all these changes

frontend/node_modules/skmeans/dist/browser/skmeans.js

@@ -0,0 +1,339 @@
+"use strict";
+
+(function e(t, n, r) {
+	function s(o, u) {
+		if (!n[o]) {
+			if (!t[o]) {
+				var a = typeof require == "function" && require;if (!u && a) return a(o, !0);if (i) return i(o, !0);var f = new Error("Cannot find module '" + o + "'");throw f.code = "MODULE_NOT_FOUND", f;
+			}var l = n[o] = { exports: {} };t[o][0].call(l.exports, function (e) {
+				var n = t[o][1][e];return s(n ? n : e);
+			}, l, l.exports, e, t, n, r);
+		}return n[o].exports;
+	}var i = typeof require == "function" && require;for (var o = 0; o < r.length; o++) {
+		s(r[o]);
+	}return s;
+})({ 1: [function (require, module, exports) {
+		"use strict";
+
+		(function () {
+			var root = this;
+			var previous_skmeans = root.skmeans;
+			var skmeans = require('./main.js');
+
+			if (typeof exports !== 'undefined') {
+				if (typeof module !== 'undefined' && module.exports) {
+					exports = module.exports = skmeans;
+				}
+				exports.skmeans = skmeans;
+			}
+
+			if (typeof window !== 'undefined') {
+				window.skmeans = skmeans;
+			}
+		}).call(this);
+	}, { "./main.js": 4 }], 2: [function (require, module, exports) {
+		module.exports = {
+			/**
+    * Euclidean distance
+    */
+			eudist: function eudist(v1, v2, sqrt) {
+				var len = v1.length;
+				var sum = 0;
+
+				for (var i = 0; i < len; i++) {
+					var d = (v1[i] || 0) - (v2[i] || 0);
+					sum += d * d;
+				}
+				// Square root not really needed
+				return sqrt ? Math.sqrt(sum) : sum;
+			},
+			mandist: function mandist(v1, v2, sqrt) {
+				var len = v1.length;
+				var sum = 0;
+
+				for (var i = 0; i < len; i++) {
+					sum += Math.abs((v1[i] || 0) - (v2[i] || 0));
+				}
+
+				// Square root not really needed
+				return sqrt ? Math.sqrt(sum) : sum;
+			},
+
+
+			/**
+    * Unidimensional distance
+    */
+			dist: function dist(v1, v2, sqrt) {
+				var d = Math.abs(v1 - v2);
+				return sqrt ? d : d * d;
+			}
+		};
+	}, {}], 3: [function (require, module, exports) {
+		var Distance = require("./distance.js"),
+		    eudist = Distance.eudist,
+		    dist = Distance.dist;
+
+		module.exports = {
+			kmrand: function kmrand(data, k) {
+				var map = {},
+				    ks = [],
+				    t = k << 2;
+				var len = data.length;
+				var multi = data[0].length > 0;
+
+				while (ks.length < k && t-- > 0) {
+					var d = data[Math.floor(Math.random() * len)];
+					var key = multi ? d.join("_") : "" + d;
+					if (!map[key]) {
+						map[key] = true;
+						ks.push(d);
+					}
+				}
+
+				if (ks.length < k) throw new Error("Error initializating clusters");else return ks;
+			},
+
+
+			/**
+    * K-means++ initial centroid selection
+    */
+			kmpp: function kmpp(data, k) {
+				var distance = data[0].length ? eudist : dist;
+				var ks = [],
+				    len = data.length;
+				var multi = data[0].length > 0;
+				var map = {};
+
+				// First random centroid
+				var c = data[Math.floor(Math.random() * len)];
+				var key = multi ? c.join("_") : "" + c;
+				ks.push(c);
+				map[key] = true;
+
+				// Retrieve next centroids
+				while (ks.length < k) {
+					// Min Distances between current centroids and data points
+					var dists = [],
+					    lk = ks.length;
+					var dsum = 0,
+					    prs = [];
+
+					for (var i = 0; i < len; i++) {
+						var min = Infinity;
+						for (var j = 0; j < lk; j++) {
+							var _dist = distance(data[i], ks[j]);
+							if (_dist <= min) min = _dist;
+						}
+						dists[i] = min;
+					}
+
+					// Sum all min distances
+					for (var _i = 0; _i < len; _i++) {
+						dsum += dists[_i];
+					}
+
+					// Probabilities and cummulative prob (cumsum)
+					for (var _i2 = 0; _i2 < len; _i2++) {
+						prs[_i2] = { i: _i2, v: data[_i2], pr: dists[_i2] / dsum, cs: 0 };
+					}
+
+					// Sort Probabilities
+					prs.sort(function (a, b) {
+						return a.pr - b.pr;
+					});
+
+					// Cummulative Probabilities
+					prs[0].cs = prs[0].pr;
+					for (var _i3 = 1; _i3 < len; _i3++) {
+						prs[_i3].cs = prs[_i3 - 1].cs + prs[_i3].pr;
+					}
+
+					// Randomize
+					var rnd = Math.random();
+
+					// Gets only the items whose cumsum >= rnd
+					var idx = 0;
+					while (idx < len - 1 && prs[idx++].cs < rnd) {}
+					ks.push(prs[idx - 1].v);
+					/*
+     let done = false;
+     while(!done) {
+     	// this is our new centroid
+     	c = prs[idx-1].v
+     	key = multi? c.join("_") : `${c}`;
+     	if(!map[key]) {
+     		map[key] = true;
+     		ks.push(c);
+     		done = true;
+     	}
+     	else {
+     		idx++;
+     	}
+     }
+     */
+				}
+
+				return ks;
+			}
+		};
+	}, { "./distance.js": 2 }], 4: [function (require, module, exports) {
+		/*jshint esversion: 6 */
+
+		var Distance = require("./distance.js"),
+		    ClusterInit = require("./kinit.js"),
+		    eudist = Distance.eudist,
+		    mandist = Distance.mandist,
+		    dist = Distance.dist,
+		    kmrand = ClusterInit.kmrand,
+		    kmpp = ClusterInit.kmpp;
+
+		var MAX = 10000;
+
+		/**
+   * Inits an array with values
+   */
+		function init(len, val, v) {
+			v = v || [];
+			for (var i = 0; i < len; i++) {
+				v[i] = val;
+			}return v;
+		}
+
+		function skmeans(data, k, initial, maxit) {
+			var ks = [],
+			    old = [],
+			    idxs = [],
+			    dist = [];
+			var conv = false,
+			    it = maxit || MAX;
+			var len = data.length,
+			    vlen = data[0].length,
+			    multi = vlen > 0;
+			var count = [];
+
+			if (!initial) {
+				var _idxs = {};
+				while (ks.length < k) {
+					var idx = Math.floor(Math.random() * len);
+					if (!_idxs[idx]) {
+						_idxs[idx] = true;
+						ks.push(data[idx]);
+					}
+				}
+			} else if (initial == "kmrand") {
+				ks = kmrand(data, k);
+			} else if (initial == "kmpp") {
+				ks = kmpp(data, k);
+			} else {
+				ks = initial;
+			}
+
+			do {
+				// Reset k count
+				init(k, 0, count);
+
+				// For each value in data, find the nearest centroid
+				for (var i = 0; i < len; i++) {
+					var min = Infinity,
+					    _idx = 0;
+					for (var j = 0; j < k; j++) {
+						// Multidimensional or unidimensional
+						var dist = multi ? eudist(data[i], ks[j]) : Math.abs(data[i] - ks[j]);
+						if (dist <= min) {
+							min = dist;
+							_idx = j;
+						}
+					}
+					idxs[i] = _idx; // Index of the selected centroid for that value
+					count[_idx]++; // Number of values for this centroid
+				}
+
+				// Recalculate centroids
+				var sum = [],
+				    old = [],
+				    dif = 0;
+				for (var _j = 0; _j < k; _j++) {
+					// Multidimensional or unidimensional
+					sum[_j] = multi ? init(vlen, 0, sum[_j]) : 0;
+					old[_j] = ks[_j];
+				}
+
+				// If multidimensional
+				if (multi) {
+					for (var _j2 = 0; _j2 < k; _j2++) {
+						ks[_j2] = [];
+					} // Sum values and count for each centroid
+					for (var _i4 = 0; _i4 < len; _i4++) {
+						var _idx2 = idxs[_i4],
+						    // Centroid for that item
+						vsum = sum[_idx2],
+						    // Sum values for this centroid
+						vect = data[_i4]; // Current vector
+
+						// Accumulate value on the centroid for current vector
+						for (var h = 0; h < vlen; h++) {
+							vsum[h] += vect[h];
+						}
+					}
+					// Calculate the average for each centroid
+					conv = true;
+					for (var _j3 = 0; _j3 < k; _j3++) {
+						var ksj = ks[_j3],
+						    // Current centroid
+						sumj = sum[_j3],
+						    // Accumulated centroid values
+						oldj = old[_j3],
+						    // Old centroid value
+						cj = count[_j3]; // Number of elements for this centroid
+
+						// New average
+						for (var _h = 0; _h < vlen; _h++) {
+							ksj[_h] = sumj[_h] / cj || 0; // New centroid
+						}
+
+						// Find if centroids have moved
+						if (conv) {
+							for (var _h2 = 0; _h2 < vlen; _h2++) {
+								if (oldj[_h2] != ksj[_h2]) {
+									conv = false;
+									break;
+								}
+							}
+						}
+					}
+				}
+				// If unidimensional
+				else {
+						// Sum values and count for each centroid
+						for (var _i5 = 0; _i5 < len; _i5++) {
+							var _idx3 = idxs[_i5];
+							sum[_idx3] += data[_i5];
+						}
+						// Calculate the average for each centroid
+						for (var _j4 = 0; _j4 < k; _j4++) {
+							ks[_j4] = sum[_j4] / count[_j4] || 0; // New centroid
+						}
+						// Find if centroids have moved
+						conv = true;
+						for (var _j5 = 0; _j5 < k; _j5++) {
+							if (old[_j5] != ks[_j5]) {
+								conv = false;
+								break;
+							}
+						}
+					}
+
+				conv = conv || --it <= 0;
+			} while (!conv);
+
+			return {
+				it: MAX - it,
+				k: k,
+				idxs: idxs,
+				centroids: ks
+			};
+		}
+
+		module.exports = skmeans;
+	}, { "./distance.js": 2, "./kinit.js": 3 }] }, {}, [1]);
+//# sourceMappingURL=skmeans.js.map

frontend/node_modules/skmeans/dist/browser/skmeans.min.js

@@ -0,0 +1,3 @@
+/*! skmeans 2017-08-03 */
+
+"use strict";!function r(t,n,o){function a(i,f){if(!n[i]){if(!t[i]){var s="function"==typeof require&&require;if(!f&&s)return s(i,!0);if(e)return e(i,!0);var u=new Error("Cannot find module '"+i+"'");throw u.code="MODULE_NOT_FOUND",u}var v=n[i]={exports:{}};t[i][0].call(v.exports,function(r){var n=t[i][1][r];return a(n||r)},v,v.exports,r,t,n,o)}return n[i].exports}for(var e="function"==typeof require&&require,i=0;i<o.length;i++)a(o[i]);return a}({1:[function(r,t,n){(function(){this.skmeans;var o=r("./main.js");void 0!==n&&(void 0!==t&&t.exports&&(n=t.exports=o),n.skmeans=o),"undefined"!=typeof window&&(window.skmeans=o)}).call(this)},{"./main.js":4}],2:[function(r,t,n){t.exports={eudist:function(r,t,n){for(var o=r.length,a=0,e=0;e<o;e++){var i=(r[e]||0)-(t[e]||0);a+=i*i}return n?Math.sqrt(a):a},mandist:function(r,t,n){for(var o=r.length,a=0,e=0;e<o;e++)a+=Math.abs((r[e]||0)-(t[e]||0));return n?Math.sqrt(a):a},dist:function(r,t,n){var o=Math.abs(r-t);return n?o:o*o}}},{}],3:[function(r,t,n){var o=r("./distance.js"),a=o.eudist,e=o.dist;t.exports={kmrand:function(r,t){for(var n={},o=[],a=t<<2,e=r.length,i=r[0].length>0;o.length<t&&a-- >0;){var f=r[Math.floor(Math.random()*e)],s=i?f.join("_"):""+f;n[s]||(n[s]=!0,o.push(f))}if(o.length<t)throw new Error("Error initializating clusters");return o},kmpp:function(r,t){var n=r[0].length?a:e,o=[],i=r.length,f=r[0].length>0,s={},u=r[Math.floor(Math.random()*i)],v=f?u.join("_"):""+u;for(o.push(u),s[v]=!0;o.length<t;){for(var h=[],c=o.length,d=0,l=[],p=0;p<i;p++){for(var m=1/0,g=0;g<c;g++){var k=n(r[p],o[g]);k<=m&&(m=k)}h[p]=m}for(var M=0;M<i;M++)d+=h[M];for(var j=0;j<i;j++)l[j]={i:j,v:r[j],pr:h[j]/d,cs:0};l.sort(function(r,t){return r.pr-t.pr}),l[0].cs=l[0].pr;for(var x=1;x<i;x++)l[x].cs=l[x-1].cs+l[x].pr;for(var w=Math.random(),q=0;q<i-1&&l[q++].cs<w;);o.push(l[q-1].v)}return o}}},{"./distance.js":2}],4:[function(r,t,n){function o(r,t,n){n=n||[];for(var o=0;o<r;o++)n[o]=t;return n}var a=r("./distance.js"),e=r("./kinit.js"),i=a.eudist,f=(a.mandist,a.dist,e.kmrand),s=e.kmpp,u=1e4;t.exports=function(r,t,n,a){var e=[],v=[],h=[],c=[],d=!1,l=a||u,p=r.length,m=r[0].length,g=m>0,k=[];if(n)e="kmrand"==n?f(r,t):"kmpp"==n?s(r,t):n;else for(var M={};e.length<t;){var j=Math.floor(Math.random()*p);M[j]||(M[j]=!0,e.push(r[j]))}do{o(t,0,k);for(var x=0;x<p;x++){for(var w=1/0,q=0,b=0;b<t;b++)(c=g?i(r[x],e[b]):Math.abs(r[x]-e[b]))<=w&&(w=c,q=b);h[x]=q,k[q]++}for(var E=[],v=[],_=0;_<t;_++)E[_]=g?o(m,0,E[_]):0,v[_]=e[_];if(g){for(var y=0;y<t;y++)e[y]=[];for(var O=0;O<p;O++)for(var D=E[h[O]],N=r[O],U=0;U<m;U++)D[U]+=N[U];d=!0;for(var z=0;z<t;z++){for(var C=e[z],F=E[z],L=v[z],T=k[z],A=0;A<m;A++)C[A]=F[A]/T||0;if(d)for(var B=0;B<m;B++)if(L[B]!=C[B]){d=!1;break}}}else{for(var G=0;G<p;G++)E[h[G]]+=r[G];for(var H=0;H<t;H++)e[H]=E[H]/k[H]||0;d=!0;for(var I=0;I<t;I++)if(v[I]!=e[I]){d=!1;break}}d=d||--l<=0}while(!d);return{it:u-l,k:t,idxs:h,centroids:e}}},{"./distance.js":2,"./kinit.js":3}]},{},[1]);

frontend/node_modules/skmeans/dist/index.html

@@ -0,0 +1,15 @@
+<!doctype html>
+<html>
+<head>
+	<script src="browser/skmeans.js"></script>
+</head>
+<body>
+	<script>
+		var data = [1,12,13,4,25,21,22,3,14,5,11,2,23,24,15];
+		var res = skmeans(data,3);
+
+		console.log(data);
+		console.log(res);
+	</script>
+</body>
+</html>

frontend/node_modules/skmeans/dist/node/Gruntfile.js

@@ -0,0 +1,49 @@
+'use strict';
+
+module.exports = function (grunt) {
+	require('load-grunt-tasks')(grunt);
+
+	// Project configuration.
+	grunt.initConfig({
+		pkg: grunt.file.readJSON('package.json'),
+		browserify: {
+			dist: {
+				watch: true,
+				keepAlive: true,
+				files: {
+					'dist/browser/skmeans.js': ['browser.js']
+				}
+			}
+		},
+		babel: {
+			options: {
+				sourceMap: true,
+				presets: ['es2015']
+			},
+			dist: {
+				files: [{
+					expand: true,
+					src: ['*.js'],
+					dest: 'dist/node',
+					ext: '.js'
+				}, {
+					'dist/browser/skmeans.js': ['dist/browser/skmeans.js']
+				}]
+			}
+		},
+		uglify: {
+			options: {
+				banner: '/*! <%= pkg.name %> <%= grunt.template.today("yyyy-mm-dd") %> */\n'
+			},
+			dist: {
+				files: {
+					'dist/browser/skmeans.min.js': ['dist/browser/skmeans.js']
+				}
+			}
+		},
+		clean: ['dist/browser/*.js', 'dist/browser/*.map']
+	});
+
+	grunt.registerTask('default', ['browserify', 'babel', 'uglify']);
+};
+//# sourceMappingURL=Gruntfile.js.map

frontend/node_modules/skmeans/dist/node/Gruntfile.js.map

@@ -0,0 +1 @@
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frontend/node_modules/skmeans/dist/node/browser.js

@@ -0,0 +1,19 @@
+"use strict";
+
+(function () {
+  var root = this;
+  var previous_skmeans = root.skmeans;
+  var skmeans = require('./main.js');
+
+  if (typeof exports !== 'undefined') {
+    if (typeof module !== 'undefined' && module.exports) {
+      exports = module.exports = skmeans;
+    }
+    exports.skmeans = skmeans;
+  }
+
+  if (typeof window !== 'undefined') {
+    window.skmeans = skmeans;
+  }
+}).call(undefined);
+//# sourceMappingURL=browser.js.map

frontend/node_modules/skmeans/dist/node/browser.js.map

@@ -0,0 +1 @@
+{"version":3,"sources":["../../browser.js"],"names":["root","previous_skmeans","skmeans","require","exports","module","window","call"],"mappings":"AAAA;;AAEA,CAAC,YAAW;AACV,MAAIA,OAAO,IAAX;AACA,MAAIC,mBAAmBD,KAAKE,OAA5B;AACD,MAAIA,UAAUC,QAAQ,WAAR,CAAd;;AAEA,MAAI,OAAOC,OAAP,KAAmB,WAAvB,EAAqC;AAClC,QAAI,OAAOC,MAAP,KAAkB,WAAlB,IAAiCA,OAAOD,OAA5C,EAAsD;AACpDA,gBAAUC,OAAOD,OAAP,GAAiBF,OAA3B;AACD;AACDE,YAAQF,OAAR,GAAkBA,OAAlB;AACD;;AAEF,MAAG,OAAOI,MAAP,KAAkB,WAArB,EAAkC;AAC/BA,WAAOJ,OAAP,GAAiBA,OAAjB;AACD;AAEF,CAhBD,EAgBGK,IAhBH","file":"browser.js","sourcesContent":["\"use strict\";\n\n(function() {\n  var root = this\n  var previous_skmeans = root.skmeans;\n\tvar skmeans = require('./main.js');\n\n\tif( typeof exports !== 'undefined' ) {\n    if( typeof module !== 'undefined' && module.exports ) {\n      exports = module.exports = skmeans;\n    }\n    exports.skmeans = skmeans;\n  }\n\n\tif(typeof window !== 'undefined') {\n    window.skmeans = skmeans;\n  }\n\n}).call(this);\n"]}

frontend/node_modules/skmeans/dist/node/distance.js

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+"use strict";
+
+module.exports = {
+	/**
+  * Euclidean distance
+  */
+	eudist: function eudist(v1, v2, sqrt) {
+		var len = v1.length;
+		var sum = 0;
+
+		for (var i = 0; i < len; i++) {
+			var d = (v1[i] || 0) - (v2[i] || 0);
+			sum += d * d;
+		}
+		// Square root not really needed
+		return sqrt ? Math.sqrt(sum) : sum;
+	},
+	mandist: function mandist(v1, v2, sqrt) {
+		var len = v1.length;
+		var sum = 0;
+
+		for (var i = 0; i < len; i++) {
+			sum += Math.abs((v1[i] || 0) - (v2[i] || 0));
+		}
+
+		// Square root not really needed
+		return sqrt ? Math.sqrt(sum) : sum;
+	},
+
+
+	/**
+  * Unidimensional distance
+  */
+	dist: function dist(v1, v2, sqrt) {
+		var d = Math.abs(v1 - v2);
+		return sqrt ? d : d * d;
+	}
+};
+//# sourceMappingURL=distance.js.map

frontend/node_modules/skmeans/dist/node/distance.js.map

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+{"version":3,"sources":["../../distance.js"],"names":["module","exports","eudist","v1","v2","sqrt","len","length","sum","i","d","Math","mandist","abs","dist"],"mappings":";;AAAAA,OAAOC,OAAP,GAAiB;AAChB;;;AAGAC,OAJgB,kBAITC,EAJS,EAINC,EAJM,EAIHC,IAJG,EAIG;AAClB,MAAIC,MAAMH,GAAGI,MAAb;AACA,MAAIC,MAAM,CAAV;;AAEA,OAAI,IAAIC,IAAE,CAAV,EAAYA,IAAEH,GAAd,EAAkBG,GAAlB,EAAuB;AACtB,OAAIC,IAAI,CAACP,GAAGM,CAAH,KAAO,CAAR,KAAcL,GAAGK,CAAH,KAAO,CAArB,CAAR;AACAD,UAAOE,IAAEA,CAAT;AACA;AACD;AACA,SAAOL,OAAMM,KAAKN,IAAL,CAAUG,GAAV,CAAN,GAAuBA,GAA9B;AACA,EAde;AAgBhBI,QAhBgB,mBAgBRT,EAhBQ,EAgBLC,EAhBK,EAgBFC,IAhBE,EAgBI;AACnB,MAAIC,MAAMH,GAAGI,MAAb;AACA,MAAIC,MAAM,CAAV;;AAEA,OAAI,IAAIC,IAAE,CAAV,EAAYA,IAAEH,GAAd,EAAkBG,GAAlB,EAAuB;AACtBD,UAAOG,KAAKE,GAAL,CAAS,CAACV,GAAGM,CAAH,KAAO,CAAR,KAAcL,GAAGK,CAAH,KAAO,CAArB,CAAT,CAAP;AACA;;AAED;AACA,SAAOJ,OAAMM,KAAKN,IAAL,CAAUG,GAAV,CAAN,GAAuBA,GAA9B;AACA,EA1Be;;;AA4BhB;;;AAGAM,KA/BgB,gBA+BXX,EA/BW,EA+BRC,EA/BQ,EA+BLC,IA/BK,EA+BC;AAChB,MAAIK,IAAIC,KAAKE,GAAL,CAASV,KAAGC,EAAZ,CAAR;AACA,SAAOC,OAAMK,CAAN,GAAUA,IAAEA,CAAnB;AACA;AAlCe,CAAjB","file":"distance.js","sourcesContent":["module.exports = {\n\t/**\n\t * Euclidean distance\n\t */\n\teudist(v1,v2,sqrt) {\n\t\tvar len = v1.length;\n\t\tvar sum = 0;\n\n\t\tfor(let i=0;i<len;i++) {\n\t\t\tvar d = (v1[i]||0) - (v2[i]||0);\n\t\t\tsum += d*d;\n\t\t}\n\t\t// Square root not really needed\n\t\treturn sqrt? Math.sqrt(sum) : sum;\n\t},\n\n\tmandist(v1,v2,sqrt) {\n\t\tvar len = v1.length;\n\t\tvar sum = 0;\n\n\t\tfor(let i=0;i<len;i++) {\n\t\t\tsum += Math.abs((v1[i]||0) - (v2[i]||0));\n\t\t}\n\n\t\t// Square root not really needed\n\t\treturn sqrt? Math.sqrt(sum) : sum;\n\t},\n\n\t/**\n\t * Unidimensional distance\n\t */\n\tdist(v1,v2,sqrt) {\n\t\tvar d = Math.abs(v1-v2);\n\t\treturn sqrt? d : d*d;\n\t}\n\n}\n"]}

frontend/node_modules/skmeans/dist/node/kinit.js

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+"use strict";
+
+var Distance = require("./distance.js"),
+    eudist = Distance.eudist,
+    dist = Distance.dist;
+
+module.exports = {
+	kmrand: function kmrand(data, k) {
+		var map = {},
+		    ks = [],
+		    t = k << 2;
+		var len = data.length;
+		var multi = data[0].length > 0;
+
+		while (ks.length < k && t-- > 0) {
+			var d = data[Math.floor(Math.random() * len)];
+			var key = multi ? d.join("_") : "" + d;
+			if (!map[key]) {
+				map[key] = true;
+				ks.push(d);
+			}
+		}
+
+		if (ks.length < k) throw new Error("Error initializating clusters");else return ks;
+	},
+
+
+	/**
+  * K-means++ initial centroid selection
+  */
+	kmpp: function kmpp(data, k) {
+		var distance = data[0].length ? eudist : dist;
+		var ks = [],
+		    len = data.length;
+		var multi = data[0].length > 0;
+		var map = {};
+
+		// First random centroid
+		var c = data[Math.floor(Math.random() * len)];
+		var key = multi ? c.join("_") : "" + c;
+		ks.push(c);
+		map[key] = true;
+
+		// Retrieve next centroids
+		while (ks.length < k) {
+			// Min Distances between current centroids and data points
+			var dists = [],
+			    lk = ks.length;
+			var dsum = 0,
+			    prs = [];
+
+			for (var i = 0; i < len; i++) {
+				var min = Infinity;
+				for (var j = 0; j < lk; j++) {
+					var _dist = distance(data[i], ks[j]);
+					if (_dist <= min) min = _dist;
+				}
+				dists[i] = min;
+			}
+
+			// Sum all min distances
+			for (var _i = 0; _i < len; _i++) {
+				dsum += dists[_i];
+			}
+
+			// Probabilities and cummulative prob (cumsum)
+			for (var _i2 = 0; _i2 < len; _i2++) {
+				prs[_i2] = { i: _i2, v: data[_i2], pr: dists[_i2] / dsum, cs: 0 };
+			}
+
+			// Sort Probabilities
+			prs.sort(function (a, b) {
+				return a.pr - b.pr;
+			});
+
+			// Cummulative Probabilities
+			prs[0].cs = prs[0].pr;
+			for (var _i3 = 1; _i3 < len; _i3++) {
+				prs[_i3].cs = prs[_i3 - 1].cs + prs[_i3].pr;
+			}
+
+			// Randomize
+			var rnd = Math.random();
+
+			// Gets only the items whose cumsum >= rnd
+			var idx = 0;
+			while (idx < len - 1 && prs[idx++].cs < rnd) {}
+			ks.push(prs[idx - 1].v);
+			/*
+   let done = false;
+   while(!done) {
+   	// this is our new centroid
+   	c = prs[idx-1].v
+   	key = multi? c.join("_") : `${c}`;
+   	if(!map[key]) {
+   		map[key] = true;
+   		ks.push(c);
+   		done = true;
+   	}
+   	else {
+   		idx++;
+   	}
+   }
+   */
+		}
+
+		return ks;
+	}
+};
+//# sourceMappingURL=kinit.js.map

frontend/node_modules/skmeans/dist/node/main.js

@@ -0,0 +1,161 @@
+"use strict";
+
+/*jshint esversion: 6 */
+
+var Distance = require("./distance.js"),
+    ClusterInit = require("./kinit.js"),
+    eudist = Distance.eudist,
+    mandist = Distance.mandist,
+    dist = Distance.dist,
+    kmrand = ClusterInit.kmrand,
+    kmpp = ClusterInit.kmpp;
+
+var MAX = 10000;
+
+/**
+ * Inits an array with values
+ */
+function init(len, val, v) {
+	v = v || [];
+	for (var i = 0; i < len; i++) {
+		v[i] = val;
+	}return v;
+}
+
+function skmeans(data, k, initial, maxit) {
+	var ks = [],
+	    old = [],
+	    idxs = [],
+	    dist = [];
+	var conv = false,
+	    it = maxit || MAX;
+	var len = data.length,
+	    vlen = data[0].length,
+	    multi = vlen > 0;
+	var count = [];
+
+	if (!initial) {
+		var _idxs = {};
+		while (ks.length < k) {
+			var idx = Math.floor(Math.random() * len);
+			if (!_idxs[idx]) {
+				_idxs[idx] = true;
+				ks.push(data[idx]);
+			}
+		}
+	} else if (initial == "kmrand") {
+		ks = kmrand(data, k);
+	} else if (initial == "kmpp") {
+		ks = kmpp(data, k);
+	} else {
+		ks = initial;
+	}
+
+	do {
+		// Reset k count
+		init(k, 0, count);
+
+		// For each value in data, find the nearest centroid
+		for (var i = 0; i < len; i++) {
+			var min = Infinity,
+			    _idx = 0;
+			for (var j = 0; j < k; j++) {
+				// Multidimensional or unidimensional
+				var dist = multi ? eudist(data[i], ks[j]) : Math.abs(data[i] - ks[j]);
+				if (dist <= min) {
+					min = dist;
+					_idx = j;
+				}
+			}
+			idxs[i] = _idx; // Index of the selected centroid for that value
+			count[_idx]++; // Number of values for this centroid
+		}
+
+		// Recalculate centroids
+		var sum = [],
+		    old = [],
+		    dif = 0;
+		for (var _j = 0; _j < k; _j++) {
+			// Multidimensional or unidimensional
+			sum[_j] = multi ? init(vlen, 0, sum[_j]) : 0;
+			old[_j] = ks[_j];
+		}
+
+		// If multidimensional
+		if (multi) {
+			for (var _j2 = 0; _j2 < k; _j2++) {
+				ks[_j2] = [];
+			} // Sum values and count for each centroid
+			for (var _i = 0; _i < len; _i++) {
+				var _idx2 = idxs[_i],
+				    // Centroid for that item
+				vsum = sum[_idx2],
+				    // Sum values for this centroid
+				vect = data[_i]; // Current vector
+
+				// Accumulate value on the centroid for current vector
+				for (var h = 0; h < vlen; h++) {
+					vsum[h] += vect[h];
+				}
+			}
+			// Calculate the average for each centroid
+			conv = true;
+			for (var _j3 = 0; _j3 < k; _j3++) {
+				var ksj = ks[_j3],
+				    // Current centroid
+				sumj = sum[_j3],
+				    // Accumulated centroid values
+				oldj = old[_j3],
+				    // Old centroid value
+				cj = count[_j3]; // Number of elements for this centroid
+
+				// New average
+				for (var _h = 0; _h < vlen; _h++) {
+					ksj[_h] = sumj[_h] / cj || 0; // New centroid
+				}
+
+				// Find if centroids have moved
+				if (conv) {
+					for (var _h2 = 0; _h2 < vlen; _h2++) {
+						if (oldj[_h2] != ksj[_h2]) {
+							conv = false;
+							break;
+						}
+					}
+				}
+			}
+		}
+		// If unidimensional
+		else {
+				// Sum values and count for each centroid
+				for (var _i2 = 0; _i2 < len; _i2++) {
+					var _idx3 = idxs[_i2];
+					sum[_idx3] += data[_i2];
+				}
+				// Calculate the average for each centroid
+				for (var _j4 = 0; _j4 < k; _j4++) {
+					ks[_j4] = sum[_j4] / count[_j4] || 0; // New centroid
+				}
+				// Find if centroids have moved
+				conv = true;
+				for (var _j5 = 0; _j5 < k; _j5++) {
+					if (old[_j5] != ks[_j5]) {
+						conv = false;
+						break;
+					}
+				}
+			}
+
+		conv = conv || --it <= 0;
+	} while (!conv);
+
+	return {
+		it: MAX - it,
+		k: k,
+		idxs: idxs,
+		centroids: ks
+	};
+}
+
+module.exports = skmeans;
+//# sourceMappingURL=main.js.map