skmeans - npm Package Compare versions

Comparing version 0.6.1 to 0.7.0

dist/skmeans.js

@@ -38,3 +38,4 @@ "use strict";
 			}
-			return Math.sqrt(sum);
+			// Square root not really needed
+			return sum; //Math.sqrt(sum);
 		}
@@ -55,2 +56,9 @@
 
+		function equals(v1, v2, multi) {
+			var l = v1.length;
+			for (var i = 0; i < l; i++) {
+				if (v1[i] != v2[i]) return false;
+			}return true;
+		}
+
 		/**
@@ -68,7 +76,9 @@ * Inits an array with values
 			var ks = [],
+			    old = [],
 			    idxs = [],
-			    len = data.length;
+			    dist = [];
 			var conv = false,
 			    it = maxit || MAX;
-			var vlen = data[0].length,
+			var len = data.length,
+			    vlen = data[0].length,
 			    multi = vlen > 0;
@@ -119,20 +129,27 @@
 						var _idx = idxs[_i2],
-						    vsum = sum[_idx],
-						    vect = data[_i2];
+						    // Centroid for that item
+						vsum = sum[_idx],
+						    // Sum values for this centroid
+						vect = data[_i2]; // Current vector
+
+						// Accumulate value on the centroid for current vector
 						for (var h = 0; h < vlen; h++) {
 							vsum[h] += vect[h];
 						}
-						count[_idx]++;
+						count[_idx]++; // Number of values for this centroid
 					}
-					// Calculate de average for each centroid
-					// and de distance between old and new centroids
+					// Calculate the average for each centroid
 					conv = true;
 					for (var _j3 = 0; _j3 < k; _j3++) {
 						var ksj = ks[_j3],
-						    sumj = sum[_j3],
-						    oldj = old[_j3],
-						    cj = count[_j3];
+						    // Current centroid
+						sumj = sum[_j3],
+						    // Accumulated centroid values
+						oldj = old[_j3],
+						    // Old centroid value
+						cj = count[_j3]; // Number of elements for this centrois
+
 						// New average
 						for (var _h = 0; _h < vlen; _h++) {
-							ksj[_h] = sumj[_h] / cj || 0;
+							ksj[_h] = sumj[_h] / cj || 0; // New centroid
 						}
@@ -154,9 +171,9 @@ // Find if centroids have moved
 						for (var _i3 = 0; _i3 < len; _i3++) {
-							sum[idxs[_i3]] += data[_i3];
-							count[idxs[_i3]]++;
+							var _idx2 = idxs[_i3];
+							sum[_idx2] += data[_i3];
+							count[_idx2]++;
 						}
-						// Calculate de average for each centroid
-						// and de distance between old and new centroids
+						// Calculate the average for each centroid
 						for (var _j4 = 0; _j4 < k; _j4++) {
-							ks[_j4] = sum[_j4] / count[_j4] || 0;
+							ks[_j4] = sum[_j4] / count[_j4] || 0; // New centroid
 						}
@@ -163,0 +180,0 @@ // Find if centroids have moved

dist/skmeans.min.js

@@ -1,3 +0,3 @@
-/*! skmeans 2017-06-18 */
+/*! skmeans 2017-07-14 */
 
-"use strict";!function r(o,n,e){function t(a,i){if(!n[a]){if(!o[a]){var u="function"==typeof require&&require;if(!i&&u)return u(a,!0);if(f)return f(a,!0);var v=new Error("Cannot find module '"+a+"'");throw v.code="MODULE_NOT_FOUND",v}var s=n[a]={exports:{}};o[a][0].call(s.exports,function(r){var n=o[a][1][r];return t(n||r)},s,s.exports,r,o,n,e)}return n[a].exports}for(var f="function"==typeof require&&require,a=0;a<e.length;a++)t(e[a]);return t}({1:[function(r,o,n){!function(o){var n=r("./main.js");o.skmeans=n}(window)},{"./main.js":2}],2:[function(r,o,n){function e(r,o){for(var n=r.length,e=0,t=0;t<n;t++){var f=(r[t]||0)-(o[t]||0);e+=f*f}return Math.sqrt(e)}function t(r,o,n){n=n||[];for(var e=0;e<r;e++)n[e]=o;return n}var f=1e4;o.exports=function(r,o,n,a){var i=[],u=[],v=r.length,s=!1,c=a||f,h=r[0].length,l=h>0;if(n)i=n;else for(var d=0;d<o;d++)i.push(r[Math.floor(Math.random()*v)]);do{for(var p=0;p<v;p++){for(var x=1/0,m=0,q=0;q<o;q++){var w=l?e(r[p],i[q]):Math.abs(r[p]-i[q]);w<x&&(x=w,m=q)}u[p]=m}for(var M=[],g=[],k=[],b=0;b<o;b++)M[b]=0,g[b]=l?t(h,0,g[b]):0,k[b]=i[b];if(l){for(var O=0;O<o;O++)i[O]=[];for(var j=0;j<v;j++){for(var y=u[j],D=g[y],E=r[j],N=0;N<h;N++)D[N]+=E[N];M[y]++}s=!0;for(var U=0;U<o;U++){for(var _=i[U],C=g[U],F=k[U],L=M[U],T=0;T<h;T++)_[T]=C[T]/L||0;if(s)for(var z=0;z<h;z++)if(F[z]!=_[z]){s=!1;break}}}else{for(var A=0;A<v;A++)g[u[A]]+=r[A],M[u[A]]++;for(var B=0;B<o;B++)i[B]=g[B]/M[B]||0;s=!0;for(var G=0;G<o;G++)if(k[G]!=i[G]){s=!1;break}}s=s||--c<=0}while(!s);return{it:f-c,k:o,idxs:u,centroids:i}}},{}]},{},[1]);
+"use strict";!function r(o,n,e){function f(a,i){if(!n[a]){if(!o[a]){var u="function"==typeof require&&require;if(!i&&u)return u(a,!0);if(t)return t(a,!0);var v=new Error("Cannot find module '"+a+"'");throw v.code="MODULE_NOT_FOUND",v}var s=n[a]={exports:{}};o[a][0].call(s.exports,function(r){var n=o[a][1][r];return f(n||r)},s,s.exports,r,o,n,e)}return n[a].exports}for(var t="function"==typeof require&&require,a=0;a<e.length;a++)f(e[a]);return f}({1:[function(r,o,n){!function(o){var n=r("./main.js");o.skmeans=n}(window)},{"./main.js":2}],2:[function(r,o,n){function e(r,o){for(var n=r.length,e=0,f=0;f<n;f++){var t=(r[f]||0)-(o[f]||0);e+=t*t}return e}function f(r,o,n){n=n||[];for(var e=0;e<r;e++)n[e]=o;return n}var t=1e4;o.exports=function(r,o,n,a){var i=[],u=[],v=[],s=[],c=!1,l=a||t,h=r.length,d=r[0].length,p=d>0;if(n)i=n;else for(var x=0;x<o;x++)i.push(r[Math.floor(Math.random()*h)]);do{for(var m=0;m<h;m++){for(var w=1/0,g=0,k=0;k<o;k++)(s=p?e(r[m],i[k]):Math.abs(r[m]-i[k]))<w&&(w=s,g=k);v[m]=g}for(var q=[],M=[],u=[],b=0;b<o;b++)q[b]=0,M[b]=p?f(d,0,M[b]):0,u[b]=i[b];if(p){for(var O=0;O<o;O++)i[O]=[];for(var j=0;j<h;j++){for(var y=v[j],D=M[y],E=r[j],N=0;N<d;N++)D[N]+=E[N];q[y]++}c=!0;for(var U=0;U<o;U++){for(var _=i[U],C=M[U],F=u[U],L=q[U],T=0;T<d;T++)_[T]=C[T]/L||0;if(c)for(var z=0;z<d;z++)if(F[z]!=_[z]){c=!1;break}}}else{for(var A=0;A<h;A++){var B=v[A];M[B]+=r[A],q[B]++}for(var G=0;G<o;G++)i[G]=M[G]/q[G]||0;c=!0;for(var H=0;H<o;H++)if(u[H]!=i[H]){c=!1;break}}c=c||--l<=0}while(!c);return{it:t-l,k:o,idxs:v,centroids:i}}},{}]},{},[1]);

main.js

@@ -9,10 +9,11 @@ /*jshint esversion: 6 */
 function eudist(v1,v2) {
-		var len = v1.length;
-		var sum = 0;
+	var len = v1.length;
+	var sum = 0;
 
-		for(let i=0;i<len;i++) {
-			var d = (v1[i]||0) - (v2[i]||0);
-			sum += d*d;
-		}
-		return Math.sqrt(sum);
+	for(let i=0;i<len;i++) {
+		var d = (v1[i]||0) - (v2[i]||0);
+		sum += d*d;
+	}
+	// Square root not really needed
+	return sum;	//Math.sqrt(sum);
 }
@@ -24,11 +25,18 @@
 function mandist(v1,v2) {
-		var len = v1.length;
-		var sum = 0;
+	var len = v1.length;
+	var sum = 0;
 
-		for(let i=0;i<len;i++) {
-			sum += Math.abs((v1[i]||0) - (v2[i]||0));
-		}
-		return sum;
+	for(let i=0;i<len;i++) {
+		sum += Math.abs((v1[i]||0) - (v2[i]||0));
+	}
+	return sum;
 }
 
+function equals(v1,v2,multi) {
+	var l = v1.length;
+	for(var i=0;i<l;i++)
+		if(v1[i]!=v2[i]) return false;
+	return true;
+}
+
 /**
@@ -44,5 +52,5 @@ * Inits an array with values
 function skmeans(data,k,initial,maxit) {
-	var ks = [], idxs = [], len = data.length;
+	var ks = [], old = [], idxs = [], dist = [];
 	var conv = false, it = maxit || MAX;
-	var vlen = data[0].length, multi = vlen>0;
+	var len = data.length, vlen = data[0].length, multi = vlen>0;
 
@@ -88,16 +96,23 @@ if(!initial) {
 			for(let i=0;i<len;i++) {
-				let idx = idxs[i], vsum = sum[idx], vect = data[i];
+				let idx = idxs[i],	// Centroid for that item
+					vsum = sum[idx],	// Sum values for this centroid
+					vect = data[i];		// Current vector
+
+				// Accumulate value on the centroid for current vector
 				for(let h=0;h<vlen;h++) {
 					vsum[h] += vect[h];
 				}
-				count[idx]++;
+				count[idx]++;	// Number of values for this centroid
 			}
-			// Calculate de average for each centroid
-			// and de distance between old and new centroids
+			// Calculate the average for each centroid
 			conv = true;
 			for(let j=0;j<k;j++) {
-				let ksj = ks[j], sumj = sum[j],oldj = old[j], cj = count[j];
+				let ksj = ks[j],	// Current centroid
+					sumj = sum[j],	// Accumulated centroid values
+					oldj = old[j], 	// Old centroid value
+					cj = count[j];	// Number of elements for this centrois
+
 				// New average
 				for(let h=0;h<vlen;h++) {
-					ksj[h] = sumj[h]/cj || 0;
+					ksj[h] = sumj[h]/cj || 0;	// New centroid
 				}
@@ -119,9 +134,9 @@ // Find if centroids have moved
 			for(let i=0;i<len;i++) {
-				sum[idxs[i]] += data[i];
-				count[idxs[i]]++;
+				let idx = idxs[i];
+				sum[idx] += data[i];
+				count[idx]++;
 			}
-			// Calculate de average for each centroid
-			// and de distance between old and new centroids
+			// Calculate the average for each centroid
 			for(let j=0;j<k;j++) {
-				ks[j] = sum[j]/count[j] || 0;
+				ks[j] = sum[j]/count[j] || 0;	// New centroid
 			}
@@ -128,0 +143,0 @@ // Find if centroids have moved

package.json

 {
   "name": "skmeans",
-  "version": "0.6.1",
+  "version": "0.7.0",
   "description": "Super fast simple kmeans clustering for unidimiensional and multidimensional data. Works in node and browser",
@@ -5,0 +5,0 @@ "author": "David Gómez Matarrodona <[e-mail]>",

README.md

@@ -49,3 +49,3 @@ # skmeans
 ### skmeans(data,k,[centroids],[iterations])
-Calculates unidimiensional k-means clustering on *data*. Parameters are:
+Calculates unidimiensional and multidimensional k-means clustering on *data*. Parameters are:
 * **data** Unidimiensional or multidimensional array of values to be clustered. for unidimiensional data, takes the form of a simple array *[1,2,3.....,n]*. For multidimensional data, takes a
@@ -52,0 +52,0 @@ NxM array *[[1,2],[2,3]....[n,m]]*

test/multi.js

 const skmeans = require("../main.js");
 
 var data = [];
-for(let i=0;i<20;i++) {
+for(let i=0;i<2000;i++) {
 	var r = Math.random();
@@ -12,3 +12,2 @@ if(r>=0 && r<0.33) data.push([r,r*2]);
 var res = skmeans(data,3);
-console.log(data);
-console.log(res);
+console.log(res.it,res.centroids);

test/uni.js

 const skmeans = require("../main.js");
 
 var data = [];
-for(let i=0;i<20;i++) {
+for(let i=0;i<2000;i++) {
 	var r = Math.random();
@@ -11,4 +11,3 @@ if(r>=0 && r<0.33) data.push(r);
 
-var res = skmeans(data,3);
-console.log(data);
-console.log(res);
+var res = skmeans(data,3,[1,5,9]);
+console.log(res.it,res.centroids);

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Total package byte prevSize

26189

increased by7.44%

Lines of code

419

increased by6.08%

No dependency changes