source: src/main/java/agents/anac/y2015/agentBuyogV2/flanagan/analysis/RegressionDerivativeFunction.java

Last change on this file was 127, checked in by Wouter Pasman, 6 years ago

#41 ROLL BACK of rev.126 . So this version is equal to rev. 125

File size: 1.9 KB
Line 
1/*
2* Interface RegressionFunction
3*
4* The sum of squares function needed by the
5* non-linear regression methods in the class Regression
6* is supplied by means of this interface, RegressionFunction
7*
8* WRITTEN BY: Dr Michael Thomas Flanagan
9*
10* DATE: October 2008
11* MODIFIED:
12*
13* DOCUMENTATION:
14* See Michael Thomas Flanagan's Java library on-line web page:
15* http://www.ee.ucl.ac.uk/~mflanaga/java/Regression.html
16* http://www.ee.ucl.ac.uk/~mflanaga/java/
17*
18* Copyright (c) 2008
19*
20* PERMISSION TO COPY:
21*
22* Redistributions of this source code, or parts of, must retain the above
23* copyright notice, this list of conditions and the following disclaimer.
24*
25* Redistribution in binary form of all or parts of this class, must reproduce
26* the above copyright, this list of conditions and the following disclaimer in
27* the documentation and/or other materials provided with the distribution.
28*
29* Permission to use, copy and modify this software and its documentation for
30* NON-COMMERCIAL purposes is granted, without fee, provided that an acknowledgement
31* to the author, Michael Thomas Flanagan at www.ee.ucl.ac.uk/~mflanaga, appears in all
32* copies and associated documentation or publications.
33*
34* Dr Michael Thomas Flanagan makes no representations about the suitability
35* or fitness of the software for any or for a particular purpose.
36* Michael Thomas Flanagan shall not be liable for any damages suffered
37* as a result of using, modifying or distributing this software or its derivatives.
38*
39***************************************************************************************/
40
41
42package agents.anac.y2015.agentBuyogV2.flanagan.analysis;
43
44// Interface for Regression class
45// Derivative function for non-linear regression methods
46// i = index of parameter a in dy/da[i]
47
48public interface RegressionDerivativeFunction{
49 double function(double[] param, double[] x, int i);
50}
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