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| date | Tue, 04 Jan 2011 10:02:07 +0100 |
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<HTML> <BODY BGCOLOR="white"> <PRE> <FONT color="green">001</FONT> /*<a name="line.1"></a> <FONT color="green">002</FONT> * Licensed to the Apache Software Foundation (ASF) under one or more<a name="line.2"></a> <FONT color="green">003</FONT> * contributor license agreements. See the NOTICE file distributed with<a name="line.3"></a> <FONT color="green">004</FONT> * this work for additional information regarding copyright ownership.<a name="line.4"></a> <FONT color="green">005</FONT> * The ASF licenses this file to You under the Apache License, Version 2.0<a name="line.5"></a> <FONT color="green">006</FONT> * (the "License"); you may not use this file except in compliance with<a name="line.6"></a> <FONT color="green">007</FONT> * the License. You may obtain a copy of the License at<a name="line.7"></a> <FONT color="green">008</FONT> *<a name="line.8"></a> <FONT color="green">009</FONT> * http://www.apache.org/licenses/LICENSE-2.0<a name="line.9"></a> <FONT color="green">010</FONT> *<a name="line.10"></a> <FONT color="green">011</FONT> * Unless required by applicable law or agreed to in writing, software<a name="line.11"></a> <FONT color="green">012</FONT> * distributed under the License is distributed on an "AS IS" BASIS,<a name="line.12"></a> <FONT color="green">013</FONT> * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.<a name="line.13"></a> <FONT color="green">014</FONT> * See the License for the specific language governing permissions and<a name="line.14"></a> <FONT color="green">015</FONT> * limitations under the License.<a name="line.15"></a> <FONT color="green">016</FONT> */<a name="line.16"></a> <FONT color="green">017</FONT> package org.apache.commons.math.stat.descriptive.moment;<a name="line.17"></a> <FONT color="green">018</FONT> <a name="line.18"></a> <FONT color="green">019</FONT> import java.io.Serializable;<a name="line.19"></a> <FONT color="green">020</FONT> <a name="line.20"></a> <FONT color="green">021</FONT> import org.apache.commons.math.MathRuntimeException;<a name="line.21"></a> <FONT color="green">022</FONT> import org.apache.commons.math.stat.descriptive.WeightedEvaluation;<a name="line.22"></a> <FONT color="green">023</FONT> import org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic;<a name="line.23"></a> <FONT color="green">024</FONT> <a name="line.24"></a> <FONT color="green">025</FONT> /**<a name="line.25"></a> <FONT color="green">026</FONT> * Computes the variance of the available values. By default, the unbiased<a name="line.26"></a> <FONT color="green">027</FONT> * "sample variance" definitional formula is used:<a name="line.27"></a> <FONT color="green">028</FONT> * <p><a name="line.28"></a> <FONT color="green">029</FONT> * variance = sum((x_i - mean)^2) / (n - 1) </p><a name="line.29"></a> <FONT color="green">030</FONT> * <p><a name="line.30"></a> <FONT color="green">031</FONT> * where mean is the {@link Mean} and <code>n</code> is the number<a name="line.31"></a> <FONT color="green">032</FONT> * of sample observations.</p><a name="line.32"></a> <FONT color="green">033</FONT> * <p><a name="line.33"></a> <FONT color="green">034</FONT> * The definitional formula does not have good numerical properties, so<a name="line.34"></a> <FONT color="green">035</FONT> * this implementation does not compute the statistic using the definitional<a name="line.35"></a> <FONT color="green">036</FONT> * formula. <ul><a name="line.36"></a> <FONT color="green">037</FONT> * <li> The <code>getResult</code> method computes the variance using<a name="line.37"></a> <FONT color="green">038</FONT> * updating formulas based on West's algorithm, as described in<a name="line.38"></a> <FONT color="green">039</FONT> * <a href="http://doi.acm.org/10.1145/359146.359152"> Chan, T. F. and<a name="line.39"></a> <FONT color="green">040</FONT> * J. G. Lewis 1979, <i>Communications of the ACM</i>,<a name="line.40"></a> <FONT color="green">041</FONT> * vol. 22 no. 9, pp. 526-531.</a></li><a name="line.41"></a> <FONT color="green">042</FONT> * <li> The <code>evaluate</code> methods leverage the fact that they have the<a name="line.42"></a> <FONT color="green">043</FONT> * full array of values in memory to execute a two-pass algorithm.<a name="line.43"></a> <FONT color="green">044</FONT> * Specifically, these methods use the "corrected two-pass algorithm" from<a name="line.44"></a> <FONT color="green">045</FONT> * Chan, Golub, Levesque, <i>Algorithms for Computing the Sample Variance</i>,<a name="line.45"></a> <FONT color="green">046</FONT> * American Statistician, vol. 37, no. 3 (1983) pp. 242-247.</li></ul><a name="line.46"></a> <FONT color="green">047</FONT> * Note that adding values using <code>increment</code> or<a name="line.47"></a> <FONT color="green">048</FONT> * <code>incrementAll</code> and then executing <code>getResult</code> will<a name="line.48"></a> <FONT color="green">049</FONT> * sometimes give a different, less accurate, result than executing<a name="line.49"></a> <FONT color="green">050</FONT> * <code>evaluate</code> with the full array of values. The former approach<a name="line.50"></a> <FONT color="green">051</FONT> * should only be used when the full array of values is not available.</p><a name="line.51"></a> <FONT color="green">052</FONT> * <p><a name="line.52"></a> <FONT color="green">053</FONT> * The "population variance" ( sum((x_i - mean)^2) / n ) can also<a name="line.53"></a> <FONT color="green">054</FONT> * be computed using this statistic. The <code>isBiasCorrected</code><a name="line.54"></a> <FONT color="green">055</FONT> * property determines whether the "population" or "sample" value is<a name="line.55"></a> <FONT color="green">056</FONT> * returned by the <code>evaluate</code> and <code>getResult</code> methods.<a name="line.56"></a> <FONT color="green">057</FONT> * To compute population variances, set this property to <code>false.</code><a name="line.57"></a> <FONT color="green">058</FONT> * </p><a name="line.58"></a> <FONT color="green">059</FONT> * <p><a name="line.59"></a> <FONT color="green">060</FONT> * <strong>Note that this implementation is not synchronized.</strong> If<a name="line.60"></a> <FONT color="green">061</FONT> * multiple threads access an instance of this class concurrently, and at least<a name="line.61"></a> <FONT color="green">062</FONT> * one of the threads invokes the <code>increment()</code> or<a name="line.62"></a> <FONT color="green">063</FONT> * <code>clear()</code> method, it must be synchronized externally.</p><a name="line.63"></a> <FONT color="green">064</FONT> *<a name="line.64"></a> <FONT color="green">065</FONT> * @version $Revision: 908626 $ $Date: 2010-02-10 13:44:42 -0500 (Wed, 10 Feb 2010) $<a name="line.65"></a> <FONT color="green">066</FONT> */<a name="line.66"></a> <FONT color="green">067</FONT> public class Variance extends AbstractStorelessUnivariateStatistic implements Serializable, WeightedEvaluation {<a name="line.67"></a> <FONT color="green">068</FONT> <a name="line.68"></a> <FONT color="green">069</FONT> /** Serializable version identifier */<a name="line.69"></a> <FONT color="green">070</FONT> private static final long serialVersionUID = -9111962718267217978L;<a name="line.70"></a> <FONT color="green">071</FONT> <a name="line.71"></a> <FONT color="green">072</FONT> /** SecondMoment is used in incremental calculation of Variance*/<a name="line.72"></a> <FONT color="green">073</FONT> protected SecondMoment moment = null;<a name="line.73"></a> <FONT color="green">074</FONT> <a name="line.74"></a> <FONT color="green">075</FONT> /**<a name="line.75"></a> <FONT color="green">076</FONT> * Boolean test to determine if this Variance should also increment<a name="line.76"></a> <FONT color="green">077</FONT> * the second moment, this evaluates to false when this Variance is<a name="line.77"></a> <FONT color="green">078</FONT> * constructed with an external SecondMoment as a parameter.<a name="line.78"></a> <FONT color="green">079</FONT> */<a name="line.79"></a> <FONT color="green">080</FONT> protected boolean incMoment = true;<a name="line.80"></a> <FONT color="green">081</FONT> <a name="line.81"></a> <FONT color="green">082</FONT> /**<a name="line.82"></a> <FONT color="green">083</FONT> * Determines whether or not bias correction is applied when computing the<a name="line.83"></a> <FONT color="green">084</FONT> * value of the statisic. True means that bias is corrected. See<a name="line.84"></a> <FONT color="green">085</FONT> * {@link Variance} for details on the formula.<a name="line.85"></a> <FONT color="green">086</FONT> */<a name="line.86"></a> <FONT color="green">087</FONT> private boolean isBiasCorrected = true;<a name="line.87"></a> <FONT color="green">088</FONT> <a name="line.88"></a> <FONT color="green">089</FONT> /**<a name="line.89"></a> <FONT color="green">090</FONT> * Constructs a Variance with default (true) <code>isBiasCorrected</code><a name="line.90"></a> <FONT color="green">091</FONT> * property.<a name="line.91"></a> <FONT color="green">092</FONT> */<a name="line.92"></a> <FONT color="green">093</FONT> public Variance() {<a name="line.93"></a> <FONT color="green">094</FONT> moment = new SecondMoment();<a name="line.94"></a> <FONT color="green">095</FONT> }<a name="line.95"></a> <FONT color="green">096</FONT> <a name="line.96"></a> <FONT color="green">097</FONT> /**<a name="line.97"></a> <FONT color="green">098</FONT> * Constructs a Variance based on an external second moment.<a name="line.98"></a> <FONT color="green">099</FONT> *<a name="line.99"></a> <FONT color="green">100</FONT> * @param m2 the SecondMoment (Third or Fourth moments work<a name="line.100"></a> <FONT color="green">101</FONT> * here as well.)<a name="line.101"></a> <FONT color="green">102</FONT> */<a name="line.102"></a> <FONT color="green">103</FONT> public Variance(final SecondMoment m2) {<a name="line.103"></a> <FONT color="green">104</FONT> incMoment = false;<a name="line.104"></a> <FONT color="green">105</FONT> this.moment = m2;<a name="line.105"></a> <FONT color="green">106</FONT> }<a name="line.106"></a> <FONT color="green">107</FONT> <a name="line.107"></a> <FONT color="green">108</FONT> /**<a name="line.108"></a> <FONT color="green">109</FONT> * Constructs a Variance with the specified <code>isBiasCorrected</code><a name="line.109"></a> <FONT color="green">110</FONT> * property<a name="line.110"></a> <FONT color="green">111</FONT> *<a name="line.111"></a> <FONT color="green">112</FONT> * @param isBiasCorrected setting for bias correction - true means<a name="line.112"></a> <FONT color="green">113</FONT> * bias will be corrected and is equivalent to using the argumentless<a name="line.113"></a> <FONT color="green">114</FONT> * constructor<a name="line.114"></a> <FONT color="green">115</FONT> */<a name="line.115"></a> <FONT color="green">116</FONT> public Variance(boolean isBiasCorrected) {<a name="line.116"></a> <FONT color="green">117</FONT> moment = new SecondMoment();<a name="line.117"></a> <FONT color="green">118</FONT> this.isBiasCorrected = isBiasCorrected;<a name="line.118"></a> <FONT color="green">119</FONT> }<a name="line.119"></a> <FONT color="green">120</FONT> <a name="line.120"></a> <FONT color="green">121</FONT> /**<a name="line.121"></a> <FONT color="green">122</FONT> * Constructs a Variance with the specified <code>isBiasCorrected</code><a name="line.122"></a> <FONT color="green">123</FONT> * property and the supplied external second moment.<a name="line.123"></a> <FONT color="green">124</FONT> *<a name="line.124"></a> <FONT color="green">125</FONT> * @param isBiasCorrected setting for bias correction - true means<a name="line.125"></a> <FONT color="green">126</FONT> * bias will be corrected<a name="line.126"></a> <FONT color="green">127</FONT> * @param m2 the SecondMoment (Third or Fourth moments work<a name="line.127"></a> <FONT color="green">128</FONT> * here as well.)<a name="line.128"></a> <FONT color="green">129</FONT> */<a name="line.129"></a> <FONT color="green">130</FONT> public Variance(boolean isBiasCorrected, SecondMoment m2) {<a name="line.130"></a> <FONT color="green">131</FONT> incMoment = false;<a name="line.131"></a> <FONT color="green">132</FONT> this.moment = m2;<a name="line.132"></a> <FONT color="green">133</FONT> this.isBiasCorrected = isBiasCorrected;<a name="line.133"></a> <FONT color="green">134</FONT> }<a name="line.134"></a> <FONT color="green">135</FONT> <a name="line.135"></a> <FONT color="green">136</FONT> /**<a name="line.136"></a> <FONT color="green">137</FONT> * Copy constructor, creates a new {@code Variance} identical<a name="line.137"></a> <FONT color="green">138</FONT> * to the {@code original}<a name="line.138"></a> <FONT color="green">139</FONT> *<a name="line.139"></a> <FONT color="green">140</FONT> * @param original the {@code Variance} instance to copy<a name="line.140"></a> <FONT color="green">141</FONT> */<a name="line.141"></a> <FONT color="green">142</FONT> public Variance(Variance original) {<a name="line.142"></a> <FONT color="green">143</FONT> copy(original, this);<a name="line.143"></a> <FONT color="green">144</FONT> }<a name="line.144"></a> <FONT color="green">145</FONT> <a name="line.145"></a> <FONT color="green">146</FONT> /**<a name="line.146"></a> <FONT color="green">147</FONT> * {@inheritDoc}<a name="line.147"></a> <FONT color="green">148</FONT> * <p>If all values are available, it is more accurate to use<a name="line.148"></a> <FONT color="green">149</FONT> * {@link #evaluate(double[])} rather than adding values one at a time<a name="line.149"></a> <FONT color="green">150</FONT> * using this method and then executing {@link #getResult}, since<a name="line.150"></a> <FONT color="green">151</FONT> * <code>evaluate</code> leverages the fact that is has the full<a name="line.151"></a> <FONT color="green">152</FONT> * list of values together to execute a two-pass algorithm.<a name="line.152"></a> <FONT color="green">153</FONT> * See {@link Variance}.</p><a name="line.153"></a> <FONT color="green">154</FONT> */<a name="line.154"></a> <FONT color="green">155</FONT> @Override<a name="line.155"></a> <FONT color="green">156</FONT> public void increment(final double d) {<a name="line.156"></a> <FONT color="green">157</FONT> if (incMoment) {<a name="line.157"></a> <FONT color="green">158</FONT> moment.increment(d);<a name="line.158"></a> <FONT color="green">159</FONT> }<a name="line.159"></a> <FONT color="green">160</FONT> }<a name="line.160"></a> <FONT color="green">161</FONT> <a name="line.161"></a> <FONT color="green">162</FONT> /**<a name="line.162"></a> <FONT color="green">163</FONT> * {@inheritDoc}<a name="line.163"></a> <FONT color="green">164</FONT> */<a name="line.164"></a> <FONT color="green">165</FONT> @Override<a name="line.165"></a> <FONT color="green">166</FONT> public double getResult() {<a name="line.166"></a> <FONT color="green">167</FONT> if (moment.n == 0) {<a name="line.167"></a> <FONT color="green">168</FONT> return Double.NaN;<a name="line.168"></a> <FONT color="green">169</FONT> } else if (moment.n == 1) {<a name="line.169"></a> <FONT color="green">170</FONT> return 0d;<a name="line.170"></a> <FONT color="green">171</FONT> } else {<a name="line.171"></a> <FONT color="green">172</FONT> if (isBiasCorrected) {<a name="line.172"></a> <FONT color="green">173</FONT> return moment.m2 / (moment.n - 1d);<a name="line.173"></a> <FONT color="green">174</FONT> } else {<a name="line.174"></a> <FONT color="green">175</FONT> return moment.m2 / (moment.n);<a name="line.175"></a> <FONT color="green">176</FONT> }<a name="line.176"></a> <FONT color="green">177</FONT> }<a name="line.177"></a> <FONT color="green">178</FONT> }<a name="line.178"></a> <FONT color="green">179</FONT> <a name="line.179"></a> <FONT color="green">180</FONT> /**<a name="line.180"></a> <FONT color="green">181</FONT> * {@inheritDoc}<a name="line.181"></a> <FONT color="green">182</FONT> */<a name="line.182"></a> <FONT color="green">183</FONT> public long getN() {<a name="line.183"></a> <FONT color="green">184</FONT> return moment.getN();<a name="line.184"></a> <FONT color="green">185</FONT> }<a name="line.185"></a> <FONT color="green">186</FONT> <a name="line.186"></a> <FONT color="green">187</FONT> /**<a name="line.187"></a> <FONT color="green">188</FONT> * {@inheritDoc}<a name="line.188"></a> <FONT color="green">189</FONT> */<a name="line.189"></a> <FONT color="green">190</FONT> @Override<a name="line.190"></a> <FONT color="green">191</FONT> public void clear() {<a name="line.191"></a> <FONT color="green">192</FONT> if (incMoment) {<a name="line.192"></a> <FONT color="green">193</FONT> moment.clear();<a name="line.193"></a> <FONT color="green">194</FONT> }<a name="line.194"></a> <FONT color="green">195</FONT> }<a name="line.195"></a> <FONT color="green">196</FONT> <a name="line.196"></a> <FONT color="green">197</FONT> /**<a name="line.197"></a> <FONT color="green">198</FONT> * Returns the variance of the entries in the input array, or<a name="line.198"></a> <FONT color="green">199</FONT> * <code>Double.NaN</code> if the array is empty.<a name="line.199"></a> <FONT color="green">200</FONT> * <p><a name="line.200"></a> <FONT color="green">201</FONT> * See {@link Variance} for details on the computing algorithm.</p><a name="line.201"></a> <FONT color="green">202</FONT> * <p><a name="line.202"></a> <FONT color="green">203</FONT> * Returns 0 for a single-value (i.e. length = 1) sample.</p><a name="line.203"></a> <FONT color="green">204</FONT> * <p><a name="line.204"></a> <FONT color="green">205</FONT> * Throws <code>IllegalArgumentException</code> if the array is null.</p><a name="line.205"></a> <FONT color="green">206</FONT> * <p><a name="line.206"></a> <FONT color="green">207</FONT> * Does not change the internal state of the statistic.</p><a name="line.207"></a> <FONT color="green">208</FONT> *<a name="line.208"></a> <FONT color="green">209</FONT> * @param values the input array<a name="line.209"></a> <FONT color="green">210</FONT> * @return the variance of the values or Double.NaN if length = 0<a name="line.210"></a> <FONT color="green">211</FONT> * @throws IllegalArgumentException if the array is null<a name="line.211"></a> <FONT color="green">212</FONT> */<a name="line.212"></a> <FONT color="green">213</FONT> @Override<a name="line.213"></a> <FONT color="green">214</FONT> public double evaluate(final double[] values) {<a name="line.214"></a> <FONT color="green">215</FONT> if (values == null) {<a name="line.215"></a> <FONT color="green">216</FONT> throw MathRuntimeException.createIllegalArgumentException("input values array is null");<a name="line.216"></a> <FONT color="green">217</FONT> }<a name="line.217"></a> <FONT color="green">218</FONT> return evaluate(values, 0, values.length);<a name="line.218"></a> <FONT color="green">219</FONT> }<a name="line.219"></a> <FONT color="green">220</FONT> <a name="line.220"></a> <FONT color="green">221</FONT> /**<a name="line.221"></a> <FONT color="green">222</FONT> * Returns the variance of the entries in the specified portion of<a name="line.222"></a> <FONT color="green">223</FONT> * the input array, or <code>Double.NaN</code> if the designated subarray<a name="line.223"></a> <FONT color="green">224</FONT> * is empty.<a name="line.224"></a> <FONT color="green">225</FONT> * <p><a name="line.225"></a> <FONT color="green">226</FONT> * See {@link Variance} for details on the computing algorithm.</p><a name="line.226"></a> <FONT color="green">227</FONT> * <p><a name="line.227"></a> <FONT color="green">228</FONT> * Returns 0 for a single-value (i.e. length = 1) sample.</p><a name="line.228"></a> <FONT color="green">229</FONT> * <p><a name="line.229"></a> <FONT color="green">230</FONT> * Does not change the internal state of the statistic.</p><a name="line.230"></a> <FONT color="green">231</FONT> * <p><a name="line.231"></a> <FONT color="green">232</FONT> * Throws <code>IllegalArgumentException</code> if the array is null.</p><a name="line.232"></a> <FONT color="green">233</FONT> *<a name="line.233"></a> <FONT color="green">234</FONT> * @param values the input array<a name="line.234"></a> <FONT color="green">235</FONT> * @param begin index of the first array element to include<a name="line.235"></a> <FONT color="green">236</FONT> * @param length the number of elements to include<a name="line.236"></a> <FONT color="green">237</FONT> * @return the variance of the values or Double.NaN if length = 0<a name="line.237"></a> <FONT color="green">238</FONT> * @throws IllegalArgumentException if the array is null or the array index<a name="line.238"></a> <FONT color="green">239</FONT> * parameters are not valid<a name="line.239"></a> <FONT color="green">240</FONT> */<a name="line.240"></a> <FONT color="green">241</FONT> @Override<a name="line.241"></a> <FONT color="green">242</FONT> public double evaluate(final double[] values, final int begin, final int length) {<a name="line.242"></a> <FONT color="green">243</FONT> <a name="line.243"></a> <FONT color="green">244</FONT> double var = Double.NaN;<a name="line.244"></a> <FONT color="green">245</FONT> <a name="line.245"></a> <FONT color="green">246</FONT> if (test(values, begin, length)) {<a name="line.246"></a> <FONT color="green">247</FONT> clear();<a name="line.247"></a> <FONT color="green">248</FONT> if (length == 1) {<a name="line.248"></a> <FONT color="green">249</FONT> var = 0.0;<a name="line.249"></a> <FONT color="green">250</FONT> } else if (length > 1) {<a name="line.250"></a> <FONT color="green">251</FONT> Mean mean = new Mean();<a name="line.251"></a> <FONT color="green">252</FONT> double m = mean.evaluate(values, begin, length);<a name="line.252"></a> <FONT color="green">253</FONT> var = evaluate(values, m, begin, length);<a name="line.253"></a> <FONT color="green">254</FONT> }<a name="line.254"></a> <FONT color="green">255</FONT> }<a name="line.255"></a> <FONT color="green">256</FONT> return var;<a name="line.256"></a> <FONT color="green">257</FONT> }<a name="line.257"></a> <FONT color="green">258</FONT> <a name="line.258"></a> <FONT color="green">259</FONT> /**<a name="line.259"></a> <FONT color="green">260</FONT> * <p>Returns the weighted variance of the entries in the specified portion of<a name="line.260"></a> <FONT color="green">261</FONT> * the input array, or <code>Double.NaN</code> if the designated subarray<a name="line.261"></a> <FONT color="green">262</FONT> * is empty.</p><a name="line.262"></a> <FONT color="green">263</FONT> * <p><a name="line.263"></a> <FONT color="green">264</FONT> * Uses the formula <pre><a name="line.264"></a> <FONT color="green">265</FONT> * &Sigma;(weights[i]*(values[i] - weightedMean)<sup>2</sup>)/(&Sigma;(weights[i]) - 1)<a name="line.265"></a> <FONT color="green">266</FONT> * </pre><a name="line.266"></a> <FONT color="green">267</FONT> * where weightedMean is the weighted mean</p><a name="line.267"></a> <FONT color="green">268</FONT> * <p><a name="line.268"></a> <FONT color="green">269</FONT> * This formula will not return the same result as the unweighted variance when all<a name="line.269"></a> <FONT color="green">270</FONT> * weights are equal, unless all weights are equal to 1. The formula assumes that<a name="line.270"></a> <FONT color="green">271</FONT> * weights are to be treated as "expansion values," as will be the case if for example<a name="line.271"></a> <FONT color="green">272</FONT> * the weights represent frequency counts. To normalize weights so that the denominator<a name="line.272"></a> <FONT color="green">273</FONT> * in the variance computation equals the length of the input vector minus one, use <pre><a name="line.273"></a> <FONT color="green">274</FONT> * <code>evaluate(values, MathUtils.normalizeArray(weights, values.length)); </code><a name="line.274"></a> <FONT color="green">275</FONT> * </pre><a name="line.275"></a> <FONT color="green">276</FONT> * <p><a name="line.276"></a> <FONT color="green">277</FONT> * Returns 0 for a single-value (i.e. length = 1) sample.</p><a name="line.277"></a> <FONT color="green">278</FONT> * <p><a name="line.278"></a> <FONT color="green">279</FONT> * Throws <code>IllegalArgumentException</code> if any of the following are true:<a name="line.279"></a> <FONT color="green">280</FONT> * <ul><li>the values array is null</li><a name="line.280"></a> <FONT color="green">281</FONT> * <li>the weights array is null</li><a name="line.281"></a> <FONT color="green">282</FONT> * <li>the weights array does not have the same length as the values array</li><a name="line.282"></a> <FONT color="green">283</FONT> * <li>the weights array contains one or more infinite values</li><a name="line.283"></a> <FONT color="green">284</FONT> * <li>the weights array contains one or more NaN values</li><a name="line.284"></a> <FONT color="green">285</FONT> * <li>the weights array contains negative values</li><a name="line.285"></a> <FONT color="green">286</FONT> * <li>the start and length arguments do not determine a valid array</li><a name="line.286"></a> <FONT color="green">287</FONT> * </ul></p><a name="line.287"></a> <FONT color="green">288</FONT> * <p><a name="line.288"></a> <FONT color="green">289</FONT> * Does not change the internal state of the statistic.</p><a name="line.289"></a> <FONT color="green">290</FONT> * <p><a name="line.290"></a> <FONT color="green">291</FONT> * Throws <code>IllegalArgumentException</code> if either array is null.</p><a name="line.291"></a> <FONT color="green">292</FONT> *<a name="line.292"></a> <FONT color="green">293</FONT> * @param values the input array<a name="line.293"></a> <FONT color="green">294</FONT> * @param weights the weights array<a name="line.294"></a> <FONT color="green">295</FONT> * @param begin index of the first array element to include<a name="line.295"></a> <FONT color="green">296</FONT> * @param length the number of elements to include<a name="line.296"></a> <FONT color="green">297</FONT> * @return the weighted variance of the values or Double.NaN if length = 0<a name="line.297"></a> <FONT color="green">298</FONT> * @throws IllegalArgumentException if the parameters are not valid<a name="line.298"></a> <FONT color="green">299</FONT> * @since 2.1<a name="line.299"></a> <FONT color="green">300</FONT> */<a name="line.300"></a> <FONT color="green">301</FONT> public double evaluate(final double[] values, final double[] weights,<a name="line.301"></a> <FONT color="green">302</FONT> final int begin, final int length) {<a name="line.302"></a> <FONT color="green">303</FONT> <a name="line.303"></a> <FONT color="green">304</FONT> double var = Double.NaN;<a name="line.304"></a> <FONT color="green">305</FONT> <a name="line.305"></a> <FONT color="green">306</FONT> if (test(values, weights,begin, length)) {<a name="line.306"></a> <FONT color="green">307</FONT> clear();<a name="line.307"></a> <FONT color="green">308</FONT> if (length == 1) {<a name="line.308"></a> <FONT color="green">309</FONT> var = 0.0;<a name="line.309"></a> <FONT color="green">310</FONT> } else if (length > 1) {<a name="line.310"></a> <FONT color="green">311</FONT> Mean mean = new Mean();<a name="line.311"></a> <FONT color="green">312</FONT> double m = mean.evaluate(values, weights, begin, length);<a name="line.312"></a> <FONT color="green">313</FONT> var = evaluate(values, weights, m, begin, length);<a name="line.313"></a> <FONT color="green">314</FONT> }<a name="line.314"></a> <FONT color="green">315</FONT> }<a name="line.315"></a> <FONT color="green">316</FONT> return var;<a name="line.316"></a> <FONT color="green">317</FONT> }<a name="line.317"></a> <FONT color="green">318</FONT> <a name="line.318"></a> <FONT color="green">319</FONT> /**<a name="line.319"></a> <FONT color="green">320</FONT> * <p><a name="line.320"></a> <FONT color="green">321</FONT> * Returns the weighted variance of the entries in the the input array.</p><a name="line.321"></a> <FONT color="green">322</FONT> * <p><a name="line.322"></a> <FONT color="green">323</FONT> * Uses the formula <pre><a name="line.323"></a> <FONT color="green">324</FONT> * &Sigma;(weights[i]*(values[i] - weightedMean)<sup>2</sup>)/(&Sigma;(weights[i]) - 1)<a name="line.324"></a> <FONT color="green">325</FONT> * </pre><a name="line.325"></a> <FONT color="green">326</FONT> * where weightedMean is the weighted mean</p><a name="line.326"></a> <FONT color="green">327</FONT> * <p><a name="line.327"></a> <FONT color="green">328</FONT> * This formula will not return the same result as the unweighted variance when all<a name="line.328"></a> <FONT color="green">329</FONT> * weights are equal, unless all weights are equal to 1. The formula assumes that<a name="line.329"></a> <FONT color="green">330</FONT> * weights are to be treated as "expansion values," as will be the case if for example<a name="line.330"></a> <FONT color="green">331</FONT> * the weights represent frequency counts. To normalize weights so that the denominator<a name="line.331"></a> <FONT color="green">332</FONT> * in the variance computation equals the length of the input vector minus one, use <pre><a name="line.332"></a> <FONT color="green">333</FONT> * <code>evaluate(values, MathUtils.normalizeArray(weights, values.length)); </code><a name="line.333"></a> <FONT color="green">334</FONT> * </pre><a name="line.334"></a> <FONT color="green">335</FONT> * <p><a name="line.335"></a> <FONT color="green">336</FONT> * Returns 0 for a single-value (i.e. length = 1) sample.</p><a name="line.336"></a> <FONT color="green">337</FONT> * <p><a name="line.337"></a> <FONT color="green">338</FONT> * Throws <code>IllegalArgumentException</code> if any of the following are true:<a name="line.338"></a> <FONT color="green">339</FONT> * <ul><li>the values array is null</li><a name="line.339"></a> <FONT color="green">340</FONT> * <li>the weights array is null</li><a name="line.340"></a> <FONT color="green">341</FONT> * <li>the weights array does not have the same length as the values array</li><a name="line.341"></a> <FONT color="green">342</FONT> * <li>the weights array contains one or more infinite values</li><a name="line.342"></a> <FONT color="green">343</FONT> * <li>the weights array contains one or more NaN values</li><a name="line.343"></a> <FONT color="green">344</FONT> * <li>the weights array contains negative values</li><a name="line.344"></a> <FONT color="green">345</FONT> * </ul></p><a name="line.345"></a> <FONT color="green">346</FONT> * <p><a name="line.346"></a> <FONT color="green">347</FONT> * Does not change the internal state of the statistic.</p><a name="line.347"></a> <FONT color="green">348</FONT> * <p><a name="line.348"></a> <FONT color="green">349</FONT> * Throws <code>IllegalArgumentException</code> if either array is null.</p><a name="line.349"></a> <FONT color="green">350</FONT> *<a name="line.350"></a> <FONT color="green">351</FONT> * @param values the input array<a name="line.351"></a> <FONT color="green">352</FONT> * @param weights the weights array<a name="line.352"></a> <FONT color="green">353</FONT> * @return the weighted variance of the values<a name="line.353"></a> <FONT color="green">354</FONT> * @throws IllegalArgumentException if the parameters are not valid<a name="line.354"></a> <FONT color="green">355</FONT> * @since 2.1<a name="line.355"></a> <FONT color="green">356</FONT> */<a name="line.356"></a> <FONT color="green">357</FONT> public double evaluate(final double[] values, final double[] weights) {<a name="line.357"></a> <FONT color="green">358</FONT> return evaluate(values, weights, 0, values.length);<a name="line.358"></a> <FONT color="green">359</FONT> }<a name="line.359"></a> <FONT color="green">360</FONT> <a name="line.360"></a> <FONT color="green">361</FONT> /**<a name="line.361"></a> <FONT color="green">362</FONT> * Returns the variance of the entries in the specified portion of<a name="line.362"></a> <FONT color="green">363</FONT> * the input array, using the precomputed mean value. Returns<a name="line.363"></a> <FONT color="green">364</FONT> * <code>Double.NaN</code> if the designated subarray is empty.<a name="line.364"></a> <FONT color="green">365</FONT> * <p><a name="line.365"></a> <FONT color="green">366</FONT> * See {@link Variance} for details on the computing algorithm.</p><a name="line.366"></a> <FONT color="green">367</FONT> * <p><a name="line.367"></a> <FONT color="green">368</FONT> * The formula used assumes that the supplied mean value is the arithmetic<a name="line.368"></a> <FONT color="green">369</FONT> * mean of the sample data, not a known population parameter. This method<a name="line.369"></a> <FONT color="green">370</FONT> * is supplied only to save computation when the mean has already been<a name="line.370"></a> <FONT color="green">371</FONT> * computed.</p><a name="line.371"></a> <FONT color="green">372</FONT> * <p><a name="line.372"></a> <FONT color="green">373</FONT> * Returns 0 for a single-value (i.e. length = 1) sample.</p><a name="line.373"></a> <FONT color="green">374</FONT> * <p><a name="line.374"></a> <FONT color="green">375</FONT> * Throws <code>IllegalArgumentException</code> if the array is null.</p><a name="line.375"></a> <FONT color="green">376</FONT> * <p><a name="line.376"></a> <FONT color="green">377</FONT> * Does not change the internal state of the statistic.</p><a name="line.377"></a> <FONT color="green">378</FONT> *<a name="line.378"></a> <FONT color="green">379</FONT> * @param values the input array<a name="line.379"></a> <FONT color="green">380</FONT> * @param mean the precomputed mean value<a name="line.380"></a> <FONT color="green">381</FONT> * @param begin index of the first array element to include<a name="line.381"></a> <FONT color="green">382</FONT> * @param length the number of elements to include<a name="line.382"></a> <FONT color="green">383</FONT> * @return the variance of the values or Double.NaN if length = 0<a name="line.383"></a> <FONT color="green">384</FONT> * @throws IllegalArgumentException if the array is null or the array index<a name="line.384"></a> <FONT color="green">385</FONT> * parameters are not valid<a name="line.385"></a> <FONT color="green">386</FONT> */<a name="line.386"></a> <FONT color="green">387</FONT> public double evaluate(final double[] values, final double mean,<a name="line.387"></a> <FONT color="green">388</FONT> final int begin, final int length) {<a name="line.388"></a> <FONT color="green">389</FONT> <a name="line.389"></a> <FONT color="green">390</FONT> double var = Double.NaN;<a name="line.390"></a> <FONT color="green">391</FONT> <a name="line.391"></a> <FONT color="green">392</FONT> if (test(values, begin, length)) {<a name="line.392"></a> <FONT color="green">393</FONT> if (length == 1) {<a name="line.393"></a> <FONT color="green">394</FONT> var = 0.0;<a name="line.394"></a> <FONT color="green">395</FONT> } else if (length > 1) {<a name="line.395"></a> <FONT color="green">396</FONT> double accum = 0.0;<a name="line.396"></a> <FONT color="green">397</FONT> double dev = 0.0;<a name="line.397"></a> <FONT color="green">398</FONT> double accum2 = 0.0;<a name="line.398"></a> <FONT color="green">399</FONT> for (int i = begin; i < begin + length; i++) {<a name="line.399"></a> <FONT color="green">400</FONT> dev = values[i] - mean;<a name="line.400"></a> <FONT color="green">401</FONT> accum += dev * dev;<a name="line.401"></a> <FONT color="green">402</FONT> accum2 += dev;<a name="line.402"></a> <FONT color="green">403</FONT> }<a name="line.403"></a> <FONT color="green">404</FONT> double len = length;<a name="line.404"></a> <FONT color="green">405</FONT> if (isBiasCorrected) {<a name="line.405"></a> <FONT color="green">406</FONT> var = (accum - (accum2 * accum2 / len)) / (len - 1.0);<a name="line.406"></a> <FONT color="green">407</FONT> } else {<a name="line.407"></a> <FONT color="green">408</FONT> var = (accum - (accum2 * accum2 / len)) / len;<a name="line.408"></a> <FONT color="green">409</FONT> }<a name="line.409"></a> <FONT color="green">410</FONT> }<a name="line.410"></a> <FONT color="green">411</FONT> }<a name="line.411"></a> <FONT color="green">412</FONT> return var;<a name="line.412"></a> <FONT color="green">413</FONT> }<a name="line.413"></a> <FONT color="green">414</FONT> <a name="line.414"></a> <FONT color="green">415</FONT> /**<a name="line.415"></a> <FONT color="green">416</FONT> * Returns the variance of the entries in the input array, using the<a name="line.416"></a> <FONT color="green">417</FONT> * precomputed mean value. Returns <code>Double.NaN</code> if the array<a name="line.417"></a> <FONT color="green">418</FONT> * is empty.<a name="line.418"></a> <FONT color="green">419</FONT> * <p><a name="line.419"></a> <FONT color="green">420</FONT> * See {@link Variance} for details on the computing algorithm.</p><a name="line.420"></a> <FONT color="green">421</FONT> * <p><a name="line.421"></a> <FONT color="green">422</FONT> * If <code>isBiasCorrected</code> is <code>true</code> the formula used<a name="line.422"></a> <FONT color="green">423</FONT> * assumes that the supplied mean value is the arithmetic mean of the<a name="line.423"></a> <FONT color="green">424</FONT> * sample data, not a known population parameter. If the mean is a known<a name="line.424"></a> <FONT color="green">425</FONT> * population parameter, or if the "population" version of the variance is<a name="line.425"></a> <FONT color="green">426</FONT> * desired, set <code>isBiasCorrected</code> to <code>false</code> before<a name="line.426"></a> <FONT color="green">427</FONT> * invoking this method.</p><a name="line.427"></a> <FONT color="green">428</FONT> * <p><a name="line.428"></a> <FONT color="green">429</FONT> * Returns 0 for a single-value (i.e. length = 1) sample.</p><a name="line.429"></a> <FONT color="green">430</FONT> * <p><a name="line.430"></a> <FONT color="green">431</FONT> * Throws <code>IllegalArgumentException</code> if the array is null.</p><a name="line.431"></a> <FONT color="green">432</FONT> * <p><a name="line.432"></a> <FONT color="green">433</FONT> * Does not change the internal state of the statistic.</p><a name="line.433"></a> <FONT color="green">434</FONT> *<a name="line.434"></a> <FONT color="green">435</FONT> * @param values the input array<a name="line.435"></a> <FONT color="green">436</FONT> * @param mean the precomputed mean value<a name="line.436"></a> <FONT color="green">437</FONT> * @return the variance of the values or Double.NaN if the array is empty<a name="line.437"></a> <FONT color="green">438</FONT> * @throws IllegalArgumentException if the array is null<a name="line.438"></a> <FONT color="green">439</FONT> */<a name="line.439"></a> <FONT color="green">440</FONT> public double evaluate(final double[] values, final double mean) {<a name="line.440"></a> <FONT color="green">441</FONT> return evaluate(values, mean, 0, values.length);<a name="line.441"></a> <FONT color="green">442</FONT> }<a name="line.442"></a> <FONT color="green">443</FONT> <a name="line.443"></a> <FONT color="green">444</FONT> /**<a name="line.444"></a> <FONT color="green">445</FONT> * Returns the weighted variance of the entries in the specified portion of<a name="line.445"></a> <FONT color="green">446</FONT> * the input array, using the precomputed weighted mean value. Returns<a name="line.446"></a> <FONT color="green">447</FONT> * <code>Double.NaN</code> if the designated subarray is empty.<a name="line.447"></a> <FONT color="green">448</FONT> * <p><a name="line.448"></a> <FONT color="green">449</FONT> * Uses the formula <pre><a name="line.449"></a> <FONT color="green">450</FONT> * &Sigma;(weights[i]*(values[i] - mean)<sup>2</sup>)/(&Sigma;(weights[i]) - 1)<a name="line.450"></a> <FONT color="green">451</FONT> * </pre></p><a name="line.451"></a> <FONT color="green">452</FONT> * <p><a name="line.452"></a> <FONT color="green">453</FONT> * The formula used assumes that the supplied mean value is the weighted arithmetic<a name="line.453"></a> <FONT color="green">454</FONT> * mean of the sample data, not a known population parameter. This method<a name="line.454"></a> <FONT color="green">455</FONT> * is supplied only to save computation when the mean has already been<a name="line.455"></a> <FONT color="green">456</FONT> * computed.</p><a name="line.456"></a> <FONT color="green">457</FONT> * <p><a name="line.457"></a> <FONT color="green">458</FONT> * This formula will not return the same result as the unweighted variance when all<a name="line.458"></a> <FONT color="green">459</FONT> * weights are equal, unless all weights are equal to 1. The formula assumes that<a name="line.459"></a> <FONT color="green">460</FONT> * weights are to be treated as "expansion values," as will be the case if for example<a name="line.460"></a> <FONT color="green">461</FONT> * the weights represent frequency counts. To normalize weights so that the denominator<a name="line.461"></a> <FONT color="green">462</FONT> * in the variance computation equals the length of the input vector minus one, use <pre><a name="line.462"></a> <FONT color="green">463</FONT> * <code>evaluate(values, MathUtils.normalizeArray(weights, values.length), mean); </code><a name="line.463"></a> <FONT color="green">464</FONT> * </pre><a name="line.464"></a> <FONT color="green">465</FONT> * <p><a name="line.465"></a> <FONT color="green">466</FONT> * Returns 0 for a single-value (i.e. length = 1) sample.</p><a name="line.466"></a> <FONT color="green">467</FONT> * <p><a name="line.467"></a> <FONT color="green">468</FONT> * Throws <code>IllegalArgumentException</code> if any of the following are true:<a name="line.468"></a> <FONT color="green">469</FONT> * <ul><li>the values array is null</li><a name="line.469"></a> <FONT color="green">470</FONT> * <li>the weights array is null</li><a name="line.470"></a> <FONT color="green">471</FONT> * <li>the weights array does not have the same length as the values array</li><a name="line.471"></a> <FONT color="green">472</FONT> * <li>the weights array contains one or more infinite values</li><a name="line.472"></a> <FONT color="green">473</FONT> * <li>the weights array contains one or more NaN values</li><a name="line.473"></a> <FONT color="green">474</FONT> * <li>the weights array contains negative values</li><a name="line.474"></a> <FONT color="green">475</FONT> * <li>the start and length arguments do not determine a valid array</li><a name="line.475"></a> <FONT color="green">476</FONT> * </ul></p><a name="line.476"></a> <FONT color="green">477</FONT> * <p><a name="line.477"></a> <FONT color="green">478</FONT> * Does not change the internal state of the statistic.</p><a name="line.478"></a> <FONT color="green">479</FONT> *<a name="line.479"></a> <FONT color="green">480</FONT> * @param values the input array<a name="line.480"></a> <FONT color="green">481</FONT> * @param weights the weights array<a name="line.481"></a> <FONT color="green">482</FONT> * @param mean the precomputed weighted mean value<a name="line.482"></a> <FONT color="green">483</FONT> * @param begin index of the first array element to include<a name="line.483"></a> <FONT color="green">484</FONT> * @param length the number of elements to include<a name="line.484"></a> <FONT color="green">485</FONT> * @return the variance of the values or Double.NaN if length = 0<a name="line.485"></a> <FONT color="green">486</FONT> * @throws IllegalArgumentException if the parameters are not valid<a name="line.486"></a> <FONT color="green">487</FONT> * @since 2.1<a name="line.487"></a> <FONT color="green">488</FONT> */<a name="line.488"></a> <FONT color="green">489</FONT> public double evaluate(final double[] values, final double[] weights,<a name="line.489"></a> <FONT color="green">490</FONT> final double mean, final int begin, final int length) {<a name="line.490"></a> <FONT color="green">491</FONT> <a name="line.491"></a> <FONT color="green">492</FONT> double var = Double.NaN;<a name="line.492"></a> <FONT color="green">493</FONT> <a name="line.493"></a> <FONT color="green">494</FONT> if (test(values, weights, begin, length)) {<a name="line.494"></a> <FONT color="green">495</FONT> if (length == 1) {<a name="line.495"></a> <FONT color="green">496</FONT> var = 0.0;<a name="line.496"></a> <FONT color="green">497</FONT> } else if (length > 1) {<a name="line.497"></a> <FONT color="green">498</FONT> double accum = 0.0;<a name="line.498"></a> <FONT color="green">499</FONT> double dev = 0.0;<a name="line.499"></a> <FONT color="green">500</FONT> double accum2 = 0.0;<a name="line.500"></a> <FONT color="green">501</FONT> for (int i = begin; i < begin + length; i++) {<a name="line.501"></a> <FONT color="green">502</FONT> dev = values[i] - mean;<a name="line.502"></a> <FONT color="green">503</FONT> accum += weights[i] * (dev * dev);<a name="line.503"></a> <FONT color="green">504</FONT> accum2 += weights[i] * dev;<a name="line.504"></a> <FONT color="green">505</FONT> }<a name="line.505"></a> <FONT color="green">506</FONT> <a name="line.506"></a> <FONT color="green">507</FONT> double sumWts = 0;<a name="line.507"></a> <FONT color="green">508</FONT> for (int i = 0; i < weights.length; i++) {<a name="line.508"></a> <FONT color="green">509</FONT> sumWts += weights[i];<a name="line.509"></a> <FONT color="green">510</FONT> }<a name="line.510"></a> <FONT color="green">511</FONT> <a name="line.511"></a> <FONT color="green">512</FONT> if (isBiasCorrected) {<a name="line.512"></a> <FONT color="green">513</FONT> var = (accum - (accum2 * accum2 / sumWts)) / (sumWts - 1.0);<a name="line.513"></a> <FONT color="green">514</FONT> } else {<a name="line.514"></a> <FONT color="green">515</FONT> var = (accum - (accum2 * accum2 / sumWts)) / sumWts;<a name="line.515"></a> <FONT color="green">516</FONT> }<a name="line.516"></a> <FONT color="green">517</FONT> }<a name="line.517"></a> <FONT color="green">518</FONT> }<a name="line.518"></a> <FONT color="green">519</FONT> return var;<a name="line.519"></a> <FONT color="green">520</FONT> }<a name="line.520"></a> <FONT color="green">521</FONT> <a name="line.521"></a> <FONT color="green">522</FONT> /**<a name="line.522"></a> <FONT color="green">523</FONT> * <p>Returns the weighted variance of the values in the input array, using<a name="line.523"></a> <FONT color="green">524</FONT> * the precomputed weighted mean value.</p><a name="line.524"></a> <FONT color="green">525</FONT> * <p><a name="line.525"></a> <FONT color="green">526</FONT> * Uses the formula <pre><a name="line.526"></a> <FONT color="green">527</FONT> * &Sigma;(weights[i]*(values[i] - mean)<sup>2</sup>)/(&Sigma;(weights[i]) - 1)<a name="line.527"></a> <FONT color="green">528</FONT> * </pre></p><a name="line.528"></a> <FONT color="green">529</FONT> * <p><a name="line.529"></a> <FONT color="green">530</FONT> * The formula used assumes that the supplied mean value is the weighted arithmetic<a name="line.530"></a> <FONT color="green">531</FONT> * mean of the sample data, not a known population parameter. This method<a name="line.531"></a> <FONT color="green">532</FONT> * is supplied only to save computation when the mean has already been<a name="line.532"></a> <FONT color="green">533</FONT> * computed.</p><a name="line.533"></a> <FONT color="green">534</FONT> * <p><a name="line.534"></a> <FONT color="green">535</FONT> * This formula will not return the same result as the unweighted variance when all<a name="line.535"></a> <FONT color="green">536</FONT> * weights are equal, unless all weights are equal to 1. The formula assumes that<a name="line.536"></a> <FONT color="green">537</FONT> * weights are to be treated as "expansion values," as will be the case if for example<a name="line.537"></a> <FONT color="green">538</FONT> * the weights represent frequency counts. To normalize weights so that the denominator<a name="line.538"></a> <FONT color="green">539</FONT> * in the variance computation equals the length of the input vector minus one, use <pre><a name="line.539"></a> <FONT color="green">540</FONT> * <code>evaluate(values, MathUtils.normalizeArray(weights, values.length), mean); </code><a name="line.540"></a> <FONT color="green">541</FONT> * </pre><a name="line.541"></a> <FONT color="green">542</FONT> * <p><a name="line.542"></a> <FONT color="green">543</FONT> * Returns 0 for a single-value (i.e. length = 1) sample.</p><a name="line.543"></a> <FONT color="green">544</FONT> * <p><a name="line.544"></a> <FONT color="green">545</FONT> * Throws <code>IllegalArgumentException</code> if any of the following are true:<a name="line.545"></a> <FONT color="green">546</FONT> * <ul><li>the values array is null</li><a name="line.546"></a> <FONT color="green">547</FONT> * <li>the weights array is null</li><a name="line.547"></a> <FONT color="green">548</FONT> * <li>the weights array does not have the same length as the values array</li><a name="line.548"></a> <FONT color="green">549</FONT> * <li>the weights array contains one or more infinite values</li><a name="line.549"></a> <FONT color="green">550</FONT> * <li>the weights array contains one or more NaN values</li><a name="line.550"></a> <FONT color="green">551</FONT> * <li>the weights array contains negative values</li><a name="line.551"></a> <FONT color="green">552</FONT> * </ul></p><a name="line.552"></a> <FONT color="green">553</FONT> * <p><a name="line.553"></a> <FONT color="green">554</FONT> * Does not change the internal state of the statistic.</p><a name="line.554"></a> <FONT color="green">555</FONT> *<a name="line.555"></a> <FONT color="green">556</FONT> * @param values the input array<a name="line.556"></a> <FONT color="green">557</FONT> * @param weights the weights array<a name="line.557"></a> <FONT color="green">558</FONT> * @param mean the precomputed weighted mean value<a name="line.558"></a> <FONT color="green">559</FONT> * @return the variance of the values or Double.NaN if length = 0<a name="line.559"></a> <FONT color="green">560</FONT> * @throws IllegalArgumentException if the parameters are not valid<a name="line.560"></a> <FONT color="green">561</FONT> * @since 2.1<a name="line.561"></a> <FONT color="green">562</FONT> */<a name="line.562"></a> <FONT color="green">563</FONT> public double evaluate(final double[] values, final double[] weights, final double mean) {<a name="line.563"></a> <FONT color="green">564</FONT> return evaluate(values, weights, mean, 0, values.length);<a name="line.564"></a> <FONT color="green">565</FONT> }<a name="line.565"></a> <FONT color="green">566</FONT> <a name="line.566"></a> <FONT color="green">567</FONT> /**<a name="line.567"></a> <FONT color="green">568</FONT> * @return Returns the isBiasCorrected.<a name="line.568"></a> <FONT color="green">569</FONT> */<a name="line.569"></a> <FONT color="green">570</FONT> public boolean isBiasCorrected() {<a name="line.570"></a> <FONT color="green">571</FONT> return isBiasCorrected;<a name="line.571"></a> <FONT color="green">572</FONT> }<a name="line.572"></a> <FONT color="green">573</FONT> <a name="line.573"></a> <FONT color="green">574</FONT> /**<a name="line.574"></a> <FONT color="green">575</FONT> * @param biasCorrected The isBiasCorrected to set.<a name="line.575"></a> <FONT color="green">576</FONT> */<a name="line.576"></a> <FONT color="green">577</FONT> public void setBiasCorrected(boolean biasCorrected) {<a name="line.577"></a> <FONT color="green">578</FONT> this.isBiasCorrected = biasCorrected;<a name="line.578"></a> <FONT color="green">579</FONT> }<a name="line.579"></a> <FONT color="green">580</FONT> <a name="line.580"></a> <FONT color="green">581</FONT> /**<a name="line.581"></a> <FONT color="green">582</FONT> * {@inheritDoc}<a name="line.582"></a> <FONT color="green">583</FONT> */<a name="line.583"></a> <FONT color="green">584</FONT> @Override<a name="line.584"></a> <FONT color="green">585</FONT> public Variance copy() {<a name="line.585"></a> <FONT color="green">586</FONT> Variance result = new Variance();<a name="line.586"></a> <FONT color="green">587</FONT> copy(this, result);<a name="line.587"></a> <FONT color="green">588</FONT> return result;<a name="line.588"></a> <FONT color="green">589</FONT> }<a name="line.589"></a> <FONT color="green">590</FONT> <a name="line.590"></a> <FONT color="green">591</FONT> <a name="line.591"></a> <FONT color="green">592</FONT> /**<a name="line.592"></a> <FONT color="green">593</FONT> * Copies source to dest.<a name="line.593"></a> <FONT color="green">594</FONT> * <p>Neither source nor dest can be null.</p><a name="line.594"></a> <FONT color="green">595</FONT> *<a name="line.595"></a> <FONT color="green">596</FONT> * @param source Variance to copy<a name="line.596"></a> <FONT color="green">597</FONT> * @param dest Variance to copy to<a name="line.597"></a> <FONT color="green">598</FONT> * @throws NullPointerException if either source or dest is null<a name="line.598"></a> <FONT color="green">599</FONT> */<a name="line.599"></a> <FONT color="green">600</FONT> public static void copy(Variance source, Variance dest) {<a name="line.600"></a> <FONT color="green">601</FONT> dest.moment = source.moment.copy();<a name="line.601"></a> <FONT color="green">602</FONT> dest.isBiasCorrected = source.isBiasCorrected;<a name="line.602"></a> <FONT color="green">603</FONT> dest.incMoment = source.incMoment;<a name="line.603"></a> <FONT color="green">604</FONT> }<a name="line.604"></a> <FONT color="green">605</FONT> <a name="line.605"></a> <FONT color="green">606</FONT> }<a name="line.606"></a> </PRE> </BODY> </HTML>