Published
Edited
Aug 26, 2021
t = 216
get(dataEmbed, t + tp).deaths_per
AB2 = {
const meanDistance = d3.mean(neighbors, d => d[1])
let A = []
let b = []
let ws = []
for (const nearest of neighbors.map(d => d[0])) {
const dist = math.distance(nearest.point, xt.point)
const w = Math.exp(-theta * dist / meanDistance)
//const sc = math.norm(math.subtract(nearest.point, xt.point))
const sc = 1
// if (arrIntersect(xt.ts, nearest.ts).length > 0) {
// continue
// }
b.push(w * sc * get(dataEmbed, nearest.t + tp)["val0"])
A.push(math.multiply([1].concat([...nearest.point].reverse()), w * sc)) // TODO: Note the point flipping!
ws.push(w)
}
return [A, b, ws]
}
w = {
const w = Array.from({length: nn}, () => 0)
const meanDistance = d3.mean(neighbors, d => d[1])
for (let i = 0; i < nn; i++) {
const dist = math.distance(neighbors[i][0].point, xt.point)
w[i] = Math.exp(-theta * dist / meanDistance)
}
return w
}
AB = {
let A = []
let b = []
for (const nearest of neighbors.map(d => d[0])) {
const p = nearest.point
A.push([1].concat(p))
b.push(get(dataEmbed, nearest.t + tp)["val0"])
}
return [math.multiply(math.diag(w), A), math.multiply(math.diag(w), b)]
}
ABx = {
const meanDistance = d3.mean(neighbors, d => d[1])
let A = []
let b = []
for (const nearest of neighbors.map(d => d[0])) {
const dist = math.distance(nearest.point, xt.point)
const w = Math.exp(-theta * dist / meanDistance)
const p = nearest.point
A.push(math.multiply(w, [1].concat(p)))
b.push(w * get(dataEmbed, nearest.t + tp)["val0"])
}
//return [math.multiply(math.diag(w), A), math.multiply(math.diag(w), b)]
return [A, b]
}
// TODO: Add zField
function autoSmap(dataEmbed, t, vField, args = {}) {
args = {
zField: null,
tp: 1,
E: null,
theta: null,
nn: null,
...args
}
const {zField, tp, E, theta, nn} = args
const trainPoints = dataEmbed.filter(d => d.t < t - (tp - 1))
const nowRow = get(dataEmbed, t)
const dims = Array.from({length: E}, (_,i) => `${vField}_t-${i}`)
dims.push("point")
const tree = new kdTree.kdTree([...trainPoints], (a, b) => math.distance(a["point"], b["point"]), dims)
const neighbors = tree.nearest(nowRow, nn)
const meanDistance = d3.mean(neighbors, d => d[1])
let A = []
let b = []
for (const nearest of neighbors.map(d => d[0])) {
const dist = math.distance(nearest.point, nowRow.point)
const w = Math.exp(-theta * dist / meanDistance)
const p = nearest.point
A.push(math.multiply(w, [1].concat(p)))
b.push(w * get(dataEmbed, nearest.t + tp)[vField])
}
const C = leastSquares(A, b)
const yh = math.multiply([[1].concat(get(dataEmbed, t).point)], math.transpose([C]))[0][0]
return {
baseT: t,
baseVal: nowRow[vField],
t: t + tp,
pred: yh,
neighbors: neighbors,
params: {E: E, theta: theta, nn: nn}
// TODO: Add z
}
}
function crudeAll(dataEmbed, f, vField, ts, args = {}) {
const preds = []
for (const t of ts) {
const pred = f(dataEmbed, t, vField, args)
preds.push(pred)
}
return preds
}
crudeAll(dataEmbed2, autoSmap, vField, Array.from({length: 10}, (_,i) => i+100), {E: E, tp: tp, theta: theta, nn: nn})
autoSmap(dataEmbed2, t, "deaths_per", {E: E, tp: tp, theta: theta, nn: nn})
{
const A = [[1, 4, 1],
[1, 1, 1],
[1,-1, 0],
[1, 0, 0],
[1, 5, 1],
[1, 5, 1],
[1, 6, 0],
[1, 4, 0]]
const b = [52,45,58,50,61,50,62,49]
return leastSquares(A, b)
}
function delayEmbed(data, fields, dim, args={}) {
args = {
tau: 1,
zField:
null,
period: null,
prefix: "",
...args
}
const {tau, zField, period, prefix} = args
const embedData = []
const dataMap = d3.group(data, d => d[zField])
for (const series of dataMap.values()) {
for (let i = (dim-1)*tau; i < series.length; i++) {
const row = {...series[i]}
for (const field of fields) {
const point = []
const ts = []
for (let j = (dim-1); j >= 0; j--) {
const p = series[i-j*tau]
point.push(p[field])
ts.push(p.t)
//row[field + j] = p[field]
row["val" + j] = p[field]
}
row["point"] = point
row["ts"] = ts
//row[field + "Point"] = point
//row[field + "Ts"] = ts
}
embedData.push(row)
}
}
return embedData
}
function delayEmbed2(data, fields, dim, args={}) {
args = {
tau: 1,
zField: null,
period: null,
prefix: "",
...args
}
const {tau, zField, period, prefix} = args
const embedData = []
const dataMap = d3.group(data, d => d[zField])
for (const series of dataMap.values()) {
for (let i = (dim-1)*tau; i < series.length; i++) {
const row = {...series[i]}
for (const field of fields) {
const point = []
const ts = []
for (let j = 0; j < dim; j++) {
const p = series[i-j*tau]
point.push(p[field])
ts.push(p.t)
row[`${field}_t-${j}`] = p[field]
//row["val" + j] = p[field]
}
row["point"] = point
row["ts"] = ts
//row[field + "Point"] = point
//row[field + "Ts"] = ts
}
embedData.push(row)
}
}
return embedData
}
dataEmbed2 = delayEmbed2(dataUs, [vField], E, {tau: tau, zField: zField})
//function sMap(
function leastSquares(A, b) {
const svd = SVD(A)
const U = svd.u
const S = math.diag(svd.q)
const V = svd.v
const Sinv = math.inv(S)
const x = math.multiply(V, math.multiply(math.inv(S), math.multiply(math.transpose(U), b)))
return x
}
//regression = require('regression@2.0.1/dist/regression.js').catch(() => window["regression"])
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.
