orangeduck · niccokunzmann · Dec 3, 2012 · Dec 16, 2012 · Dec 16, 2012 · Dec 24, 2012
diff --git a/.gitattributes b/.gitattributes
@@ -0,0 +1,12 @@
+*.sh            text eol=lf
+*.php           text eol=lf
+*.inc           text eol=lf
+*.html          text eol=lf
+*.js            text eol=lf
+*.css           text eol=lf
+*.ini           text eol=lf
+*.txt           text eol=lf
+*.xml           text eol=lf
+*.md            text eol=lf
+*.markdown      text eol=lf
+
diff --git a/.gitignore b/.gitignore
@@ -19,6 +19,9 @@
 *.rar
 *.tar
 *.zip
+
+# installed NPM packages	     
+node_modules/
 
 # Emacs backups
 *~

diff --git a/README.md b/README.md
@@ -1,5 +1,48 @@
-Numeric Javascript by Sébastien Loisel
-======================================
+This is a fork of Numeric Javascript by Sébastien Loisel.
+
+Changes
+------
+
+Notable changes compared with numeric-1.2.6.js
+
+- numeric.jacobi(A, maxiter), that diagonalizes symmetric real matrices (real hermitian). No problems with repeated eigenvalues/symmetry.
+
+- numeric.eigh(A, maxiter), mirrors numeric.jacobi.
+
+- numeric.jacobinorm(A), helper for numeric.jacobi, computes the weight in the upper triangular part of a square matrix.
+
+- extra iterative solvers:
+
+  + numeric.ccsMV(A, x), CCS matrix mul dense vector helper
+
+  + numeric.bicgstab(A, b, maxIters, residue), BICGSTAB algorithm
+
+  + numeric.cg(A, b, maxIters, residue), conjugate gradient (CG) solver. Supports both full matrix and sparse matrix.
+
+  + numeric.sor(A, b, relax, maxIters, residue), SOR solver. The performance of the SOR solver is not as good as CG and BiCGSTAB, but similar to ccsLUPSolve. In practice, CG and BiCGSTAB should always be the best options.
+
+  + numeric.newtonSolve(F, A, x), Newton solver. Ported from GSL(GNU Scientific Library)'s globally convergent Newton method (gsl-1.15\multiroots\gnewton.c). This Newton solver has been successfully used to implement a simple geometric constraint solver in project Rena (https://github.com/kaige/Rena).
+
+  + numeric.uniroot(func, lowerLimit, upperLimit, errorTol, maxIter), search the interval from <tt>lowerLimit</tt> to <tt>upperLimit</tt> for a root (i.e., zero) of the function <tt>func</tt> with respect to its first argument using Brent's method root-finding algorithm.
+
+- numeric.logspace(), another fill routine akin to numeric.linspace() but now for logarithmic space fills.
+
+- numeric.zeros(), numeric.ones(), numeric.empty(), numeric.range(): methods to fill a vector or Array with fixed values.
+
+- numeric.roll(), numeric.flip(), numeric.fliplr(), numeric.flipud(), numeric.rot90(): methods to flip/rotate Arrays (matrices).
+
+- numeric.kron(x, y), Kronecker's method applied to a matrix.
+
+- numeric.ccsDiag(diag), numeric.ccsTranspose(A): numeric.ccsScatter helpers
+
+- numeric.pi, numeric.e: high precision math constants
+
+- CSV I/O bugfixes
+
+
+
+Introduction
+------
 
 Numeric Javascript is a javascript library for doing numerical
 analysis in the browser. Because Numeric Javascript uses only the
@@ -12,7 +55,7 @@ asking a server to compute something. Indeed, you do not need a
 powerful server (or any server at all) since your web app will perform
 all its calculations in the client.
 
-For further information, see http://www.numericjs.com/
+The original website, see http://www.numericjs.com/
 Discussion forum: http://groups.google.com/group/numericjs
 
 License
@@ -47,5 +90,4 @@ the numericjs.com web site, which you probably won't need.
 Building and testing
 --------------------
 
-If you tweak the code, you can build and test the library by running the script /tools/build.sh. If you plan
-to send me patches, please at least run this build script and check that all the tests pass.
+If you tweak the code, you can build and test the library by running the script /tools/build.sh.
diff --git a/benchmark.html b/benchmark.html
@@ -1,6 +1,10 @@
-<!doctype html>
+<!DOCTYPE html>
 <html>
 <head>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<meta name="description" content="Numerical analysis in javascript: library and console." />
+<meta name="keywords" content="Javascript,HTML,simplex,matrix,vector,linear algebra" />
+<meta name="author" content="S�bastien Loisel" />
 <link rel="SHORTCUT ICON" href="favicon.ico">
 <link href='http://fonts.googleapis.com/css?family=Lato' rel='stylesheet' type='text/css'>
 <link rel="stylesheet" type="text/css" href="resources/style.css">
@@ -25,8 +29,8 @@
 <!--<script src="lib/numeric.js"></script>
 <script src="tools/sylvester.js"></script>
 <script src="tools/trunk/closure/goog/base.js"></script>
-<script src="tools/jquery-1.7.1.min.js"></script>
-<script src="tools/jquery.flot.min.js"></script>-->
+<script src="tools/jquery-1.7.1.js"></script>
+<script src="tools/jquery.flot.js"></script>-->
 <script src="tools/benchlib.js"></script>
 
 <script>
@@ -49,59 +53,59 @@
 var mkA = function(n) { return numeric.random([n,n]); };
 var mkV = function(n) { return numeric.random([n]); };
 var benchmarks = [
-	[
-	'abs(vector)', [3,10,30,100,300,1000,3000],
-	function(n) { var V = mkV(n); return bench(function() { numeric.abs(V); }); },
-	function(n) { var V = new goog.math.Matrix([mkV(n)]); return bench(function() { V.toArray().map(Math.abs); }); },
-	function(n) { var V = $V(mkV(n)); return bench(function() { V.map(Math.abs); }); }
-	],
-	[
-	'Create identity', [3,10,30,100,300,1000],
-	function(n) { return bench(function() { numeric.identity(n); }); },
-	function(n) { return bench(function() { goog.math.Matrix.createIdentityMatrix(n); }); },
-	function(n) { return bench(function() { Matrix.I(n); }); }
-	],
-	[
-	'Matrix transpose', [3,10,30,100,300,1000],
-	function(n) { var A = mkA(n); return bench(function() { numeric.transpose(A); }); },
-	function(n) { var A = new goog.math.Matrix(mkA(n)); return bench(function() { A.getTranspose(); }); },
-	function(n) { var A = $M(mkA(n)); return bench(function() { A.transpose(); }); }
-	],
-	[
-	'Matrix-Vector product', [3,10,30,100,300,1000],
-	function(n) { var A = mkA(n), V = mkV(n); return bench(function() { numeric.dot(A,V); }); },
-	function(n) { var A = new goog.math.Matrix(mkA(n)), V = new goog.math.Matrix([mkV(n)]).getTranspose(); return bench(function() { A.multiply(V); }); },
-	function(n) { var A = $M(mkA(n)), V = $V(mkV(n)); return bench(function() { A.multiply(V); }); }
-	],
-	[
-	'Vector-Matrix product', [3,10,30,100,300,1000],
-	function(n) { var A = mkA(n), V = mkV(n); return bench(function() { numeric.dot(V,A); }); },
-	function(n) { var A = new goog.math.Matrix(mkA(n)), V = new goog.math.Matrix([mkV(n)]); return bench(function() { V.multiply(A); }); },
-	function(n) { var A = $M(mkA(n)), V = $V(mkV(n)); return bench(function() { A.transpose().multiply(V); }); }
-	],
-	['Ax+b', [3,10,30,100,300,1000],
-	function(n) { var A = mkA(n), x = mkV(n), b = mkV(n); return bench(function() { numeric.addeq(numeric.dot(A,x),b); }); },
-	function(n) { var A = new goog.math.Matrix(mkA(n)), x = new goog.math.Matrix([mkV(n)]).getTranspose(), b = new goog.math.Matrix([mkV(n)]).getTranspose(); return bench(function() { A.multiply(x).add(b); }); },
-	function(n) { var A = $M(mkA(n)), x = $V(mkV(n)), b = $V(mkV(n)); return bench(function() { A.multiply(x).add(b); }); }
-	],
-	[
-	'Matrix-Matrix product', [3,5,10,20,30,50,75,100],
-	function(n) { var A = mkA(n), B = mkA(n); return bench(function() { numeric.dot(A,B); }); },
-	function(n) { var A = new goog.math.Matrix(mkA(n)), B = new goog.math.Matrix(mkA(n)); return bench(function() { A.multiply(B); }); },
-	function(n) { var A = $M(mkA(n)), B = $M(mkA(n)); return bench(function() { A.multiply(B); }); }
-	],
-	[
-	'Matrix-Matrix sum', [3,5,10,20,30,50,75,100],
-	function(n) { var A = mkA(n); return bench(function() { numeric.add(A,A); }); },
-	function(n) { var A = new goog.math.Matrix(mkA(n)); return bench(function() { A.add(A); }); },
-	function(n) { var A = $M(mkA(n)); return bench(function() { A.add(A); }); }
-	],
-	[
-	'Matrix inverse', [3,5,10,20,30,50,75,100],
-	function(n) { var A = mkA(n); return bench(function() { numeric.inv(A); }); },
-	function(n) { var A = new goog.math.Matrix(mkA(n)); return bench(function() { A.getInverse(); }); },
-	function(n) { var A = $M(mkA(n)); return bench(function() { A.inv(); }); }
-	],
+    [
+    'abs(vector)', [3,10,30,100,300,1000,3000],
+    function(n) { var V = mkV(n); return bench(function() { numeric.abs(V); }); },
+    function(n) { var V = new goog.math.Matrix([mkV(n)]); return bench(function() { V.toArray().map(Math.abs); }); },
+    function(n) { var V = $V(mkV(n)); return bench(function() { V.map(Math.abs); }); }
+    ],
+    [
+    'Create identity', [3,10,30,100,300,1000],
+    function(n) { return bench(function() { numeric.identity(n); }); },
+    function(n) { return bench(function() { goog.math.Matrix.createIdentityMatrix(n); }); },
+    function(n) { return bench(function() { Matrix.I(n); }); }
+    ],
+    [
+    'Matrix transpose', [3,10,30,100,300,1000],
+    function(n) { var A = mkA(n); return bench(function() { numeric.transpose(A); }); },
+    function(n) { var A = new goog.math.Matrix(mkA(n)); return bench(function() { A.getTranspose(); }); },
+    function(n) { var A = $M(mkA(n)); return bench(function() { A.transpose(); }); }
+    ],
+    [
+    'Matrix-Vector product', [3,10,30,100,300,1000],
+    function(n) { var A = mkA(n), V = mkV(n); return bench(function() { numeric.dot(A,V); }); },
+    function(n) { var A = new goog.math.Matrix(mkA(n)), V = new goog.math.Matrix([mkV(n)]).getTranspose(); return bench(function() { A.multiply(V); }); },
+    function(n) { var A = $M(mkA(n)), V = $V(mkV(n)); return bench(function() { A.multiply(V); }); }
+    ],
+    [
+    'Vector-Matrix product', [3,10,30,100,300,1000],
+    function(n) { var A = mkA(n), V = mkV(n); return bench(function() { numeric.dot(V,A); }); },
+    function(n) { var A = new goog.math.Matrix(mkA(n)), V = new goog.math.Matrix([mkV(n)]); return bench(function() { V.multiply(A); }); },
+    function(n) { var A = $M(mkA(n)), V = $V(mkV(n)); return bench(function() { A.transpose().multiply(V); }); }
+    ],
+    ['Ax+b', [3,10,30,100,300,1000],
+    function(n) { var A = mkA(n), x = mkV(n), b = mkV(n); return bench(function() { numeric.addeq(numeric.dot(A,x),b); }); },
+    function(n) { var A = new goog.math.Matrix(mkA(n)), x = new goog.math.Matrix([mkV(n)]).getTranspose(), b = new goog.math.Matrix([mkV(n)]).getTranspose(); return bench(function() { A.multiply(x).add(b); }); },
+    function(n) { var A = $M(mkA(n)), x = $V(mkV(n)), b = $V(mkV(n)); return bench(function() { A.multiply(x).add(b); }); }
+    ],
+    [
+    'Matrix-Matrix product', [3,5,10,20,30,50,75,100],
+    function(n) { var A = mkA(n), B = mkA(n); return bench(function() { numeric.dot(A,B); }); },
+    function(n) { var A = new goog.math.Matrix(mkA(n)), B = new goog.math.Matrix(mkA(n)); return bench(function() { A.multiply(B); }); },
+    function(n) { var A = $M(mkA(n)), B = $M(mkA(n)); return bench(function() { A.multiply(B); }); }
+    ],
+    [
+    'Matrix-Matrix sum', [3,5,10,20,30,50,75,100],
+    function(n) { var A = mkA(n); return bench(function() { numeric.add(A,A); }); },
+    function(n) { var A = new goog.math.Matrix(mkA(n)); return bench(function() { A.add(A); }); },
+    function(n) { var A = $M(mkA(n)); return bench(function() { A.add(A); }); }
+    ],
+    [
+    'Matrix inverse', [3,5,10,20,30,50,75,100],
+    function(n) { var A = mkA(n); return bench(function() { numeric.inv(A); }); },
+    function(n) { var A = new goog.math.Matrix(mkA(n)); return bench(function() { A.getInverse(); }); },
+    function(n) { var A = $M(mkA(n)); return bench(function() { A.inv(); }); }
+    ],
     [
     'Sparse Laplacian LU', [5,10,20,30],
     function(n) { var A = numeric.ccsScatter(numeric.cdelsq(numeric.cgrid(n))); return bench(function() { numeric.ccsLUP(A); }); },
@@ -123,23 +127,23 @@
 var datasets = [];
 var l;
 var colors = ["#000","#00f","#0f0","#f80","#f0f","#0ff"];
-for(l=0;l<libs.length;l++) { 
-	datasets[l] = { 
-		data: [], 
-		label: libs[l], 
-		color: colors[l], 
-		points: { show: true }, 
-		lines: { show: true } 
-		};
+for(l=0;l<libs.length;l++) {
+    datasets[l] = {
+        data: [],
+        label: libs[l],
+        color: colors[l],
+        points: { show: true },
+        lines: { show: true }
+        };
 }
 l=1;
 var l0,xticks = [];
 for(b=0;b<benchmarks.length;b++) {
-	l0 = l;
-	for(k=0;k<benchmarks[b][1].length;k++) {
-		l++;
-	}
-	xticks.push([(l0+l)*0.5,benchmarks[b][0]]);
+    l0 = l;
+    for(k=0;k<benchmarks[b][1].length;k++) {
+        l++;
+    }
+    xticks.push([(l0+l)*0.5,benchmarks[b][0]]);
 }
 k=0;
 b=0;
@@ -149,66 +153,66 @@
 var c0 = [[],[],[]];
 var counts = [0,0,0];
 function invbench(b,k,lib,rep) {
-	var ks,sz = benchmarks[b][1][k];
-	var i,j,foo;
-	ks = sz.toString();
-	if(rep>0 && c0[lib][rep-1] < 10) { c0[lib][rep] = c0[lib][rep-1]; }
-	else { foo = benchmarks[b][lib+2]; if(k===0) foo(sz); c0[lib][rep] = foo(sz); }
-	rep++;
-	if(rep === 1) { rep = 0; lib++; }
-	if(lib+2 === benchmarks[b].length) {
-		k++;
-		lib=0;
-		var cps = [];
-		var mi = 1e6/Math.pow(benchmarks[b][1][k-1],pwr[b]);
-		for(i=0;i<c0.length;i++) {
-		    cps[i] = 0;
-		    for(j=0;j<c0[i].length;j++) cps[i] += c0[i][j];
-			cps[i] /= (c0[i].length*mi);
-		}
-		for(i=0;i<cps.length;i++) {
-			if(MSIE || cps[i]) datasets[i].data.push([count,cps[i]]);
-			if(cps[i]) {
-			    counts[i]++;
-			    geometricmeans[i] += Math.log(cps[i]);
-			}
-		}
-		var foo = '<td>n='+ks+'</td>';
-		var color = '', uncolor='';
-		for(i=0;i<cps.length;i++) {
-			foo +=  '<td>'+cps[i].toPrecision(8)+'</td>';
-		}
-		if(!MSIE) {
-			var t = document.getElementById('bench');
-			var r = t.insertRow(-1);
-			if(k === 1) { 
-				r.innerHTML = ('<td width=200px><b>'+benchmarks[b][0]+'</b></td>'
-			    	          +'<td width=125px><b>Numeric</b></td>'
-		            	      +'<td width=125px><b>Google Closure</b></td>'
-		                	  +'<td width=125px><b>Sylvester</b></td>');
-				r = t.insertRow(-1); 
-			}
-			r.innerHTML = foo;
-		}
-		$.plot($("#placeholder"), datasets, 
-		    { 
-		   	  legend: {container: '#legend'}, 
-	    	  xaxis:  {ticks:xticks, tickLength:0, min:1, max: l-1},
-	    	  yaxis:  {ticks:20}
-    		});
-	    c0 = [[],[],[]];
-		count++;
-	}
-	if(k === benchmarks[b][1].length) {
-		for(i=0;i<cps.length;i++) datasets[i].data.push(null);
-		k=0; b++;
-	}
-	if(b < benchmarks.length) { 
-		setTimeout('invbench('+b.toString()
-		                  +','+k.toString()
-		                  +','+lib.toString()
-		                  +','+rep.toString()
-		                  +')',MSIE?10:0); 
+    var ks,sz = benchmarks[b][1][k];
+    var i,j,foo;
+    ks = sz.toString();
+    if(rep>0 && c0[lib][rep-1] < 10) { c0[lib][rep] = c0[lib][rep-1]; }
+    else { foo = benchmarks[b][lib+2]; if(k===0) foo(sz); c0[lib][rep] = foo(sz); }
+    rep++;
+    if(rep === 1) { rep = 0; lib++; }
+    if(lib+2 === benchmarks[b].length) {
+        k++;
+        lib=0;
+        var cps = [];
+        var mi = 1e6/Math.pow(benchmarks[b][1][k-1],pwr[b]);
+        for(i=0;i<c0.length;i++) {
+            cps[i] = 0;
+            for(j=0;j<c0[i].length;j++) cps[i] += c0[i][j];
+            cps[i] /= (c0[i].length*mi);
+        }
+        for(i=0;i<cps.length;i++) {
+            if(MSIE || cps[i]) datasets[i].data.push([count,cps[i]]);
+            if(cps[i]) {
+                counts[i]++;
+                geometricmeans[i] += Math.log(cps[i]);
+            }
+        }
+        var foo = '<td>n='+ks+'</td>';
+        var color = '', uncolor='';
+        for(i=0;i<cps.length;i++) {
+            foo +=  '<td>'+cps[i].toPrecision(8)+'</td>';
+        }
+        if(!MSIE) {
+            var t = document.getElementById('bench');
+            var r = t.insertRow(-1);
+            if(k === 1) {
+                r.innerHTML = ('<td width=200px><b>'+benchmarks[b][0]+'</b></td>'
+                              +'<td width=125px><b>Numeric</b></td>'
+                              +'<td width=125px><b>Google Closure</b></td>'
+                              +'<td width=125px><b>Sylvester</b></td>');
+                r = t.insertRow(-1);
+            }
+            r.innerHTML = foo;
+        }
+        $.plot($("#placeholder"), datasets,
+            {
+              legend: {container: '#legend'},
+              xaxis:  {ticks:xticks, tickLength:0, min:1, max: l-1},
+              yaxis:  {ticks:20}
+            });
+        c0 = [[],[],[]];
+        count++;
+    }
+    if(k === benchmarks[b][1].length) {
+        for(i=0;i<cps.length;i++) datasets[i].data.push(null);
+        k=0; b++;
+    }
+    if(b < benchmarks.length) {
+        setTimeout('invbench('+b.toString()
+                          +','+k.toString()
+                          +','+lib.toString()
+                          +','+rep.toString()
+                          +')',MSIE?10:0);
     } else {
         geometricmeans = numeric.exp(numeric.div(geometricmeans,counts));
         $('#meanscore')[0].innerHTML += numeric.prettyPrint(geometricmeans)+'MFLOPS';
-Original file line number
+Diff line change
@@ Expand Up / @@ -19,6 +19,9 @@ @@
     *.rar
     *.tar
     *.zip
+    # installed NPM packages
+    node_modules/
     # Emacs backups
     *~
@@ Expand Down @@