do eslint fix and update readme

README.md

@@ -14,6 +14,10 @@ Most of the other implementations available in npm take a N x M matrix (a 2d arr
 
 ## What's New
 
+#### 1.4.0
+
+Support options to provide initial centroids. See details in [pull request](https://github.com/stanleyfok/kmeans-engine/pull/2)
+
 #### 1.3.0
 
 Update to newer version of [vector-object](https://www.npmjs.com/package/vector-object)
@@ -62,6 +66,7 @@ const engineers = [
 // accepted options:
 // k: number of clusters
 // maxIterations (optional): max number of iterations
+// initialCentroids (optional): an array of initial centroids in length of k
 // debug (optional): show debug message in console or not, default is false
 kmeans.clusterize(engineers, { k: 4, maxIterations: 5, debug: true }, (err, res) => {
   console.log('----- Results -----');

lib/KMeansEngine.js

@@ -45,7 +45,7 @@ class KMeansEngine {
 
     if (this.initialCentroids !== undefined) {
       if (!_.isArray(this.initialCentroids) || (this.initialCentroids.length !== this.k)) {
-        throw new Error('Initial centroids should be array of length equal to cluster size')
+        throw new Error('Initial centroids should be array of length equal to cluster size');
       } else {
         // Initial centroids  should be array of objects
         for (let i = 0; i < this.initialCentroids.length; i += 1) {

package.json

@@ -1,6 +1,6 @@
 {
   "name": "kmeans-engine",
-  "version": "1.3.1",
+  "version": "1.4.0",
   "description": "Scalable kmeans clustering algorithm in js using objects as input vectors, tailor made for sparse matrix",
   "homepage": "https://github.com/stanleyfok/kmeans-engine",
   "repository": {

test/KMeansEngine.js

@@ -1,4 +1,3 @@
-const _ = require('underscore');
 const chai = require('chai');
 
 const should = chai.should();
@@ -124,8 +123,13 @@ describe('KMeansEngine', () => {
     });
 
     it('should return same result given same initial centroids', (done) => {
-      const initialCentroids = [{x: 1, y: 0}, {x: 0, y: 1}, {x: 1, y: 1}];
-      kmeans.clusterize(set2, { k: 3, maxIterations: 1, initialCentroids: initialCentroids}, (err1, res1) => {
+      const initialCentroids = [{ x: 1, y: 0 }, { x: 0, y: 1 }, { x: 1, y: 1 }];
+
+      kmeans.clusterize(set2, {
+        k: 3,
+        maxIterations: 1,
+        initialCentroids,
+      }, (err1, res1) => {
         should.not.exist(err1);
         res1.should.to.have.property('iterations');
         res1.should.to.have.property('clusters');
@@ -136,7 +140,11 @@ describe('KMeansEngine', () => {
           cluster1.should.to.have.property('vectorIds');
         });
 
-        kmeans.clusterize(set2, { k: 3, maxIterations: 1, initialCentroids: initialCentroids }, (err2, res2) => {
+        kmeans.clusterize(set2, {
+          k: 3,
+          maxIterations: 1,
+          initialCentroids,
+        }, (err2, res2) => {
           should.not.exist(err2);
           res2.should.to.have.property('iterations');
           res2.should.to.have.property('clusters');