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snoyman.com-content/static/reverse-bench.html
Michael Snoyman 5d08a2c315
Add reverse-bench.html
2017-05-18 09:41:47 -07:00

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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<title>criterion report</title>
<script language="javascript" type="text/javascript">
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i=0,position;position=positions[i];i++){if(position.x==x&&position.y==y){position.active=true;return}}position={active:true,rendered:false,element:positions.length?info.element.clone():info.element,x:x,y:y};positions.push(position);position.element.css({top:Math.round(y),left:Math.round(x),"text-align":halign})};Canvas.prototype.removeText=function(layer,x,y,text,font,angle){if(text==null){var layerCache=this._textCache[layer];if(layerCache!=null){for(var styleKey in layerCache){if(hasOwnProperty.call(layerCache,styleKey)){var styleCache=layerCache[styleKey];for(var key in styleCache){if(hasOwnProperty.call(styleCache,key)){var positions=styleCache[key].positions;for(var i=0,position;position=positions[i];i++){position.active=false}}}}}}}else{var positions=this.getTextInfo(layer,text,font,angle).positions;for(var i=0,position;position=positions[i];i++){if(position.x==x&&position.y==y){position.active=false}}}};function Plot(placeholder,data_,options_,plugins){var series=[],options={colors:["#edc240","#afd8f8","#cb4b4b","#4da74d","#9440ed"],legend:{show:true,noColumns:1,labelFormatter:null,labelBoxBorderColor:"#ccc",container:null,position:"ne",margin:5,backgroundColor:null,backgroundOpacity:.85,sorted:null},xaxis:{show:null,position:"bottom",mode:null,font:null,color:null,tickColor:null,transform:null,inverseTransform:null,min:null,max:null,autoscaleMargin:null,ticks:null,tickFormatter:null,labelWidth:null,labelHeight:null,reserveSpace:null,tickLength:null,alignTicksWithAxis:null,tickDecimals:null,tickSize:null,minTickSize:null},yaxis:{autoscaleMargin:.02,position:"left"},xaxes:[],yaxes:[],series:{points:{show:false,radius:3,lineWidth:2,fill:true,fillColor:"#ffffff",symbol:"circle"},lines:{lineWidth:2,fill:false,fillColor:null,steps:false},bars:{show:false,lineWidth:2,barWidth:1,fill:true,fillColor:null,align:"left",horizontal:false,zero:true},shadowSize:3,highlightColor:null},grid:{show:true,aboveData:false,color:"#545454",backgroundColor:null,borderColor:null,tickColor:null,margin:0,labelMargin:5,axisMargin:8,borderWidth:2,minBorderMargin:null,markings:null,markingsColor:"#f4f4f4",markingsLineWidth:2,clickable:false,hoverable:false,autoHighlight:true,mouseActiveRadius:10},interaction:{redrawOverlayInterval:1e3/60},hooks:{}},surface=null,overlay=null,eventHolder=null,ctx=null,octx=null,xaxes=[],yaxes=[],plotOffset={left:0,right:0,top:0,bottom:0},plotWidth=0,plotHeight=0,hooks={processOptions:[],processRawData:[],processDatapoints:[],processOffset:[],drawBackground:[],drawSeries:[],draw:[],bindEvents:[],drawOverlay:[],shutdown:[]},plot=this;plot.setData=setData;plot.setupGrid=setupGrid;plot.draw=draw;plot.getPlaceholder=function(){return placeholder};plot.getCanvas=function(){return surface.element};plot.getPlotOffset=function(){return plotOffset};plot.width=function(){return plotWidth};plot.height=function(){return plotHeight};plot.offset=function(){var o=eventHolder.offset();o.left+=plotOffset.left;o.top+=plotOffset.top;return o};plot.getData=function(){return series};plot.getAxes=function(){var res={},i;$.each(xaxes.concat(yaxes),function(_,axis){if(axis)res[axis.direction+(axis.n!=1?axis.n:"")+"axis"]=axis});return res};plot.getXAxes=function(){return xaxes};plot.getYAxes=function(){return yaxes};plot.c2p=canvasToAxisCoords;plot.p2c=axisToCanvasCoords;plot.getOptions=function(){return options};plot.highlight=highlight;plot.unhighlight=unhighlight;plot.triggerRedrawOverlay=triggerRedrawOverlay;plot.pointOffset=function(point){return{left:parseInt(xaxes[axisNumber(point,"x")-1].p2c(+point.x)+plotOffset.left,10),top:parseInt(yaxes[axisNumber(point,"y")-1].p2c(+point.y)+plotOffset.top,10)}};plot.shutdown=shutdown;plot.destroy=function(){shutdown();placeholder.removeData("plot").empty();series=[];options=null;surface=null;overlay=null;eventHolder=null;ctx=null;octx=null;xaxes=[];yaxes=[];hooks=null;highlights=[];plot=null};plot.resize=function(){var width=placeholder.width(),height=placeholder.height();surface.resize(width,height);overlay.resize(width,height)};plot.hooks=hooks;initPlugins(plot);parseOptions(options_);setupCanvases();setData(data_);setupGrid();draw();bindEvents();function executeHooks(hook,args){args=[plot].concat(args);for(var i=0;i<hook.length;++i)hook[i].apply(this,args)}function initPlugins(){var classes={Canvas:Canvas};for(var i=0;i<plugins.length;++i){var p=plugins[i];p.init(plot,classes);if(p.options)$.extend(true,options,p.options)}}function parseOptions(opts){$.extend(true,options,opts);if(opts&&opts.colors){options.colors=opts.colors}if(options.xaxis.color==null)options.xaxis.color=$.color.parse(options.grid.color).scale("a",.22).toString();if(options.yaxis.color==null)options.yaxis.color=$.color.parse(options.grid.color).scale("a",.22).toString();if(options.xaxis.tickColor==null)options.xaxis.tickColor=options.grid.tickColor||options.xaxis.color;if(options.yaxis.tickColor==null)options.yaxis.tickColor=options.grid.tickColor||options.yaxis.color;if(options.grid.borderColor==null)options.grid.borderColor=options.grid.color;if(options.grid.tickColor==null)options.grid.tickColor=$.color.parse(options.grid.color).scale("a",.22).toString();var i,axisOptions,axisCount,fontSize=placeholder.css("font-size"),fontSizeDefault=fontSize?+fontSize.replace("px",""):13,fontDefaults={style:placeholder.css("font-style"),size:Math.round(.8*fontSizeDefault),variant:placeholder.css("font-variant"),weight:placeholder.css("font-weight"),family:placeholder.css("font-family")};axisCount=options.xaxes.length||1;for(i=0;i<axisCount;++i){axisOptions=options.xaxes[i];if(axisOptions&&!axisOptions.tickColor){axisOptions.tickColor=axisOptions.color}axisOptions=$.extend(true,{},options.xaxis,axisOptions);options.xaxes[i]=axisOptions;if(axisOptions.font){axisOptions.font=$.extend({},fontDefaults,axisOptions.font);if(!axisOptions.font.color){axisOptions.font.color=axisOptions.color}if(!axisOptions.font.lineHeight){axisOptions.font.lineHeight=Math.round(axisOptions.font.size*1.15)}}}axisCount=options.yaxes.length||1;for(i=0;i<axisCount;++i){axisOptions=options.yaxes[i];if(axisOptions&&!axisOptions.tickColor){axisOptions.tickColor=axisOptions.color}axisOptions=$.extend(true,{},options.yaxis,axisOptions);options.yaxes[i]=axisOptions;if(axisOptions.font){axisOptions.font=$.extend({},fontDefaults,axisOptions.font);if(!axisOptions.font.color){axisOptions.font.color=axisOptions.color}if(!axisOptions.font.lineHeight){axisOptions.font.lineHeight=Math.round(axisOptions.font.size*1.15)}}}if(options.xaxis.noTicks&&options.xaxis.ticks==null)options.xaxis.ticks=options.xaxis.noTicks;if(options.yaxis.noTicks&&options.yaxis.ticks==null)options.yaxis.ticks=options.yaxis.noTicks;if(options.x2axis){options.xaxes[1]=$.extend(true,{},options.xaxis,options.x2axis);options.xaxes[1].position="top";if(options.x2axis.min==null){options.xaxes[1].min=null}if(options.x2axis.max==null){options.xaxes[1].max=null}}if(options.y2axis){options.yaxes[1]=$.extend(true,{},options.yaxis,options.y2axis);options.yaxes[1].position="right";if(options.y2axis.min==null){options.yaxes[1].min=null}if(options.y2axis.max==null){options.yaxes[1].max=null}}if(options.grid.coloredAreas)options.grid.markings=options.grid.coloredAreas;if(options.grid.coloredAreasColor)options.grid.markingsColor=options.grid.coloredAreasColor;if(options.lines)$.extend(true,options.series.lines,options.lines);if(options.points)$.extend(true,options.series.points,options.points);if(options.bars)$.extend(true,options.series.bars,options.bars);if(options.shadowSize!=null)options.series.shadowSize=options.shadowSize;if(options.highlightColor!=null)options.series.highlightColor=options.highlightColor;for(i=0;i<options.xaxes.length;++i)getOrCreateAxis(xaxes,i+1).options=options.xaxes[i];for(i=0;i<options.yaxes.length;++i)getOrCreateAxis(yaxes,i+1).options=options.yaxes[i];for(var n in hooks)if(options.hooks[n]&&options.hooks[n].length)hooks[n]=hooks[n].concat(options.hooks[n]);executeHooks(hooks.processOptions,[options])}function setData(d){series=parseData(d);fillInSeriesOptions();processData()}function parseData(d){var res=[];for(var i=0;i<d.length;++i){var s=$.extend(true,{},options.series);if(d[i].data!=null){s.data=d[i].data;delete d[i].data;$.extend(true,s,d[i]);d[i].data=s.data}else s.data=d[i];res.push(s)}return res}function axisNumber(obj,coord){var a=obj[coord+"axis"];if(typeof a=="object")a=a.n;if(typeof a!="number")a=1;return a}function allAxes(){return $.grep(xaxes.concat(yaxes),function(a){return a})}function canvasToAxisCoords(pos){var res={},i,axis;for(i=0;i<xaxes.length;++i){axis=xaxes[i];if(axis&&axis.used)res["x"+axis.n]=axis.c2p(pos.left)}for(i=0;i<yaxes.length;++i){axis=yaxes[i];if(axis&&axis.used)res["y"+axis.n]=axis.c2p(pos.top)}if(res.x1!==undefined)res.x=res.x1;if(res.y1!==undefined)res.y=res.y1;return res}function axisToCanvasCoords(pos){var res={},i,axis,key;for(i=0;i<xaxes.length;++i){axis=xaxes[i];if(axis&&axis.used){key="x"+axis.n;if(pos[key]==null&&axis.n==1)key="x";if(pos[key]!=null){res.left=axis.p2c(pos[key]);break}}}for(i=0;i<yaxes.length;++i){axis=yaxes[i];if(axis&&axis.used){key="y"+axis.n;if(pos[key]==null&&axis.n==1)key="y";if(pos[key]!=null){res.top=axis.p2c(pos[key]);break}}}return res}function getOrCreateAxis(axes,number){if(!axes[number-1])axes[number-1]={n:number,direction:axes==xaxes?"x":"y",options:$.extend(true,{},axes==xaxes?options.xaxis:options.yaxis)};return axes[number-1]}function fillInSeriesOptions(){var neededColors=series.length,maxIndex=-1,i;for(i=0;i<series.length;++i){var sc=series[i].color;if(sc!=null){neededColors--;if(typeof sc=="number"&&sc>maxIndex){maxIndex=sc}}}if(neededColors<=maxIndex){neededColors=maxIndex+1}var c,colors=[],colorPool=options.colors,colorPoolSize=colorPool.length,variation=0;for(i=0;i<neededColors;i++){c=$.color.parse(colorPool[i%colorPoolSize]||"#666");if(i%colorPoolSize==0&&i){if(variation>=0){if(variation<.5){variation=-variation-.2}else variation=0}else variation=-variation}colors[i]=c.scale("rgb",1+variation)}var colori=0,s;for(i=0;i<series.length;++i){s=series[i];if(s.color==null){s.color=colors[colori].toString();++colori}else if(typeof s.color=="number")s.color=colors[s.color].toString();if(s.lines.show==null){var v,show=true;for(v in s)if(s[v]&&s[v].show){show=false;break}if(show)s.lines.show=true}if(s.lines.zero==null){s.lines.zero=!!s.lines.fill}s.xaxis=getOrCreateAxis(xaxes,axisNumber(s,"x"));s.yaxis=getOrCreateAxis(yaxes,axisNumber(s,"y"))}}function processData(){var topSentry=Number.POSITIVE_INFINITY,bottomSentry=Number.NEGATIVE_INFINITY,fakeInfinity=Number.MAX_VALUE,i,j,k,m,length,s,points,ps,x,y,axis,val,f,p,data,format;function updateAxis(axis,min,max){if(min<axis.datamin&&min!=-fakeInfinity)axis.datamin=min;if(max>axis.datamax&&max!=fakeInfinity)axis.datamax=max}$.each(allAxes(),function(_,axis){axis.datamin=topSentry;axis.datamax=bottomSentry;axis.used=false});for(i=0;i<series.length;++i){s=series[i];s.datapoints={points:[]};executeHooks(hooks.processRawData,[s,s.data,s.datapoints])}for(i=0;i<series.length;++i){s=series[i];data=s.data;format=s.datapoints.format;if(!format){format=[];format.push({x:true,number:true,required:true});format.push({y:true,number:true,required:true});if(s.bars.show||s.lines.show&&s.lines.fill){var autoscale=!!(s.bars.show&&s.bars.zero||s.lines.show&&s.lines.zero);format.push({y:true,number:true,required:false,defaultValue:0,autoscale:autoscale});if(s.bars.horizontal){delete format[format.length-1].y;format[format.length-1].x=true}}s.datapoints.format=format}if(s.datapoints.pointsize!=null)continue;s.datapoints.pointsize=format.length;ps=s.datapoints.pointsize;points=s.datapoints.points;var insertSteps=s.lines.show&&s.lines.steps;s.xaxis.used=s.yaxis.used=true;for(j=k=0;j<data.length;++j,k+=ps){p=data[j];var nullify=p==null;if(!nullify){for(m=0;m<ps;++m){val=p[m];f=format[m];if(f){if(f.number&&val!=null){val=+val;if(isNaN(val))val=null;else if(val==Infinity)val=fakeInfinity;else if(val==-Infinity)val=-fakeInfinity}if(val==null){if(f.required)nullify=true;if(f.defaultValue!=null)val=f.defaultValue}}points[k+m]=val}}if(nullify){for(m=0;m<ps;++m){val=points[k+m];if(val!=null){f=format[m];if(f.autoscale!==false){if(f.x){updateAxis(s.xaxis,val,val)}if(f.y){updateAxis(s.yaxis,val,val)}}}points[k+m]=null}}else{if(insertSteps&&k>0&&points[k-ps]!=null&&points[k-ps]!=points[k]&&points[k-ps+1]!=points[k+1]){for(m=0;m<ps;++m)points[k+ps+m]=points[k+m];points[k+1]=points[k-ps+1];k+=ps}}}}for(i=0;i<series.length;++i){s=series[i];executeHooks(hooks.processDatapoints,[s,s.datapoints])}for(i=0;i<series.length;++i){s=series[i];points=s.datapoints.points;ps=s.datapoints.pointsize;format=s.datapoints.format;var xmin=topSentry,ymin=topSentry,xmax=bottomSentry,ymax=bottomSentry;for(j=0;j<points.length;j+=ps){if(points[j]==null)continue;for(m=0;m<ps;++m){val=points[j+m];f=format[m];if(!f||f.autoscale===false||val==fakeInfinity||val==-fakeInfinity)continue;if(f.x){if(val<xmin)xmin=val;if(val>xmax)xmax=val}if(f.y){if(val<ymin)ymin=val;if(val>ymax)ymax=val}}}if(s.bars.show){var delta;switch(s.bars.align){case"left":delta=0;break;case"right":delta=-s.bars.barWidth;break;default:delta=-s.bars.barWidth/2}if(s.bars.horizontal){ymin+=delta;ymax+=delta+s.bars.barWidth}else{xmin+=delta;xmax+=delta+s.bars.barWidth}}updateAxis(s.xaxis,xmin,xmax);updateAxis(s.yaxis,ymin,ymax)}$.each(allAxes(),function(_,axis){if(axis.datamin==topSentry)axis.datamin=null;if(axis.datamax==bottomSentry)axis.datamax=null})}function setupCanvases(){placeholder.css("padding",0).children().filter(function(){return!$(this).hasClass("flot-overlay")&&!$(this).hasClass("flot-base")}).remove();if(placeholder.css("position")=="static")placeholder.css("position","relative");surface=new Canvas("flot-base",placeholder);overlay=new Canvas("flot-overlay",placeholder);ctx=surface.context;octx=overlay.context;eventHolder=$(overlay.element).unbind();var existing=placeholder.data("plot");if(existing){existing.shutdown();overlay.clear()}placeholder.data("plot",plot)}function bindEvents(){if(options.grid.hoverable){eventHolder.mousemove(onMouseMove);eventHolder.bind("mouseleave",onMouseLeave)}if(options.grid.clickable)eventHolder.click(onClick);executeHooks(hooks.bindEvents,[eventHolder])}function shutdown(){if(redrawTimeout)clearTimeout(redrawTimeout);eventHolder.unbind("mousemove",onMouseMove);eventHolder.unbind("mouseleave",onMouseLeave);eventHolder.unbind("click",onClick);executeHooks(hooks.shutdown,[eventHolder])}function setTransformationHelpers(axis){function identity(x){return x}var s,m,t=axis.options.transform||identity,it=axis.options.inverseTransform;if(axis.direction=="x"){s=axis.scale=plotWidth/Math.abs(t(axis.max)-t(axis.min));m=Math.min(t(axis.max),t(axis.min))}else{s=axis.scale=plotHeight/Math.abs(t(axis.max)-t(axis.min));s=-s;m=Math.max(t(axis.max),t(axis.min))}if(t==identity)axis.p2c=function(p){return(p-m)*s};else axis.p2c=function(p){return(t(p)-m)*s};if(!it)axis.c2p=function(c){return m+c/s};else axis.c2p=function(c){return it(m+c/s)}}function measureTickLabels(axis){var opts=axis.options,ticks=axis.ticks||[],labelWidth=opts.labelWidth||0,labelHeight=opts.labelHeight||0,maxWidth=labelWidth||(axis.direction=="x"?Math.floor(surface.width/(ticks.length||1)):null),legacyStyles=axis.direction+"Axis "+axis.direction+axis.n+"Axis",layer="flot-"+axis.direction+"-axis flot-"+axis.direction+axis.n+"-axis "+legacyStyles,font=opts.font||"flot-tick-label tickLabel";for(var i=0;i<ticks.length;++i){var t=ticks[i];if(!t.label)continue;var info=surface.getTextInfo(layer,t.label,font,null,maxWidth);labelWidth=Math.max(labelWidth,info.width);labelHeight=Math.max(labelHeight,info.height)}axis.labelWidth=opts.labelWidth||labelWidth;axis.labelHeight=opts.labelHeight||labelHeight}function allocateAxisBoxFirstPhase(axis){var lw=axis.labelWidth,lh=axis.labelHeight,pos=axis.options.position,isXAxis=axis.direction==="x",tickLength=axis.options.tickLength,axisMargin=options.grid.axisMargin,padding=options.grid.labelMargin,innermost=true,outermost=true,first=true,found=false;$.each(isXAxis?xaxes:yaxes,function(i,a){if(a&&(a.show||a.reserveSpace)){if(a===axis){found=true}else if(a.options.position===pos){if(found){outermost=false}else{innermost=false}}if(!found){first=false}}});if(outermost){axisMargin=0}if(tickLength==null){tickLength=first?"full":5}if(!isNaN(+tickLength))padding+=+tickLength;if(isXAxis){lh+=padding;if(pos=="bottom"){plotOffset.bottom+=lh+axisMargin;axis.box={top:surface.height-plotOffset.bottom,height:lh}}else{axis.box={top:plotOffset.top+axisMargin,height:lh};plotOffset.top+=lh+axisMargin}}else{lw+=padding;if(pos=="left"){axis.box={left:plotOffset.left+axisMargin,width:lw};plotOffset.left+=lw+axisMargin}else{plotOffset.right+=lw+axisMargin;axis.box={left:surface.width-plotOffset.right,width:lw}}}axis.position=pos;axis.tickLength=tickLength;axis.box.padding=padding;axis.innermost=innermost}function allocateAxisBoxSecondPhase(axis){if(axis.direction=="x"){axis.box.left=plotOffset.left-axis.labelWidth/2;axis.box.width=surface.width-plotOffset.left-plotOffset.right+axis.labelWidth}else{axis.box.top=plotOffset.top-axis.labelHeight/2;axis.box.height=surface.height-plotOffset.bottom-plotOffset.top+axis.labelHeight}}function adjustLayoutForThingsStickingOut(){var minMargin=options.grid.minBorderMargin,axis,i;if(minMargin==null){minMargin=0;for(i=0;i<series.length;++i)minMargin=Math.max(minMargin,2*(series[i].points.radius+series[i].points.lineWidth/2))}var margins={left:minMargin,right:minMargin,top:minMargin,bottom:minMargin};$.each(allAxes(),function(_,axis){if(axis.reserveSpace&&axis.ticks&&axis.ticks.length){if(axis.direction==="x"){margins.left=Math.max(margins.left,axis.labelWidth/2);margins.right=Math.max(margins.right,axis.labelWidth/2)}else{margins.bottom=Math.max(margins.bottom,axis.labelHeight/2);margins.top=Math.max(margins.top,axis.labelHeight/2)}}});plotOffset.left=Math.ceil(Math.max(margins.left,plotOffset.left));plotOffset.right=Math.ceil(Math.max(margins.right,plotOffset.right));plotOffset.top=Math.ceil(Math.max(margins.top,plotOffset.top));plotOffset.bottom=Math.ceil(Math.max(margins.bottom,plotOffset.bottom))}function setupGrid(){var i,axes=allAxes(),showGrid=options.grid.show;for(var a in plotOffset){var margin=options.grid.margin||0;plotOffset[a]=typeof margin=="number"?margin:margin[a]||0}executeHooks(hooks.processOffset,[plotOffset]);for(var a in plotOffset){if(typeof options.grid.borderWidth=="object"){plotOffset[a]+=showGrid?options.grid.borderWidth[a]:0}else{plotOffset[a]+=showGrid?options.grid.borderWidth:0}}$.each(axes,function(_,axis){var axisOpts=axis.options;axis.show=axisOpts.show==null?axis.used:axisOpts.show;axis.reserveSpace=axisOpts.reserveSpace==null?axis.show:axisOpts.reserveSpace;setRange(axis)});if(showGrid){var allocatedAxes=$.grep(axes,function(axis){return axis.show||axis.reserveSpace});$.each(allocatedAxes,function(_,axis){setupTickGeneration(axis);setTicks(axis);snapRangeToTicks(axis,axis.ticks);measureTickLabels(axis)});for(i=allocatedAxes.length-1;i>=0;--i)allocateAxisBoxFirstPhase(allocatedAxes[i]);adjustLayoutForThingsStickingOut();$.each(allocatedAxes,function(_,axis){allocateAxisBoxSecondPhase(axis)})}plotWidth=surface.width-plotOffset.left-plotOffset.right;plotHeight=surface.height-plotOffset.bottom-plotOffset.top;$.each(axes,function(_,axis){setTransformationHelpers(axis)});if(showGrid){drawAxisLabels()}insertLegend()}function setRange(axis){var opts=axis.options,min=+(opts.min!=null?opts.min:axis.datamin),max=+(opts.max!=null?opts.max:axis.datamax),delta=max-min;if(delta==0){var widen=max==0?1:.01;if(opts.min==null)min-=widen;if(opts.max==null||opts.min!=null)max+=widen}else{var margin=opts.autoscaleMargin;if(margin!=null){if(opts.min==null){min-=delta*margin;if(min<0&&axis.datamin!=null&&axis.datamin>=0)min=0}if(opts.max==null){max+=delta*margin;if(max>0&&axis.datamax!=null&&axis.datamax<=0)max=0}}}axis.min=min;axis.max=max}function setupTickGeneration(axis){var opts=axis.options;var noTicks;if(typeof opts.ticks=="number"&&opts.ticks>0)noTicks=opts.ticks;else noTicks=.3*Math.sqrt(axis.direction=="x"?surface.width:surface.height);var delta=(axis.max-axis.min)/noTicks,dec=-Math.floor(Math.log(delta)/Math.LN10),maxDec=opts.tickDecimals;if(maxDec!=null&&dec>maxDec){dec=maxDec}var magn=Math.pow(10,-dec),norm=delta/magn,size;if(norm<1.5){size=1}else if(norm<3){size=2;if(norm>2.25&&(maxDec==null||dec+1<=maxDec)){size=2.5;++dec}}else if(norm<7.5){size=5}else{size=10}size*=magn;if(opts.minTickSize!=null&&size<opts.minTickSize){size=opts.minTickSize}axis.delta=delta;axis.tickDecimals=Math.max(0,maxDec!=null?maxDec:dec);axis.tickSize=opts.tickSize||size;if(opts.mode=="time"&&!axis.tickGenerator){throw new Error("Time mode requires the flot.time plugin.")}if(!axis.tickGenerator){axis.tickGenerator=function(axis){var ticks=[],start=floorInBase(axis.min,axis.tickSize),i=0,v=Number.NaN,prev;do{prev=v;v=start+i*axis.tickSize;ticks.push(v);++i}while(v<axis.max&&v!=prev);return ticks};axis.tickFormatter=function(value,axis){var factor=axis.tickDecimals?Math.pow(10,axis.tickDecimals):1;var formatted=""+Math.round(value*factor)/factor;if(axis.tickDecimals!=null){var decimal=formatted.indexOf(".");var precision=decimal==-1?0:formatted.length-decimal-1;if(precision<axis.tickDecimals){return(precision?formatted:formatted+".")+(""+factor).substr(1,axis.tickDecimals-precision)}}return formatted}}if($.isFunction(opts.tickFormatter))axis.tickFormatter=function(v,axis){return""+opts.tickFormatter(v,axis)};if(opts.alignTicksWithAxis!=null){var otherAxis=(axis.direction=="x"?xaxes:yaxes)[opts.alignTicksWithAxis-1];if(otherAxis&&otherAxis.used&&otherAxis!=axis){var niceTicks=axis.tickGenerator(axis);if(niceTicks.length>0){if(opts.min==null)axis.min=Math.min(axis.min,niceTicks[0]);if(opts.max==null&&niceTicks.length>1)axis.max=Math.max(axis.max,niceTicks[niceTicks.length-1])}axis.tickGenerator=function(axis){var ticks=[],v,i;for(i=0;i<otherAxis.ticks.length;++i){v=(otherAxis.ticks[i].v-otherAxis.min)/(otherAxis.max-otherAxis.min);v=axis.min+v*(axis.max-axis.min);ticks.push(v)}return ticks};if(!axis.mode&&opts.tickDecimals==null){var extraDec=Math.max(0,-Math.floor(Math.log(axis.delta)/Math.LN10)+1),ts=axis.tickGenerator(axis);if(!(ts.length>1&&/\..*0$/.test((ts[1]-ts[0]).toFixed(extraDec))))axis.tickDecimals=extraDec}}}}function setTicks(axis){var oticks=axis.options.ticks,ticks=[];if(oticks==null||typeof oticks=="number"&&oticks>0)ticks=axis.tickGenerator(axis);else if(oticks){if($.isFunction(oticks))ticks=oticks(axis);else ticks=oticks}var i,v;axis.ticks=[];for(i=0;i<ticks.length;++i){var label=null;var t=ticks[i];if(typeof t=="object"){v=+t[0];if(t.length>1)label=t[1]}else v=+t;if(label==null)label=axis.tickFormatter(v,axis);if(!isNaN(v))axis.ticks.push({v:v,label:label})}}function snapRangeToTicks(axis,ticks){if(axis.options.autoscaleMargin&&ticks.length>0){if(axis.options.min==null)axis.min=Math.min(axis.min,ticks[0].v);if(axis.options.max==null&&ticks.length>1)axis.max=Math.max(axis.max,ticks[ticks.length-1].v)}}function draw(){surface.clear();executeHooks(hooks.drawBackground,[ctx]);var grid=options.grid;if(grid.show&&grid.backgroundColor)drawBackground();if(grid.show&&!grid.aboveData){drawGrid()}for(var i=0;i<series.length;++i){executeHooks(hooks.drawSeries,[ctx,series[i]]);drawSeries(series[i])}executeHooks(hooks.draw,[ctx]);if(grid.show&&grid.aboveData){drawGrid()}surface.render();triggerRedrawOverlay()}function extractRange(ranges,coord){var axis,from,to,key,axes=allAxes();for(var i=0;i<axes.length;++i){axis=axes[i];if(axis.direction==coord){key=coord+axis.n+"axis";if(!ranges[key]&&axis.n==1)key=coord+"axis";if(ranges[key]){from=ranges[key].from;to=ranges[key].to;break}}}if(!ranges[key]){axis=coord=="x"?xaxes[0]:yaxes[0];from=ranges[coord+"1"];to=ranges[coord+"2"]}if(from!=null&&to!=null&&from>to){var tmp=from;from=to;to=tmp}return{from:from,to:to,axis:axis}}function drawBackground(){ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.fillStyle=getColorOrGradient(options.grid.backgroundColor,plotHeight,0,"rgba(255, 255, 255, 0)");ctx.fillRect(0,0,plotWidth,plotHeight);ctx.restore()}function drawGrid(){var i,axes,bw,bc;ctx.save();ctx.translate(plotOffset.left,plotOffset.top);var markings=options.grid.markings;if(markings){if($.isFunction(markings)){axes=plot.getAxes();axes.xmin=axes.xaxis.min;axes.xmax=axes.xaxis.max;axes.ymin=axes.yaxis.min;axes.ymax=axes.yaxis.max;markings=markings(axes)}for(i=0;i<markings.length;++i){var m=markings[i],xrange=extractRange(m,"x"),yrange=extractRange(m,"y");if(xrange.from==null)xrange.from=xrange.axis.min;if(xrange.to==null)xrange.to=xrange.axis.max;
if(yrange.from==null)yrange.from=yrange.axis.min;if(yrange.to==null)yrange.to=yrange.axis.max;if(xrange.to<xrange.axis.min||xrange.from>xrange.axis.max||yrange.to<yrange.axis.min||yrange.from>yrange.axis.max)continue;xrange.from=Math.max(xrange.from,xrange.axis.min);xrange.to=Math.min(xrange.to,xrange.axis.max);yrange.from=Math.max(yrange.from,yrange.axis.min);yrange.to=Math.min(yrange.to,yrange.axis.max);var xequal=xrange.from===xrange.to,yequal=yrange.from===yrange.to;if(xequal&&yequal){continue}xrange.from=Math.floor(xrange.axis.p2c(xrange.from));xrange.to=Math.floor(xrange.axis.p2c(xrange.to));yrange.from=Math.floor(yrange.axis.p2c(yrange.from));yrange.to=Math.floor(yrange.axis.p2c(yrange.to));if(xequal||yequal){var lineWidth=m.lineWidth||options.grid.markingsLineWidth,subPixel=lineWidth%2?.5:0;ctx.beginPath();ctx.strokeStyle=m.color||options.grid.markingsColor;ctx.lineWidth=lineWidth;if(xequal){ctx.moveTo(xrange.to+subPixel,yrange.from);ctx.lineTo(xrange.to+subPixel,yrange.to)}else{ctx.moveTo(xrange.from,yrange.to+subPixel);ctx.lineTo(xrange.to,yrange.to+subPixel)}ctx.stroke()}else{ctx.fillStyle=m.color||options.grid.markingsColor;ctx.fillRect(xrange.from,yrange.to,xrange.to-xrange.from,yrange.from-yrange.to)}}}axes=allAxes();bw=options.grid.borderWidth;for(var j=0;j<axes.length;++j){var axis=axes[j],box=axis.box,t=axis.tickLength,x,y,xoff,yoff;if(!axis.show||axis.ticks.length==0)continue;ctx.lineWidth=1;if(axis.direction=="x"){x=0;if(t=="full")y=axis.position=="top"?0:plotHeight;else y=box.top-plotOffset.top+(axis.position=="top"?box.height:0)}else{y=0;if(t=="full")x=axis.position=="left"?0:plotWidth;else x=box.left-plotOffset.left+(axis.position=="left"?box.width:0)}if(!axis.innermost){ctx.strokeStyle=axis.options.color;ctx.beginPath();xoff=yoff=0;if(axis.direction=="x")xoff=plotWidth+1;else yoff=plotHeight+1;if(ctx.lineWidth==1){if(axis.direction=="x"){y=Math.floor(y)+.5}else{x=Math.floor(x)+.5}}ctx.moveTo(x,y);ctx.lineTo(x+xoff,y+yoff);ctx.stroke()}ctx.strokeStyle=axis.options.tickColor;ctx.beginPath();for(i=0;i<axis.ticks.length;++i){var v=axis.ticks[i].v;xoff=yoff=0;if(isNaN(v)||v<axis.min||v>axis.max||t=="full"&&(typeof bw=="object"&&bw[axis.position]>0||bw>0)&&(v==axis.min||v==axis.max))continue;if(axis.direction=="x"){x=axis.p2c(v);yoff=t=="full"?-plotHeight:t;if(axis.position=="top")yoff=-yoff}else{y=axis.p2c(v);xoff=t=="full"?-plotWidth:t;if(axis.position=="left")xoff=-xoff}if(ctx.lineWidth==1){if(axis.direction=="x")x=Math.floor(x)+.5;else y=Math.floor(y)+.5}ctx.moveTo(x,y);ctx.lineTo(x+xoff,y+yoff)}ctx.stroke()}if(bw){bc=options.grid.borderColor;if(typeof bw=="object"||typeof bc=="object"){if(typeof bw!=="object"){bw={top:bw,right:bw,bottom:bw,left:bw}}if(typeof bc!=="object"){bc={top:bc,right:bc,bottom:bc,left:bc}}if(bw.top>0){ctx.strokeStyle=bc.top;ctx.lineWidth=bw.top;ctx.beginPath();ctx.moveTo(0-bw.left,0-bw.top/2);ctx.lineTo(plotWidth,0-bw.top/2);ctx.stroke()}if(bw.right>0){ctx.strokeStyle=bc.right;ctx.lineWidth=bw.right;ctx.beginPath();ctx.moveTo(plotWidth+bw.right/2,0-bw.top);ctx.lineTo(plotWidth+bw.right/2,plotHeight);ctx.stroke()}if(bw.bottom>0){ctx.strokeStyle=bc.bottom;ctx.lineWidth=bw.bottom;ctx.beginPath();ctx.moveTo(plotWidth+bw.right,plotHeight+bw.bottom/2);ctx.lineTo(0,plotHeight+bw.bottom/2);ctx.stroke()}if(bw.left>0){ctx.strokeStyle=bc.left;ctx.lineWidth=bw.left;ctx.beginPath();ctx.moveTo(0-bw.left/2,plotHeight+bw.bottom);ctx.lineTo(0-bw.left/2,0);ctx.stroke()}}else{ctx.lineWidth=bw;ctx.strokeStyle=options.grid.borderColor;ctx.strokeRect(-bw/2,-bw/2,plotWidth+bw,plotHeight+bw)}}ctx.restore()}function drawAxisLabels(){$.each(allAxes(),function(_,axis){var box=axis.box,legacyStyles=axis.direction+"Axis "+axis.direction+axis.n+"Axis",layer="flot-"+axis.direction+"-axis flot-"+axis.direction+axis.n+"-axis "+legacyStyles,font=axis.options.font||"flot-tick-label tickLabel",tick,x,y,halign,valign;surface.removeText(layer);if(!axis.show||axis.ticks.length==0)return;for(var i=0;i<axis.ticks.length;++i){tick=axis.ticks[i];if(!tick.label||tick.v<axis.min||tick.v>axis.max)continue;if(axis.direction=="x"){halign="center";x=plotOffset.left+axis.p2c(tick.v);if(axis.position=="bottom"){y=box.top+box.padding}else{y=box.top+box.height-box.padding;valign="bottom"}}else{valign="middle";y=plotOffset.top+axis.p2c(tick.v);if(axis.position=="left"){x=box.left+box.width-box.padding;halign="right"}else{x=box.left+box.padding}}surface.addText(layer,x,y,tick.label,font,null,null,halign,valign)}})}function drawSeries(series){if(series.lines.show)drawSeriesLines(series);if(series.bars.show)drawSeriesBars(series);if(series.points.show)drawSeriesPoints(series)}function drawSeriesLines(series){function plotLine(datapoints,xoffset,yoffset,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize,prevx=null,prevy=null;ctx.beginPath();for(var i=ps;i<points.length;i+=ps){var x1=points[i-ps],y1=points[i-ps+1],x2=points[i],y2=points[i+1];if(x1==null||x2==null)continue;if(y1<=y2&&y1<axisy.min){if(y2<axisy.min)continue;x1=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.min}else if(y2<=y1&&y2<axisy.min){if(y1<axisy.min)continue;x2=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.min}if(y1>=y2&&y1>axisy.max){if(y2>axisy.max)continue;x1=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.max}else if(y2>=y1&&y2>axisy.max){if(y1>axisy.max)continue;x2=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.max}if(x1<=x2&&x1<axisx.min){if(x2<axisx.min)continue;y1=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.min}else if(x2<=x1&&x2<axisx.min){if(x1<axisx.min)continue;y2=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.min}if(x1>=x2&&x1>axisx.max){if(x2>axisx.max)continue;y1=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.max}else if(x2>=x1&&x2>axisx.max){if(x1>axisx.max)continue;y2=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.max}if(x1!=prevx||y1!=prevy)ctx.moveTo(axisx.p2c(x1)+xoffset,axisy.p2c(y1)+yoffset);prevx=x2;prevy=y2;ctx.lineTo(axisx.p2c(x2)+xoffset,axisy.p2c(y2)+yoffset)}ctx.stroke()}function plotLineArea(datapoints,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize,bottom=Math.min(Math.max(0,axisy.min),axisy.max),i=0,top,areaOpen=false,ypos=1,segmentStart=0,segmentEnd=0;while(true){if(ps>0&&i>points.length+ps)break;i+=ps;var x1=points[i-ps],y1=points[i-ps+ypos],x2=points[i],y2=points[i+ypos];if(areaOpen){if(ps>0&&x1!=null&&x2==null){segmentEnd=i;ps=-ps;ypos=2;continue}if(ps<0&&i==segmentStart+ps){ctx.fill();areaOpen=false;ps=-ps;ypos=1;i=segmentStart=segmentEnd+ps;continue}}if(x1==null||x2==null)continue;if(x1<=x2&&x1<axisx.min){if(x2<axisx.min)continue;y1=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.min}else if(x2<=x1&&x2<axisx.min){if(x1<axisx.min)continue;y2=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.min}if(x1>=x2&&x1>axisx.max){if(x2>axisx.max)continue;y1=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.max}else if(x2>=x1&&x2>axisx.max){if(x1>axisx.max)continue;y2=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.max}if(!areaOpen){ctx.beginPath();ctx.moveTo(axisx.p2c(x1),axisy.p2c(bottom));areaOpen=true}if(y1>=axisy.max&&y2>=axisy.max){ctx.lineTo(axisx.p2c(x1),axisy.p2c(axisy.max));ctx.lineTo(axisx.p2c(x2),axisy.p2c(axisy.max));continue}else if(y1<=axisy.min&&y2<=axisy.min){ctx.lineTo(axisx.p2c(x1),axisy.p2c(axisy.min));ctx.lineTo(axisx.p2c(x2),axisy.p2c(axisy.min));continue}var x1old=x1,x2old=x2;if(y1<=y2&&y1<axisy.min&&y2>=axisy.min){x1=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.min}else if(y2<=y1&&y2<axisy.min&&y1>=axisy.min){x2=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.min}if(y1>=y2&&y1>axisy.max&&y2<=axisy.max){x1=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.max}else if(y2>=y1&&y2>axisy.max&&y1<=axisy.max){x2=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.max}if(x1!=x1old){ctx.lineTo(axisx.p2c(x1old),axisy.p2c(y1))}ctx.lineTo(axisx.p2c(x1),axisy.p2c(y1));ctx.lineTo(axisx.p2c(x2),axisy.p2c(y2));if(x2!=x2old){ctx.lineTo(axisx.p2c(x2),axisy.p2c(y2));ctx.lineTo(axisx.p2c(x2old),axisy.p2c(y2))}}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.lineJoin="round";var lw=series.lines.lineWidth,sw=series.shadowSize;if(lw>0&&sw>0){ctx.lineWidth=sw;ctx.strokeStyle="rgba(0,0,0,0.1)";var angle=Math.PI/18;plotLine(series.datapoints,Math.sin(angle)*(lw/2+sw/2),Math.cos(angle)*(lw/2+sw/2),series.xaxis,series.yaxis);ctx.lineWidth=sw/2;plotLine(series.datapoints,Math.sin(angle)*(lw/2+sw/4),Math.cos(angle)*(lw/2+sw/4),series.xaxis,series.yaxis)}ctx.lineWidth=lw;ctx.strokeStyle=series.color;var fillStyle=getFillStyle(series.lines,series.color,0,plotHeight);if(fillStyle){ctx.fillStyle=fillStyle;plotLineArea(series.datapoints,series.xaxis,series.yaxis)}if(lw>0)plotLine(series.datapoints,0,0,series.xaxis,series.yaxis);ctx.restore()}function drawSeriesPoints(series){function plotPoints(datapoints,radius,fillStyle,offset,shadow,axisx,axisy,symbol){var points=datapoints.points,ps=datapoints.pointsize;for(var i=0;i<points.length;i+=ps){var x=points[i],y=points[i+1];if(x==null||x<axisx.min||x>axisx.max||y<axisy.min||y>axisy.max)continue;ctx.beginPath();x=axisx.p2c(x);y=axisy.p2c(y)+offset;if(symbol=="circle")ctx.arc(x,y,radius,0,shadow?Math.PI:Math.PI*2,false);else symbol(ctx,x,y,radius,shadow);ctx.closePath();if(fillStyle){ctx.fillStyle=fillStyle;ctx.fill()}ctx.stroke()}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);var lw=series.points.lineWidth,sw=series.shadowSize,radius=series.points.radius,symbol=series.points.symbol;if(lw==0)lw=1e-4;if(lw>0&&sw>0){var w=sw/2;ctx.lineWidth=w;ctx.strokeStyle="rgba(0,0,0,0.1)";plotPoints(series.datapoints,radius,null,w+w/2,true,series.xaxis,series.yaxis,symbol);ctx.strokeStyle="rgba(0,0,0,0.2)";plotPoints(series.datapoints,radius,null,w/2,true,series.xaxis,series.yaxis,symbol)}ctx.lineWidth=lw;ctx.strokeStyle=series.color;plotPoints(series.datapoints,radius,getFillStyle(series.points,series.color),0,false,series.xaxis,series.yaxis,symbol);ctx.restore()}function drawBar(x,y,b,barLeft,barRight,fillStyleCallback,axisx,axisy,c,horizontal,lineWidth){var left,right,bottom,top,drawLeft,drawRight,drawTop,drawBottom,tmp;if(horizontal){drawBottom=drawRight=drawTop=true;drawLeft=false;left=b;right=x;top=y+barLeft;bottom=y+barRight;if(right<left){tmp=right;right=left;left=tmp;drawLeft=true;drawRight=false}}else{drawLeft=drawRight=drawTop=true;drawBottom=false;left=x+barLeft;right=x+barRight;bottom=b;top=y;if(top<bottom){tmp=top;top=bottom;bottom=tmp;drawBottom=true;drawTop=false}}if(right<axisx.min||left>axisx.max||top<axisy.min||bottom>axisy.max)return;if(left<axisx.min){left=axisx.min;drawLeft=false}if(right>axisx.max){right=axisx.max;drawRight=false}if(bottom<axisy.min){bottom=axisy.min;drawBottom=false}if(top>axisy.max){top=axisy.max;drawTop=false}left=axisx.p2c(left);bottom=axisy.p2c(bottom);right=axisx.p2c(right);top=axisy.p2c(top);if(fillStyleCallback){c.fillStyle=fillStyleCallback(bottom,top);c.fillRect(left,top,right-left,bottom-top)}if(lineWidth>0&&(drawLeft||drawRight||drawTop||drawBottom)){c.beginPath();c.moveTo(left,bottom);if(drawLeft)c.lineTo(left,top);else c.moveTo(left,top);if(drawTop)c.lineTo(right,top);else c.moveTo(right,top);if(drawRight)c.lineTo(right,bottom);else c.moveTo(right,bottom);if(drawBottom)c.lineTo(left,bottom);else c.moveTo(left,bottom);c.stroke()}}function drawSeriesBars(series){function plotBars(datapoints,barLeft,barRight,fillStyleCallback,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize;for(var i=0;i<points.length;i+=ps){if(points[i]==null)continue;drawBar(points[i],points[i+1],points[i+2],barLeft,barRight,fillStyleCallback,axisx,axisy,ctx,series.bars.horizontal,series.bars.lineWidth)}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.lineWidth=series.bars.lineWidth;ctx.strokeStyle=series.color;var barLeft;switch(series.bars.align){case"left":barLeft=0;break;case"right":barLeft=-series.bars.barWidth;break;default:barLeft=-series.bars.barWidth/2}var fillStyleCallback=series.bars.fill?function(bottom,top){return 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<h1>criterion performance measurements</h1>
<h2>overview</h2>
<p><a href="#grokularation">want to understand this report?</a></p>
<div id="overview" class="ovchart" style="width:900px;height:100px;"></div>
<h2><a name="b0">5/reverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde0" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time0" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle0" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb0">xxx</span></td>
<td><span class="olstimept0">xxx</span></td>
<td><span class="confinterval olstimeub0">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb0">xxx</span></td>
<td><span class="olsr2pt0">xxx</span></td>
<td><span class="confinterval olsr2ub0">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">2.1825676261906123e-8</span></td>
<td><span class="time">2.2358490252398955e-8</span></td>
<td><span class="confinterval citime">2.37844799339728e-8</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.0410718832927102e-9</span></td>
<td><span class="time">2.679739388766841e-9</span></td>
<td><span class="confinterval citime">5.028759887265124e-9</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.9412781297226944</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b1">5/myReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde1" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time1" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle1" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb1">xxx</span></td>
<td><span class="olstimept1">xxx</span></td>
<td><span class="confinterval olstimeub1">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb1">xxx</span></td>
<td><span class="olsr2pt1">xxx</span></td>
<td><span class="confinterval olsr2ub1">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">4.3976666496170086e-8</span></td>
<td><span class="time">4.4436941145299556e-8</span></td>
<td><span class="confinterval citime">4.485953844319826e-8</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.1846412312215601e-9</span></td>
<td><span class="time">1.4129818567543152e-9</span></td>
<td><span class="confinterval citime">1.7437750926423863e-9</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.5048047041113867</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b2">5/betterReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde2" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time2" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle2" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb2">xxx</span></td>
<td><span class="olstimept2">xxx</span></td>
<td><span class="confinterval olstimeub2">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb2">xxx</span></td>
<td><span class="olsr2pt2">xxx</span></td>
<td><span class="confinterval olsr2ub2">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">2.2468511683032136e-8</span></td>
<td><span class="time">2.275122735723411e-8</span></td>
<td><span class="confinterval citime">2.308338369611327e-8</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">8.368738485812947e-10</span></td>
<td><span class="time">1.0551141730817191e-9</span></td>
<td><span class="confinterval citime">1.4280336158690675e-9</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.7000303924411976</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b3">5/vectorReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde3" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time3" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle3" class="cyclechart"
style="width:300px;height:278px;"></div></td>
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</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb3">xxx</span></td>
<td><span class="olstimept3">xxx</span></td>
<td><span class="confinterval olstimeub3">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb3">xxx</span></td>
<td><span class="olsr2pt3">xxx</span></td>
<td><span class="confinterval olsr2ub3">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">4.113268125528561e-7</span></td>
<td><span class="time">4.1671732796036996e-7</span></td>
<td><span class="confinterval citime">4.2302707007282097e-7</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.497270976211413e-8</span></td>
<td><span class="time">2.0058069721556883e-8</span></td>
<td><span class="confinterval citime">2.7911287812056826e-8</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.6633637246704727</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b4">5/svectorReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde4" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time4" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle4" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb4">xxx</span></td>
<td><span class="olstimept4">xxx</span></td>
<td><span class="confinterval olstimeub4">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb4">xxx</span></td>
<td><span class="olsr2pt4">xxx</span></td>
<td><span class="confinterval olsr2ub4">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">6.920398407902705e-8</span></td>
<td><span class="time">6.975841936879646e-8</span></td>
<td><span class="confinterval citime">7.046564991423694e-8</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.8066599476484786e-9</span></td>
<td><span class="time">2.172896214537708e-9</span></td>
<td><span class="confinterval citime">2.6467403234113016e-9</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.48839555023650844</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b5">5/uvectorReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde5" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time5" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle5" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb5">xxx</span></td>
<td><span class="olstimept5">xxx</span></td>
<td><span class="confinterval olstimeub5">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb5">xxx</span></td>
<td><span class="olsr2pt5">xxx</span></td>
<td><span class="confinterval olsr2ub5">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">6.851679597092373e-8</span></td>
<td><span class="time">6.925843908280533e-8</span></td>
<td><span class="confinterval citime">7.01740396232455e-8</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">2.210327892769043e-9</span></td>
<td><span class="time">2.927763932650304e-9</span></td>
<td><span class="confinterval citime">4.326065221198047e-9</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.6400701063900864</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b6">100/reverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde6" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time6" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle6" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb6">xxx</span></td>
<td><span class="olstimept6">xxx</span></td>
<td><span class="confinterval olstimeub6">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb6">xxx</span></td>
<td><span class="olsr2pt6">xxx</span></td>
<td><span class="confinterval olsr2ub6">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">4.4247980578490166e-7</span></td>
<td><span class="time">4.486931689301164e-7</span></td>
<td><span class="confinterval citime">4.715765238594911e-7</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.384237597896298e-8</span></td>
<td><span class="time">3.4265713859801746e-8</span></td>
<td><span class="confinterval citime">7.91561949401906e-8</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.8337723416690731</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b7">100/myReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde7" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time7" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle7" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb7">xxx</span></td>
<td><span class="olstimept7">xxx</span></td>
<td><span class="confinterval olstimeub7">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb7">xxx</span></td>
<td><span class="olsr2pt7">xxx</span></td>
<td><span class="confinterval olsr2ub7">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">1.007891519190093e-6</span></td>
<td><span class="time">1.0195661875784653e-6</span></td>
<td><span class="confinterval citime">1.039020302638846e-6</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">3.817967123630337e-8</span></td>
<td><span class="time">5.2243289403871843e-8</span></td>
<td><span class="confinterval citime">7.427452481277e-8</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.6738845808731594</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b8">100/betterReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde8" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time8" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle8" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb8">xxx</span></td>
<td><span class="olstimept8">xxx</span></td>
<td><span class="confinterval olstimeub8">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb8">xxx</span></td>
<td><span class="olsr2pt8">xxx</span></td>
<td><span class="confinterval olsr2ub8">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">4.295364364477159e-7</span></td>
<td><span class="time">4.3298605299216234e-7</span></td>
<td><span class="confinterval citime">4.3799217841438554e-7</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.112311803558022e-8</span></td>
<td><span class="time">1.3612046474153825e-8</span></td>
<td><span class="confinterval citime">1.7582641093090672e-8</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.4540266643025085</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b9">100/vectorReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde9" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time9" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle9" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb9">xxx</span></td>
<td><span class="olstimept9">xxx</span></td>
<td><span class="confinterval olstimeub9">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb9">xxx</span></td>
<td><span class="olsr2pt9">xxx</span></td>
<td><span class="confinterval olsr2ub9">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">3.3057291168636383e-6</span></td>
<td><span class="time">3.38274136179026e-6</span></td>
<td><span class="confinterval citime">3.548252976134029e-6</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.0608095029319091e-7</span></td>
<td><span class="time">3.541439684399015e-7</span></td>
<td><span class="confinterval citime">6.096734294907109e-7</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.8869680939351119</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b10">100/svectorReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde10" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time10" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle10" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb10">xxx</span></td>
<td><span class="olstimept10">xxx</span></td>
<td><span class="confinterval olstimeub10">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb10">xxx</span></td>
<td><span class="olsr2pt10">xxx</span></td>
<td><span class="confinterval olsr2ub10">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">6.866557693995546e-7</span></td>
<td><span class="time">6.934741716236048e-7</span></td>
<td><span class="confinterval citime">7.021896276554826e-7</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">2.1479242834733937e-8</span></td>
<td><span class="time">2.4882765696493914e-8</span></td>
<td><span class="confinterval citime">3.0244553441558444e-8</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.5098333358544204</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b11">100/uvectorReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde11" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time11" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle11" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb11">xxx</span></td>
<td><span class="olstimept11">xxx</span></td>
<td><span class="confinterval olstimeub11">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb11">xxx</span></td>
<td><span class="olsr2pt11">xxx</span></td>
<td><span class="confinterval olsr2ub11">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">6.6949455396143e-7</span></td>
<td><span class="time">6.763147099577562e-7</span></td>
<td><span class="confinterval citime">6.8492834272938e-7</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">2.090563167335844e-8</span></td>
<td><span class="time">2.6756765803271828e-8</span></td>
<td><span class="confinterval citime">3.911103210894066e-8</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.560253509340352</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b12">10000/reverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde12" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time12" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle12" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb12">xxx</span></td>
<td><span class="olstimept12">xxx</span></td>
<td><span class="confinterval olstimeub12">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb12">xxx</span></td>
<td><span class="olsr2pt12">xxx</span></td>
<td><span class="confinterval olsr2ub12">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">8.226367938376666e-5</span></td>
<td><span class="time">8.404068339648452e-5</span></td>
<td><span class="confinterval citime">8.709085964902236e-5</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">4.522568050534064e-6</span></td>
<td><span class="time">7.86691139839666e-6</span></td>
<td><span class="confinterval citime">1.430260632818422e-5</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.8002504670977215</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b13">10000/myReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde13" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time13" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle13" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb13">xxx</span></td>
<td><span class="olstimept13">xxx</span></td>
<td><span class="confinterval olstimeub13">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb13">xxx</span></td>
<td><span class="olsr2pt13">xxx</span></td>
<td><span class="confinterval olsr2ub13">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">9.89039039905634e-4</span></td>
<td><span class="time">1.018843258186528e-3</span></td>
<td><span class="confinterval citime">1.0672437545241342e-3</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">8.176285380106038e-5</span></td>
<td><span class="time">1.257903479559935e-4</span></td>
<td><span class="confinterval citime">2.101625919477303e-4</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.8045762396300628</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b14">10000/betterReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde14" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time14" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle14" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb14">xxx</span></td>
<td><span class="olstimept14">xxx</span></td>
<td><span class="confinterval olstimeub14">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb14">xxx</span></td>
<td><span class="olsr2pt14">xxx</span></td>
<td><span class="confinterval olsr2ub14">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">8.037007935207724e-5</span></td>
<td><span class="time">8.094225189166519e-5</span></td>
<td><span class="confinterval citime">8.184021433892617e-5</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.6701071654065847e-6</span></td>
<td><span class="time">2.23963860459826e-6</span></td>
<td><span class="confinterval citime">3.3025989615865156e-6</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.25266616390534796</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b15">10000/vectorReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde15" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time15" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle15" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb15">xxx</span></td>
<td><span class="olstimept15">xxx</span></td>
<td><span class="confinterval olstimeub15">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb15">xxx</span></td>
<td><span class="olsr2pt15">xxx</span></td>
<td><span class="confinterval olsr2ub15">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">4.671121652397968e-4</span></td>
<td><span class="time">4.874406423516324e-4</span></td>
<td><span class="confinterval citime">5.260025225863243e-4</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">5.3060569609557984e-5</span></td>
<td><span class="time">8.807508602942347e-5</span></td>
<td><span class="confinterval citime">1.399993272699004e-4</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.9129058424341375</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b16">10000/svectorReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde16" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time16" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle16" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb16">xxx</span></td>
<td><span class="olstimept16">xxx</span></td>
<td><span class="confinterval olstimeub16">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb16">xxx</span></td>
<td><span class="olsr2pt16">xxx</span></td>
<td><span class="confinterval olsr2ub16">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">8.363981457710693e-5</span></td>
<td><span class="time">8.465469716167873e-5</span></td>
<td><span class="confinterval citime">8.572704740430688e-5</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">2.8557446230897197e-6</span></td>
<td><span class="time">3.46327049505151e-6</span></td>
<td><span class="confinterval citime">4.740792412320742e-6</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.4309142876000203</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b17">10000/uvectorReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde17" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time17" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle17" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb17">xxx</span></td>
<td><span class="olstimept17">xxx</span></td>
<td><span class="confinterval olstimeub17">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb17">xxx</span></td>
<td><span class="olsr2pt17">xxx</span></td>
<td><span class="confinterval olsr2ub17">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">8.180549446549271e-5</span></td>
<td><span class="time">8.302433383267748e-5</span></td>
<td><span class="confinterval citime">8.507035683076922e-5</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">3.0654232002257616e-6</span></td>
<td><span class="time">5.168280070836902e-6</span></td>
<td><span class="confinterval citime">8.482840131547177e-6</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.6386877066044123</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b18">1000000/reverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde18" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time18" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle18" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb18">xxx</span></td>
<td><span class="olstimept18">xxx</span></td>
<td><span class="confinterval olstimeub18">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb18">xxx</span></td>
<td><span class="olsr2pt18">xxx</span></td>
<td><span class="confinterval olsr2ub18">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">5.82335887983178e-2</span></td>
<td><span class="time">6.070360608160647e-2</span></td>
<td><span class="confinterval citime">6.351763956607893e-2</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">3.493123458566975e-3</span></td>
<td><span class="time">4.869657126862904e-3</span></td>
<td><span class="confinterval citime">6.661851617464323e-3</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.23735406305053863</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b19">1000000/myReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde19" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time19" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle19" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb19">xxx</span></td>
<td><span class="olstimept19">xxx</span></td>
<td><span class="confinterval olstimeub19">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb19">xxx</span></td>
<td><span class="olsr2pt19">xxx</span></td>
<td><span class="confinterval olsr2ub19">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">0.1521053190443865</span></td>
<td><span class="time">0.15616956000529855</span></td>
<td><span class="confinterval citime">0.16591606631843145</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.4696875602100575e-3</span></td>
<td><span class="time">8.218916793373316e-3</span></td>
<td><span class="confinterval citime">1.2116156633981663e-2</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.12641003608244392</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b20">1000000/betterReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde20" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time20" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle20" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb20">xxx</span></td>
<td><span class="olstimept20">xxx</span></td>
<td><span class="confinterval olstimeub20">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb20">xxx</span></td>
<td><span class="olsr2pt20">xxx</span></td>
<td><span class="confinterval olsr2ub20">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">5.8424205701204296e-2</span></td>
<td><span class="time">6.046765499354529e-2</span></td>
<td><span class="confinterval citime">6.389965309082378e-2</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">2.7554816954297187e-3</span></td>
<td><span class="time">4.6048879250182835e-3</span></td>
<td><span class="confinterval citime">6.9992153869534645e-3</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.23596776268571917</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b21">1000000/vectorReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde21" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time21" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle21" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb21">xxx</span></td>
<td><span class="olstimept21">xxx</span></td>
<td><span class="confinterval olstimeub21">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb21">xxx</span></td>
<td><span class="olsr2pt21">xxx</span></td>
<td><span class="confinterval olsr2ub21">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">5.391284686073217e-2</span></td>
<td><span class="time">5.609325219775799e-2</span></td>
<td><span class="confinterval citime">5.804273318320527e-2</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">2.5813403288497466e-3</span></td>
<td><span class="time">3.581623161602982e-3</span></td>
<td><span class="confinterval citime">5.10770701971325e-3</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.15779515270543848</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b22">1000000/svectorReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde22" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time22" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle22" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb22">xxx</span></td>
<td><span class="olstimept22">xxx</span></td>
<td><span class="confinterval olstimeub22">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb22">xxx</span></td>
<td><span class="olsr2pt22">xxx</span></td>
<td><span class="confinterval olsr2ub22">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">1.3087657836357023e-2</span></td>
<td><span class="time">1.3486536819947735e-2</span></td>
<td><span class="confinterval citime">1.4577670676972554e-2</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">6.632674890790757e-4</span></td>
<td><span class="time">1.5583727257841432e-3</span></td>
<td><span class="confinterval citime">2.871073235275729e-3</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.5619420802481624</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b23">1000000/uvectorReverse</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde23" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time23" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle23" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb23">xxx</span></td>
<td><span class="olstimept23">xxx</span></td>
<td><span class="confinterval olstimeub23">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb23">xxx</span></td>
<td><span class="olsr2pt23">xxx</span></td>
<td><span class="confinterval olsr2ub23">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">1.207082513587815e-2</span></td>
<td><span class="time">1.2224248519543568e-2</span></td>
<td><span class="confinterval citime">1.2365601516715435e-2</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">3.05961188737579e-4</span></td>
<td><span class="time">3.8840682394674676e-4</span></td>
<td><span class="confinterval citime">4.906517806114332e-4</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.10403423030173367</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="grokularation">understanding this report</a></h2>
<p>In this report, each function benchmarked by criterion is assigned
a section of its own. The charts in each section are active; if
you hover your mouse over data points and annotations, you will see
more details.</p>
<ul>
<li>The chart on the left is a
<a href="http://en.wikipedia.org/wiki/Kernel_density_estimation">kernel
density estimate</a> (also known as a KDE) of time
measurements. This graphs the probability of any given time
measurement occurring. A spike indicates that a measurement of a
particular time occurred; its height indicates how often that
measurement was repeated.</li>
<li>The chart on the right is the raw data from which the kernel
density estimate is built. The <i>x</i> axis indicates the
number of loop iterations, while the <i>y</i> axis shows measured
execution time for the given number of loop iterations. The
line behind the values is the linear regression prediction of
execution time for a given number of iterations. Ideally, all
measurements will be on (or very near) this line.</li>
</ul>
<p>Under the charts is a small table.
The first two rows are the results of a linear regression run
on the measurements displayed in the right-hand chart.</p>
<ul>
<li><i>OLS regression</i> indicates the
time estimated for a single loop iteration using an ordinary
least-squares regression model. This number is more accurate
than the <i>mean</i> estimate below it, as it more effectively
eliminates measurement overhead and other constant factors.</li>
<li><i>R&#xb2; goodness-of-fit</i> is a measure of how
accurately the linear regression model fits the observed
measurements. If the measurements are not too noisy, R&#xb2;
should lie between 0.99 and 1, indicating an excellent fit. If
the number is below 0.99, something is confounding the accuracy
of the linear model.</li>
<li><i>Mean execution time</i> and <i>standard deviation</i> are
statistics calculated from execution time
divided by number of iterations.</li>
</ul>
<p>We use a statistical technique called
the <a href="http://en.wikipedia.org/wiki/Bootstrapping_(statistics)">bootstrap</a>
to provide confidence intervals on our estimates. The
bootstrap-derived upper and lower bounds on estimates let you see
how accurate we believe those estimates to be. (Hover the mouse
over the table headers to see the confidence levels.)</p>
<p>A noisy benchmarking environment can cause some or many
measurements to fall far from the mean. These outlying
measurements can have a significant inflationary effect on the
estimate of the standard deviation. We calculate and display an
estimate of the extent to which the standard deviation has been
inflated by outliers.</p>
<script type="text/javascript">
$(function () {
function mangulate(rpt) {
var measured = function(key) {
var idx = rpt.reportKeys.indexOf(key);
return rpt.reportMeasured.map(function(r) { return r[idx]; });
};
var number = rpt.reportNumber;
var name = rpt.reportName;
var mean = rpt.reportAnalysis.anMean.estPoint;
var iters = measured("iters");
var times = measured("time");
var kdetimes = rpt.reportKDEs[0].kdeValues;
var kdepdf = rpt.reportKDEs[0].kdePDF;
var meanSecs = mean;
var units = $.timeUnits(mean);
var rgrs = rpt.reportAnalysis.anRegress[0];
var scale = units[0];
var olsTime = rgrs.regCoeffs.iters;
$(".olstimept" + number).text(function() {
return $.renderTime(olsTime.estPoint);
});
$(".olstimelb" + number).text(function() {
return $.renderTime(olsTime.estLowerBound);
});
$(".olstimeub" + number).text(function() {
return $.renderTime(olsTime.estUpperBound);
});
$(".olsr2pt" + number).text(function() {
return rgrs.regRSquare.estPoint.toFixed(3);
});
$(".olsr2lb" + number).text(function() {
return rgrs.regRSquare.estLowerBound.toFixed(3);
});
$(".olsr2ub" + number).text(function() {
return rgrs.regRSquare.estUpperBound.toFixed(3);
});
mean *= scale;
kdetimes = $.scaleBy(scale, kdetimes);
var kq = $("#kde" + number);
var k = $.plot(kq,
[{ label: name + " time densities",
data: $.zip(kdetimes, kdepdf),
}],
{ xaxis: { tickFormatter: $.unitFormatter(scale) },
yaxis: { ticks: false },
grid: { borderColor: "#777",
hoverable: true, markings: [ { color: '#6fd3fb',
lineWidth: 1.5, xaxis: { from: mean, to: mean } } ] },
});
var o = k.pointOffset({ x: mean, y: 0});
kq.append('<div class="meanlegend" title="' + $.renderTime(meanSecs) +
'" style="position:absolute;left:' + (o.left + 4) +
'px;bottom:139px;">mean</div>');
$.addTooltip("#kde" + number,
function(secs) { return $.renderTime(secs / scale); });
var timepairs = new Array(times.length);
var lastiter = iters[iters.length-1];
var olspairs = [[0,0], [lastiter, lastiter * scale * olsTime.estPoint]];
for (var i = 0; i < times.length; i++)
timepairs[i] = [iters[i],times[i]*scale];
iterFormatter = function() {
var denom = 0;
return function(iters) {
if (iters == 0)
return '';
if (denom > 0)
return (iters / denom).toFixed();
var power;
if (iters >= 1e9) {
denom = '1e9'; power = '&#x2079;';
}
if (iters >= 1e6) {
denom = '1e6'; power = '&#x2076;';
}
else if (iters >= 1e3) {
denom = '1e3'; power = '&#xb3;';
}
else denom = 1;
if (denom > 1) {
iters = (iters / denom).toFixed();
iters += '&times;10' + power + ' iters';
} else {
iters += ' iters';
}
return iters;
};
};
$.plot($("#time" + number),
[{ label: "regression", data: olspairs,
lines: { show: true } },
{ label: name + " times", data: timepairs,
points: { show: true } }],
{ grid: { borderColor: "#777", hoverable: true },
xaxis: { tickFormatter: iterFormatter() },
yaxis: { tickFormatter: $.unitFormatter(scale) } });
$.addTooltip("#time" + number,
function(iters,secs) {
return ($.renderTime(secs / scale) + ' / ' +
iters.toLocaleString() + ' iters');
});
if (0) {
var cyclepairs = new Array(cycles.length);
for (var i = 0; i < cycles.length; i++)
cyclepairs[i] = [cycles[i],i];
$.plot($("#cycle" + number),
[{ label: name + " cycles",
data: cyclepairs }],
{ points: { show: true },
grid: { borderColor: "#777", hoverable: true },
xaxis: { tickFormatter:
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yaxis: { ticks: false },
});
$.addTooltip("#cycles" + number, function(x,y) { return x + ' cycles'; });
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<div class="footfirst">
<h2>colophon</h2>
<p>This report was created using the
<a href="http://hackage.haskell.org/package/criterion">criterion</a>
benchmark execution and performance analysis tool.</p>
<p>Criterion is developed and maintained
by <a href="http://www.serpentine.com/blog/">Bryan O'Sullivan</a>.</p>
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