brian/d3-spring-model
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แก้ coding style ภาค 2

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This commit is contained in:
Pitchaya Boonsarngsuk
2018-03-22 16:12:25 +00:00
parent 2256af7448
commit 0cdd927444
12 changed files with 160 additions and 130 deletions
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51
src/barnesHut.js
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@@ -1,7 +1,7 @@
import constant from "./constant";
import jiggle from "./jiggle";
import {x, y} from "./xy";
import {quadtree} from "d3-quadtree";
import constant from './constant';
import jiggle from './jiggle';
import {x, y} from './xy';
import {quadtree} from 'd3-quadtree';
/**
* The refinement of the existing Barnes-Hut implementation in D3
@@ -12,7 +12,7 @@ import {quadtree} from "d3-quadtree";
* The check to see if the nodes are far away was also changed to the one described in original Barnes-Hut paper.
* @return {force} calculated forces.
*/
export default function() {
export default function () {
var nodes,
node,
alpha,
@@ -25,7 +25,7 @@ export default function() {
* @param {number} _ - controls the stopping of the
* particle simulations.
*/
function force(_) {
function force (_) {
var i, n = nodes.length, tree = quadtree(nodes, x, y).visitAfter(accumulate);
for (alpha = _, i = 0; i < n; ++i) {
node = nodes[i], tree.visit(apply);
@@ -38,7 +38,7 @@ export default function() {
* nodes accumulate forces from coincident quadrants.
* @param {quadrant} quad - node representing the quadrant in quadtree.
*/
function accumulate(quad) {
function accumulate (quad) {
var q, d, children = [];
// For internal nodes, accumulate forces from child quadrants.
@@ -69,8 +69,7 @@ export default function() {
* @param {number} x2 - upper x bound of the node.
* @return {boolean} - true if the approximation was applied.
*/
function apply(quad, x1, _, x2) {
function apply (quad, x1, _, x2) {
var x = quad.data.x + quad.data.vx - node.x - node.vx,
y = quad.data.y + quad.data.vy - node.y - node.vy,
w = x2 - x1,
@@ -98,13 +97,15 @@ export default function() {
if (y === 0) y = jiggle(), l += y * y;
}
do if (quad.data !== node) {
l = (l - +distance(node, quad.data)) / l * alpha;
x *= l, y *= l;
quad.data.vx -= x;
quad.data.vy -= y;
node.vx += x;
node.vy += y;
do {
if (quad.data !== node) {
l = (l - +distance(node, quad.data)) / l * alpha;
x *= l, y *= l;
quad.data.vx -= x;
quad.data.vy -= y;
node.vx += x;
node.vy += y;
}
} while (quad = quad.next);
}
@@ -114,37 +115,37 @@ export default function() {
* the better layout.
* @return {number} - stress of the layout.
*/
function getStress() {
function getStress () {
var totalDiffSq = 0, totalHighDistSq = 0;
for (var i = 0, source, target, realDist, highDist; i < nodes.length; i++) {
for (var j = 0; j < nodes.length; j++) {
if (i !== j) {
source = nodes[i], target = nodes[j];
realDist = Math.hypot(target.x-source.x, target.y-source.y);
realDist = Math.hypot(target.x - source.x, target.y - source.y);
highDist = +distance(nodes[i], nodes[j]);
totalDiffSq += Math.pow(realDist-highDist, 2);
totalDiffSq += Math.pow(realDist - highDist, 2);
totalHighDistSq += highDist * highDist;
}
}
}
return Math.sqrt(totalDiffSq/totalHighDistSq);
return Math.sqrt(totalDiffSq / totalHighDistSq);
}
// API for initializing the algorithm, setting parameters and querying
// metrics.
force.initialize = function(_) {
force.initialize = function (_) {
nodes = _;
};
force.distance = function(_) {
return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), force) : distance;
force.distance = function (_) {
return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), force) : distance;
};
force.theta = function(_) {
force.theta = function (_) {
return arguments.length ? (theta = _, force) : theta;
};
force.stress = function() {
force.stress = function () {
return getStress();
};

4
src/constant.js
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@@ -1,8 +1,8 @@
/**
* @return a constant defined by x.
*/
export default function(x) {
return function() {
export default function (x) {
return function () {
return x;
};
}

16
src/interpolation/helpers.js
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@@ -8,7 +8,7 @@
sample is the list of selected objects while
remainder is the list of those unselected.
*/
export function takeSampleFrom(sourceList, amount) {
export function takeSampleFrom (sourceList, amount) {
let randElements = [],
max = sourceList.length,
swap = false;
@@ -18,7 +18,7 @@ export function takeSampleFrom(sourceList, amount) {
}
// If picking more than half of the entire set, random to pick the remainder instead
if (amount > Math.ceil(max/2)){
if (amount > Math.ceil(max / 2)) {
amount = max - amount;
swap = true;
}
@@ -35,7 +35,7 @@ export function takeSampleFrom(sourceList, amount) {
return !randElements.includes(obj);
});
if(swap) {
if (swap) {
return {
sample: remainder,
remainder: randElements
@@ -57,14 +57,14 @@ export function takeSampleFrom(sourceList, amount) {
* @param {number} r
* @return {object} - coordinate {x: number, y: number} of the point
*/
export function pointOnCircle(h, k, angle, r) {
export function pointOnCircle (h, k, angle, r) {
return {
x: h + r*Math.cos(toRadians(angle)),
y: k + r*Math.sin(toRadians(angle))
x: h + r * Math.cos(toRadians(angle)),
y: k + r * Math.sin(toRadians(angle))
};
}
function toRadians(degrees) {
function toRadians (degrees) {
return degrees * (Math.PI / 180);
}
@@ -79,7 +79,7 @@ function toRadians(degrees) {
that of samples.
* @return {number} - Sum of distances differences
*/
export function sumDistError(node, samples, realDistances) {
export function sumDistError (node, samples, realDistances) {
let total = 0.0;
for (let i = 0; i < samples.length; i++) {
let sample = samples[i];

2
src/jiggle.js
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@@ -1,7 +1,7 @@
/**
* @return {number} a very small non-zero random number.
*/
export default function() {
export default function () {
let rand;
do {
rand = (Math.random() - 0.5) * 1e-6;

62
src/link.js
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@@ -1,5 +1,5 @@
import constant from "./constant";
import jiggle from "./jiggle";
import constant from './constant';
import jiggle from './jiggle';
/**
* Modified link force algorithm
@@ -8,7 +8,7 @@ import jiggle from "./jiggle";
* - removed other unused functions
* Alpha should be constant 1 for accurate simulation
*/
export default function() {
export default function () {
var dataSizeFactor,
distance = constant(30),
distances = [],
@@ -18,60 +18,62 @@ export default function() {
latestVelocityDiff = 0,
iterations = 1;
function force(alpha) {
function force (alpha) {
let n = nodes.length;
// Cache old velocity for comparison later
if (stableVeloHandler!==null && stableVelocity>=0) {
for (let i = n-1, node; i>=0; i--) {
if (stableVeloHandler !== null && stableVelocity >= 0) {
for (let i = n - 1, node; i >= 0; i--) {
node = nodes[i];
node.oldvx = node.vx;
node.oldvy = node.vy;
}
}
// Each iteration in a tick
for (var k = 0, source, target, i, j, x, y, l; k < iterations; ++k) {
// For each link
for (i = 1; i < n; i++) for (j = 0; j < i; j++) {
for (i = 1; i < n; i++) {
for (j = 0; j < i; j++) {
// jiggle so l won't be zero and divide by zero error after this
source = nodes[i];
target = nodes[j];
x = target.x + target.vx - source.x - source.vx || jiggle();
y = target.y + target.vy - source.y - source.vy || jiggle();
l = Math.sqrt(x * x + y * y);
l = (l - distances[i*(i-1)/2+j]) / l * dataSizeFactor * alpha;
x *= l, y *= l;
target.vx -= x;
target.vy -= y;
source.vx += x;
source.vy += y;
source = nodes[i];
target = nodes[j];
x = target.x + target.vx - source.x - source.vx || jiggle();
y = target.y + target.vy - source.y - source.vy || jiggle();
l = Math.sqrt(x * x + y * y);
l = (l - distances[i * (i - 1) / 2 + j]) / l * dataSizeFactor * alpha;
x *= l, y *= l;
target.vx -= x;
target.vy -= y;
source.vx += x;
source.vy += y;
}
}
}
// Calculate velocity changes, aka force applied.
if (stableVeloHandler!==null && stableVelocity>=0) {
if (stableVeloHandler !== null && stableVelocity >= 0) {
let velocityDiff = 0;
for (let i = n-1, node; i>=0; i--) {
for (let i = n - 1, node; i >= 0; i--) {
node = nodes[i];
velocityDiff += Math.abs(Math.hypot(node.vx-node.oldvx, node.vy-node.oldvy));
velocityDiff += Math.abs(Math.hypot(node.vx - node.oldvx, node.vy - node.oldvy));
}
velocityDiff /= n;
latestVelocityDiff = velocityDiff;
if(velocityDiff<stableVelocity){
if (velocityDiff < stableVelocity) {
stableVeloHandler();
}
}
}
function initialize() {
function initialize () {
if (!nodes) return;
// 0.5 to divide the force to two part for source and target node
dataSizeFactor = 0.5/(nodes.length-1);
dataSizeFactor = 0.5 / (nodes.length - 1);
initializeDistance();
}
function initializeDistance() {
function initializeDistance () {
if (!nodes) return;
for (let i = 1, n = nodes.length; i < n; i++) {
for (let j = 0; j < i; j++) {
@@ -80,17 +82,17 @@ export default function() {
}
}
force.initialize = function(_) {
force.initialize = function (_) {
nodes = _;
initialize();
};
force.iterations = function(_) {
force.iterations = function (_) {
return arguments.length ? (iterations = +_, force) : iterations;
};
force.distance = function(_) {
return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), initializeDistance(), force) : distance;
force.distance = function (_) {
return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), initializeDistance(), force) : distance;
};
force.latestAccel = function () {

43
src/neighbourSampling.js
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@@ -1,12 +1,12 @@
import constant from "./constant";
import jiggle from "./jiggle";
import constant from './constant';
import jiggle from './jiggle';
/**
* An implementation of Chalmers' 1996 Neighbour and Sampling algorithm.
* It uses random sampling to find the most suited neighbours from the
* data set.
*/
function sortDistances(a, b) {
function sortDistances (a, b) {
return b[1] - a[1];
}
@@ -25,18 +25,18 @@ export default function () {
* Apply spring forces at each simulation iteration.
* @param {number} alpha - multiplier for amount of force applied
*/
function force(alpha) {
function force (alpha) {
let n = nodes.length;
// Cache old velocity for comparison later
if (stableVeloHandler!==null && stableVelocity>=0) {
for (let i = n-1, node; i>=0; i--) {
if (stableVeloHandler !== null && stableVelocity >= 0) {
for (let i = n - 1, node; i >= 0; i--) {
node = nodes[i];
node.oldvx = node.vx;
node.oldvy = node.vy;
}
}
for (let i = n-1, node, samples; i>=0; i--) {
for (let i = n - 1, node, samples; i >= 0; i--) {
node = nodes[i];
samples = createRandomSamples(i);
@@ -52,16 +52,16 @@ export default function () {
}
// Calculate velocity changes, aka force applied.
if (stableVeloHandler!==null && stableVelocity>=0) {
if (stableVeloHandler !== null && stableVelocity >= 0) {
let velocityDiff = 0;
for (let i = n-1, node; i>=0; i--) {
for (let i = n - 1, node; i >= 0; i--) {
node = nodes[i];
velocityDiff += Math.abs(Math.hypot(node.vx-node.oldvx, node.vy-node.oldvy));
velocityDiff += Math.abs(Math.hypot(node.vx - node.oldvx, node.vy - node.oldvy));
}
velocityDiff /= n;
latestVelocityDiff = velocityDiff;
if(velocityDiff<stableVelocity){
if (velocityDiff < stableVelocity) {
stableVeloHandler();
}
}
@@ -74,7 +74,7 @@ export default function () {
* @param {number} dist - high dimensional distance between the two nodes
* @param {number} alpha - multiplier for the amount of force applied
*/
function setVelocity(source, target, dist, alpha) {
function setVelocity (source, target, dist, alpha) {
let x, y, l;
// jiggle so l won't be zero and divide by zero error after this
x = target.x + target.vx - source.x - source.vx || jiggle();
@@ -90,11 +90,11 @@ export default function () {
}
// Called on nodes change and added to a simulation
function initialize() {
function initialize () {
if (!nodes) return;
// Initialize for each node some random neighbours.
for (let i = nodes.length-1; i>=0; i--) {
for (let i = nodes.length - 1; i >= 0; i--) {
let neighbs = pickRandomNodesFor(i, [i], neighbourSize);
// Sort the neighbour set by the distances.
neighbours[i] = new Map(neighbs.sort(sortDistances));
@@ -103,8 +103,8 @@ export default function () {
initDataSizeFactor();
}
function initDataSizeFactor(){
dataSizeFactor = 0.5/(neighbourSize+sampleSize);
function initDataSizeFactor () {
dataSizeFactor = 0.5 / (neighbourSize + sampleSize);
}
/**
@@ -116,7 +116,7 @@ export default function () {
* @param {number} size - max number of elements in the map to return.
* @return {array}
*/
function pickRandomNodesFor(index, exclude, size) {
function pickRandomNodesFor (index, exclude, size) {
let randElements = [];
let max = nodes.length;
@@ -133,7 +133,7 @@ export default function () {
}
randElements.push(rand);
}
for(let i=randElements.length-1, rand; i>=0; i--){
for (let i = randElements.length - 1, rand; i >= 0; i--) {
rand = randElements[i];
randElements[i] = [rand, distance(nodes[index], nodes[rand])];
}
@@ -146,7 +146,7 @@ export default function () {
* @param {number} index - index of the node to generate sample for
* @return {map}
*/
function createRandomSamples(index) {
function createRandomSamples (index) {
// Ignore the current neighbours of the node and itself.
let exclude = [index];
exclude = exclude.concat(Array.from(neighbours[index].keys()));
@@ -160,13 +160,12 @@ export default function () {
* @param {map} samples - map of samples
* @return {map} - new map of neighbours
*/
function findNewNeighbours(neighbours, samples) {
function findNewNeighbours (neighbours, samples) {
let combined = [...neighbours.entries()].concat([...samples.entries()]);
combined = combined.sort(sortDistances);
return new Map(combined.slice(0, neighbourSize));
}
// API for initializing the algorithm and setting parameters
force.initialize = function (_) {
nodes = _;
@@ -186,7 +185,7 @@ export default function () {
};
force.distance = function (_) {
return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), force) : distance;
return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), force) : distance;
};
force.latestAccel = function () {

6
src/stress.js
View File

@@ -4,10 +4,10 @@
* to the better layout.
* @return {number} - stress of the layout.
*/
export function getStress(nodes, distance) {
let sumDiffSq = 0
export function getStress (nodes, distance) {
let sumDiffSq = 0;
let sumLowDDistSq = 0;
for (let j = nodes.length-1; j >= 1; j--) {
for (let j = nodes.length - 1; j >= 1; j--) {
for (let i = 0; i < j; i++) {
let source = nodes[i], target = nodes[j];
let lowDDist = Math.hypot(target.x - source.x, target.y - source.y);

47
src/t-sne.js
View File

@@ -1,5 +1,5 @@
/* eslint-disable block-scoped-var */
import constant from "./constant";
import constant from './constant';
/**
* Set the node id accessor to the specified i.
@@ -7,7 +7,7 @@ import constant from "./constant";
* @param {accessor} i - id accessor.
* @return {accessor} - node id accessor.
*/
function index(d, i) {
function index (d, i) {
return i;
}
@@ -15,7 +15,7 @@ function index(d, i) {
* t-SNE implementation in D3 by using the code existing in tsnejs
* (https://github.com/karpathy/tsnejs) to compute the solution.
*/
export default function() {
export default function () {
var id = index,
distance = constant(300),
nodes,
@@ -33,7 +33,7 @@ export default function() {
* Make a step in t-SNE algorithm and set the velocities for the nodes
* to accumulate the values from solution.
*/
function force() {
function force () {
// Make a step at each iteration.
step();
var solution = getSolution();
@@ -49,7 +49,7 @@ export default function() {
* Calculates the random number from Gaussian distribution.
* @return {number} random number.
*/
function gaussRandom() {
function gaussRandom () {
let u = 2 * Math.random() - 1;
let v = 2 * Math.random() - 1;
let r = u * u + v * v;
@@ -63,11 +63,11 @@ export default function() {
* Return the normalized number.
* @return {number} normalized random number from Gaussian distribution.
*/
function randomN() {
function randomN () {
return gaussRandom() * 1e-4;
}
function sign(x) {
function sign (x) {
return x > 0 ? 1 : x < 0 ? -1 : 0;
}
@@ -76,7 +76,7 @@ export default function() {
* @param {number} n - length of array.
* @return {Float64Array} - array of zeros with length n.
*/
function zeros(n) {
function zeros (n) {
if (typeof n === 'undefined' || isNaN(n)) {
return [];
}
@@ -90,7 +90,7 @@ export default function() {
* @param {number} d - columns.
* @return {array} - 2d array
*/
function random2d(n, d) {
function random2d (n, d) {
var x = [];
for (var i = 0; i < n; i++) {
var y = [];
@@ -109,7 +109,7 @@ export default function() {
* @param {number} tol - limit for entropy difference.
* @return {2d array} - 2d matrix containing probabilities.
*/
function d2p(data, perplexity, tol) {
function d2p (data, perplexity, tol) {
N = Math.floor(data.length);
var Htarget = Math.log(perplexity); // target entropy of distribution.
var P1 = zeros(N * N); // temporary probability matrix.
@@ -161,7 +161,6 @@ export default function() {
} else {
beta = (beta + betamax) / 2;
}
} else {
// Converse case. Make distrubtion less peaky.
betamax = beta;
@@ -183,7 +182,6 @@ export default function() {
for (j = 0; j < N; j++) {
P1[i * N + j] = prow[j];
}
}
// Symmetrize P and normalize it to sum to 1 over all ij
@@ -200,7 +198,7 @@ export default function() {
/**
* Initialize a starting (random) solution.
*/
function initSolution() {
function initSolution () {
Y = random2d(N, dim);
// Step gains to accelerate progress in unchanging directions.
gains = random2d(N, dim, 1.0);
@@ -212,7 +210,7 @@ export default function() {
/**
* @return {2d array} the solution.
*/
function getSolution() {
function getSolution () {
return Y;
}
@@ -220,7 +218,7 @@ export default function() {
* Do a single step (iteration) for the layout.
* @return {number} the current cost.
*/
function step() {
function step () {
iteration += 1;
var cg = costGrad(Y); // Evaluate gradient.
@@ -269,8 +267,7 @@ export default function() {
* @param {2d array} Y - the current solution to evaluate.
* @return {object} that contains a cost and a gradient.
*/
function costGrad(Y) {
function costGrad (Y) {
var pmul = iteration < 100 ? 4 : 1;
// Compute current Q distribution, unnormalized first.
@@ -326,7 +323,7 @@ export default function() {
* the better layout.
* @return {number} - stress of the layout.
*/
function getStress() {
function getStress () {
var totalDiffSq = 0,
totalHighDistSq = 0;
for (var i = 0, source, target, realDist, highDist; i < nodes.length; i++) {
@@ -345,7 +342,7 @@ export default function() {
// API for initializing the algorithm, setting parameters and querying
// metrics.
force.initialize = function(_) {
force.initialize = function (_) {
nodes = _;
N = nodes.length;
// Initialize the probability matrix.
@@ -353,23 +350,23 @@ export default function() {
initSolution();
};
force.id = function(_) {
force.id = function (_) {
return arguments.length ? (id = _, force) : id;
};
force.distance = function(_) {
return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), force) : distance;
force.distance = function (_) {
return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), force) : distance;
};
force.stress = function() {
force.stress = function () {
return getStress();
};
force.learningRate = function(_) {
force.learningRate = function (_) {
return arguments.length ? (learningRate = +_, force) : learningRate;
};
force.perplexity = function(_) {
force.perplexity = function (_) {
return arguments.length ? (perplexity = +_, force) : perplexity;
};

4
src/xy.js
View File

@@ -1,13 +1,13 @@
/**
* @return x value of a node
*/
export function x(d) {
export function x (d) {
return d.x;
}
/**
* @return y value of a node
*/
export function y(d) {
export function y (d) {
return d.y;
}