Move d3.stats to external lib, science.stats.js!
This commit is contained in:
Jason Davies
Родитель
bb21bbd8ce
Коммит
e0a0455f7b
16
Makefile
16
Makefile
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@ -6,8 +6,6 @@ all: \
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d3.min.js \
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d3.min.js \
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d3.behavior.js \
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d3.behavior.js \
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d3.behavior.min.js \
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d3.behavior.min.js \
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d3.stats.js \
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d3.stats.min.js \
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d3.chart.js \
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d3.chart.js \
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d3.chart.min.js \
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d3.chart.min.js \
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d3.layout.js \
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d3.layout.js \
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@ -101,20 +99,6 @@ d3.behavior.js: \
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src/behavior/zoom.js \
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src/behavior/zoom.js \
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src/end.js
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src/end.js
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d3.stats.js: \
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src/start.js \
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src/stats/stats.js \
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src/stats/bandwidth.js \
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src/stats/kernel.js \
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src/stats/kde.js \
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src/stats/iqr.js \
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src/stats/mean.js \
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src/stats/median.js \
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src/stats/mode.js \
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src/stats/quantiles.js \
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src/stats/variance.js \
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src/end.js
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d3.chart.js: \
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d3.chart.js: \
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src/start.js \
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src/start.js \
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src/chart/chart.js \
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src/chart/chart.js \
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@ -291,7 +291,11 @@ function d3_chart_boxWhiskers(d) {
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function d3_chart_boxQuartiles(d) {
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function d3_chart_boxQuartiles(d) {
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// TODO avoid sorting data twice?
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// TODO avoid sorting data twice?
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return d3.stats.quantiles(d, [.25, .5, .75]);
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var n = d.length;
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return [.25, .5, .75].map(function(q) {
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q *= n;
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return ~~q === q ? (d[q] + d[q + 1]) / 2 : d[Math.round(q)];
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});
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}
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}
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// ranges (bad, satisfactory, good)
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// ranges (bad, satisfactory, good)
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// measures (actual, forecast)
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// measures (actual, forecast)
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Различия файлов скрыты, потому что одна или несколько строк слишком длинны
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@ -1 +0,0 @@
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(function(){d3.stats={},d3.stats.bandwidth={nrd0:function(a){var b=Math.sqrt(d3.stats.variance(a));(lo=Math.min(b,d3.stats.iqr(a)/1.34))||(lo=b)||(lo=Math.abs(a[1]))||(lo=1);return.9*lo*Math.pow(a.length,-0.2)},nrd:function(a){var b=d3.stats.iqr(a)/1.34;return 1.06*Math.min(Math.sqrt(d3.stats.variance(a)),b)*Math.pow(a.length,-0.2)}},d3.stats.kernel={uniform:function(a){if(a<=1&&a>=-1)return.5;return 0},triangular:function(a){if(a<=1&&a>=-1)return 1-Math.abs(a);return 0},epanechnikov:function(a){if(a<=1&&a>=-1)return.75*(1-a*a);return 0},quartic:function(a){if(a<=1&&a>=-1){var b=1-a*a;return.9375*b*b}return 0},triweight:function(a){if(a<=1&&a>=-1){var b=1-a*a;return 35/32*b*b*b}return 0},gaussian:function(a){return 1/Math.sqrt(2*Math.PI)*Math.exp(-0.5*a*a)},cosine:function(a){if(a<=1&&a>=-1)return Math.PI/4*Math.cos(Math.PI/2*a);return 0}},d3.stats.kde=function(){function d(d,e){var f=c.call(this,b);return d.map(function(c){var d=-1,e=0,g=b.length;while(++d<g)e+=a((c-b[d])/f);return[c,e/f/g]})}var a=d3.stats.kernel.gaussian,b=[],c=d3.stats.bandwidth.nrd;d.kernel=function(b){if(!arguments.length)return a;a=b;return d},d.sample=function(a){if(!arguments.length)return b;b=a;return d},d.bandwidth=function(a){if(!arguments.length)return c;c=d3.functor(a);return d};return d},d3.stats.iqr=function(a){var b=d3.stats.quantiles(a,[.25,.75]);return b[1]-b[0]},d3.stats.mean=function(a){var b=a.length;if(b===0)return NaN;var c=0,d=-1;while(++d<b)c+=(a[d]-c)/(d+1);return c},d3.stats.median=function(a){return d3.stats.quantiles(a,[.5])[0]},d3.stats.mode=function(a){a=a.slice().sort(d3.ascending);var b,c=a.length,d=-1,e=d,f=null,g=0,h,i;while(++d<c)(i=a[d])!==f&&((h=d-e)>g&&(g=h,b=f),f=i,e=d);return b},d3.stats.quantiles=function(a,b){a=a.slice().sort(d3.ascending);var c=a.length-1;return b.map(function(b){if(b===0)return a[0];if(b===1)return a[c];var e=1+b*c,f=Math.floor(e),g=e-f,h=a[f-1];return g===0?h:h+g*(a[f]-h)})},d3.stats.variance=function(a){var b=a.length;if(b<1)return NaN;if(b===1)return 0;var c=d3.stats.mean(a),d=-1,e=0;while(++d<b){var f=a[d]-c;e+=f*f}return e/(b-1)}})()
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@ -3,7 +3,6 @@
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<head>
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<head>
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<title>Box Plot Charts</title>
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<title>Box Plot Charts</title>
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<script type="text/javascript" src="../../d3.js"></script>
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<script type="text/javascript" src="../../d3.js"></script>
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<script type="text/javascript" src="../../d3.stats.js"></script>
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<script type="text/javascript" src="../../d3.chart.js"></script>
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<script type="text/javascript" src="../../d3.chart.js"></script>
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<script type="text/javascript" src="../../d3.csv.js"></script>
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<script type="text/javascript" src="../../d3.csv.js"></script>
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<link type="text/css" rel="stylesheet" href="../button.css"/>
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<link type="text/css" rel="stylesheet" href="../button.css"/>
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@ -5,7 +5,8 @@
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<link rel="stylesheet" type="text/css" href="kde.css"/>
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<link rel="stylesheet" type="text/css" href="kde.css"/>
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<script type="text/javascript" src="../../d3.js"></script>
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<script type="text/javascript" src="../../d3.js"></script>
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<script type="text/javascript" src="../../d3.layout.js"></script>
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<script type="text/javascript" src="../../d3.layout.js"></script>
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<script type="text/javascript" src="../../d3.stats.js"></script>
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<script type="text/javascript" src="../../lib/science/science.js"></script>
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<script type="text/javascript" src="../../lib/science/science.stats.js"></script>
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</head>
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</head>
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<body>
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<body>
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<script type="text/javascript" src="kde.js"></script>
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<script type="text/javascript" src="kde.js"></script>
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@ -4,10 +4,10 @@ d3.json("faithful.json", function(faithful) {
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var w = 800,
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var w = 800,
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h = 400,
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h = 400,
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x = d3.scale.linear().domain([30, 110]).range([0, w]);
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x = d3.scale.linear().domain([30, 110]).range([0, w]);
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bins = d3.layout.histogram().frequency(false).ticks(x.ticks(60))(data),
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bins = d3.layout.histogram().frequency(true).bins(x.ticks(60))(data),
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max = d3.max(bins, function(d) { return d.y; }),
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max = d3.max(bins, function(d) { return d.y; }),
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y = d3.scale.linear().domain([0, .1]).range([0, h]),
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y = d3.scale.linear().domain([0, .1]).range([0, h]),
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kde = d3.stats.kde().sample(data);
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kde = science.stats.kde().sample(data);
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var vis = d3.select("body")
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var vis = d3.select("body")
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.append("svg:svg")
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.append("svg:svg")
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@ -32,7 +32,7 @@ d3.json("faithful.json", function(faithful) {
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.y(function(d) { return h - y(d[1]); });
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.y(function(d) { return h - y(d[1]); });
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vis.selectAll("path")
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vis.selectAll("path")
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.data(d3.values(d3.stats.bandwidth))
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.data(d3.values(science.stats.bandwidth))
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.enter().append("svg:path")
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.enter().append("svg:path")
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.attr("d", function(h) {
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.attr("d", function(h) {
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return line(kde.bandwidth(h)(d3.range(30, 110, .1)));
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return line(kde.bandwidth(h)(d3.range(30, 110, .1)));
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@ -5,6 +5,8 @@
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<script type="text/javascript" src="../../d3.js"></script>
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<script type="text/javascript" src="../../d3.js"></script>
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<script type="text/javascript" src="../../d3.chart.js"></script>
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<script type="text/javascript" src="../../d3.chart.js"></script>
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<script type="text/javascript" src="../../d3.stats.js"></script>
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<script type="text/javascript" src="../../d3.stats.js"></script>
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<script type="text/javascript" src="../../lib/science/science.js"></script>
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<script type="text/javascript" src="../../lib/science/science.stats.js"></script>
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<link type="text/css" rel="stylesheet" href="../button.css"/>
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<link type="text/css" rel="stylesheet" href="../button.css"/>
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<link type="text/css" rel="stylesheet" href="qq.css"/>
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<link type="text/css" rel="stylesheet" href="qq.css"/>
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</head>
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</head>
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@ -15,8 +15,8 @@ var vis = d3.select("#chart")
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.attr("transform", "translate(" + m[3] + "," + m[0] + ")");
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.attr("transform", "translate(" + m[3] + "," + m[0] + ")");
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d3.json("turkers.json", function(turkers) {
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d3.json("turkers.json", function(turkers) {
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var tm = d3.stats.mean(turkers),
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var tm = science.stats.mean(turkers),
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td = Math.sqrt(d3.stats.variance(turkers)),
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td = Math.sqrt(science.stats.variance(turkers)),
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dd = [
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dd = [
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[0.10306430789206111, 0.0036139086950272735, 0.30498647327844536],
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[0.10306430789206111, 0.0036139086950272735, 0.30498647327844536],
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[0.5924252668569606, 0.0462763685758622, 0.4340870312025223],
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[0.5924252668569606, 0.0462763685758622, 0.4340870312025223],
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@ -0,0 +1,26 @@
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Copyright (c) 2011, Jason Davies
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* The name Jason Davies may not be used to endorse or promote products
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derived from this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL JASON DAVIES BE LIABLE FOR ANY DIRECT, INDIRECT,
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INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
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OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
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ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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@ -0,0 +1,5 @@
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(function(){science = {version: "0.0.1"}; // semver
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science.functor = function(v) {
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return typeof v === "function" ? v : function() { return v; };
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};
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})()
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@ -0,0 +1 @@
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(function(){science={version:"0.0.1"},science.functor=function(a){return typeof a=="function"?a:function(){return a}}})()
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@ -1,12 +1,12 @@
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(function(){d3.stats = {};
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(function(){science.stats = {};
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// Bandwidth selectors for Gaussian kernels.
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// Bandwidth selectors for Gaussian kernels.
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// Based on R's implementations in `stats.bw`.
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// Based on R's implementations in `stats.bw`.
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d3.stats.bandwidth = {
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science.stats.bandwidth = {
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// Silverman, B. W. (1986) Density Estimation. London: Chapman and Hall.
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// Silverman, B. W. (1986) Density Estimation. London: Chapman and Hall.
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nrd0: function(x) {
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nrd0: function(x) {
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var hi = Math.sqrt(d3.stats.variance(x));
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var hi = Math.sqrt(science.stats.variance(x));
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if (!(lo = Math.min(hi, d3.stats.iqr(x) / 1.34)))
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if (!(lo = Math.min(hi, science.stats.iqr(x) / 1.34)))
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(lo = hi) || (lo = Math.abs(x[1])) || (lo = 1);
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(lo = hi) || (lo = Math.abs(x[1])) || (lo = 1);
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return .9 * lo * Math.pow(x.length, -.2);
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return .9 * lo * Math.pow(x.length, -.2);
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},
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},
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@ -14,13 +14,13 @@ d3.stats.bandwidth = {
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// Scott, D. W. (1992) Multivariate Density Estimation: Theory, Practice, and
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// Scott, D. W. (1992) Multivariate Density Estimation: Theory, Practice, and
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// Visualization. Wiley.
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// Visualization. Wiley.
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nrd: function(x) {
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nrd: function(x) {
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var h = d3.stats.iqr(x) / 1.34;
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var h = science.stats.iqr(x) / 1.34;
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return 1.06 * Math.min(Math.sqrt(d3.stats.variance(x)), h)
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return 1.06 * Math.min(Math.sqrt(science.stats.variance(x)), h)
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* Math.pow(x.length, -1/5);
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* Math.pow(x.length, -1/5);
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}
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}
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};
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};
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// See <http://en.wikipedia.org/wiki/Kernel_(statistics)>.
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// See <http://en.wikipedia.org/wiki/Kernel_(statistics)>.
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d3.stats.kernel = {
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science.stats.kernel = {
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uniform: function(u) {
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uniform: function(u) {
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if (u <= 1 && u >= -1) return .5;
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if (u <= 1 && u >= -1) return .5;
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return 0;
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return 0;
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@ -56,10 +56,10 @@ d3.stats.kernel = {
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}
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}
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};
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};
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// http://exploringdata.net/den_trac.htm
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// http://exploringdata.net/den_trac.htm
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d3.stats.kde = function() {
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science.stats.kde = function() {
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var kernel = d3.stats.kernel.gaussian,
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var kernel = science.stats.kernel.gaussian,
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sample = [],
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sample = [],
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bandwidth = d3.stats.bandwidth.nrd;
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bandwidth = science.stats.bandwidth.nrd;
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function kde(points, i) {
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function kde(points, i) {
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var bw = bandwidth.call(this, sample);
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var bw = bandwidth.call(this, sample);
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@ -88,18 +88,18 @@ d3.stats.kde = function() {
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kde.bandwidth = function(x) {
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kde.bandwidth = function(x) {
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if (!arguments.length) return bandwidth;
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if (!arguments.length) return bandwidth;
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bandwidth = d3.functor(x);
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bandwidth = science.functor(x);
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return kde;
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return kde;
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};
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};
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return kde;
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return kde;
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};
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};
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d3.stats.iqr = function(x) {
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science.stats.iqr = function(x) {
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var quartiles = d3.stats.quantiles(x, [.25, .75]);
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var quartiles = science.stats.quantiles(x, [.25, .75]);
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return quartiles[1] - quartiles[0];
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return quartiles[1] - quartiles[0];
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};
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};
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// Welford's algorithm.
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// Welford's algorithm.
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d3.stats.mean = function(x) {
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science.stats.mean = function(x) {
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var n = x.length;
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var n = x.length;
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if (n === 0) return NaN;
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if (n === 0) return NaN;
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var m = 0,
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var m = 0,
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@ -107,11 +107,11 @@ d3.stats.mean = function(x) {
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while (++i < n) m += (x[i] - m) / (i + 1);
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while (++i < n) m += (x[i] - m) / (i + 1);
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return m;
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return m;
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};
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};
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d3.stats.median = function(x) {
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science.stats.median = function(x) {
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return d3.stats.quantiles(x, [.5])[0];
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return science.stats.quantiles(x, [.5])[0];
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};
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};
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d3.stats.mode = function(x) {
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science.stats.mode = function(x) {
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x = x.slice().sort(d3.ascending);
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x = x.slice().sort(science.ascending);
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var mode,
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var mode,
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n = x.length,
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n = x.length,
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i = -1,
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i = -1,
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@ -133,8 +133,8 @@ d3.stats.mode = function(x) {
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return mode;
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return mode;
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};
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};
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// Uses R's quantile algorithm type=7.
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// Uses R's quantile algorithm type=7.
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d3.stats.quantiles = function(d, quantiles) {
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science.stats.quantiles = function(d, quantiles) {
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d = d.slice().sort(d3.ascending);
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d = d.slice().sort(science.ascending);
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var n_1 = d.length - 1;
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var n_1 = d.length - 1;
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return quantiles.map(function(q) {
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return quantiles.map(function(q) {
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if (q === 0) return d[0];
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if (q === 0) return d[0];
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@ -150,11 +150,11 @@ d3.stats.quantiles = function(d, quantiles) {
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};
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};
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// Unbiased estimate of a sample's variance.
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// Unbiased estimate of a sample's variance.
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// Also known as the sample variance, where the denominator is n - 1.
|
// Also known as the sample variance, where the denominator is n - 1.
|
||||||
d3.stats.variance = function(x) {
|
science.stats.variance = function(x) {
|
||||||
var n = x.length;
|
var n = x.length;
|
||||||
if (n < 1) return NaN;
|
if (n < 1) return NaN;
|
||||||
if (n === 1) return 0;
|
if (n === 1) return 0;
|
||||||
var mean = d3.stats.mean(x),
|
var mean = science.stats.mean(x),
|
||||||
i = -1,
|
i = -1,
|
||||||
s = 0;
|
s = 0;
|
||||||
while (++i < n) {
|
while (++i < n) {
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
(function(){science.stats={},science.stats.bandwidth={nrd0:function(a){var b=Math.sqrt(science.stats.variance(a));(lo=Math.min(b,science.stats.iqr(a)/1.34))||(lo=b)||(lo=Math.abs(a[1]))||(lo=1);return.9*lo*Math.pow(a.length,-0.2)},nrd:function(a){var b=science.stats.iqr(a)/1.34;return 1.06*Math.min(Math.sqrt(science.stats.variance(a)),b)*Math.pow(a.length,-0.2)}},science.stats.kernel={uniform:function(a){if(a<=1&&a>=-1)return.5;return 0},triangular:function(a){if(a<=1&&a>=-1)return 1-Math.abs(a);return 0},epanechnikov:function(a){if(a<=1&&a>=-1)return.75*(1-a*a);return 0},quartic:function(a){if(a<=1&&a>=-1){var b=1-a*a;return.9375*b*b}return 0},triweight:function(a){if(a<=1&&a>=-1){var b=1-a*a;return 35/32*b*b*b}return 0},gaussian:function(a){return 1/Math.sqrt(2*Math.PI)*Math.exp(-0.5*a*a)},cosine:function(a){if(a<=1&&a>=-1)return Math.PI/4*Math.cos(Math.PI/2*a);return 0}},science.stats.kde=function(){function d(d,e){var f=c.call(this,b);return d.map(function(c){var d=-1,e=0,g=b.length;while(++d<g)e+=a((c-b[d])/f);return[c,e/f/g]})}var a=science.stats.kernel.gaussian,b=[],c=science.stats.bandwidth.nrd;d.kernel=function(b){if(!arguments.length)return a;a=b;return d},d.sample=function(a){if(!arguments.length)return b;b=a;return d},d.bandwidth=function(a){if(!arguments.length)return c;c=science.functor(a);return d};return d},science.stats.iqr=function(a){var b=science.stats.quantiles(a,[.25,.75]);return b[1]-b[0]},science.stats.mean=function(a){var b=a.length;if(b===0)return NaN;var c=0,d=-1;while(++d<b)c+=(a[d]-c)/(d+1);return c},science.stats.median=function(a){return science.stats.quantiles(a,[.5])[0]},science.stats.mode=function(a){a=a.slice().sort(science.ascending);var b,c=a.length,d=-1,e=d,f=null,g=0,h,i;while(++d<c)(i=a[d])!==f&&((h=d-e)>g&&(g=h,b=f),f=i,e=d);return b},science.stats.quantiles=function(a,b){a=a.slice().sort(science.ascending);var c=a.length-1;return b.map(function(b){if(b===0)return a[0];if(b===1)return a[c];var e=1+b*c,f=Math.floor(e),g=e-f,h=a[f-1];return g===0?h:h+g*(a[f]-h)})},science.stats.variance=function(a){var b=a.length;if(b<1)return NaN;if(b===1)return 0;var c=science.stats.mean(a),d=-1,e=0;while(++d<b){var f=a[d]-c;e+=f*f}return e/(b-1)}})()
|
||||||
|
|
@ -290,5 +290,9 @@ function d3_chart_boxWhiskers(d) {
|
||||||
|
|
||||||
function d3_chart_boxQuartiles(d) {
|
function d3_chart_boxQuartiles(d) {
|
||||||
// TODO avoid sorting data twice?
|
// TODO avoid sorting data twice?
|
||||||
return d3.stats.quantiles(d, [.25, .5, .75]);
|
var n = d.length;
|
||||||
|
return [.25, .5, .75].map(function(q) {
|
||||||
|
q *= n;
|
||||||
|
return ~~q === q ? (d[q] + d[q + 1]) / 2 : d[Math.round(q)];
|
||||||
|
});
|
||||||
}
|
}
|
||||||
|
|
|
||||||
|
|
@ -1,20 +0,0 @@
|
||||||
// Bandwidth selectors for Gaussian kernels.
|
|
||||||
// Based on R's implementations in `stats.bw`.
|
|
||||||
d3.stats.bandwidth = {
|
|
||||||
|
|
||||||
// Silverman, B. W. (1986) Density Estimation. London: Chapman and Hall.
|
|
||||||
nrd0: function(x) {
|
|
||||||
var hi = Math.sqrt(d3.stats.variance(x));
|
|
||||||
if (!(lo = Math.min(hi, d3.stats.iqr(x) / 1.34)))
|
|
||||||
(lo = hi) || (lo = Math.abs(x[1])) || (lo = 1);
|
|
||||||
return .9 * lo * Math.pow(x.length, -.2);
|
|
||||||
},
|
|
||||||
|
|
||||||
// Scott, D. W. (1992) Multivariate Density Estimation: Theory, Practice, and
|
|
||||||
// Visualization. Wiley.
|
|
||||||
nrd: function(x) {
|
|
||||||
var h = d3.stats.iqr(x) / 1.34;
|
|
||||||
return 1.06 * Math.min(Math.sqrt(d3.stats.variance(x)), h)
|
|
||||||
* Math.pow(x.length, -1/5);
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
@ -1,4 +0,0 @@
|
||||||
d3.stats.iqr = function(x) {
|
|
||||||
var quartiles = d3.stats.quantiles(x, [.25, .75]);
|
|
||||||
return quartiles[1] - quartiles[0];
|
|
||||||
};
|
|
||||||
|
|
@ -1,39 +0,0 @@
|
||||||
// http://exploringdata.net/den_trac.htm
|
|
||||||
d3.stats.kde = function() {
|
|
||||||
var kernel = d3.stats.kernel.gaussian,
|
|
||||||
sample = [],
|
|
||||||
bandwidth = d3.stats.bandwidth.nrd;
|
|
||||||
|
|
||||||
function kde(points, i) {
|
|
||||||
var bw = bandwidth.call(this, sample);
|
|
||||||
return points.map(function(x) {
|
|
||||||
var i = -1,
|
|
||||||
y = 0,
|
|
||||||
n = sample.length;
|
|
||||||
while (++i < n) {
|
|
||||||
y += kernel((x - sample[i]) / bw);
|
|
||||||
}
|
|
||||||
return [x, y / bw / n];
|
|
||||||
});
|
|
||||||
}
|
|
||||||
|
|
||||||
kde.kernel = function(x) {
|
|
||||||
if (!arguments.length) return kernel;
|
|
||||||
kernel = x;
|
|
||||||
return kde;
|
|
||||||
};
|
|
||||||
|
|
||||||
kde.sample = function(x) {
|
|
||||||
if (!arguments.length) return sample;
|
|
||||||
sample = x;
|
|
||||||
return kde;
|
|
||||||
};
|
|
||||||
|
|
||||||
kde.bandwidth = function(x) {
|
|
||||||
if (!arguments.length) return bandwidth;
|
|
||||||
bandwidth = d3.functor(x);
|
|
||||||
return kde;
|
|
||||||
};
|
|
||||||
|
|
||||||
return kde;
|
|
||||||
};
|
|
||||||
|
|
@ -1,36 +0,0 @@
|
||||||
// See <http://en.wikipedia.org/wiki/Kernel_(statistics)>.
|
|
||||||
d3.stats.kernel = {
|
|
||||||
uniform: function(u) {
|
|
||||||
if (u <= 1 && u >= -1) return .5;
|
|
||||||
return 0;
|
|
||||||
},
|
|
||||||
triangular: function(u) {
|
|
||||||
if (u <= 1 && u >= -1) return 1 - Math.abs(u);
|
|
||||||
return 0;
|
|
||||||
},
|
|
||||||
epanechnikov: function(u) {
|
|
||||||
if (u <= 1 && u >= -1) return .75 * (1 - u * u);
|
|
||||||
return 0;
|
|
||||||
},
|
|
||||||
quartic: function(u) {
|
|
||||||
if (u <= 1 && u >= -1) {
|
|
||||||
var tmp = 1 - u * u;
|
|
||||||
return (15 / 16) * tmp * tmp;
|
|
||||||
}
|
|
||||||
return 0;
|
|
||||||
},
|
|
||||||
triweight: function(u) {
|
|
||||||
if (u <= 1 && u >= -1) {
|
|
||||||
var tmp = 1 - u * u;
|
|
||||||
return (35 / 32) * tmp * tmp * tmp;
|
|
||||||
}
|
|
||||||
return 0;
|
|
||||||
},
|
|
||||||
gaussian: function(u) {
|
|
||||||
return 1 / Math.sqrt(2 * Math.PI) * Math.exp(-.5 * u * u);
|
|
||||||
},
|
|
||||||
cosine: function(u) {
|
|
||||||
if (u <= 1 && u >= -1) return Math.PI / 4 * Math.cos(Math.PI / 2 * u);
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
@ -1,9 +0,0 @@
|
||||||
// Welford's algorithm.
|
|
||||||
d3.stats.mean = function(x) {
|
|
||||||
var n = x.length;
|
|
||||||
if (n === 0) return NaN;
|
|
||||||
var m = 0,
|
|
||||||
i = -1;
|
|
||||||
while (++i < n) m += (x[i] - m) / (i + 1);
|
|
||||||
return m;
|
|
||||||
};
|
|
||||||
|
|
@ -1,3 +0,0 @@
|
||||||
d3.stats.median = function(x) {
|
|
||||||
return d3.stats.quantiles(x, [.5])[0];
|
|
||||||
};
|
|
||||||
|
|
@ -1,22 +0,0 @@
|
||||||
d3.stats.mode = function(x) {
|
|
||||||
x = x.slice().sort(d3.ascending);
|
|
||||||
var mode,
|
|
||||||
n = x.length,
|
|
||||||
i = -1,
|
|
||||||
l = i,
|
|
||||||
last = null,
|
|
||||||
max = 0,
|
|
||||||
tmp,
|
|
||||||
v;
|
|
||||||
while (++i < n) {
|
|
||||||
if ((v = x[i]) !== last) {
|
|
||||||
if ((tmp = i - l) > max) {
|
|
||||||
max = tmp;
|
|
||||||
mode = last;
|
|
||||||
}
|
|
||||||
last = v;
|
|
||||||
l = i;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
return mode;
|
|
||||||
};
|
|
||||||
|
|
@ -1,16 +0,0 @@
|
||||||
// Uses R's quantile algorithm type=7.
|
|
||||||
d3.stats.quantiles = function(d, quantiles) {
|
|
||||||
d = d.slice().sort(d3.ascending);
|
|
||||||
var n_1 = d.length - 1;
|
|
||||||
return quantiles.map(function(q) {
|
|
||||||
if (q === 0) return d[0];
|
|
||||||
else if (q === 1) return d[n_1];
|
|
||||||
|
|
||||||
var index = 1 + q * n_1,
|
|
||||||
lo = Math.floor(index),
|
|
||||||
h = index - lo,
|
|
||||||
a = d[lo - 1];
|
|
||||||
|
|
||||||
return h === 0 ? a : a + h * (d[lo] - a);
|
|
||||||
});
|
|
||||||
};
|
|
||||||
|
|
@ -1 +0,0 @@
|
||||||
d3.stats = {};
|
|
||||||
|
|
@ -1,15 +0,0 @@
|
||||||
// Unbiased estimate of a sample's variance.
|
|
||||||
// Also known as the sample variance, where the denominator is n - 1.
|
|
||||||
d3.stats.variance = function(x) {
|
|
||||||
var n = x.length;
|
|
||||||
if (n < 1) return NaN;
|
|
||||||
if (n === 1) return 0;
|
|
||||||
var mean = d3.stats.mean(x),
|
|
||||||
i = -1,
|
|
||||||
s = 0;
|
|
||||||
while (++i < n) {
|
|
||||||
var v = x[i] - mean;
|
|
||||||
s += v * v;
|
|
||||||
}
|
|
||||||
return s / (n - 1);
|
|
||||||
};
|
|
||||||
|
|
@ -1,13 +0,0 @@
|
||||||
require("./../lib/env-js/envjs/node");
|
|
||||||
require("./../d3");
|
|
||||||
require("./../d3.stats");
|
|
||||||
|
|
||||||
var data = [1, 2, 3, 4];
|
|
||||||
|
|
||||||
console.log("nrd0:");
|
|
||||||
console.log(" ", d3.stats.bandwidth.nrd0(data));
|
|
||||||
console.log("");
|
|
||||||
|
|
||||||
console.log("nrd:");
|
|
||||||
console.log(" ", d3.stats.bandwidth.nrd(data));
|
|
||||||
console.log("");
|
|
||||||
|
|
@ -1,6 +0,0 @@
|
||||||
nrd0:
|
|
||||||
0.7635139420854616
|
|
||||||
|
|
||||||
nrd:
|
|
||||||
0.899249754011766
|
|
||||||
|
|
||||||
|
|
@ -1,12 +0,0 @@
|
||||||
require("./../lib/env-js/envjs/node");
|
|
||||||
require("./../d3");
|
|
||||||
require("./../d3.stats");
|
|
||||||
|
|
||||||
var data = [1, 2, 3, 4, 5, 6];
|
|
||||||
|
|
||||||
for (var i = 0; i <= data.length; i++) {
|
|
||||||
var d = data.slice(0, i);
|
|
||||||
console.log("iqr [" + d + "]:");
|
|
||||||
console.log(" ", d3.stats.iqr(d));
|
|
||||||
console.log("");
|
|
||||||
}
|
|
||||||
|
|
@ -1,21 +0,0 @@
|
||||||
iqr []:
|
|
||||||
NaN
|
|
||||||
|
|
||||||
iqr [1]:
|
|
||||||
0
|
|
||||||
|
|
||||||
iqr [1,2]:
|
|
||||||
0.5
|
|
||||||
|
|
||||||
iqr [1,2,3]:
|
|
||||||
1
|
|
||||||
|
|
||||||
iqr [1,2,3,4]:
|
|
||||||
1.5
|
|
||||||
|
|
||||||
iqr [1,2,3,4,5]:
|
|
||||||
2
|
|
||||||
|
|
||||||
iqr [1,2,3,4,5,6]:
|
|
||||||
2.5
|
|
||||||
|
|
||||||
|
|
@ -1,12 +0,0 @@
|
||||||
require("./../lib/env-js/envjs/node");
|
|
||||||
require("./../d3");
|
|
||||||
require("./../d3.stats");
|
|
||||||
|
|
||||||
var data = [1, 2, 3, 4, 5];
|
|
||||||
|
|
||||||
for (var i = 0; i <= data.length; i++) {
|
|
||||||
var d = data.slice(0, i);
|
|
||||||
console.log("median [" + d + "]:");
|
|
||||||
console.log(" ", d3.stats.median(d));
|
|
||||||
console.log("");
|
|
||||||
}
|
|
||||||
|
|
@ -1,18 +0,0 @@
|
||||||
median []:
|
|
||||||
NaN
|
|
||||||
|
|
||||||
median [1]:
|
|
||||||
1
|
|
||||||
|
|
||||||
median [1,2]:
|
|
||||||
1.5
|
|
||||||
|
|
||||||
median [1,2,3]:
|
|
||||||
2
|
|
||||||
|
|
||||||
median [1,2,3,4]:
|
|
||||||
2.5
|
|
||||||
|
|
||||||
median [1,2,3,4,5]:
|
|
||||||
3
|
|
||||||
|
|
||||||
|
|
@ -1,12 +0,0 @@
|
||||||
require("./../lib/env-js/envjs/node");
|
|
||||||
require("./../d3");
|
|
||||||
require("./../d3.stats");
|
|
||||||
|
|
||||||
var data = [1, 2, 3, 4, 5];
|
|
||||||
|
|
||||||
for (var i = 0; i <= data.length; i++) {
|
|
||||||
var d = data.slice(0, i);
|
|
||||||
console.log("variance [" + d + "]:");
|
|
||||||
console.log(" ", d3.stats.variance(d));
|
|
||||||
console.log("");
|
|
||||||
}
|
|
||||||
|
|
@ -1,18 +0,0 @@
|
||||||
variance []:
|
|
||||||
NaN
|
|
||||||
|
|
||||||
variance [1]:
|
|
||||||
0
|
|
||||||
|
|
||||||
variance [1,2]:
|
|
||||||
0.5
|
|
||||||
|
|
||||||
variance [1,2,3]:
|
|
||||||
1
|
|
||||||
|
|
||||||
variance [1,2,3,4]:
|
|
||||||
1.6666666666666667
|
|
||||||
|
|
||||||
variance [1,2,3,4,5]:
|
|
||||||
2.5
|
|
||||||
|
|
||||||
Загрузка…
Ссылка в новой задаче