lsqnonlin added

1 files changed, 83 insertions, 0 deletions

diff --git a/tests/unit_tests/lsqnonlin.dia.ref b/tests/unit_tests/lsqnonlin.dia.ref
new file mode 100644
index 0000000..916d9dc
--- /dev/null
+++ b/tests/unit_tests/lsqnonlin.dia.ref
@@ -0,0 +1,83 @@
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// Author: Harpreet Singh
+// Organization: FOSSEE, IIT Bombay
+// Email: harpreet.mertia@gmail.com
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+
+// <-- JVM NOT MANDATORY -->
+// <-- ENGLISH IMPOSED -->
+
+
+//
+// assert_close --
+//   Returns 1 if the two real matrices computed and expected are close,
+//   i.e. if the relative distance between computed and expected is lesser than epsilon.
+// Arguments
+//   computed, expected : the two matrices to compare
+//   epsilon : a small number
+//
+function flag = assert_close ( computed, expected, epsilon )
+  if expected==0.0 then
+    shift = norm(computed-expected);
+  else
+    shift = norm(computed-expected)/norm(expected);
+  end
+//  if shift < epsilon then
+//    flag = 1;
+//  else
+//    flag = 0;
+//  end
+//  if flag <> 1 then pause,end
+    flag = assert_checktrue ( shift < epsilon );
+endfunction
+//
+// assert_equal --
+//   Returns 1 if the two real matrices computed and expected are equal.
+// Arguments
+//   computed, expected : the two matrices to compare
+//   epsilon : a small number
+//
+//function flag = assert_equal ( computed , expected )
+//  if computed==expected then
+//    flag = 1;
+//  else
+//    flag = 0;
+//  end
+//  if flag <> 1 then pause,end
+//endfunction
+
+//A simple non-linear least square example taken from leastsq default present in scilab
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// measure weights (here all equal to 1...)
+wm = ones(m,1);
+// and we want to find the parameters x such that the model fits the given
+// data in the least square sense:
+//
+//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
+// initial parameters guess
+x0 = [1.5 ; 0.8];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+// the simplest call
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0)
+
+assert_close ( xopt , [ 0.9940629 0.9904811 ]' , 0.0005 );
+assert_close ( residual , [-0.0139785 0.0158061 0.0029263 0.0091929 -0.0017872 -0.0050049 0.0056439 -0.0128825 -0.0129584 0.0035627]' , 0.0005 );
+assert_close ( resnorm , [ 0.0009450] , 0.0005 );
+assert_checkequal( exitflag , int32(0) );
+printf("Test Successful");