unless she does
distance2d = function(p1, p2){
let dx = p2[0] - p1[0];
let dy = p2[1] - p1[1];
return Math.sqrt(dx * dx + dy * dy);
}
distance2d(animal('capybara'), animal('panda bear')) // panda and capybara
distance2d(animal('tarantula'), animal('elephant')) // tarantula and elephant
colors['olive']
colors['red']
colors['black']
distance([10, 1], [5, 2]);
subtractv([10, 1], [5, 2])
addv([10, 1], [5, 2])
meanv([[0, 1], [2, 2], [4, 3]])
distance(colors['red'], colors['green']) > distance(colors['red'], colors['pink'])
closest(colors, colors.red)
closest(colors, [150, 60, 150])
closest(colors, subtractv(colors['purple'], colors['red']))
closest(colors, addv(colors['blue'], colors['green']))
// the average of black and white: medium grey
closest(colors, meanv([colors['black'], colors['white']]))
{
// an analogy: pink is to red as X is to blue
let pinkToRed = subtractv(colors['pink'], colors['red'])
return closest(colors, addv(pinkToRed, colors['blue']))
}
{
// another example:
let navyToBlue = subtractv(colors['navy'], colors['blue'])
return closest(colors, addv(navyToBlue, colors['green']))
}
avgColor = {
let doc = await gutenFetch('http://www.gutenberg.org/cache/epub/345/pg345.txt');
let words = RiTa.tokenize(doc), dracColors = [];
words.forEach((w,i) => colors[w.toLowerCase()]
&& dracColors.push(colors[w.toLowerCase()]));
return meanv(dracColors);
}
closest(colors, avgColor)
closest(colors, colors['mauve']).map(cname => cname + " trousers");
tokens = RiTa.tokens(await gutenFetch('http://www.gutenberg.org/cache/epub/345/pg345.txt'));
gloVe.getVector("cheese")
cosine = function(v1, v2) {
let vec1 = tf.tensor1d(v1);
let vec2 = tf.tensor1d(v2);
let dot = vec1.dot(vec2).asScalar();
let sim = dot.div(vec1.norm(2)).div(vec2.norm(2));
return 1 - sim.dataSync()[0];
}
cosine(vec('dog'), vec('puppy')) < cosine(vec('trousers'), vec('octopus'))
gloVeClosest = async function(tokenList, vector, n=10) {
let tensor = tf.tensor1d(vector);
let neighbors = await gloVe._getNearestNeighbors(tensor, n*100, true);
return neighbors.filter(t => tokenList.includes(t.word)).slice(0, n).map(n => n.word);
}
gloVeClosest(tokens, vec("basketball"))
gloVeClosest(tokens, meanv([vec("day"), vec("night")]))
gloVeClosest(tokens, vec("wine"))
gloVeClosest(tokens, subtractv(vec("wine"), vec("alcohol"))) // hmmm, something off here
gloVeClosest(tokens, vec("water"))
gloVeClosest(tokens, addv(vec("water"),vec("frozen")))
gloVeClosest(tokens, vec("grass"))
{
// analogy: blue is to sky as X is to grass
let blueToSky = subtractv(vec("blue"), vec("sky"))
return gloVeClosest(tokens, addv(blueToSky, vec("grass")))
}
sentVec = function(s) {
let words = RiTa.tokenize(s);
return meanv(words.map(w => gloVe.getVector(w)));
}
sentences = RiTa.sentences(await gutenFetch('http://www.gutenberg.org/cache/epub/345/pg345.txt'));
gloVeClosestSent = function(space, inputStr, n=10) {
let inputVec = sentVec(inputStr);
let dists = {};
console.time("cos");
space = space.slice(0,500);
space.forEach(x => dists[x] = cosine(sentVec(x), inputVec));
console.timeEnd("cos");
return Object.keys(dists).sort((a,b) => dists[a] - dists[b]).slice(0,n);
}
animal = function(name) { return data.filter(d => d.name === name).map(o => [o.cuteness, o.size])[0] }
RiTa = require('rita@2.4.86');
height = 400
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.
