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Testsuite: remove useless debug mode from several .m tests 

The output is always useful (and even needed in case of test failure).
remove-priordens
Sébastien Villemot 2023-11-16 09:33:27 +01:00
parent 742a3101b1
commit 56ed5bff43
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GPG Key ID: 2CECE9350ECEBE4A
4 changed files with 52 additions and 114 deletions
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46
tests/cyclereduction.m
View File

@ -1,5 +1,3 @@
debug = false;
source_dir = getenv('source_root'); source_dir = getenv('source_root');
addpath([source_dir filesep 'matlab']); addpath([source_dir filesep 'matlab']);
@ -7,10 +5,8 @@ dynare_config;
testFailed = 0; testFailed = 0;
if ~debug skipline()
skipline() disp('*** TESTING: cyclereduction.m ***');
disp('*** TESTING: cyclereduction.m ***');
end
matlab_cr_path = [source_dir filesep 'matlab' filesep 'missing' filesep 'mex' filesep 'cycle_reduction']; matlab_cr_path = [source_dir filesep 'matlab' filesep 'missing' filesep 'mex' filesep 'cycle_reduction'];
addpath(matlab_cr_path); addpath(matlab_cr_path);
@ -37,15 +33,11 @@ try
R = norm(C+B*X1+A*X1*X1,1); R = norm(C+B*X1+A*X1*X1,1);
if (R > cvg_tol) if (R > cvg_tol)
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Matlab cycle_reduction solution is wrong')
dprintf('Matlab cycle_reduction solution is wrong')
end
end end
catch catch
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Matlab cycle_reduction failed')
dprintf('Matlab cycle_reduction failed')
end
end end
% 2. Solve the equation with the Fortran cycle reduction algorithm % 2. Solve the equation with the Fortran cycle reduction algorithm
@ -56,36 +48,28 @@ try
R = norm(C+B*X2+A*X2*X2,1); R = norm(C+B*X2+A*X2*X2,1);
if (R > cvg_tol) if (R > cvg_tol)
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Fortran cycle_reduction solution is wrong')
dprintf('Fortran cycle_reduction solution is wrong')
end
end end
catch catch
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Fortran cycle_reduction failed')
dprintf('Fortran cycle_reduction failed')
end
end end
% 3. Compare solutions of the Fortran and Matlab routines % 3. Compare solutions of the Fortran and Matlab routines
if (norm(X1 - X2, 1) > cvg_tol) if (norm(X1 - X2, 1) > cvg_tol)
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Fortran and Matlab cycle reduction solutions differ');
dprintf('Fortran and Matlab cycle reduction solutions differ');
end
end end
% Compare the Fortran and Matlab execution time % Compare the Fortran and Matlab execution time
if debug if tElapsed1<tElapsed2
if tElapsed1<tElapsed2 skipline()
skipline() dprintf('Matlab cyclic reduction is %5.2f times faster than its Fortran counterpart.', tElapsed2/tElapsed1)
dprintf('Matlab cyclic reduction is %5.2f times faster than its Fortran counterpart.', tElapsed2/tElapsed1) skipline()
skipline() else
else skipline()
skipline() dprintf('Fortran cyclic reduction is %5.2f times faster than its Matlab counterpart.', tElapsed1/tElapsed2)
dprintf('Fortran cyclic reduction is %5.2f times faster than its Matlab counterpart.', tElapsed1/tElapsed2) skipline()
skipline()
end
end end
t1 = clock; t1 = clock;

46
tests/logarithmicreduction.m
View File

@ -1,5 +1,3 @@
debug = false;
source_dir = getenv('source_root'); source_dir = getenv('source_root');
addpath([source_dir filesep 'matlab']); addpath([source_dir filesep 'matlab']);
@ -7,10 +5,8 @@ dynare_config;
testFailed = 0; testFailed = 0;
if ~debug skipline()
skipline() disp('*** TESTING: logarithmicreduction.m ***');
disp('*** TESTING: logarithmicreduction.m ***');
end
matlab_cr_path = [source_dir filesep 'matlab' filesep 'missing' filesep 'mex' filesep 'logarithmic_reduction']; matlab_cr_path = [source_dir filesep 'matlab' filesep 'missing' filesep 'mex' filesep 'logarithmic_reduction'];
addpath(matlab_cr_path); addpath(matlab_cr_path);
@ -37,15 +33,11 @@ try
R = norm(C+B*X1+A*X1*X1,1); R = norm(C+B*X1+A*X1*X1,1);
if (R > cvg_tol) if (R > cvg_tol)
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Matlab logarithmic_reduction solution is wrong')
dprintf('Matlab logarithmic_reduction solution is wrong')
end
end end
catch catch
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Matlab logarithmic_reduction failed')
dprintf('Matlab logarithmic_reduction failed')
end
end end
% 2. Solve the equation with the Fortran logarithmic reduction algorithm % 2. Solve the equation with the Fortran logarithmic reduction algorithm
@ -56,36 +48,28 @@ try
R = norm(C+B*X2+A*X2*X2,1); R = norm(C+B*X2+A*X2*X2,1);
if (R > cvg_tol) if (R > cvg_tol)
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Fortran logarithmic_reduction solution is wrong')
dprintf('Fortran logarithmic_reduction solution is wrong')
end
end end
catch catch
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Fortran logarithmic_reduction failed')
dprintf('Fortran logarithmic_reduction failed')
end
end end
% 3. Compare solutions of the Fortran and Matlab routines % 3. Compare solutions of the Fortran and Matlab routines
if (norm(X1 - X2, 1) > cvg_tol) if (norm(X1 - X2, 1) > cvg_tol)
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Fortran and Matlab logarithmic reduction solutions differ');
dprintf('Fortran and Matlab logarithmic reduction solutions differ');
end
end end
% Compare the Fortran and Matlab execution time % Compare the Fortran and Matlab execution time
if debug if tElapsed1<tElapsed2
if tElapsed1<tElapsed2 skipline()
skipline() dprintf('Matlab logarithmic reduction is %5.2f times faster than its Fortran counterpart.', tElapsed2/tElapsed1)
dprintf('Matlab logarithmic reduction is %5.2f times faster than its Fortran counterpart.', tElapsed2/tElapsed1) skipline()
skipline() else
else skipline()
skipline() dprintf('Fortran logarithmic reduction is %5.2f times faster than its Matlab counterpart.', tElapsed1/tElapsed2)
dprintf('Fortran logarithmic reduction is %5.2f times faster than its Matlab counterpart.', tElapsed1/tElapsed2) skipline()
skipline()
end
end end
t1 = clock; t1 = clock;

40
tests/nonlinearsolvers.m
View File

@ -1,5 +1,3 @@
debug = false;
source_dir = getenv('source_root'); source_dir = getenv('source_root');
addpath([source_dir filesep 'matlab']); addpath([source_dir filesep 'matlab']);
@ -9,10 +7,8 @@ cd solver-test-functions
testFailed = 0; testFailed = 0;
if ~debug skipline()
skipline() disp('*** TESTING: nonlinearsolvers.m ***');
disp('*** TESTING: nonlinearsolvers.m ***');
end
tolf = 1e-6; tolf = 1e-6;
tolx = 1e-6; tolx = 1e-6;
@ -62,31 +58,23 @@ for i=1:length(objfun)
if isequal(func2str(objfun{i}), 'powell2') if isequal(func2str(objfun{i}), 'powell2')
if ~errorflag if ~errorflag
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Nonlinear solver is expected to fail on %s function but did not return an error.', func2str(objfun{i}))
dprintf('Nonlinear solver is expected to fail on %s function but did not return an error.', func2str(objfun{i}))
end
end end
elseif isequal(func2str(objfun{i}), 'trigonometric') elseif isequal(func2str(objfun{i}), 'trigonometric')
% FIXME block_trust_region (mex) fails, with exit code equal to 4, but not trust_region (matlab). Would be nice to undertsand the difference. % FIXME block_trust_region (mex) fails, with exit code equal to 4, but not trust_region (matlab). Would be nice to undertsand the difference.
if ~errorflag if ~errorflag
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Nonlinear solver is expected to fail on %s function but did not return an error.', func2str(objfun{i}))
dprintf('Nonlinear solver is expected to fail on %s function but did not return an error.', func2str(objfun{i}))
end
end end
else else
if errorflag || norm(objfun{i}(x))>tolf if errorflag || norm(objfun{i}(x))>tolf
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Nonlinear solver (mex) failed on %s function (norm(f(x))=%s).', func2str(objfun{i}), num2str(norm(objfun{i}(x))))
dprintf('Nonlinear solver (mex) failed on %s function (norm(f(x))=%s).', func2str(objfun{i}), num2str(norm(objfun{i}(x))))
end
end end
end end
catch catch
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Nonlinear solver (mex) failed on %s function.', func2str(objfun{i}))
dprintf('Nonlinear solver (mex) failed on %s function.', func2str(objfun{i}))
end
end end
end end
@ -111,29 +99,21 @@ for i=1:length(objfun)
if isequal(func2str(objfun{i}), 'powell2') if isequal(func2str(objfun{i}), 'powell2')
if ~errorflag if ~errorflag
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Nonlinear solver is expected to fail on %s function but did not return an error.', func2str(objfun{i}))
dprintf('Nonlinear solver is expected to fail on %s function but did not return an error.', func2str(objfun{i}))
end
end end
if info~=3 if info~=3
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Nonlinear solver is expected to fail on %s function with info==3 but did not the correct value of info.', func2str(objfun{i}))
dprintf('Nonlinear solver is expected to fail on %s function with info==3 but did not the correct value of info.', func2str(objfun{i}))
end
end end
else else
if errorflag if errorflag
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Nonlinear solver failed on %s function (info=%s).', func2str(objfun{i}), int2str(info))
dprintf('Nonlinear solver failed on %s function (info=%s).', func2str(objfun{i}), int2str(info))
end
end end
end end
catch catch
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Nonlinear solver failed on %s function.', func2str(objfun{i}))
dprintf('Nonlinear solver failed on %s function.', func2str(objfun{i}))
end
end end
end end

34
tests/riccatiupdate.m
View File

@ -1,5 +1,3 @@
debug = true;
source_dir = getenv('source_root'); source_dir = getenv('source_root');
addpath([source_dir filesep 'matlab']); addpath([source_dir filesep 'matlab']);
@ -7,10 +5,8 @@ dynare_config;
testFailed = 0; testFailed = 0;
if ~debug skipline()
skipline() disp('*** TESTING: riccatiupdate.m ***');
disp('*** TESTING: riccatiupdate.m ***');
end
t0 = clock; t0 = clock;
@ -57,28 +53,22 @@ try
R = norm(Ptmp_fortran-Ptmp_matlab,1); R = norm(Ptmp_fortran-Ptmp_matlab,1);
if (R > tol) if (R > tol)
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('The Fortran Riccati update is wrong')
dprintf('The Fortran Riccati update is wrong')
end
end end
catch catch
testFailed = testFailed+1; testFailed = testFailed+1;
if debug dprintf('Fortran Riccati update failed')
dprintf('Fortran Riccati update failed')
end
end end
% Compare the Fortran and Matlab execution time % Compare the Fortran and Matlab execution time
if debug if tElapsed1<tElapsed2
if tElapsed1<tElapsed2 skipline()
skipline() dprintf('Matlab Riccati update is %5.2f times faster than its Fortran counterpart.', tElapsed2/tElapsed1)
dprintf('Matlab Riccati update is %5.2f times faster than its Fortran counterpart.', tElapsed2/tElapsed1) skipline()
skipline() else
else skipline()
skipline() dprintf('Fortran Riccati update is %5.2f times faster than its Matlab counterpart.', tElapsed1/tElapsed2)
dprintf('Fortran Riccati update is %5.2f times faster than its Matlab counterpart.', tElapsed1/tElapsed2) skipline()
skipline()
end
end end
% Compare results after multiple calls % Compare results after multiple calls