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| <pre><span class="sourceLineNo">001</span>/*-<a name="line.1"></a> |
| <span class="sourceLineNo">002</span> * Copyright 2016 Diamond Light Source Ltd.<a name="line.2"></a> |
| <span class="sourceLineNo">003</span> *<a name="line.3"></a> |
| <span class="sourceLineNo">004</span> * All rights reserved. This program and the accompanying materials<a name="line.4"></a> |
| <span class="sourceLineNo">005</span> * are made available under the terms of the Eclipse Public License v1.0<a name="line.5"></a> |
| <span class="sourceLineNo">006</span> * which accompanies this distribution, and is available at<a name="line.6"></a> |
| <span class="sourceLineNo">007</span> * http://www.eclipse.org/legal/epl-v10.html<a name="line.7"></a> |
| <span class="sourceLineNo">008</span> */<a name="line.8"></a> |
| <span class="sourceLineNo">009</span><a name="line.9"></a> |
| <span class="sourceLineNo">010</span>package org.eclipse.january.dataset;<a name="line.10"></a> |
| <span class="sourceLineNo">011</span><a name="line.11"></a> |
| <span class="sourceLineNo">012</span>/**<a name="line.12"></a> |
| <span class="sourceLineNo">013</span> * Estimators of the scale of a Dataset.<a name="line.13"></a> |
| <span class="sourceLineNo">014</span> * <p><a name="line.14"></a> |
| <span class="sourceLineNo">015</span> * A class of static methods to produce estimations of the scale of variation within a Dataset.<a name="line.15"></a> |
| <span class="sourceLineNo">016</span> * The available estimators are:<a name="line.16"></a> |
| <span class="sourceLineNo">017</span> * <ul><a name="line.17"></a> |
| <span class="sourceLineNo">018</span> * <li> Median Absolute Deviation </li><a name="line.18"></a> |
| <span class="sourceLineNo">019</span> * <li> S<sub>n</sub> of Croux and Rousseeuw (1992).</li><a name="line.19"></a> |
| <span class="sourceLineNo">020</span> * </ul> <a name="line.20"></a> |
| <span class="sourceLineNo">021</span> * <p><a name="line.21"></a> |
| <span class="sourceLineNo">022</span> * Croux, C. and P. J. Rousseeuw, "Time-efficient algorithms for two highly robust estimators of scale", Computational Statistics, Volume 1, eds. Y. Dodge and J.Whittaker, Physica-Verlag, Heidelberg, pp411--428 (1992).<a name="line.22"></a> |
| <span class="sourceLineNo">023</span> */<a name="line.23"></a> |
| <span class="sourceLineNo">024</span>public class Outliers {<a name="line.24"></a> |
| <span class="sourceLineNo">025</span><a name="line.25"></a> |
| <span class="sourceLineNo">026</span> private final static double MADSCALEFACTOR = 1.4826;<a name="line.26"></a> |
| <span class="sourceLineNo">027</span> private final static double SNSCALEFACTOR = 1.1926;<a name="line.27"></a> |
| <span class="sourceLineNo">028</span> <a name="line.28"></a> |
| <span class="sourceLineNo">029</span> /**<a name="line.29"></a> |
| <span class="sourceLineNo">030</span> * Returns the Median Absolute Deviation (MAD) and the median. <a name="line.30"></a> |
| <span class="sourceLineNo">031</span> * @param data<a name="line.31"></a> |
| <span class="sourceLineNo">032</span> * The data for which the median and the MAD are to be calculated<a name="line.32"></a> |
| <span class="sourceLineNo">033</span> * @return A two-element array of doubles, consisting of the MAD and the median of the data<a name="line.33"></a> |
| <span class="sourceLineNo">034</span> */<a name="line.34"></a> |
| <span class="sourceLineNo">035</span> public static double[] medianAbsoluteDeviation(Dataset data) {<a name="line.35"></a> |
| <span class="sourceLineNo">036</span> <a name="line.36"></a> |
| <span class="sourceLineNo">037</span> double median = (Double)Stats.median(data);<a name="line.37"></a> |
| <span class="sourceLineNo">038</span> data = Maths.subtract(data, median);<a name="line.38"></a> |
| <span class="sourceLineNo">039</span> data = Maths.abs(data);<a name="line.39"></a> |
| <span class="sourceLineNo">040</span> double median2 = (Double)Stats.median(data);<a name="line.40"></a> |
| <span class="sourceLineNo">041</span> double mad = MADSCALEFACTOR * median2;<a name="line.41"></a> |
| <span class="sourceLineNo">042</span> <a name="line.42"></a> |
| <span class="sourceLineNo">043</span> return new double[]{mad, median};<a name="line.43"></a> |
| <span class="sourceLineNo">044</span> }<a name="line.44"></a> |
| <span class="sourceLineNo">045</span> <a name="line.45"></a> |
| <span class="sourceLineNo">046</span> /**<a name="line.46"></a> |
| <span class="sourceLineNo">047</span> * Returns the Sn estimator of Croux and Rousseeuw.<a name="line.47"></a> |
| <span class="sourceLineNo">048</span> * <p><a name="line.48"></a> |
| <span class="sourceLineNo">049</span> * This is the simple O(n²) version of the calculation algorithm.<a name="line.49"></a> |
| <span class="sourceLineNo">050</span> * @param data<a name="line.50"></a> |
| <span class="sourceLineNo">051</span> * The data for which the estimator is to be calculated.<a name="line.51"></a> |
| <span class="sourceLineNo">052</span> * @return The value of the Sn estimator for the data<a name="line.52"></a> |
| <span class="sourceLineNo">053</span> */<a name="line.53"></a> |
| <span class="sourceLineNo">054</span> public static double snNaive(Dataset data) {<a name="line.54"></a> |
| <span class="sourceLineNo">055</span> <a name="line.55"></a> |
| <span class="sourceLineNo">056</span> Dataset medAbs = DatasetFactory.zeros(data);<a name="line.56"></a> |
| <span class="sourceLineNo">057</span> Dataset dif = DatasetFactory.zeros(data);<a name="line.57"></a> |
| <span class="sourceLineNo">058</span> <a name="line.58"></a> |
| <span class="sourceLineNo">059</span> IndexIterator it = data.getIterator();<a name="line.59"></a> |
| <span class="sourceLineNo">060</span> int count = 0;<a name="line.60"></a> |
| <span class="sourceLineNo">061</span> while (it.hasNext()) {<a name="line.61"></a> |
| <span class="sourceLineNo">062</span> double val = data.getElementDoubleAbs(it.index);<a name="line.62"></a> |
| <span class="sourceLineNo">063</span> Maths.subtract(data, val, dif);<a name="line.63"></a> |
| <span class="sourceLineNo">064</span> Maths.abs(dif,dif);<a name="line.64"></a> |
| <span class="sourceLineNo">065</span> //Lower median - Math.floor((n/2)+1) of sorted<a name="line.65"></a> |
| <span class="sourceLineNo">066</span> dif.sort(null);<a name="line.66"></a> |
| <span class="sourceLineNo">067</span> medAbs.setObjectAbs(count++, lowMed(dif));<a name="line.67"></a> |
| <span class="sourceLineNo">068</span> }<a name="line.68"></a> |
| <span class="sourceLineNo">069</span> <a name="line.69"></a> |
| <span class="sourceLineNo">070</span> <a name="line.70"></a> |
| <span class="sourceLineNo">071</span> //Higher median - Math.floor((n+1)/2) of sorted<a name="line.71"></a> |
| <span class="sourceLineNo">072</span> medAbs.sort(null);<a name="line.72"></a> |
| <span class="sourceLineNo">073</span> double median = highMed(medAbs);<a name="line.73"></a> |
| <span class="sourceLineNo">074</span> <a name="line.74"></a> |
| <span class="sourceLineNo">075</span> return median * SNSCALEFACTOR;<a name="line.75"></a> |
| <span class="sourceLineNo">076</span> }<a name="line.76"></a> |
| <span class="sourceLineNo">077</span> <a name="line.77"></a> |
| <span class="sourceLineNo">078</span> /**<a name="line.78"></a> |
| <span class="sourceLineNo">079</span> * Returns the Sn estimator of Croux and Rousseeuw.<a name="line.79"></a> |
| <span class="sourceLineNo">080</span> * <p><a name="line.80"></a> |
| <span class="sourceLineNo">081</span> * This is the complex O(nlog n) version of the calculation algorithm.<a name="line.81"></a> |
| <span class="sourceLineNo">082</span> * @param data<a name="line.82"></a> |
| <span class="sourceLineNo">083</span> * The data for which the estimator is to be calculated.<a name="line.83"></a> |
| <span class="sourceLineNo">084</span> * @return The value of the Sn estimator for the data<a name="line.84"></a> |
| <span class="sourceLineNo">085</span> */<a name="line.85"></a> |
| <span class="sourceLineNo">086</span> public static double snFast(Dataset data) {<a name="line.86"></a> |
| <span class="sourceLineNo">087</span> <a name="line.87"></a> |
| <span class="sourceLineNo">088</span> Dataset sorted = data.clone();<a name="line.88"></a> |
| <span class="sourceLineNo">089</span> sorted.sort(null);<a name="line.89"></a> |
| <span class="sourceLineNo">090</span> <a name="line.90"></a> |
| <span class="sourceLineNo">091</span> Dataset medAbs = DatasetFactory.zeros(data);<a name="line.91"></a> |
| <span class="sourceLineNo">092</span> <a name="line.92"></a> |
| <span class="sourceLineNo">093</span> IndexIterator it = data.getIterator();<a name="line.93"></a> |
| <span class="sourceLineNo">094</span> int count = 0;<a name="line.94"></a> |
| <span class="sourceLineNo">095</span> while (it.hasNext()) {<a name="line.95"></a> |
| <span class="sourceLineNo">096</span> MedianOfTwoSortedSets snuff = new MedianForSn(sorted, it.index);<a name="line.96"></a> |
| <span class="sourceLineNo">097</span> medAbs.setObjectAbs(count++, snuff.get());<a name="line.97"></a> |
| <span class="sourceLineNo">098</span> }<a name="line.98"></a> |
| <span class="sourceLineNo">099</span> <a name="line.99"></a> |
| <span class="sourceLineNo">100</span> <a name="line.100"></a> |
| <span class="sourceLineNo">101</span> //Higher median - Math.floor((n+1)/2) of sorted<a name="line.101"></a> |
| <span class="sourceLineNo">102</span> medAbs.sort(null);<a name="line.102"></a> |
| <span class="sourceLineNo">103</span> double median = highMed(medAbs);<a name="line.103"></a> |
| <span class="sourceLineNo">104</span> <a name="line.104"></a> |
| <span class="sourceLineNo">105</span> return median * SNSCALEFACTOR;<a name="line.105"></a> |
| <span class="sourceLineNo">106</span> }<a name="line.106"></a> |
| <span class="sourceLineNo">107</span> <a name="line.107"></a> |
| <span class="sourceLineNo">108</span> /**<a name="line.108"></a> |
| <span class="sourceLineNo">109</span> * Returns the lomed<a name="line.109"></a> |
| <span class="sourceLineNo">110</span> * <p><a name="line.110"></a> |
| <span class="sourceLineNo">111</span> * Returns the lomed (low median) of a sorted Dataset.<a name="line.111"></a> |
| <span class="sourceLineNo">112</span> * @param data<a name="line.112"></a> |
| <span class="sourceLineNo">113</span> * A sorted Dataset for which the low median is to be calculated. <a name="line.113"></a> |
| <span class="sourceLineNo">114</span> * @return<a name="line.114"></a> |
| <span class="sourceLineNo">115</span> * The value of the lomed of the data<a name="line.115"></a> |
| <span class="sourceLineNo">116</span> */<a name="line.116"></a> |
| <span class="sourceLineNo">117</span> public static double lowMed(Dataset data) {<a name="line.117"></a> |
| <span class="sourceLineNo">118</span> return data.getElementDoubleAbs((int)Math.floor((data.getSize()/2)));<a name="line.118"></a> |
| <span class="sourceLineNo">119</span> }<a name="line.119"></a> |
| <span class="sourceLineNo">120</span> <a name="line.120"></a> |
| <span class="sourceLineNo">121</span> /**<a name="line.121"></a> |
| <span class="sourceLineNo">122</span> * Returns the himed<a name="line.122"></a> |
| <span class="sourceLineNo">123</span> * <p><a name="line.123"></a> |
| <span class="sourceLineNo">124</span> * Returns the himed (high median) of a sorted Dataset.<a name="line.124"></a> |
| <span class="sourceLineNo">125</span> * @param data<a name="line.125"></a> |
| <span class="sourceLineNo">126</span> * A sorted Dataset for which the low median is to be calculated. <a name="line.126"></a> |
| <span class="sourceLineNo">127</span> * @return<a name="line.127"></a> |
| <span class="sourceLineNo">128</span> * The value of the himed of the data<a name="line.128"></a> |
| <span class="sourceLineNo">129</span> */<a name="line.129"></a> |
| <span class="sourceLineNo">130</span> public static double highMed(Dataset data) {<a name="line.130"></a> |
| <span class="sourceLineNo">131</span> return data.getElementDoubleAbs((int)Math.floor((data.getSize()+1)/2-1));<a name="line.131"></a> |
| <span class="sourceLineNo">132</span> }<a name="line.132"></a> |
| <span class="sourceLineNo">133</span><a name="line.133"></a> |
| <span class="sourceLineNo">134</span> /**<a name="line.134"></a> |
| <span class="sourceLineNo">135</span> * Calculates the overall median of two double arrays<a name="line.135"></a> |
| <span class="sourceLineNo">136</span> * @param a<a name="line.136"></a> |
| <span class="sourceLineNo">137</span> * @param b<a name="line.137"></a> |
| <span class="sourceLineNo">138</span> * the two arrays for which the overall median is desired. <a name="line.138"></a> |
| <span class="sourceLineNo">139</span> * @return the overall median of the two arrays<a name="line.139"></a> |
| <span class="sourceLineNo">140</span> */<a name="line.140"></a> |
| <span class="sourceLineNo">141</span> public static double medianOFTwoPrimitiveArrays (double[] a, double[] b) {<a name="line.141"></a> |
| <span class="sourceLineNo">142</span> MedianOfTwoArrays medio = new MedianOfTwoArrays(a, b);<a name="line.142"></a> |
| <span class="sourceLineNo">143</span> return medio.get();<a name="line.143"></a> |
| <span class="sourceLineNo">144</span> }<a name="line.144"></a> |
| <span class="sourceLineNo">145</span>}<a name="line.145"></a> |
| <span class="sourceLineNo">146</span><a name="line.146"></a> |
| <span class="sourceLineNo">147</span>/**<a name="line.147"></a> |
| <span class="sourceLineNo">148</span> * Allows the calculation of the median of two arrays.<a name="line.148"></a> |
| <span class="sourceLineNo">149</span> * <p><a name="line.149"></a> |
| <span class="sourceLineNo">150</span> * Subclasses must implement getA() and getB() to return the elements of A or B<a name="line.150"></a> |
| <span class="sourceLineNo">151</span> * at the given index. The length of a must be less than or equal to that of b.<a name="line.151"></a> |
| <span class="sourceLineNo">152</span> * The constructor must set the sizes nA and nB.<a name="line.152"></a> |
| <span class="sourceLineNo">153</span> */<a name="line.153"></a> |
| <span class="sourceLineNo">154</span>abstract class MedianOfTwoSortedSets{<a name="line.154"></a> |
| <span class="sourceLineNo">155</span> int nB, nA, diff, diffLeft;<a name="line.155"></a> |
| <span class="sourceLineNo">156</span> <a name="line.156"></a> |
| <span class="sourceLineNo">157</span> public final double get() {<a name="line.157"></a> |
| <span class="sourceLineNo">158</span> // Initialize the left and right markers for the set of candidate <a name="line.158"></a> |
| <span class="sourceLineNo">159</span> // values. These are inclusive on both left and right.<a name="line.159"></a> |
| <span class="sourceLineNo">160</span> int leftA = 0, leftB = 0;<a name="line.160"></a> |
| <span class="sourceLineNo">161</span> @SuppressWarnings("unused") // keep rightA for symmetry<a name="line.161"></a> |
| <span class="sourceLineNo">162</span> int rightA = nB-1, rightB = nB-1;<a name="line.162"></a> |
| <span class="sourceLineNo">163</span> <a name="line.163"></a> |
| <span class="sourceLineNo">164</span> while (nB > 1) {<a name="line.164"></a> |
| <span class="sourceLineNo">165</span> // For 0-based indexing, the lomed is the element at floor((n+1)/2)-1<a name="line.165"></a> |
| <span class="sourceLineNo">166</span> int medianIndex = (int) Math.floor((nB+1)/2)-1;<a name="line.166"></a> |
| <span class="sourceLineNo">167</span> int medianAIndex = leftA + medianIndex,<a name="line.167"></a> |
| <span class="sourceLineNo">168</span> medianBIndex = leftB + medianIndex;<a name="line.168"></a> |
| <span class="sourceLineNo">169</span> double medA = getAm(medianAIndex),<a name="line.169"></a> |
| <span class="sourceLineNo">170</span> medB = getBm(medianBIndex);<a name="line.170"></a> |
| <span class="sourceLineNo">171</span><a name="line.171"></a> |
| <span class="sourceLineNo">172</span> int smallerShift = 0;<a name="line.172"></a> |
| <span class="sourceLineNo">173</span> if (nB % 2 == 0) {<a name="line.173"></a> |
| <span class="sourceLineNo">174</span> // N even: the smaller lomed, as well as anything smaller than it, cannot be the overall median<a name="line.174"></a> |
| <span class="sourceLineNo">175</span> smallerShift = +1;<a name="line.175"></a> |
| <span class="sourceLineNo">176</span> }<a name="line.176"></a> |
| <span class="sourceLineNo">177</span> <a name="line.177"></a> |
| <span class="sourceLineNo">178</span> if (medA >= medB) {<a name="line.178"></a> |
| <span class="sourceLineNo">179</span> rightA = medianAIndex;<a name="line.179"></a> |
| <span class="sourceLineNo">180</span> leftB = medianBIndex + smallerShift;<a name="line.180"></a> |
| <span class="sourceLineNo">181</span> } else {<a name="line.181"></a> |
| <span class="sourceLineNo">182</span> rightB = medianBIndex;<a name="line.182"></a> |
| <span class="sourceLineNo">183</span> leftA = medianAIndex + smallerShift;<a name="line.183"></a> |
| <span class="sourceLineNo">184</span> }<a name="line.184"></a> |
| <span class="sourceLineNo">185</span> <a name="line.185"></a> |
| <span class="sourceLineNo">186</span> // Different lengths<a name="line.186"></a> |
| <span class="sourceLineNo">187</span> // It should be floor((l_m-1 + 1)/2))<a name="line.187"></a> |
| <span class="sourceLineNo">188</span> // this is newLength, defined above<a name="line.188"></a> |
| <span class="sourceLineNo">189</span> // Difference between left and right<a name="line.189"></a> |
| <span class="sourceLineNo">190</span> nB = rightB - leftB + 1;<a name="line.190"></a> |
| <span class="sourceLineNo">191</span> }<a name="line.191"></a> |
| <span class="sourceLineNo">192</span><a name="line.192"></a> |
| <span class="sourceLineNo">193</span> // when the array is length 1, right and left will be the same.<a name="line.193"></a> |
| <span class="sourceLineNo">194</span> // The lomed of a two element array is the smaller of the two<a name="line.194"></a> |
| <span class="sourceLineNo">195</span> return Math.min(getAm(leftA), getBm(leftB));<a name="line.195"></a> |
| <span class="sourceLineNo">196</span> }<a name="line.196"></a> |
| <span class="sourceLineNo">197</span> <a name="line.197"></a> |
| <span class="sourceLineNo">198</span> // Get the value in the expanded array<a name="line.198"></a> |
| <span class="sourceLineNo">199</span> private double getAm(int i) {<a name="line.199"></a> |
| <span class="sourceLineNo">200</span> int firstElement = diffLeft,<a name="line.200"></a> |
| <span class="sourceLineNo">201</span> lastElement = diffLeft + nA - 1;<a name="line.201"></a> |
| <span class="sourceLineNo">202</span> if (i < firstElement) {<a name="line.202"></a> |
| <span class="sourceLineNo">203</span> return Double.NEGATIVE_INFINITY;<a name="line.203"></a> |
| <span class="sourceLineNo">204</span> } else if (i > lastElement) {<a name="line.204"></a> |
| <span class="sourceLineNo">205</span> return Double.POSITIVE_INFINITY;<a name="line.205"></a> |
| <span class="sourceLineNo">206</span> } else {<a name="line.206"></a> |
| <span class="sourceLineNo">207</span> return getA(i - diffLeft);<a name="line.207"></a> |
| <span class="sourceLineNo">208</span> }<a name="line.208"></a> |
| <span class="sourceLineNo">209</span> }<a name="line.209"></a> |
| <span class="sourceLineNo">210</span> <a name="line.210"></a> |
| <span class="sourceLineNo">211</span> private double getBm(int i) {<a name="line.211"></a> |
| <span class="sourceLineNo">212</span> return getB(i);<a name="line.212"></a> |
| <span class="sourceLineNo">213</span> }<a name="line.213"></a> |
| <span class="sourceLineNo">214</span><a name="line.214"></a> |
| <span class="sourceLineNo">215</span> // Get the values in the original arrays<a name="line.215"></a> |
| <span class="sourceLineNo">216</span> protected abstract double getA(int i); <a name="line.216"></a> |
| <span class="sourceLineNo">217</span> protected abstract double getB(int i);<a name="line.217"></a> |
| <span class="sourceLineNo">218</span> <a name="line.218"></a> |
| <span class="sourceLineNo">219</span> // Call this to set up the length difference variables. <a name="line.219"></a> |
| <span class="sourceLineNo">220</span> protected void setDiffs() {<a name="line.220"></a> |
| <span class="sourceLineNo">221</span> diff = nB - nA;<a name="line.221"></a> |
| <span class="sourceLineNo">222</span> diffLeft = diff/2;<a name="line.222"></a> |
| <span class="sourceLineNo">223</span> }<a name="line.223"></a> |
| <span class="sourceLineNo">224</span>}<a name="line.224"></a> |
| <span class="sourceLineNo">225</span><a name="line.225"></a> |
| <span class="sourceLineNo">226</span>class MedianOfTwoArrays extends MedianOfTwoSortedSets {<a name="line.226"></a> |
| <span class="sourceLineNo">227</span><a name="line.227"></a> |
| <span class="sourceLineNo">228</span> double[] a, b;<a name="line.228"></a> |
| <span class="sourceLineNo">229</span><a name="line.229"></a> |
| <span class="sourceLineNo">230</span> public MedianOfTwoArrays(double[] ain, double[] bin) {<a name="line.230"></a> |
| <span class="sourceLineNo">231</span> if (bin.length >= ain.length) {<a name="line.231"></a> |
| <span class="sourceLineNo">232</span> this.a = ain;<a name="line.232"></a> |
| <span class="sourceLineNo">233</span> nA = ain.length;<a name="line.233"></a> |
| <span class="sourceLineNo">234</span> this.b = bin;<a name="line.234"></a> |
| <span class="sourceLineNo">235</span> nB = bin.length;<a name="line.235"></a> |
| <span class="sourceLineNo">236</span> } else {<a name="line.236"></a> |
| <span class="sourceLineNo">237</span> this.a = bin;<a name="line.237"></a> |
| <span class="sourceLineNo">238</span> nA = bin.length;<a name="line.238"></a> |
| <span class="sourceLineNo">239</span> this.b = ain;<a name="line.239"></a> |
| <span class="sourceLineNo">240</span> nB = ain.length;<a name="line.240"></a> |
| <span class="sourceLineNo">241</span> }<a name="line.241"></a> |
| <span class="sourceLineNo">242</span> setDiffs();<a name="line.242"></a> |
| <span class="sourceLineNo">243</span> }<a name="line.243"></a> |
| <span class="sourceLineNo">244</span> @Override<a name="line.244"></a> |
| <span class="sourceLineNo">245</span> protected double getA(int i) {<a name="line.245"></a> |
| <span class="sourceLineNo">246</span> return a[i];<a name="line.246"></a> |
| <span class="sourceLineNo">247</span> }<a name="line.247"></a> |
| <span class="sourceLineNo">248</span> @Override<a name="line.248"></a> |
| <span class="sourceLineNo">249</span> protected double getB(int i) {<a name="line.249"></a> |
| <span class="sourceLineNo">250</span> return b[i];<a name="line.250"></a> |
| <span class="sourceLineNo">251</span> }<a name="line.251"></a> |
| <span class="sourceLineNo">252</span>}<a name="line.252"></a> |
| <span class="sourceLineNo">253</span><a name="line.253"></a> |
| <span class="sourceLineNo">254</span>class MedianForSn extends MedianOfTwoSortedSets {<a name="line.254"></a> |
| <span class="sourceLineNo">255</span> Dataset xj;<a name="line.255"></a> |
| <span class="sourceLineNo">256</span> int referenceIndex;<a name="line.256"></a> |
| <span class="sourceLineNo">257</span> boolean lowerIsBigger;<a name="line.257"></a> |
| <span class="sourceLineNo">258</span><a name="line.258"></a> |
| <span class="sourceLineNo">259</span> public MedianForSn(Dataset xj, int referenceIndex) {<a name="line.259"></a> |
| <span class="sourceLineNo">260</span> this.xj = xj;<a name="line.260"></a> |
| <span class="sourceLineNo">261</span> this.referenceIndex = referenceIndex;<a name="line.261"></a> |
| <span class="sourceLineNo">262</span><a name="line.262"></a> |
| <span class="sourceLineNo">263</span> // determine which of the two halves of the array is larger<a name="line.263"></a> |
| <span class="sourceLineNo">264</span> int lowerSize = referenceIndex, upperSize = xj.getSize() - referenceIndex - 1;<a name="line.264"></a> |
| <span class="sourceLineNo">265</span> lowerIsBigger = lowerSize > upperSize;<a name="line.265"></a> |
| <span class="sourceLineNo">266</span><a name="line.266"></a> |
| <span class="sourceLineNo">267</span> // Set the array sizes<a name="line.267"></a> |
| <span class="sourceLineNo">268</span> if (lowerIsBigger) {<a name="line.268"></a> |
| <span class="sourceLineNo">269</span> nA = upperSize;<a name="line.269"></a> |
| <span class="sourceLineNo">270</span> nB = lowerSize;<a name="line.270"></a> |
| <span class="sourceLineNo">271</span> } else {<a name="line.271"></a> |
| <span class="sourceLineNo">272</span> nA = lowerSize;<a name="line.272"></a> |
| <span class="sourceLineNo">273</span> nB = upperSize;<a name="line.273"></a> |
| <span class="sourceLineNo">274</span> }<a name="line.274"></a> |
| <span class="sourceLineNo">275</span> setDiffs();<a name="line.275"></a> |
| <span class="sourceLineNo">276</span> }<a name="line.276"></a> |
| <span class="sourceLineNo">277</span><a name="line.277"></a> |
| <span class="sourceLineNo">278</span> @Override<a name="line.278"></a> |
| <span class="sourceLineNo">279</span> protected double getA(int i) {<a name="line.279"></a> |
| <span class="sourceLineNo">280</span> return (!lowerIsBigger) ? getLower(i) : getUpper(i);<a name="line.280"></a> |
| <span class="sourceLineNo">281</span> }<a name="line.281"></a> |
| <span class="sourceLineNo">282</span> @Override<a name="line.282"></a> |
| <span class="sourceLineNo">283</span> protected double getB(int i) {<a name="line.283"></a> |
| <span class="sourceLineNo">284</span> return (!lowerIsBigger) ? getUpper(i) : getLower(i);<a name="line.284"></a> |
| <span class="sourceLineNo">285</span> }<a name="line.285"></a> |
| <span class="sourceLineNo">286</span> <a name="line.286"></a> |
| <span class="sourceLineNo">287</span> private double getLower(int i) {<a name="line.287"></a> |
| <span class="sourceLineNo">288</span> return xj.getDouble(referenceIndex) - xj.getDouble(referenceIndex - 1 - i);<a name="line.288"></a> |
| <span class="sourceLineNo">289</span> }<a name="line.289"></a> |
| <span class="sourceLineNo">290</span> private double getUpper(int i) {<a name="line.290"></a> |
| <span class="sourceLineNo">291</span> return xj.getDouble(i + referenceIndex + 1) - xj.getDouble(referenceIndex);<a name="line.291"></a> |
| <span class="sourceLineNo">292</span> }<a name="line.292"></a> |
| <span class="sourceLineNo">293</span>}<a name="line.293"></a> |
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| </pre> |
| </div> |
| </body> |
| </html> |
