แก้ coding style ภาค 2

src/t-sne.js

@@ -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;
   };