skmeans - npm Package Compare versions

Comparing version 0.10.0 to 0.10.1

dist/browser/skmeans.js

 "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 () {
+  function r(e, n, t) {
+    function o(i, f) {
+      if (!n[i]) {
+        if (!e[i]) {
+          var c = "function" == typeof require && require;
+          if (!f && c) return c(i, !0);
+          if (u) return u(i, !0);
+          var a = new Error("Cannot find module '" + i + "'");
+          throw a.code = "MODULE_NOT_FOUND", a;
+        }
 
-		(function () {
-			var root = this;
-			var previous_skmeans = root.skmeans;
-			var skmeans = require('./main.js');
+        var p = n[i] = {
+          exports: {}
+        };
+        e[i][0].call(p.exports, function (r) {
+          var n = e[i][1][r];
+          return o(n || r);
+        }, p, p.exports, r, e, n, t);
+      }
 
-			if (typeof exports !== 'undefined') {
-				if (typeof module !== 'undefined' && module.exports) {
-					exports = module.exports = skmeans;
-				}
-				exports.skmeans = skmeans;
-			}
+      return n[i].exports;
+    }
 
-			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 u = "function" == typeof require && require, i = 0; i < t.length; i++) {
+      o(t[i]);
+    }
 
-				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;
+    return o;
+  }
 
-				for (var i = 0; i < len; i++) {
-					sum += Math.abs((v1[i] || 0) - (v2[i] || 0));
-				}
+  return r;
+})()({
+  1: [function (require, module, exports) {
+    "use strict";
 
-				// Square root not really needed
-				return sqrt ? Math.sqrt(sum) : sum;
-			},
+    (function () {
+      var root = this;
+      var previous_skmeans = root.skmeans;
 
+      var skmeans = require('./main.js');
 
-			/**
-    * 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;
+      if (typeof exports !== 'undefined') {
+        if (typeof module !== 'undefined' && module.exports) {
+          exports = module.exports = skmeans;
+        }
 
-		module.exports = {
-			kmrand: function kmrand(data, k) {
-				var map = {},
-				    ks = [],
-				    t = k << 2;
-				var len = data.length;
-				var multi = data[0].length > 0;
+        exports.skmeans = skmeans;
+      }
 
-				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 (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;
 
-				if (ks.length < k) throw new Error("Error initializating clusters");else return ks;
-			},
+        for (var i = 0; i < len; i++) {
+          var d = (v1[i] || 0) - (v2[i] || 0);
+          sum += d * d;
+        } // Square root not really needed
 
 
-			/**
-    * 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 = {};
+        return sqrt ? Math.sqrt(sum) : sum;
+      },
+      mandist: function mandist(v1, v2, sqrt) {
+        var len = v1.length;
+        var sum = 0;
 
-				// First random centroid
-				var c = data[Math.floor(Math.random() * len)];
-				var key = multi ? c.join("_") : "" + c;
-				ks.push(c);
-				map[key] = true;
+        for (var i = 0; i < len; i++) {
+          sum += Math.abs((v1[i] || 0) - (v2[i] || 0));
+        } // Square root not really needed
 
-				// 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;
-					}
+        return sqrt ? Math.sqrt(sum) : sum;
+      },
 
-					// Sum all min distances
-					for (var _i = 0; _i < len; _i++) {
-						dsum += dists[_i];
-					}
+      /**
+       * 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;
 
-					// 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 };
-					}
+    module.exports = {
+      kmrand: function kmrand(data, k) {
+        var map = {},
+            ks = [],
+            t = k << 2;
+        var len = data.length;
+        var multi = data[0].length > 0;
 
-					// Sort Probabilities
-					prs.sort(function (a, b) {
-						return a.pr - b.pr;
-					});
+        while (ks.length < k && t-- > 0) {
+          var d = data[Math.floor(Math.random() * len)];
+          var key = multi ? d.join("_") : "".concat(d);
 
-					// 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;
-					}
+          if (!map[key]) {
+            map[key] = true;
+            ks.push(d);
+          }
+        }
 
-					// Randomize
-					var rnd = Math.random();
+        if (ks.length < k) throw new Error("Error initializating clusters");else return ks;
+      },
 
-					// 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++;
-     	}
-     }
+      /**
+       * 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("_") : "".concat(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
      */
-				}
 
-				return ks;
-			}
-		};
-	}, { "./distance.js": 2 }], 4: [function (require, module, exports) {
-		/*jshint esversion: 6 */
+    function init(len, val, v) {
+      v = v || [];
 
-		var Distance = require("./distance.js"),
-		    ClusterInit = require("./kinit.js"),
-		    eudist = Distance.eudist,
-		    mandist = Distance.mandist,
-		    dist = Distance.dist,
-		    kmrand = ClusterInit.kmrand,
-		    kmpp = ClusterInit.kmpp;
+      for (var i = 0; i < len; i++) {
+        v[i] = val;
+      }
 
-		var MAX = 10000;
+      return v;
+    }
 
-		/**
-   * 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, fndist) {
+      var ks = [],
+          old = [],
+          idxs = [],
+          dist = [];
+      var conv = false,
+          it = maxit || MAX;
+      var len = data.length,
+          vlen = data[0].length,
+          multi = vlen > 0;
+      var count = [];
 
-		function skmeans(data, k, initial, maxit, fndist) {
-			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 = {};
 
-			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;
-			}
+        while (ks.length < k) {
+          var idx = Math.floor(Math.random() * len);
 
-			do {
-				// Reset k count
-				init(k, 0, count);
+          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;
+      }
 
-				// 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++) {
-						// Custom, Multidimensional or unidimensional
-						var dist = fndist ? fndist(data[i], ks[k]) : multi ? eudist(data[i], ks[j]) : Math.abs(data[i] - ks[j]);
+      do {
+        // Reset k count
+        init(k, 0, count); // For each value in data, find the nearest centroid
 
-						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
-				}
+        for (var i = 0; i < len; i++) {
+          var min = Infinity,
+              _idx = 0;
 
-				// 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];
-				}
+          for (var j = 0; j < k; j++) {
+            // Custom, Multidimensional or unidimensional
+            var dist = fndist ? fndist(data[i], ks[k]) : multi ? eudist(data[i], ks[j]) : Math.abs(data[i] - 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
+            if (dist <= min) {
+              min = dist;
+              _idx = j;
+            }
+          }
 
-						// 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
+          idxs[i] = _idx; // Index of the selected centroid for that value
 
-						// New average
-						for (var _h = 0; _h < vlen; _h++) {
-							ksj[_h] = sumj[_h] / cj || 0; // New centroid
-						}
+          count[_idx]++; // Number of values for this centroid
+        } // Recalculate centroids
 
-						// 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);
+        var sum = [],
+            old = [],
+            dif = 0;
 
-			return {
-				it: MAX - it,
-				k: k,
-				idxs: idxs,
-				centroids: ks
-			};
-		}
+        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
 
-		module.exports = skmeans;
-	}, { "./distance.js": 2, "./kinit.js": 3 }] }, {}, [1]);
+
+        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

dist/browser/skmeans.min.js

@@ -1,3 +0,3 @@
-/*! skmeans 2019-03-03 */
+/*! skmeans 2019-04-13 */
 
-"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,e){var v=[],h=[],c=[],d=[],l=!1,p=a||u,m=r.length,g=r[0].length,k=g>0,M=[];if(n)v="kmrand"==n?f(r,t):"kmpp"==n?s(r,t):n;else for(var j={};v.length<t;){var x=Math.floor(Math.random()*m);j[x]||(j[x]=!0,v.push(r[x]))}do{o(t,0,M);for(var w=0;w<m;w++){for(var q=1/0,b=0,E=0;E<t;E++)(d=e?e(r[w],v[t]):k?i(r[w],v[E]):Math.abs(r[w]-v[E]))<=q&&(q=d,b=E);c[w]=b,M[b]++}for(var _=[],h=[],y=0;y<t;y++)_[y]=k?o(g,0,_[y]):0,h[y]=v[y];if(k){for(var O=0;O<t;O++)v[O]=[];for(var D=0;D<m;D++)for(var N=_[c[D]],U=r[D],z=0;z<g;z++)N[z]+=U[z];l=!0;for(var C=0;C<t;C++){for(var F=v[C],L=_[C],T=h[C],A=M[C],B=0;B<g;B++)F[B]=L[B]/A||0;if(l)for(var G=0;G<g;G++)if(T[G]!=F[G]){l=!1;break}}}else{for(var H=0;H<m;H++)_[c[H]]+=r[H];for(var I=0;I<t;I++)v[I]=_[I]/M[I]||0;l=!0;for(var J=0;J<t;J++)if(h[J]!=v[J]){l=!1;break}}l=l||--p<=0}while(!l);return{it:u-p,k:t,idxs:c,centroids:v}}},{"./distance.js":2,"./kinit.js":3}]},{},[1]);
+"use strict";!function e(i,f,s){function u(t,r){if(!f[t]){if(!i[t]){var n="function"==typeof require&&require;if(!r&&n)return n(t,!0);if(v)return v(t,!0);var o=new Error("Cannot find module '"+t+"'");throw o.code="MODULE_NOT_FOUND",o}var a=f[t]={exports:{}};i[t][0].call(a.exports,function(r){return u(i[t][1][r]||r)},a,a.exports,e,i,f,s)}return f[t].exports}for(var v="function"==typeof require&&require,r=0;r<s.length;r++)u(s[r]);return u}({1:[function(t,n,o){(function(){this.skmeans;var r=t("./main.js");void 0!==o&&(void 0!==n&&n.exports&&(o=n.exports=r),o.skmeans=r),"undefined"!=typeof window&&(window.skmeans=r)}).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"),j=o.eudist,x=o.dist;t.exports={kmrand:function(r,t){for(var n={},o=[],a=t<<2,e=r.length,i=0<r[0].length;o.length<t&&0<a--;){var f=r[Math.floor(Math.random()*e)],s=i?f.join("_"):"".concat(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?j:x,o=[],a=r.length,e=0<r[0].length,i=r[Math.floor(Math.random()*a)];e?i.join("_"):"".concat(i);for(o.push(i);o.length<t;){for(var f=[],s=o.length,u=0,v=[],c=0;c<a;c++){for(var h=1/0,d=0;d<s;d++){var l=n(r[c],o[d]);l<=h&&(h=l)}f[c]=h}for(var p=0;p<a;p++)u+=f[p];for(var m=0;m<a;m++)v[m]={i:m,v:r[m],pr:f[m]/u,cs:0};v.sort(function(r,t){return r.pr-t.pr}),v[0].cs=v[0].pr;for(var g=1;g<a;g++)v[g].cs=v[g-1].cs+v[g].pr;for(var k=Math.random(),M=0;M<a-1&&v[M++].cs<k;);o.push(v[M-1].v)}return o}}},{"./distance.js":2}],4:[function(r,t,n){var o=r("./distance.js"),a=r("./kinit.js"),B=o.eudist,G=(o.mandist,o.dist,a.kmrand),H=a.kmpp;function I(r,t,n){n=n||[];for(var o=0;o<r;o++)n[o]=t;return n}t.exports=function(r,t,n,o,a){var e=[],i=[],f=[],s=[],u=!1,v=o||1e4,c=r.length,h=r[0].length,d=0<h,l=[];if(n)e="kmrand"==n?G(r,t):"kmpp"==n?H(r,t):n;else for(var p={};e.length<t;){var m=Math.floor(Math.random()*c);p[m]||(p[m]=!0,e.push(r[m]))}do{I(t,0,l);for(var g=0;g<c;g++){for(var k=1/0,M=0,j=0;j<t;j++)(s=a?a(r[g],e[t]):d?B(r[g],e[j]):Math.abs(r[g]-e[j]))<=k&&(k=s,M=j);l[f[g]=M]++}for(var x=[],w=(i=[],0);w<t;w++)x[w]=d?I(h,0,x[w]):0,i[w]=e[w];if(d){for(var q=0;q<t;q++)e[q]=[];for(var b=0;b<c;b++)for(var E=x[f[b]],_=r[b],y=0;y<h;y++)E[y]+=_[y];u=!0;for(var O=0;O<t;O++){for(var D=e[O],N=x[O],U=i[O],z=l[O],C=0;C<h;C++)D[C]=N[C]/z||0;if(u)for(var F=0;F<h;F++)if(U[F]!=D[F]){u=!1;break}}}else{for(var L=0;L<c;L++)x[f[L]]+=r[L];for(var T=0;T<t;T++)e[T]=x[T]/l[T]||0;u=!0;for(var A=0;A<t;A++)if(i[A]!=e[A]){u=!1;break}}u=u||--v<=0}while(!u);return{it:1e4-v,k:t,idxs:f,centroids:e}}},{"./distance.js":2,"./kinit.js":3}]},{},[1]);

dist/node/browser.js

@@ -6,2 +6,3 @@ "use strict";
   var previous_skmeans = root.skmeans;
+
   var skmeans = require('./main.js');
@@ -13,2 +14,3 @@
     }
+
     exports.skmeans = skmeans;
@@ -20,3 +22,3 @@ }
   }
-}).call(undefined);
+}).call(void 0);
 //# sourceMappingURL=browser.js.map

dist/node/distance.js

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

dist/node/Gruntfile.js

@@ -1,49 +0,48 @@
-'use strict';
+"use strict";
 
 module.exports = function (grunt) {
-	require('load-grunt-tasks')(grunt);
+  require('load-grunt-tasks')(grunt); // Project configuration.
 
-	// 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']);
+  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: ['@babel/preset-env']
+      },
+      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

dist/node/kinit.js

@@ -8,104 +8,113 @@ "use strict";
 module.exports = {
-	kmrand: function kmrand(data, k) {
-		var map = {},
-		    ks = [],
-		    t = k << 2;
-		var len = data.length;
-		var multi = data[0].length > 0;
+  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);
-			}
-		}
+    while (ks.length < k && t-- > 0) {
+      var d = data[Math.floor(Math.random() * len)];
+      var key = multi ? d.join("_") : "".concat(d);
 
-		if (ks.length < k) throw new Error("Error initializating clusters");else return ks;
-	},
+      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 = {};
+  /**
+   * 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
 
-		// First random centroid
-		var c = data[Math.floor(Math.random() * len)];
-		var key = multi ? c.join("_") : "" + c;
-		ks.push(c);
-		map[key] = true;
+    var c = data[Math.floor(Math.random() * len)];
+    var key = multi ? c.join("_") : "".concat(c);
+    ks.push(c);
+    map[key] = true; // Retrieve next centroids
 
-		// Retrieve next centroids
-		while (ks.length < k) {
-			// Min Distances between current centroids and data points
-			var dists = [],
-			    lk = ks.length;
-			var dsum = 0,
-			    prs = [];
+    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;
-			}
+      for (var i = 0; i < len; i++) {
+        var min = Infinity;
 
-			// Sum all min distances
-			for (var _i = 0; _i < len; _i++) {
-				dsum += dists[_i];
-			}
+        for (var j = 0; j < lk; j++) {
+          var _dist = distance(data[i], ks[j]);
 
-			// 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 };
-			}
+          if (_dist <= min) min = _dist;
+        }
 
-			// Sort Probabilities
-			prs.sort(function (a, b) {
-				return a.pr - b.pr;
-			});
+        dists[i] = min;
+      } // Sum all min distances
 
-			// 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();
+      for (var _i = 0; _i < len; _i++) {
+        dsum += dists[_i];
+      } // Probabilities and cummulative prob (cumsum)
 
-			// 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;
-	}
+      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

dist/node/main.js

 "use strict";
 
 /*jshint esversion: 6 */
-
 var Distance = require("./distance.js"),
@@ -14,147 +13,161 @@ ClusterInit = require("./kinit.js"),
 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;
+  v = v || [];
+
+  for (var i = 0; i < len; i++) {
+    v[i] = val;
+  }
+
+  return v;
 }
 
 function skmeans(data, k, initial, maxit, fndist) {
-	var ks = [],
-	    old = [],
-	    idxs = [],
-	    dist = [];
-	var conv = false,
-	    it = maxit || MAX;
-	var len = data.length,
-	    vlen = data[0].length,
-	    multi = vlen > 0;
-	var count = [];
+  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;
-	}
+  if (!initial) {
+    var _idxs = {};
 
-	do {
-		// Reset k count
-		init(k, 0, count);
+    while (ks.length < k) {
+      var idx = Math.floor(Math.random() * len);
 
-		// 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++) {
-				// Custom, Multidimensional or unidimensional
-				var dist = fndist ? fndist(data[i], ks[k]) : multi ? eudist(data[i], ks[j]) : Math.abs(data[i] - ks[j]);
+      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;
+  }
 
-				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
-		}
+  do {
+    // Reset k count
+    init(k, 0, count); // For each value in data, find the nearest 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];
-		}
+    for (var i = 0; i < len; i++) {
+      var min = Infinity,
+          _idx = 0;
 
-		// 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
+      for (var j = 0; j < k; j++) {
+        // Custom, Multidimensional or unidimensional
+        var dist = fndist ? fndist(data[i], ks[k]) : multi ? eudist(data[i], ks[j]) : Math.abs(data[i] - ks[j]);
 
-				// 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
+        if (dist <= min) {
+          min = dist;
+          _idx = j;
+        }
+      }
 
-				// New average
-				for (var _h = 0; _h < vlen; _h++) {
-					ksj[_h] = sumj[_h] / cj || 0; // New centroid
-				}
+      idxs[i] = _idx; // Index of the selected centroid for that value
 
-				// 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;
-					}
-				}
-			}
+      count[_idx]++; // Number of values for this centroid
+    } // Recalculate centroids
 
-		conv = conv || --it <= 0;
-	} while (!conv);
 
-	return {
-		it: MAX - it,
-		k: k,
-		idxs: idxs,
-		centroids: ks
-	};
+    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
+  };
 }
@@ -161,0 +174,0 @@

Gruntfile.js

@@ -19,3 +19,3 @@ module.exports = function(grunt) {
 				sourceMap: true,
-				presets: ['es2015']
+				presets: ['@babel/preset-env']
 			},
@@ -22,0 +22,0 @@ dist: {

package.json

 {
   "name": "skmeans",
-  "version": "0.10.0",
+  "version": "0.10.1",
   "description": "Super fast simple k-means and k-means++ clustering for unidimiensional and multidimensional data. Works in node and browser",
@@ -25,11 +25,11 @@ "author": "David Gómez Matarrodona <[e-mail]>",
   "devDependencies": {
-    "babel-preset-es2015": "^6.24.1",
-    "grunt": "^1.0.1",
-    "grunt-babel": "^6.0.0",
-    "grunt-browserify": "^5.0.0",
-    "grunt-contrib-clean": "^1.1.0",
-    "grunt-contrib-uglify": "^3.0.1",
-    "load-grunt-tasks": "^3.5.2",
-    "uglify": "^0.1.5"
+    "@babel/core": "^7.4.3",
+    "@babel/preset-env": "^7.4.3",
+    "grunt": "^1.0.4",
+    "grunt-babel": "^8.0.0",
+    "grunt-browserify": "^5.3.0",
+    "grunt-contrib-clean": "^2.0.0",
+    "grunt-contrib-uglify": "^4.0.1",
+    "load-grunt-tasks": "^4.0.0"
   }
 }

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increased by7.82%

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increased by0.54%

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