { beta = (beta + betamin) / 2; } } // stopping conditions: too many tries or got a good precision num++; if(Math.abs(Hhere - Htarget) < tol) { done = true; } if(num >= maxtries) { done = true; } } // console.log('data point ' + i + ' gets precision ' + beta + ' after ' + num + ' binary search steps.'); // copy over the final prow to P at row i for(var j=0;j 0 ? 1 : x < 0 ? -1 : 0; } var tSNE = function(opt) { var opt = opt || {}; this.perplexity = getopt(opt, "perplexity", 30); // effective number of nearest neighbors this.dim = getopt(opt, "dim", 2); // by default 2-D tSNE this.epsilon = getopt(opt, "epsilon", 10); // learning rate this.iter = 0; } tSNE.prototype = { // this function takes a set of high-dimensional points // and creates matrix P from them using gaussian kernel initDataRaw: function(X) { var N = X.length; var D = X[0].length; assert(N > 0, " X is empty? You must have some data!"); assert(D > 0, " X[0] is empty? Where is the data?"); var dists = xtod(X); // convert X to distances using gaussian kernel this.P = d2p(dists, this.perplexity, 1e-4); // attach to object this.N = N; // back up the size of the dataset this.initSolution(); // refresh this }, // this function takes a given distance matrix and creates // matrix P from them. // D is assumed to be provided as a list of lists, and should be symmetric initDataDist: function(D) { var N = D.length; assert(N > 0, " X is empty? You must have some data!"); // convert D to a (fast) typed array version var dists = zeros(N * N); // allocate contiguous array for(var i=0;i ({ ...memo, [d.cluster]: true }), {})\n ).sort((a, b) => a > b ? 1 : a < b ? -1 : 0)\n]) ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":233,"value":"viewof anchor = DOM.select([\n 'all',\n ...Object.keys(\n nodes.reduce((memo, d) => ({ ...memo, [d.anchor]: true }), {})\n ).sort((a, b) => a > b ? 1 : a < b ? -1 : 0)\n]) ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":225,"value":"viewof city = DOM.select([\n 'all',\n ...Object.keys(\n nodes.reduce((memo, d) => ({ ...memo, [d.city]: true }), {})\n ).sort((a, b) => a > b ? 1 : a < b ? -1 : 0)\n]) ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":55,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":242,"value":"md`\n#### utils\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":245,"value":"isSelected = (node) => {\n let selected = true\n\n if (cluster !== 'all' && node.cluster !== cluster) return false\n if (anchor !== 'all' && node.anchor !== anchor) return false\n if (city !== 'all' && node.city !== city) return false\n \n return selected\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":7,"value":"md`\n#### data\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":11,"value":"metadata = d3.csv('https://gist.githubusercontent.com/yinshanyang/849049a60884cbe789e27d2455b78621/raw/10cea5dc79eccff5499671e0c38b663747b31707/metadata.csv')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":14,"value":"vectors = d3.csv('https://gist.githubusercontent.com/yinshanyang/849049a60884cbe789e27d2455b78621/raw/10cea5dc79eccff5499671e0c38b663747b31707/vectors.csv', (row) => Object.keys(row).map((index) => +row[index]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":43,"value":"nodes = metadata.map((d, index) => ({ ...d, vectors: vectors[index] }))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":239,"value":"positions = {\n const tsne = new tSNE.tSNE({\n dim: 2,\n epsilon: 10,\n perplexity: perplexity \n })\n \n tsne.initDataRaw(vectors)\n \n for (let i = iterations; i--;) {\n tsne.step()\n }\n \n return tsne.getSolution()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":9,"value":"md`\n#### imports & variables\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":90,"value":"width = 960","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":92,"value":"height = 960","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":182,"value":"margin = 32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4,"value":"d3 = require(\"https://d3js.org/d3.v5.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":99,"value":"tsne = require('https://scienceai.github.io/tsne-js/tsne.min.js')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":164,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W33","stdlib":"1","titling":"auto"}} 30:"$2f:body"