ml-kmeans - npm Package Compare versions

Comparing version 0.1.0 to 1.0.0

package.json

 {
   "name": "ml-kmeans",
-  "version": "0.1.0",
+  "version": "1.0.0",
   "description": "K-means in Javascript",
-  "main": "src/index.js",
+  "main": "src/kmeans.js",
   "directories": {
@@ -11,3 +11,6 @@ "lib": "src",
   "scripts": {
-    "test": "mocha --require should --reporter mocha-better-spec-reporter --recursive"
+    "eslint": "eslint src test",
+    "eslint-fix": "npm run eslint -- --fix",
+    "test": "npm run test-mocha && npm run eslint",
+    "test-mocha": "mocha --require should --reporter mocha-better-spec-reporter --recursive"
   },
@@ -27,3 +30,3 @@ "repository": {
   ],
-  "author": "Miguel Asencio",
+  "author": "Miguel Asencio <[e-mail]> (https://github.com/maasencioh)",
   "license": "MIT",
@@ -34,7 +37,14 @@ "bugs": {
   "homepage": "https://github.com/mljs/kmeans",
+  "tonicExampleFilename": "./tonic.js",
   "devDependencies": {
-    "mocha": "latest",
-    "mocha-better-spec-reporter": "latest",
-    "should": "latest"
+    "eslint": "^3.4.0",
+    "eslint-config-cheminfo": "^1.2.0",
+    "eslint-plugin-no-only-tests": "^1.1.0",
+    "mocha": "^3.0.2",
+    "mocha-better-spec-reporter": "^3.0.2",
+    "should": "^11.1.0"
+  },
+  "dependencies": {
+    "ml-euclidean-distance": "^1.0.0"
   }
 }

README.md

@@ -5,3 +5,2 @@ # clustering
   [![build status][travis-image]][travis-url]
-  [![David deps][david-image]][david-url]
   [![npm download][download-image]][download-url]
@@ -15,3 +14,4 @@
 
-## Methods
+## [API Documentation](https://mljs.github.io/kmeans/)
+
 ### kmeans(data, centers, [props])
@@ -34,5 +34,5 @@ Returns an object containing the following:
 
-```js
-$ npm install
-$ npm test
+```bash
+npm install
+npm test
 ```
@@ -52,5 +52,3 @@
 [travis-url]: https://travis-ci.org/mljs/kmeans
-[david-image]: https://img.shields.io/david/mljs/kmeans.svg?style=flat-square
-[david-url]: https://david-dm.org/mljs/kmeans
 [download-image]: https://img.shields.io/npm/dm/ml-kmeans.svg?style=flat-square
 [download-url]: https://npmjs.org/package/ml-kmeans

src/kmeans.js

 'use strict';
 
-/**
- * Calculates the squared distance between two vectors
- * @param {Array<number>} vec1 - the x vector
- * @param {Array<number>} vec2 - the y vector
- * @returns {number} sum - the calculated distance
- */
-function squaredDistance(vec1, vec2) {
-    var sum = 0;
-    var dim = vec1.length;
-    for (var i = 0; i < dim; i++)
-        sum += (vec1[i] - vec2[i]) * (vec1[i] - vec2[i]);
-    return sum;
-}
+const squaredDistance = require('ml-euclidean-distance').squared;
 
 /**
  * Calculates the sum of squared errors
- * @param {Array <Array <number>>} data - the (x,y) points to cluster
- * @param {Array <Array <number>>} centers - the K centers in format (x,y)
- * @param {Array <number>} clusterID - the cluster identifier for each data dot
- * @returns {number} the sum of squared errors
+ * @param {Array <Array <Number>>} data - the (x,y) points to cluster
+ * @param {Array <Array <Number>>} centers - the K centers in format (x,y)
+ * @param {Array <Number>} clusterID - the cluster identifier for each data dot
+ * @returns {Number} the sum of squared errors
  */
 function computeSSE(data, centers, clusterID) {
-    var sse = 0;
-    var nData = data.length;
-    var c = 0;
-    for (var i = 0; i < nData;i++) {
+    let sse = 0;
+    let nData = data.length;
+    let c = 0;
+    for (let i = 0; i < nData; i++) {
         c = clusterID[i];
@@ -37,17 +25,17 @@ sse += squaredDistance(data[i], centers[c]);
  * Updates the cluster identifier based in the new data
- * @param {Array <Array <number>>} data - the (x,y) points to cluster
- * @param {Array <Array <number>>} centers - the K centers in format (x,y)
+ * @param {Array <Array <Number>>} data - the (x,y) points to cluster
+ * @param {Array <Array <Number>>} centers - the K centers in format (x,y)
  * @returns {Array} the cluster identifier for each data dot
  */
-function updateClusterID (data, centers) {
-    var nData = data.length;
-    var k = centers.length;
-    var aux = 0;
-    var clusterID = new Array(nData);
-    for (var i = 0; i < nData; i++)
+function updateClusterID(data, centers) {
+    let nData = data.length;
+    let k = centers.length;
+    let aux = 0;
+    let clusterID = new Array(nData);
+    for (let i = 0; i < nData; i++)
         clusterID[i] = 0;
-    var d = new Array(nData);
-    for (var i = 0; i < nData; i++) {
+    let d = new Array(nData);
+    for (let i = 0; i < nData; i++) {
         d[i] = new Array(k);
-        for (var j = 0; j < k; j++) {
+        for (let j = 0; j < k; j++) {
             aux = squaredDistance(data[i], centers[j]);
@@ -58,5 +46,5 @@ d[i][j] = new Array(2);
         }
-        var min = d[i][0][0];
-        var id = 0;
-        for (var j = 0; j < k; j++)
+        let min = d[i][0][0];
+        let id = 0;
+        for (let j = 0; j < k; j++)
             if (d[i][j][0] < min) {
@@ -73,30 +61,30 @@ min  = d[i][j][0];
  * Update the center values based in the new configurations of the clusters
- * @param {Array <Array <number>>} data - the (x,y) points to cluster
- * @param {Array <number>} clusterID - the cluster identifier for each data dot
- * @param K - number of clusters
+ * @param {Array <Array <Number>>} data - the (x,y) points to cluster
+ * @param {Array <Number>} clusterID - the cluster identifier for each data dot
+ * @param {Number} K - Number of clusters
  * @returns {Array} he K centers in format (x,y)
  */
 function updateCenters(data, clusterID, K) {
-    var nDim = data[0].length;
-    var nData = data.length;
-    var centers = new Array(K);
-    for (var i = 0; i < K; i++) {
+    let nDim = data[0].length;
+    let nData = data.length;
+    let centers = new Array(K);
+    for (let i = 0; i < K; i++) {
         centers[i] = new Array(nDim);
-        for (var j = 0; j < nDim; j++)
+        for (let j = 0; j < nDim; j++)
             centers[i][j] = 0;
     }
 
-    for (var k = 0; k < K; k++) {
-        var cluster = [];
-        for (var i = 0; i < nData;i++)
-            if (clusterID[i] == k)
+    for (let k = 0; k < K; k++) {
+        let cluster = [];
+        for (let i = 0; i < nData; i++)
+            if (clusterID[i] === k)
                 cluster.push(data[i]);
-        for (var d = 0; d < nDim; d++) {
-            var x = [];
-            for (var i = 0; i < nData; i++)
-                if (clusterID[i] == k)
+        for (let d = 0; d < nDim; d++) {
+            let x = [];
+            for (let i = 0; i < nData; i++)
+                if (clusterID[i] === k)
                     x.push(data[i][d]);
-            var sum = 0;
-            var l = x.length;
-            for (var i = 0; i < l; i++)
+            let sum = 0;
+            let l = x.length;
+            for (let i = 0; i < l; i++)
                 sum += x[i];
@@ -109,63 +97,63 @@ centers[k][d] = sum / l;
 
+const defaultOptions = {
+    maxIterations: 100,
+    tolerance: 1e-6,
+    withIterations: false
+};
+
 /**
  * K-means algorithm
- * @param {Array <Array <number>>} data - the (x,y) points to cluster
- * @param {Array <Array <number>>} centers - the K centers in format (x,y)
- * @param {Object} props - properties
- * @param {number} maxIter - maximum of iterations allowed
- * @param {number} tol - the error tolerance
- * @param {boolean} withIter - store clusters and centroids for each iteration
+ * @param {Array <Array <Number>>} data - the (x,y) points to cluster
+ * @param {Array <Array <Number>>} centers - the K centers in format (x,y)
+ * @param {Object} options - properties
+ * @param {Number} options.maxIterations - maximum of iterations allowed
+ * @param {Number} options.tolerance - the error tolerance
+ * @param {boolean} options.withIterations - store clusters and centroids for each iteration
  * @returns {Object} the cluster identifier for each data dot and centroids
  */
-function kmeans(data, centers, props) {
-    var maxIter, tol, withIter;
-    if (typeof props === "undefined") {
-        maxIter = 100;
-        tol = 1e-6;
-        withIter = false;
-    } else {
-        maxIter = (typeof props.maxIter === "undefined") ? 100 : props.maxIter;
-        tol = (typeof props.tol === "undefined") ? 1e-6 : props.tol;
-        withIter = (typeof props.withIter === "undefined") ? false : props.withIter;
-    }
+function kmeans(data, centers, options) {
+    if (!options) options = defaultOptions;
+    let maxIterations = options.maxIterations || defaultOptions.maxIterations;
+    let tolerance = options.tolerance || defaultOptions.tolerance;
+    let withIterations = options.withIterations || defaultOptions.withIterations;
 
-    var nData = data.length;
-    if (nData == 0) {
+    let nData = data.length;
+    if (nData === 0) {
         return [];
     }
-    var K = centers.length;
-    var clusterID = new Array(nData);
-    for (var i = 0; i < nData; i++)
+    let K = centers.length;
+    let clusterID = new Array(nData);
+    for (let i = 0; i < nData; i++)
         clusterID[i] = 0;
     if (K >= nData) {
-        for (var i = 0; i < nData; i++)
+        for (let i = 0; i < nData; i++)
             clusterID[i] = i;
         return clusterID;
     }
-    var lastDistance;
+    let lastDistance;
     lastDistance = 1e100;
-    var curDistance = 0;
-    var iterations = [];
-    for (var iter = 0; iter < maxIter; iter++) {
+    let curDistance = 0;
+    let iterations = [];
+    for (let iter = 0; iter < maxIterations; iter++) {
         clusterID = updateClusterID(data, centers);
         centers = updateCenters(data, clusterID, K);
         curDistance = computeSSE(data, centers, clusterID);
-        if (withIter) {
+        if (withIterations) {
             iterations.push({
-                "clusters": clusterID,
-                "centroids": centers
+                'clusters': clusterID,
+                'centroids': centers
             });
         }
 
-        if ((lastDistance - curDistance < tol) || ((lastDistance - curDistance)/lastDistance < tol)) {
-            if (withIter) {
+        if ((lastDistance - curDistance < tolerance) || ((lastDistance - curDistance) / lastDistance < tolerance)) {
+            if (withIterations) {
                 return {
-                    "clusters": clusterID,
-                    "centroids": centers,
-                    "iterations": iterations
+                    'clusters': clusterID,
+                    'centroids': centers,
+                    'iterations': iterations
                 };
             } else {
                 return {
-                    "clusters": clusterID,
-                    "centroids": centers
+                    'clusters': clusterID,
+                    'centroids': centers
                 };
@@ -176,12 +164,12 @@ }
     }
-    if (withIter) {
+    if (withIterations) {
         return {
-            "clusters": clusterID,
-            "centroids": centers,
-            "iterations": iterations
+            'clusters': clusterID,
+            'centroids': centers,
+            'iterations': iterations
         };
     } else {
         return {
-            "clusters": clusterID,
-            "centroids": centers
+            'clusters': clusterID,
+            'centroids': centers
         };
@@ -188,0 +176,0 @@ }

test/kmeans.js

@@ -1,8 +0,10 @@
-var kmeans = require('..');
+'use strict';
 
-var data = [[-0.0666, 3.9556], [4.76, 3.3532], [-3.1439, 11.9022], [8.8348, 11.7216], [-0.5141, -1.0318], [-0.8734, -0.0638], [1.8586, 0.1617], [2.5949, 1.7523], [-9.6961, 6.0031], [-8.9181, -5.1248], [7.1932, 12.0728], [0.0514, 0.2759], [6.9711, 13.1064], [-10.9973, 7.7045], [6.3518, 13.8093], [10.9279, 5.1009], [10.4232, -10.2709], [-3.2731, 1.5736], [-10.7068, 1.1708], [10.0436, 12.3809], [-7.3017, -8.8664], [14.3335, -0.4248], [-1.2148, 1.2219], [2.4937, 11.4931], [11.9281, -0.3832], [0.9821, 4.2688], [2.37, -9.7278], [14.6577, -2.3196], [12.0509, -1.1881], [0.4072, -1.5841], [-12.4332, 9.5092], [10.0304, -3.3084], [-3.2987, 2.6475], [-9.183, -6.732], [1.4777, -0.1288], [4.9672, 2.2459], [3.378, 11.7724], [-1.0655, -13.701], [2.1222, -14.0421], [-11.9715, 8.6394], [4.2109, 2.648], [-14.0372, 4.3083], [-3.925, 11.4749], [8.1935, 8.5127], [-0.4264, -5.4829], [-0.0024, -0.0029], [15.7082, 0.5469], [11.2737, -10.5358], [3.6512, -4.2767], [-15.1431, -2.9218], [9.0929, -12.6038], [-11.1781, -6.6943], [12.7945, 1.2342], [-6.133, 9.8605], [-12.6114, 7.7679], [-9.9677, -7.7301], [-4.6634, 12.9643], [1.5693, -12.5789], [5.7557, 9.6854], [6.6642, -8.3058], [-6.8809, -13.7961], [2.4091, 4.5483], [-2.5279, 1.7826], [-2.4686, -1.0076], [-1.627, 3.8966], [-11.2086, -10.4047], [0.4449, 0.9771], [3.1429, -11.6762], [-5.6467, -9.8625], [-0.1443, 1.1105], [11.4457, 7.1273], [-2.0661, -0.8904], [10.4427, 6.7074], [13.3232, 3.8624], [-1.3954, 10.7406], [-11.0965, 7.4209], [-7.4238, -10.2261], [12.7514, -5.1493], [4.5682, 14.3597], [0.0111, -0.2019], [4.4757, -14.9786], [5.5468, 12.642], [2.9031, 0.9922], [13.5853, 5.7207], [0.6769, 1.2887], [6.0625, 14.6495], [0.5118, -3.8389], [2.7598, -2.707], [3.0966, 1.3363], [-2.753, 0.9607], [9.6514, 5.0441], [-0.4091, 1.3676], [-9.6076, 8.0624], [-8.7501, -11.9083], [10.0703, -6.6664], [-2.775, 4.9923], [1.7828, 2.6811], [-8.3187, -9.0107], [7.869, -11.9879], [4.4372, -10.9654], [-13.9686, -3.7593], [4.7448, -0.5838], [1.523, -1.1613], [-2.7016, -12.164], [-0.101, -1.0998], [-4.298, -2.6621], [-0.4873, -4.2047], [11.2547, 9.8242], [6.1379, 12.4645], [-9.5691, 7.0038], [9.8499, 8.2545], [-5.5557, 9.7861], [1.9109, 0.5991], [2.349, -1.0844], [7.8578, -12.2351], [3.0412, 0.0989], [-0.0646, -0.1736], [-12.9012, 1.3657], [4.2466, -14.8209], [11.5388, 2.4394], [3.929, -3.9739], [0.9316, 3.905], [4.5496, 1.1866], [4.372, -2.7704], [11.455, 4.7257], [0.1667, 3.0992], [3.9864, 2.1817], [4.305, -1.6716], [7.562, -9.0314], [-14.2041, -3.4945], [-1.8313, 5.4946], [-0.2621, 2.7548], [1.2749, -5.5176], [-3.2882, -0.9906], [13.4358, 7.2966], [-0.2678, 12.9615], [-12.3131, -6.7527], [-0.6749, 12.4802], [-2.8935, -0.1917], [1.2031, 13.7469], [2.7249, -12.0991], [6.8316, 13.5634], [-12.2154, 8.1218], [-11.2229, 2.5678], [1.2659, 3.8962], [0.0282, -13.0606], [4.6974, 12.4882], [-11.1419, 4.5603], [6.2891, 7.8661], [0.6523, -0.2886], [-9.0433, -8.0838], [-1.5765, 4.7914], [6.2527, 13.5528], [-14.2127, -5.392], [2.622, 1.23], [15.2967, 1.124], [0.1564, -10.5582], [-0.0184, -1.1081], [1.3052, 2.704], [-0.4534, -4.2774], [8.2172, -9.4401], [3.2584, 11.923], [0.5783, 12.0416], [-10.394, 7.1666], [6.2146, -13.9815], [8.274, -7.4318], [5.8541, -8.6234], [-10.5098, 10.1849], [11.9771, 4.8152], [-0.1449, -0.1554], [0.142, 0.246], [-14.4965, 1.4054], [0.1092, -0.7848], [-0.9279, -0.5001], [12.741, 7.6121], [4.9104, 3.154], [11.4821, 4.2069], [-4.823, 10.66], [11.6895, -3.2821], [-11.1159, 9.4047], [9.6862, 7.0255], [4.2709, -11.0134], [0.6158, 0.1453], [0.1495, 0.3122], [-13.0746, 1.2675], [11.5982, -0.8388], [-12.8156, -2.8367], [8.8404, -11.7197], [1.9458, -10.7101], [-4.7165, -2.0864], [0.9491, -2.7463], [2.8534, -15.6712], [-10.9901, 2.373], [0.5304, 0.1503], [-0.9909, 10.8084], [-8.2816, -6.3139], [0.4797, 1.9009], [-10.8144, 4.861], [-0.3332, -10.806], [12.2777, -9.5046], [10.8568, 3.2378], [-13.6074, -1.8163], [-5.0088, -14.0002], [-5.4378, 10.2545], [-14.7814, -3.7871], [4.799, 2.5422], [1.8227, -2.1769], [-8.4743, 6.0638], [3.2532, 13.2978], [0.1051, 11.8516], [4.8026, -9.8529], [1.8074, -3.4619], [6.1632, 14.2468], [12.1793, -2.0449], [-11.8355, -8.555], [0.8006, -2.2425], [-4.8806, 10.4182], [11.7955, -0.6237], [9.7098, -4.5823], [-11.3679, -7.2694], [-0.4442, 12.2027], [-10.0847, 8.4332], [-0.2568, -12.0907], [4.5121, -0.4623], [-11.1321, -0.2562], [-7.8491, -11.8705], [8.8087, 11.5666], [1.2531, 12.7417], [1.296, 10.6887], [0.178, 4.1553], [-10.2494, 7.4416], [5.2538, -1.7206], [-1.5211, 12.4555], [-2.8232, -3.3191], [-10.55, -10.1068], [0.525, 1.0544], [-9.5425, 2.991], [-7.8366, -10.869], [10.4709, 10.306], [2.9398, -4.0552], [11.4418, 11.167], [3.8228, 10.7863], [8.819, -8.2799], [15.0613, -5.0608], [-4.5679, -3.7592], [-7.3167, -10.3306], [-1.136, 0.6528], [5.9679, 9.9295], [-5.1458, 10.8432], [3.3021, 0.6736], [-3.3911, -1.4024], [-0.6543, 2.1858], [1.9018, 1.6944], [12.3353, 5.2557], [-12.6925, 9.0565], [1.5401, -2.8375], [-12.5521, -5.8312], [-8.6292, 7.6424], [0.0011, -0.7609], [3.2544, 4.6448], [-8.9116, 8.4947], [2.2136, 11.4343], [13.7698, -7.6187], [-2.125, 15.5614], [14.7314, -2.8995], [-2.9476, 10.2836], [10.3428, -7.124], [-4.0868, -9.6519], [-13.3819, 0.1771], [2.412, 3.4523], [-9.4296, -11.8803], [5.8335, 12.6889], [-1.2018, 1.3509], [-13.8312, 0.1857], [-1.9669, -11.8927], [-5.2092, -15.1251], [12.8108, -8.4382], [-9.9785, -2.8565], [2.9082, -14.2871], [-10.1149, -10.645], [-1.314, -14.0368], [0.6984, 1.3434], [-3.3646, 9.7148], [-0.6432, -3.3273], [-9.9093, 3.7181], [-9.6247, -7.4199], [-10.6138, 3.0472], [-13.2729, -7.2328], [3.4595, 1.7802], [5.9987, -13.601], [-12.179, -2.9048], [1.3147, -0.3124], [4.9982, -9.1045], [-5.554, -13.3016], [-1.9886, 3.3253], [5.7266, -8.4147], [-10.3248, -1.2052], [-13.2403, 8.1238], [-6.2474, 11.419], [-1.2548, -14.8794]];
-var centers = [[-0.0666, 3.9556], [4.76, 3.3532]];
-var clusterID = [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0];
-var expectedCentroids = [[-8.4199, -1.6654], [4.4661, 1.3475]];
+const kmeans = require('..');
 
+let data = [[-0.0666, 3.9556], [4.76, 3.3532], [-3.1439, 11.9022], [8.8348, 11.7216], [-0.5141, -1.0318], [-0.8734, -0.0638], [1.8586, 0.1617], [2.5949, 1.7523], [-9.6961, 6.0031], [-8.9181, -5.1248], [7.1932, 12.0728], [0.0514, 0.2759], [6.9711, 13.1064], [-10.9973, 7.7045], [6.3518, 13.8093], [10.9279, 5.1009], [10.4232, -10.2709], [-3.2731, 1.5736], [-10.7068, 1.1708], [10.0436, 12.3809], [-7.3017, -8.8664], [14.3335, -0.4248], [-1.2148, 1.2219], [2.4937, 11.4931], [11.9281, -0.3832], [0.9821, 4.2688], [2.37, -9.7278], [14.6577, -2.3196], [12.0509, -1.1881], [0.4072, -1.5841], [-12.4332, 9.5092], [10.0304, -3.3084], [-3.2987, 2.6475], [-9.183, -6.732], [1.4777, -0.1288], [4.9672, 2.2459], [3.378, 11.7724], [-1.0655, -13.701], [2.1222, -14.0421], [-11.9715, 8.6394], [4.2109, 2.648], [-14.0372, 4.3083], [-3.925, 11.4749], [8.1935, 8.5127], [-0.4264, -5.4829], [-0.0024, -0.0029], [15.7082, 0.5469], [11.2737, -10.5358], [3.6512, -4.2767], [-15.1431, -2.9218], [9.0929, -12.6038], [-11.1781, -6.6943], [12.7945, 1.2342], [-6.133, 9.8605], [-12.6114, 7.7679], [-9.9677, -7.7301], [-4.6634, 12.9643], [1.5693, -12.5789], [5.7557, 9.6854], [6.6642, -8.3058], [-6.8809, -13.7961], [2.4091, 4.5483], [-2.5279, 1.7826], [-2.4686, -1.0076], [-1.627, 3.8966], [-11.2086, -10.4047], [0.4449, 0.9771], [3.1429, -11.6762], [-5.6467, -9.8625], [-0.1443, 1.1105], [11.4457, 7.1273], [-2.0661, -0.8904], [10.4427, 6.7074], [13.3232, 3.8624], [-1.3954, 10.7406], [-11.0965, 7.4209], [-7.4238, -10.2261], [12.7514, -5.1493], [4.5682, 14.3597], [0.0111, -0.2019], [4.4757, -14.9786], [5.5468, 12.642], [2.9031, 0.9922], [13.5853, 5.7207], [0.6769, 1.2887], [6.0625, 14.6495], [0.5118, -3.8389], [2.7598, -2.707], [3.0966, 1.3363], [-2.753, 0.9607], [9.6514, 5.0441], [-0.4091, 1.3676], [-9.6076, 8.0624], [-8.7501, -11.9083], [10.0703, -6.6664], [-2.775, 4.9923], [1.7828, 2.6811], [-8.3187, -9.0107], [7.869, -11.9879], [4.4372, -10.9654], [-13.9686, -3.7593], [4.7448, -0.5838], [1.523, -1.1613], [-2.7016, -12.164], [-0.101, -1.0998], [-4.298, -2.6621], [-0.4873, -4.2047], [11.2547, 9.8242], [6.1379, 12.4645], [-9.5691, 7.0038], [9.8499, 8.2545], [-5.5557, 9.7861], [1.9109, 0.5991], [2.349, -1.0844], [7.8578, -12.2351], [3.0412, 0.0989], [-0.0646, -0.1736], [-12.9012, 1.3657], [4.2466, -14.8209], [11.5388, 2.4394], [3.929, -3.9739], [0.9316, 3.905], [4.5496, 1.1866], [4.372, -2.7704], [11.455, 4.7257], [0.1667, 3.0992], [3.9864, 2.1817], [4.305, -1.6716], [7.562, -9.0314], [-14.2041, -3.4945], [-1.8313, 5.4946], [-0.2621, 2.7548], [1.2749, -5.5176], [-3.2882, -0.9906], [13.4358, 7.2966], [-0.2678, 12.9615], [-12.3131, -6.7527], [-0.6749, 12.4802], [-2.8935, -0.1917], [1.2031, 13.7469], [2.7249, -12.0991], [6.8316, 13.5634], [-12.2154, 8.1218], [-11.2229, 2.5678], [1.2659, 3.8962], [0.0282, -13.0606], [4.6974, 12.4882], [-11.1419, 4.5603], [6.2891, 7.8661], [0.6523, -0.2886], [-9.0433, -8.0838], [-1.5765, 4.7914], [6.2527, 13.5528], [-14.2127, -5.392], [2.622, 1.23], [15.2967, 1.124], [0.1564, -10.5582], [-0.0184, -1.1081], [1.3052, 2.704], [-0.4534, -4.2774], [8.2172, -9.4401], [3.2584, 11.923], [0.5783, 12.0416], [-10.394, 7.1666], [6.2146, -13.9815], [8.274, -7.4318], [5.8541, -8.6234], [-10.5098, 10.1849], [11.9771, 4.8152], [-0.1449, -0.1554], [0.142, 0.246], [-14.4965, 1.4054], [0.1092, -0.7848], [-0.9279, -0.5001], [12.741, 7.6121], [4.9104, 3.154], [11.4821, 4.2069], [-4.823, 10.66], [11.6895, -3.2821], [-11.1159, 9.4047], [9.6862, 7.0255], [4.2709, -11.0134], [0.6158, 0.1453], [0.1495, 0.3122], [-13.0746, 1.2675], [11.5982, -0.8388], [-12.8156, -2.8367], [8.8404, -11.7197], [1.9458, -10.7101], [-4.7165, -2.0864], [0.9491, -2.7463], [2.8534, -15.6712], [-10.9901, 2.373], [0.5304, 0.1503], [-0.9909, 10.8084], [-8.2816, -6.3139], [0.4797, 1.9009], [-10.8144, 4.861], [-0.3332, -10.806], [12.2777, -9.5046], [10.8568, 3.2378], [-13.6074, -1.8163], [-5.0088, -14.0002], [-5.4378, 10.2545], [-14.7814, -3.7871], [4.799, 2.5422], [1.8227, -2.1769], [-8.4743, 6.0638], [3.2532, 13.2978], [0.1051, 11.8516], [4.8026, -9.8529], [1.8074, -3.4619], [6.1632, 14.2468], [12.1793, -2.0449], [-11.8355, -8.555], [0.8006, -2.2425], [-4.8806, 10.4182], [11.7955, -0.6237], [9.7098, -4.5823], [-11.3679, -7.2694], [-0.4442, 12.2027], [-10.0847, 8.4332], [-0.2568, -12.0907], [4.5121, -0.4623], [-11.1321, -0.2562], [-7.8491, -11.8705], [8.8087, 11.5666], [1.2531, 12.7417], [1.296, 10.6887], [0.178, 4.1553], [-10.2494, 7.4416], [5.2538, -1.7206], [-1.5211, 12.4555], [-2.8232, -3.3191], [-10.55, -10.1068], [0.525, 1.0544], [-9.5425, 2.991], [-7.8366, -10.869], [10.4709, 10.306], [2.9398, -4.0552], [11.4418, 11.167], [3.8228, 10.7863], [8.819, -8.2799], [15.0613, -5.0608], [-4.5679, -3.7592], [-7.3167, -10.3306], [-1.136, 0.6528], [5.9679, 9.9295], [-5.1458, 10.8432], [3.3021, 0.6736], [-3.3911, -1.4024], [-0.6543, 2.1858], [1.9018, 1.6944], [12.3353, 5.2557], [-12.6925, 9.0565], [1.5401, -2.8375], [-12.5521, -5.8312], [-8.6292, 7.6424], [0.0011, -0.7609], [3.2544, 4.6448], [-8.9116, 8.4947], [2.2136, 11.4343], [13.7698, -7.6187], [-2.125, 15.5614], [14.7314, -2.8995], [-2.9476, 10.2836], [10.3428, -7.124], [-4.0868, -9.6519], [-13.3819, 0.1771], [2.412, 3.4523], [-9.4296, -11.8803], [5.8335, 12.6889], [-1.2018, 1.3509], [-13.8312, 0.1857], [-1.9669, -11.8927], [-5.2092, -15.1251], [12.8108, -8.4382], [-9.9785, -2.8565], [2.9082, -14.2871], [-10.1149, -10.645], [-1.314, -14.0368], [0.6984, 1.3434], [-3.3646, 9.7148], [-0.6432, -3.3273], [-9.9093, 3.7181], [-9.6247, -7.4199], [-10.6138, 3.0472], [-13.2729, -7.2328], [3.4595, 1.7802], [5.9987, -13.601], [-12.179, -2.9048], [1.3147, -0.3124], [4.9982, -9.1045], [-5.554, -13.3016], [-1.9886, 3.3253], [5.7266, -8.4147], [-10.3248, -1.2052], [-13.2403, 8.1238], [-6.2474, 11.419], [-1.2548, -14.8794]];
+let centers = [[-0.0666, 3.9556], [4.76, 3.3532]];
+let clusterID = [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0];
+let expectedCentroids = [[-8.4199, -1.6654], [4.4661, 1.3475]];
+
 describe('K-means', function () {
@@ -13,10 +15,10 @@
         it('main test', function () {
-            var ans = kmeans(data, centers);
-            clusts = ans.clusters;
-            centrs = ans.centroids;
+            let ans = kmeans(data, centers);
+            let clusts = ans.clusters;
+            let centrs = ans.centroids;
 
-            for (var i = 0, l = clusterID.length; i < l; i++) {
+            for (let i = 0, l = clusterID.length; i < l; i++) {
                 clusts[i].should.equal(clusterID[i]);
             }
-            for (var i = 0, l = centers.length; i < l; i++) {
+            for (let i = 0, l = centers.length; i < l; i++) {
                 centrs[i][0].should.be.approximately(expectedCentroids[i][0], 0.1);
@@ -32,11 +34,11 @@ centrs[i][1].should.be.approximately(expectedCentroids[i][1], 0.1);
         it('should store iterations', function () {
-            var ans = kmeans(data, centers, {"withIter": true});
-            clusts = ans.clusters;
-            centrs = ans.centroids;
-            iters = ans.iterations;
+            let ans = kmeans(data, centers, { withIterations: true });
+            let clusts = ans.clusters;
+            let centrs = ans.centroids;
+            let iters = ans.iterations;
 
-            for (var i = 0, l = clusterID.length; i < l; i++) {
+            for (let i = 0, l = clusterID.length; i < l; i++) {
                 clusts[i].should.equal(clusterID[i]);
             }
-            for (var i = 0, l = centers.length; i < l; i++) {
+            for (let i = 0, l = centers.length; i < l; i++) {
                 centrs[i][0].should.be.approximately(expectedCentroids[i][0], 0.1);
@@ -43,0 +45,0 @@ centrs[i][1].should.be.approximately(expectedCentroids[i][1], 0.1);

New alerts

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Package has recently undergone a major refactor. It may be unstable or indicate significant internal changes. Use caution when updating to versions that include significant changes.

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Improved metrics

Total package byte prevSize

1556141

increased by8429.6%

Number of package files

34

increased by325%

Lines of code

1169

increased by395.34%

Number of low quality alerts

0

decreased by-100%

Worsened metrics

Dependency count

1

increased byInfinity%

Dev dependency count

6

increased by100%

Number of low license alerts

2

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License quality

90

decreased by-10%

Number of lines in readme file

51

decreased by-3.77%

Number of medium supply chain risk alerts

5

increased by400%

Dependency changes

• + Addedml-euclidean-distance@^1.0.0

• + Addedml-euclidean-distance@1.0.0(transitive)