skmeans - npm Package Compare versions

Comparing version 0.9.2 to 0.9.3

dist/skmeans.js

@@ -81,6 +81,10 @@ "use strict";
 				    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;
 
@@ -132,4 +136,15 @@ // Retrieve next centroids
 
-					// this is our new centroid
-					ks.push(prs[idx - 1].v);
+					var 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++;
+						}
+					}
 				}
@@ -228,3 +243,3 @@
 						    // Sum values for this centroid
-						vect = data[_idx2]; // Current vector
+						vect = data[_i4]; // Current vector
 
@@ -246,6 +261,7 @@ // Accumulate value on the centroid for current vector
 						    // Old centroid value
-						cj = count[_j3]; // Number of elements for this centrois
+						cj = count[_j3]; // Number of elements for this centroid
 
 						// New average
 						for (var _h = 0; _h < vlen; _h++) {
+							//ksj[h] = (sumj[h]+oldj[h])/(cj+1) || 0;	// New centroid
 							ksj[_h] = sumj[_h] / cj || 0; // New centroid
@@ -252,0 +268,0 @@ }

dist/skmeans.min.js

@@ -1,3 +0,3 @@
-/*! skmeans 2017-07-19 */
+/*! skmeans 2017-07-20 */
 
-"use strict";!function r(n,t,o){function a(i,f){if(!t[i]){if(!n[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=t[i]={exports:{}};n[i][0].call(v.exports,function(r){var t=n[i][1][r];return a(t||r)},v,v.exports,r,n,t,o)}return t[i].exports}for(var e="function"==typeof require&&require,i=0;i<o.length;i++)a(o[i]);return a}({1:[function(r,n,t){!function(n){var t=r("./main.js");n.skmeans=t}(window)},{"./main.js":4}],2:[function(r,n,t){n.exports={eudist:function(r,n,t){for(var o=r.length,a=0,e=0;e<o;e++){var i=(r[e]||0)-(n[e]||0);a+=i*i}return t?Math.sqrt(a):a},dist:function(r,n,t){var o=Math.abs(r-n);return t?o:o*o}}},{}],3:[function(r,n,t){var o=r("./distance.js"),a=o.eudist,e=o.dist;n.exports={kmrand:function(r,n){for(var t={},o=[],a=n<<2,e=r.length,i=r[0].length>0;o.length<n&&a-- >0;){var f=r[Math.floor(Math.random()*e)],s=i?f.join("_"):""+f;t[s]||(t[s]=!0,o.push(f))}if(o.length<n)throw new Error("Error initializating clusters");return o},kmpp:function(r,n){var t=r[0].length?a:e,o=[],i=r.length,f=r[Math.floor(Math.random()*i)];for(o.push(f);o.length<n;){for(var s=[],u=o.length,v=0,c=[],h=0;h<i;h++){for(var d=1/0,l=0;l<u;l++){var p=t(r[h],o[l]);p<=d&&(d=p)}s[h]=d}for(var g=0;g<i;g++)v+=s[g];for(var m=0;m<i;m++)c[m]={i:m,v:r[m],pr:s[m]/v,cs:0};c.sort(function(r,n){return r.pr-n.pr}),c[0].cs=c[0].pr;for(var k=1;k<i;k++)c[k].cs=c[k-1].cs+c[k].pr;for(var M=Math.random(),j=0;j<i-1&&c[j++].cs>=M;);o.push(c[j-1].v)}return o}}},{"./distance.js":2}],4:[function(r,n,t){function o(r,n,t){t=t||[];for(var o=0;o<r;o++)t[o]=n;return t}var a=r("./distance.js"),e=r("./kinit.js"),i=a.eudist,f=(a.dist,e.kmrand),s=e.kmpp,u=1e4;n.exports=function(r,n,t,a){var e=[],v=[],c=[],h=[],d=!1,l=a||u,p=r.length,g=r[0].length,m=g>0;if(t)e="kmrand"==t?f(r,n):"kmpp"==t?s(r,n):t;else for(var k={};e.length<n;){var M=Math.floor(Math.random()*p);k[M]||(k[M]=!0,e.push(r[M]))}do{for(var j=0;j<p;j++){for(var x=1/0,w=0,q=0;q<n;q++)(h=m?i(r[j],e[q]):Math.abs(r[j]-e[q]))<=x&&(x=h,w=q);c[j]=w}for(var b=[],E=[],v=[],O=0;O<n;O++)b[O]=0,E[O]=m?o(g,0,E[O]):0,v[O]=e[O];if(m){for(var _=0;_<n;_++)e[_]=[];for(var y=0;y<p;y++){for(var D=c[y],N=E[D],U=r[D],z=0;z<g;z++)N[z]+=U[z];b[D]++}d=!0;for(var C=0;C<n;C++){for(var F=e[C],L=E[C],T=v[C],A=b[C],B=0;B<g;B++)F[B]=L[B]/A||0;if(d)for(var G=0;G<g;G++)if(T[G]!=F[G]){d=!1;break}}}else{for(var H=0;H<p;H++){var I=c[H];E[I]+=r[H],b[I]++}for(var J=0;J<n;J++)e[J]=E[J]/b[J]||0;d=!0;for(var K=0;K<n;K++)if(v[K]!=e[K]){d=!1;break}}d=d||--l<=0}while(!d);return{it:u-l,k:n,idxs:c,centroids:e}}},{"./distance.js":2,"./kinit.js":3}]},{},[1]);
+"use strict";!function r(n,t,o){function a(i,f){if(!t[i]){if(!n[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=t[i]={exports:{}};n[i][0].call(v.exports,function(r){var t=n[i][1][r];return a(t||r)},v,v.exports,r,n,t,o)}return t[i].exports}for(var e="function"==typeof require&&require,i=0;i<o.length;i++)a(o[i]);return a}({1:[function(r,n,t){!function(n){var t=r("./main.js");n.skmeans=t}(window)},{"./main.js":4}],2:[function(r,n,t){n.exports={eudist:function(r,n,t){for(var o=r.length,a=0,e=0;e<o;e++){var i=(r[e]||0)-(n[e]||0);a+=i*i}return t?Math.sqrt(a):a},dist:function(r,n,t){var o=Math.abs(r-n);return t?o:o*o}}},{}],3:[function(r,n,t){var o=r("./distance.js"),a=o.eudist,e=o.dist;n.exports={kmrand:function(r,n){for(var t={},o=[],a=n<<2,e=r.length,i=r[0].length>0;o.length<n&&a-- >0;){var f=r[Math.floor(Math.random()*e)],s=i?f.join("_"):""+f;t[s]||(t[s]=!0,o.push(f))}if(o.length<n)throw new Error("Error initializating clusters");return o},kmpp:function(r,n){var t=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<n;){for(var c=[],h=o.length,l=0,d=[],p=0;p<i;p++){for(var g=1/0,m=0;m<h;m++){var k=t(r[p],o[m]);k<=g&&(g=k)}c[p]=g}for(var j=0;j<i;j++)l+=c[j];for(var M=0;M<i;M++)d[M]={i:M,v:r[M],pr:c[M]/l,cs:0};d.sort(function(r,n){return r.pr-n.pr}),d[0].cs=d[0].pr;for(var x=1;x<i;x++)d[x].cs=d[x-1].cs+d[x].pr;for(var w=Math.random(),q=0;q<i-1&&d[q++].cs>=w;);for(var _=!1;!_;)u=d[q-1].v,s[v=f?u.join("_"):""+u]?q++:(s[v]=!0,o.push(u),_=!0)}return o}}},{"./distance.js":2}],4:[function(r,n,t){function o(r,n,t){t=t||[];for(var o=0;o<r;o++)t[o]=n;return t}var a=r("./distance.js"),e=r("./kinit.js"),i=a.eudist,f=(a.dist,e.kmrand),s=e.kmpp,u=1e4;n.exports=function(r,n,t,a){var e=[],v=[],c=[],h=[],l=!1,d=a||u,p=r.length,g=r[0].length,m=g>0;if(t)e="kmrand"==t?f(r,n):"kmpp"==t?s(r,n):t;else for(var k={};e.length<n;){var j=Math.floor(Math.random()*p);k[j]||(k[j]=!0,e.push(r[j]))}do{for(var M=0;M<p;M++){for(var x=1/0,w=0,q=0;q<n;q++)(h=m?i(r[M],e[q]):Math.abs(r[M]-e[q]))<=x&&(x=h,w=q);c[M]=w}for(var _=[],b=[],v=[],E=0;E<n;E++)_[E]=0,b[E]=m?o(g,0,b[E]):0,v[E]=e[E];if(m){for(var O=0;O<n;O++)e[O]=[];for(var y=0;y<p;y++){for(var D=c[y],N=b[D],U=r[y],z=0;z<g;z++)N[z]+=U[z];_[D]++}l=!0;for(var C=0;C<n;C++){for(var F=e[C],L=b[C],T=v[C],A=_[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<p;H++){var I=c[H];b[I]+=r[H],_[I]++}for(var J=0;J<n;J++)e[J]=b[J]/_[J]||0;l=!0;for(var K=0;K<n;K++)if(v[K]!=e[K]){l=!1;break}}l=l||--d<=0}while(!l);return{it:u-d,k:n,idxs:c,centroids:e}}},{"./distance.js":2,"./kinit.js":3}]},{},[1]);

kinit.js

@@ -31,6 +31,10 @@ const
 		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;
 
@@ -78,4 +82,16 @@ // Retrieve next centroids
 
-			// this is our new centroid
-			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++;
+				}
+			}
 		}
@@ -82,0 +98,0 @@

main.js

@@ -79,3 +79,3 @@ /*jshint esversion: 6 */
 					vsum = sum[idx],	// Sum values for this centroid
-					vect = data[idx];	// Current vector
+					vect = data[i];		// Current vector
 
@@ -94,7 +94,8 @@ // Accumulate value on the centroid for current vector
 					oldj = old[j], 	// Old centroid value
-					cj = count[j];	// Number of elements for this centrois
+					cj = count[j];	// Number of elements for this centroid
 
 				// New average
 				for(let h=0;h<vlen;h++) {
-					ksj[h] = sumj[h]/cj || 0;	// New centroid
+					//ksj[h] = (sumj[h]+oldj[h])/(cj+1) || 0;	// New centroid
+					ksj[h] = (sumj[h])/(cj) || 0;	// New centroid
 				}
@@ -101,0 +102,0 @@ // Find if centroids have moved

package.json

 {
   "name": "skmeans",
-  "version": "0.9.2",
+  "version": "0.9.3",
   "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]>",

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66183

increased by13.06%

Number of package files

23

increased by27.78%

Lines of code

689

increased by9.19%

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decreased by-100%

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