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comparison libs/commons-math-2.1/docs/apidocs/src-html/org/apache/commons/math/stat/correlation/Covariance.html @ 13:cbf34dd4d7e6

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commons-math-2.1 added
author dwinter
date Tue, 04 Jan 2011 10:02:07 +0100
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12:970d26a94fb7 13:cbf34dd4d7e6
1 <HTML>
2 <BODY BGCOLOR="white">
3 <PRE>
4 <FONT color="green">001</FONT> /*<a name="line.1"></a>
5 <FONT color="green">002</FONT> * Licensed to the Apache Software Foundation (ASF) under one or more<a name="line.2"></a>
6 <FONT color="green">003</FONT> * contributor license agreements. See the NOTICE file distributed with<a name="line.3"></a>
7 <FONT color="green">004</FONT> * this work for additional information regarding copyright ownership.<a name="line.4"></a>
8 <FONT color="green">005</FONT> * The ASF licenses this file to You under the Apache License, Version 2.0<a name="line.5"></a>
9 <FONT color="green">006</FONT> * (the "License"); you may not use this file except in compliance with<a name="line.6"></a>
10 <FONT color="green">007</FONT> * the License. You may obtain a copy of the License at<a name="line.7"></a>
11 <FONT color="green">008</FONT> *<a name="line.8"></a>
12 <FONT color="green">009</FONT> * http://www.apache.org/licenses/LICENSE-2.0<a name="line.9"></a>
13 <FONT color="green">010</FONT> *<a name="line.10"></a>
14 <FONT color="green">011</FONT> * Unless required by applicable law or agreed to in writing, software<a name="line.11"></a>
15 <FONT color="green">012</FONT> * distributed under the License is distributed on an "AS IS" BASIS,<a name="line.12"></a>
16 <FONT color="green">013</FONT> * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.<a name="line.13"></a>
17 <FONT color="green">014</FONT> * See the License for the specific language governing permissions and<a name="line.14"></a>
18 <FONT color="green">015</FONT> * limitations under the License.<a name="line.15"></a>
19 <FONT color="green">016</FONT> */<a name="line.16"></a>
20 <FONT color="green">017</FONT> package org.apache.commons.math.stat.correlation;<a name="line.17"></a>
21 <FONT color="green">018</FONT> <a name="line.18"></a>
22 <FONT color="green">019</FONT> import org.apache.commons.math.MathRuntimeException;<a name="line.19"></a>
23 <FONT color="green">020</FONT> import org.apache.commons.math.linear.RealMatrix;<a name="line.20"></a>
24 <FONT color="green">021</FONT> import org.apache.commons.math.linear.BlockRealMatrix;<a name="line.21"></a>
25 <FONT color="green">022</FONT> import org.apache.commons.math.stat.descriptive.moment.Mean;<a name="line.22"></a>
26 <FONT color="green">023</FONT> import org.apache.commons.math.stat.descriptive.moment.Variance;<a name="line.23"></a>
27 <FONT color="green">024</FONT> <a name="line.24"></a>
28 <FONT color="green">025</FONT> /**<a name="line.25"></a>
29 <FONT color="green">026</FONT> * Computes covariances for pairs of arrays or columns of a matrix.<a name="line.26"></a>
30 <FONT color="green">027</FONT> *<a name="line.27"></a>
31 <FONT color="green">028</FONT> * &lt;p&gt;The constructors that take &lt;code&gt;RealMatrix&lt;/code&gt; or<a name="line.28"></a>
32 <FONT color="green">029</FONT> * &lt;code&gt;double[][]&lt;/code&gt; arguments generate covariance matrices. The<a name="line.29"></a>
33 <FONT color="green">030</FONT> * columns of the input matrices are assumed to represent variable values.&lt;/p&gt;<a name="line.30"></a>
34 <FONT color="green">031</FONT> *<a name="line.31"></a>
35 <FONT color="green">032</FONT> * &lt;p&gt;The constructor argument &lt;code&gt;biasCorrected&lt;/code&gt; determines whether or<a name="line.32"></a>
36 <FONT color="green">033</FONT> * not computed covariances are bias-corrected.&lt;/p&gt;<a name="line.33"></a>
37 <FONT color="green">034</FONT> *<a name="line.34"></a>
38 <FONT color="green">035</FONT> * &lt;p&gt;Unbiased covariances are given by the formula&lt;/p&gt;<a name="line.35"></a>
39 <FONT color="green">036</FONT> * &lt;code&gt;cov(X, Y) = &amp;Sigma;[(x&lt;sub&gt;i&lt;/sub&gt; - E(X))(y&lt;sub&gt;i&lt;/sub&gt; - E(Y))] / (n - 1)&lt;/code&gt;<a name="line.36"></a>
40 <FONT color="green">037</FONT> * where &lt;code&gt;E(X)&lt;/code&gt; is the mean of &lt;code&gt;X&lt;/code&gt; and &lt;code&gt;E(Y)&lt;/code&gt;<a name="line.37"></a>
41 <FONT color="green">038</FONT> * is the mean of the &lt;code&gt;Y&lt;/code&gt; values.<a name="line.38"></a>
42 <FONT color="green">039</FONT> *<a name="line.39"></a>
43 <FONT color="green">040</FONT> * &lt;p&gt;Non-bias-corrected estimates use &lt;code&gt;n&lt;/code&gt; in place of &lt;code&gt;n - 1&lt;/code&gt;<a name="line.40"></a>
44 <FONT color="green">041</FONT> *<a name="line.41"></a>
45 <FONT color="green">042</FONT> * @version $Revision: 811685 $ $Date: 2009-09-05 13:36:48 -0400 (Sat, 05 Sep 2009) $<a name="line.42"></a>
46 <FONT color="green">043</FONT> * @since 2.0<a name="line.43"></a>
47 <FONT color="green">044</FONT> */<a name="line.44"></a>
48 <FONT color="green">045</FONT> public class Covariance {<a name="line.45"></a>
49 <FONT color="green">046</FONT> <a name="line.46"></a>
50 <FONT color="green">047</FONT> /** covariance matrix */<a name="line.47"></a>
51 <FONT color="green">048</FONT> private final RealMatrix covarianceMatrix;<a name="line.48"></a>
52 <FONT color="green">049</FONT> <a name="line.49"></a>
53 <FONT color="green">050</FONT> /**<a name="line.50"></a>
54 <FONT color="green">051</FONT> * Create an empty covariance matrix.<a name="line.51"></a>
55 <FONT color="green">052</FONT> */<a name="line.52"></a>
56 <FONT color="green">053</FONT> /** Number of observations (length of covariate vectors) */<a name="line.53"></a>
57 <FONT color="green">054</FONT> private final int n;<a name="line.54"></a>
58 <FONT color="green">055</FONT> <a name="line.55"></a>
59 <FONT color="green">056</FONT> /**<a name="line.56"></a>
60 <FONT color="green">057</FONT> * Create a Covariance with no data<a name="line.57"></a>
61 <FONT color="green">058</FONT> */<a name="line.58"></a>
62 <FONT color="green">059</FONT> public Covariance() {<a name="line.59"></a>
63 <FONT color="green">060</FONT> super();<a name="line.60"></a>
64 <FONT color="green">061</FONT> covarianceMatrix = null;<a name="line.61"></a>
65 <FONT color="green">062</FONT> n = 0;<a name="line.62"></a>
66 <FONT color="green">063</FONT> }<a name="line.63"></a>
67 <FONT color="green">064</FONT> <a name="line.64"></a>
68 <FONT color="green">065</FONT> /**<a name="line.65"></a>
69 <FONT color="green">066</FONT> * Create a Covariance matrix from a rectangular array<a name="line.66"></a>
70 <FONT color="green">067</FONT> * whose columns represent covariates.<a name="line.67"></a>
71 <FONT color="green">068</FONT> *<a name="line.68"></a>
72 <FONT color="green">069</FONT> * &lt;p&gt;The &lt;code&gt;biasCorrected&lt;/code&gt; parameter determines whether or not<a name="line.69"></a>
73 <FONT color="green">070</FONT> * covariance estimates are bias-corrected.&lt;/p&gt;<a name="line.70"></a>
74 <FONT color="green">071</FONT> *<a name="line.71"></a>
75 <FONT color="green">072</FONT> * &lt;p&gt;The input array must be rectangular with at least two columns<a name="line.72"></a>
76 <FONT color="green">073</FONT> * and two rows.&lt;/p&gt;<a name="line.73"></a>
77 <FONT color="green">074</FONT> *<a name="line.74"></a>
78 <FONT color="green">075</FONT> * @param data rectangular array with columns representing covariates<a name="line.75"></a>
79 <FONT color="green">076</FONT> * @param biasCorrected true means covariances are bias-corrected<a name="line.76"></a>
80 <FONT color="green">077</FONT> * @throws IllegalArgumentException if the input data array is not<a name="line.77"></a>
81 <FONT color="green">078</FONT> * rectangular with at least two rows and two columns.<a name="line.78"></a>
82 <FONT color="green">079</FONT> */<a name="line.79"></a>
83 <FONT color="green">080</FONT> public Covariance(double[][] data, boolean biasCorrected) {<a name="line.80"></a>
84 <FONT color="green">081</FONT> this(new BlockRealMatrix(data), biasCorrected);<a name="line.81"></a>
85 <FONT color="green">082</FONT> }<a name="line.82"></a>
86 <FONT color="green">083</FONT> <a name="line.83"></a>
87 <FONT color="green">084</FONT> /**<a name="line.84"></a>
88 <FONT color="green">085</FONT> * Create a Covariance matrix from a rectangular array<a name="line.85"></a>
89 <FONT color="green">086</FONT> * whose columns represent covariates.<a name="line.86"></a>
90 <FONT color="green">087</FONT> *<a name="line.87"></a>
91 <FONT color="green">088</FONT> * &lt;p&gt;The input array must be rectangular with at least two columns<a name="line.88"></a>
92 <FONT color="green">089</FONT> * and two rows&lt;/p&gt;<a name="line.89"></a>
93 <FONT color="green">090</FONT> *<a name="line.90"></a>
94 <FONT color="green">091</FONT> * @param data rectangular array with columns representing covariates<a name="line.91"></a>
95 <FONT color="green">092</FONT> * @throws IllegalArgumentException if the input data array is not<a name="line.92"></a>
96 <FONT color="green">093</FONT> * rectangular with at least two rows and two columns.<a name="line.93"></a>
97 <FONT color="green">094</FONT> */<a name="line.94"></a>
98 <FONT color="green">095</FONT> public Covariance(double[][] data) {<a name="line.95"></a>
99 <FONT color="green">096</FONT> this(data, true);<a name="line.96"></a>
100 <FONT color="green">097</FONT> }<a name="line.97"></a>
101 <FONT color="green">098</FONT> <a name="line.98"></a>
102 <FONT color="green">099</FONT> /**<a name="line.99"></a>
103 <FONT color="green">100</FONT> * Create a covariance matrix from a matrix whose columns<a name="line.100"></a>
104 <FONT color="green">101</FONT> * represent covariates.<a name="line.101"></a>
105 <FONT color="green">102</FONT> *<a name="line.102"></a>
106 <FONT color="green">103</FONT> * &lt;p&gt;The &lt;code&gt;biasCorrected&lt;/code&gt; parameter determines whether or not<a name="line.103"></a>
107 <FONT color="green">104</FONT> * covariance estimates are bias-corrected.&lt;/p&gt;<a name="line.104"></a>
108 <FONT color="green">105</FONT> *<a name="line.105"></a>
109 <FONT color="green">106</FONT> * &lt;p&gt;The matrix must have at least two columns and two rows&lt;/p&gt;<a name="line.106"></a>
110 <FONT color="green">107</FONT> *<a name="line.107"></a>
111 <FONT color="green">108</FONT> * @param matrix matrix with columns representing covariates<a name="line.108"></a>
112 <FONT color="green">109</FONT> * @param biasCorrected true means covariances are bias-corrected<a name="line.109"></a>
113 <FONT color="green">110</FONT> * @throws IllegalArgumentException if the input matrix does not have<a name="line.110"></a>
114 <FONT color="green">111</FONT> * at least two rows and two columns<a name="line.111"></a>
115 <FONT color="green">112</FONT> */<a name="line.112"></a>
116 <FONT color="green">113</FONT> public Covariance(RealMatrix matrix, boolean biasCorrected) {<a name="line.113"></a>
117 <FONT color="green">114</FONT> checkSufficientData(matrix);<a name="line.114"></a>
118 <FONT color="green">115</FONT> n = matrix.getRowDimension();<a name="line.115"></a>
119 <FONT color="green">116</FONT> covarianceMatrix = computeCovarianceMatrix(matrix, biasCorrected);<a name="line.116"></a>
120 <FONT color="green">117</FONT> }<a name="line.117"></a>
121 <FONT color="green">118</FONT> <a name="line.118"></a>
122 <FONT color="green">119</FONT> /**<a name="line.119"></a>
123 <FONT color="green">120</FONT> * Create a covariance matrix from a matrix whose columns<a name="line.120"></a>
124 <FONT color="green">121</FONT> * represent covariates.<a name="line.121"></a>
125 <FONT color="green">122</FONT> *<a name="line.122"></a>
126 <FONT color="green">123</FONT> * &lt;p&gt;The matrix must have at least two columns and two rows&lt;/p&gt;<a name="line.123"></a>
127 <FONT color="green">124</FONT> *<a name="line.124"></a>
128 <FONT color="green">125</FONT> * @param matrix matrix with columns representing covariates<a name="line.125"></a>
129 <FONT color="green">126</FONT> * @throws IllegalArgumentException if the input matrix does not have<a name="line.126"></a>
130 <FONT color="green">127</FONT> * at least two rows and two columns<a name="line.127"></a>
131 <FONT color="green">128</FONT> */<a name="line.128"></a>
132 <FONT color="green">129</FONT> public Covariance(RealMatrix matrix) {<a name="line.129"></a>
133 <FONT color="green">130</FONT> this(matrix, true);<a name="line.130"></a>
134 <FONT color="green">131</FONT> }<a name="line.131"></a>
135 <FONT color="green">132</FONT> <a name="line.132"></a>
136 <FONT color="green">133</FONT> /**<a name="line.133"></a>
137 <FONT color="green">134</FONT> * Returns the covariance matrix<a name="line.134"></a>
138 <FONT color="green">135</FONT> *<a name="line.135"></a>
139 <FONT color="green">136</FONT> * @return covariance matrix<a name="line.136"></a>
140 <FONT color="green">137</FONT> */<a name="line.137"></a>
141 <FONT color="green">138</FONT> public RealMatrix getCovarianceMatrix() {<a name="line.138"></a>
142 <FONT color="green">139</FONT> return covarianceMatrix;<a name="line.139"></a>
143 <FONT color="green">140</FONT> }<a name="line.140"></a>
144 <FONT color="green">141</FONT> <a name="line.141"></a>
145 <FONT color="green">142</FONT> /**<a name="line.142"></a>
146 <FONT color="green">143</FONT> * Returns the number of observations (length of covariate vectors)<a name="line.143"></a>
147 <FONT color="green">144</FONT> *<a name="line.144"></a>
148 <FONT color="green">145</FONT> * @return number of observations<a name="line.145"></a>
149 <FONT color="green">146</FONT> */<a name="line.146"></a>
150 <FONT color="green">147</FONT> <a name="line.147"></a>
151 <FONT color="green">148</FONT> public int getN() {<a name="line.148"></a>
152 <FONT color="green">149</FONT> return n;<a name="line.149"></a>
153 <FONT color="green">150</FONT> }<a name="line.150"></a>
154 <FONT color="green">151</FONT> <a name="line.151"></a>
155 <FONT color="green">152</FONT> /**<a name="line.152"></a>
156 <FONT color="green">153</FONT> * Compute a covariance matrix from a matrix whose columns represent<a name="line.153"></a>
157 <FONT color="green">154</FONT> * covariates.<a name="line.154"></a>
158 <FONT color="green">155</FONT> * @param matrix input matrix (must have at least two columns and two rows)<a name="line.155"></a>
159 <FONT color="green">156</FONT> * @param biasCorrected determines whether or not covariance estimates are bias-corrected<a name="line.156"></a>
160 <FONT color="green">157</FONT> * @return covariance matrix<a name="line.157"></a>
161 <FONT color="green">158</FONT> */<a name="line.158"></a>
162 <FONT color="green">159</FONT> protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected) {<a name="line.159"></a>
163 <FONT color="green">160</FONT> int dimension = matrix.getColumnDimension();<a name="line.160"></a>
164 <FONT color="green">161</FONT> Variance variance = new Variance(biasCorrected);<a name="line.161"></a>
165 <FONT color="green">162</FONT> RealMatrix outMatrix = new BlockRealMatrix(dimension, dimension);<a name="line.162"></a>
166 <FONT color="green">163</FONT> for (int i = 0; i &lt; dimension; i++) {<a name="line.163"></a>
167 <FONT color="green">164</FONT> for (int j = 0; j &lt; i; j++) {<a name="line.164"></a>
168 <FONT color="green">165</FONT> double cov = covariance(matrix.getColumn(i), matrix.getColumn(j), biasCorrected);<a name="line.165"></a>
169 <FONT color="green">166</FONT> outMatrix.setEntry(i, j, cov);<a name="line.166"></a>
170 <FONT color="green">167</FONT> outMatrix.setEntry(j, i, cov);<a name="line.167"></a>
171 <FONT color="green">168</FONT> }<a name="line.168"></a>
172 <FONT color="green">169</FONT> outMatrix.setEntry(i, i, variance.evaluate(matrix.getColumn(i)));<a name="line.169"></a>
173 <FONT color="green">170</FONT> }<a name="line.170"></a>
174 <FONT color="green">171</FONT> return outMatrix;<a name="line.171"></a>
175 <FONT color="green">172</FONT> }<a name="line.172"></a>
176 <FONT color="green">173</FONT> <a name="line.173"></a>
177 <FONT color="green">174</FONT> /**<a name="line.174"></a>
178 <FONT color="green">175</FONT> * Create a covariance matrix from a matrix whose columns represent<a name="line.175"></a>
179 <FONT color="green">176</FONT> * covariates. Covariances are computed using the bias-corrected formula.<a name="line.176"></a>
180 <FONT color="green">177</FONT> * @param matrix input matrix (must have at least two columns and two rows)<a name="line.177"></a>
181 <FONT color="green">178</FONT> * @return covariance matrix<a name="line.178"></a>
182 <FONT color="green">179</FONT> * @see #Covariance<a name="line.179"></a>
183 <FONT color="green">180</FONT> */<a name="line.180"></a>
184 <FONT color="green">181</FONT> protected RealMatrix computeCovarianceMatrix(RealMatrix matrix) {<a name="line.181"></a>
185 <FONT color="green">182</FONT> return computeCovarianceMatrix(matrix, true);<a name="line.182"></a>
186 <FONT color="green">183</FONT> }<a name="line.183"></a>
187 <FONT color="green">184</FONT> <a name="line.184"></a>
188 <FONT color="green">185</FONT> /**<a name="line.185"></a>
189 <FONT color="green">186</FONT> * Compute a covariance matrix from a rectangular array whose columns represent<a name="line.186"></a>
190 <FONT color="green">187</FONT> * covariates.<a name="line.187"></a>
191 <FONT color="green">188</FONT> * @param data input array (must have at least two columns and two rows)<a name="line.188"></a>
192 <FONT color="green">189</FONT> * @param biasCorrected determines whether or not covariance estimates are bias-corrected<a name="line.189"></a>
193 <FONT color="green">190</FONT> * @return covariance matrix<a name="line.190"></a>
194 <FONT color="green">191</FONT> */<a name="line.191"></a>
195 <FONT color="green">192</FONT> protected RealMatrix computeCovarianceMatrix(double[][] data, boolean biasCorrected) {<a name="line.192"></a>
196 <FONT color="green">193</FONT> return computeCovarianceMatrix(new BlockRealMatrix(data), biasCorrected);<a name="line.193"></a>
197 <FONT color="green">194</FONT> }<a name="line.194"></a>
198 <FONT color="green">195</FONT> <a name="line.195"></a>
199 <FONT color="green">196</FONT> /**<a name="line.196"></a>
200 <FONT color="green">197</FONT> * Create a covariance matrix from a rectangual array whose columns represent<a name="line.197"></a>
201 <FONT color="green">198</FONT> * covariates. Covariances are computed using the bias-corrected formula.<a name="line.198"></a>
202 <FONT color="green">199</FONT> * @param data input array (must have at least two columns and two rows)<a name="line.199"></a>
203 <FONT color="green">200</FONT> * @return covariance matrix<a name="line.200"></a>
204 <FONT color="green">201</FONT> * @see #Covariance<a name="line.201"></a>
205 <FONT color="green">202</FONT> */<a name="line.202"></a>
206 <FONT color="green">203</FONT> protected RealMatrix computeCovarianceMatrix(double[][] data) {<a name="line.203"></a>
207 <FONT color="green">204</FONT> return computeCovarianceMatrix(data, true);<a name="line.204"></a>
208 <FONT color="green">205</FONT> }<a name="line.205"></a>
209 <FONT color="green">206</FONT> <a name="line.206"></a>
210 <FONT color="green">207</FONT> /**<a name="line.207"></a>
211 <FONT color="green">208</FONT> * Computes the covariance between the two arrays.<a name="line.208"></a>
212 <FONT color="green">209</FONT> *<a name="line.209"></a>
213 <FONT color="green">210</FONT> * &lt;p&gt;Array lengths must match and the common length must be at least 2.&lt;/p&gt;<a name="line.210"></a>
214 <FONT color="green">211</FONT> *<a name="line.211"></a>
215 <FONT color="green">212</FONT> * @param xArray first data array<a name="line.212"></a>
216 <FONT color="green">213</FONT> * @param yArray second data array<a name="line.213"></a>
217 <FONT color="green">214</FONT> * @param biasCorrected if true, returned value will be bias-corrected<a name="line.214"></a>
218 <FONT color="green">215</FONT> * @return returns the covariance for the two arrays<a name="line.215"></a>
219 <FONT color="green">216</FONT> * @throws IllegalArgumentException if the arrays lengths do not match or<a name="line.216"></a>
220 <FONT color="green">217</FONT> * there is insufficient data<a name="line.217"></a>
221 <FONT color="green">218</FONT> */<a name="line.218"></a>
222 <FONT color="green">219</FONT> public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)<a name="line.219"></a>
223 <FONT color="green">220</FONT> throws IllegalArgumentException {<a name="line.220"></a>
224 <FONT color="green">221</FONT> Mean mean = new Mean();<a name="line.221"></a>
225 <FONT color="green">222</FONT> double result = 0d;<a name="line.222"></a>
226 <FONT color="green">223</FONT> int length = xArray.length;<a name="line.223"></a>
227 <FONT color="green">224</FONT> if(length == yArray.length &amp;&amp; length &gt; 1) {<a name="line.224"></a>
228 <FONT color="green">225</FONT> double xMean = mean.evaluate(xArray);<a name="line.225"></a>
229 <FONT color="green">226</FONT> double yMean = mean.evaluate(yArray);<a name="line.226"></a>
230 <FONT color="green">227</FONT> for (int i = 0; i &lt; length; i++) {<a name="line.227"></a>
231 <FONT color="green">228</FONT> double xDev = xArray[i] - xMean;<a name="line.228"></a>
232 <FONT color="green">229</FONT> double yDev = yArray[i] - yMean;<a name="line.229"></a>
233 <FONT color="green">230</FONT> result += (xDev * yDev - result) / (i + 1);<a name="line.230"></a>
234 <FONT color="green">231</FONT> }<a name="line.231"></a>
235 <FONT color="green">232</FONT> }<a name="line.232"></a>
236 <FONT color="green">233</FONT> else {<a name="line.233"></a>
237 <FONT color="green">234</FONT> throw MathRuntimeException.createIllegalArgumentException(<a name="line.234"></a>
238 <FONT color="green">235</FONT> "arrays must have the same length and both must have at " +<a name="line.235"></a>
239 <FONT color="green">236</FONT> "least two elements. xArray has size {0}, yArray has {1} elements",<a name="line.236"></a>
240 <FONT color="green">237</FONT> length, yArray.length);<a name="line.237"></a>
241 <FONT color="green">238</FONT> }<a name="line.238"></a>
242 <FONT color="green">239</FONT> return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;<a name="line.239"></a>
243 <FONT color="green">240</FONT> }<a name="line.240"></a>
244 <FONT color="green">241</FONT> <a name="line.241"></a>
245 <FONT color="green">242</FONT> /**<a name="line.242"></a>
246 <FONT color="green">243</FONT> * Computes the covariance between the two arrays, using the bias-corrected<a name="line.243"></a>
247 <FONT color="green">244</FONT> * formula.<a name="line.244"></a>
248 <FONT color="green">245</FONT> *<a name="line.245"></a>
249 <FONT color="green">246</FONT> * &lt;p&gt;Array lengths must match and the common length must be at least 2.&lt;/p&gt;<a name="line.246"></a>
250 <FONT color="green">247</FONT> *<a name="line.247"></a>
251 <FONT color="green">248</FONT> * @param xArray first data array<a name="line.248"></a>
252 <FONT color="green">249</FONT> * @param yArray second data array<a name="line.249"></a>
253 <FONT color="green">250</FONT> * @return returns the covariance for the two arrays<a name="line.250"></a>
254 <FONT color="green">251</FONT> * @throws IllegalArgumentException if the arrays lengths do not match or<a name="line.251"></a>
255 <FONT color="green">252</FONT> * there is insufficient data<a name="line.252"></a>
256 <FONT color="green">253</FONT> */<a name="line.253"></a>
257 <FONT color="green">254</FONT> public double covariance(final double[] xArray, final double[] yArray)<a name="line.254"></a>
258 <FONT color="green">255</FONT> throws IllegalArgumentException {<a name="line.255"></a>
259 <FONT color="green">256</FONT> return covariance(xArray, yArray, true);<a name="line.256"></a>
260 <FONT color="green">257</FONT> }<a name="line.257"></a>
261 <FONT color="green">258</FONT> <a name="line.258"></a>
262 <FONT color="green">259</FONT> /**<a name="line.259"></a>
263 <FONT color="green">260</FONT> * Throws IllegalArgumentException of the matrix does not have at least<a name="line.260"></a>
264 <FONT color="green">261</FONT> * two columns and two rows<a name="line.261"></a>
265 <FONT color="green">262</FONT> * @param matrix matrix to check<a name="line.262"></a>
266 <FONT color="green">263</FONT> */<a name="line.263"></a>
267 <FONT color="green">264</FONT> private void checkSufficientData(final RealMatrix matrix) {<a name="line.264"></a>
268 <FONT color="green">265</FONT> int nRows = matrix.getRowDimension();<a name="line.265"></a>
269 <FONT color="green">266</FONT> int nCols = matrix.getColumnDimension();<a name="line.266"></a>
270 <FONT color="green">267</FONT> if (nRows &lt; 2 || nCols &lt; 2) {<a name="line.267"></a>
271 <FONT color="green">268</FONT> throw MathRuntimeException.createIllegalArgumentException(<a name="line.268"></a>
272 <FONT color="green">269</FONT> "insufficient data: only {0} rows and {1} columns.",<a name="line.269"></a>
273 <FONT color="green">270</FONT> nRows, nCols);<a name="line.270"></a>
274 <FONT color="green">271</FONT> }<a name="line.271"></a>
275 <FONT color="green">272</FONT> }<a name="line.272"></a>
276 <FONT color="green">273</FONT> }<a name="line.273"></a>
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