test_basisgen_init.m

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function ok = test_basisgen_init()
% This test initializes different greedy algorithms for the generation of
% reduced basis and empirical operator interpolation spaces
%
% Test cases:
%  - 1. POD-Greedy
%  - 2. EI-Greedy (one basis for two operators)
%  - 3. PODEI-Greedy Algorithm (with one EI basis for two operators)
%  - 4. Subsequent generation of RB and CRB with algorithms 1 and 2
%  - 5. Subsequent generation of RB and two CRBs for two operators
%  - 6. PODEI-Greedy Algorithm (with two EI bases for three operators)
%
% TODO: Test 6 is disfunctional at the moment.


  ok = true;

  % declare minimal model
  mm                                  = [];
  mm.name                             = 'minimal_model';
  mm.T                                = 1;
  mm.nt                               = 10;
  mm.mu_names                         = {'par1', 'par2', 'par3'};
  mm.mu_ranges                        = {[0 1], [0 2], [0 3]};
  mm.par1                             = 0;
  mm.par2                             = 0;
  mm.par3                             = 0;
  mm.debug                            = 0;
  mm.force_delete                     = 1;
  mm.filecache_ignore_fields_in_model = {};

  mm.decomp_mode = 0;
  mm.verbose     = 0;
  mm.gridtype    = 'rectgrid';
  mm.t = 0;
  mm.tstep = 1;
  mm.dt = mm.T/mm.nt;
  mm.mass_matrix = @(x) x;
  mm.operators_ptr = @(x) x;
  mm.get_inner_product_matrix = @(x) eye(4);
  mm.init_values_algorithm = @(x, y) ones(1,4);
  mm.error_norm = 'l2';
  mm.inner_product = @fv_inner_product;

  dummyop1 = @(model, model_data, U, NU_ind) dummyop(pi,0);
  dummyop2 = @(model, model_data, U, NU_ind) dummyop(exp(0),0);
  dummyop3 = @(model, model_data, U, NU_ind) dummyop(2,1);

  bg_descr.rb_problem_type = 'Test';
  bg_descr.detailed_data_constructor = @Test.DetailedData;
  bg_descr.reduced_data_constructor = @Test.ReducedData;

  dmodel = Test.DetailedModel(mm);

  model_data = gen_model_data(dmodel);

  disp('Test1');
  t1.rbgenerator = SnapshotsGenerator.Trajectories(dmodel, 'rb', true, false);
  t1.rb_ext      = Greedy.Plugin.POD(t1.rbgenerator);
  t1.greedy_alg  = Greedy.Algorithm(t1.rb_ext);
  bg_descr.bg_algorithm = t1.greedy_alg;
  t1.rmodel      = Test.ReducedModel(dmodel, bg_descr);
  try
    evalc('prepare(t1.greedy_alg, t1.rmodel, model_data);');
    evalc('t1.dd = gen_detailed_data(t1.rmodel, model_data);');
  catch err
    ok = false;
    disp('Failed');
  end
  if ok
    disp('Success');
  end

  disp('Test2');
  t2.eigenerator1 = SnapshotsGenerator.SpaceOpEvals(dmodel, 'implicit', t1.rbgenerator, dummyop1, true, false);
  t2.eigenerator2 = SnapshotsGenerator.SpaceOpEvals(dmodel, 'explicit', t1.rbgenerator, dummyop2, true, false);
  t2.eigenerator  = t2.eigenerator1 + t2.eigenerator2;
  t2.ei_ext       = Greedy.Plugin.EI(t2.eigenerator);
  t2.greedy_alg   = Greedy.Algorithm(t2.ei_ext);
  bg_descr.bg_algorithm = t2.greedy_alg;
  t2.rmodel       = Test.ReducedModel(dmodel, bg_descr);
  try
    evalc('prepare(t2.greedy_alg, t2.rmodel, model_data);');
    evalc('t2.dd = gen_detailed_data(t2.rmodel, model_data);');
  catch err
    ok = false;
    disp('Failed');
  end
  if ok
    disp('Success');
  end

return; % currently the next takes too long.

  disp('Test3');
  t3.ext          = Greedy.Plugin.PODEI(t1.rb_ext, t2.ei_ext, t2.greedy_alg, 2);
  t3.greedy_alg   = Greedy.Algorithm(t3.ext);
  bg_descr.bg_algorithm = t3.greedy_alg;
  t3.rmodel       = Test.ReducedModel(dmodel, bg_descr, true);
  try
    evalc('prepare(t3.greedy_alg, t3.rmodel, model_data);');
    evalc('t3.dd = gen_detailed_data(t3.rmodel, model_data);');
  catch err
    ok = false;
    disp('Failed');
  end
  if ok
    disp('Success');
  end

  disp('Test4');
  t4.two_greedies = Greedy.Combined(t2.greedy_alg, t1.greedy_alg);
  bg_descr.bg_algorithm = t4.two_greedies;
  t4.rmodel       = Test.ReducedModel(dmodel, bg_descr, true);
  try
    evalc('prepare(t4.two_greedies, t4.rmodel, model_data);');
    evalc('t4.dd = gen_detailed_data(t4.rmodel, model_data);');
  catch err
    ok = false;
    disp('Failed');
  end
  if ok
    disp('Success');
  end

  disp('Test5');
  t5.ei_ext1        = Greedy.Plugin.EI(t2.eigenerator1);
  t5.ei_ext2        = Greedy.Plugin.EI(t2.eigenerator2);
  t5.ei_greedy_alg1 = Greedy.Algorithm(t5.ei_ext1);
  t5.ei_greedy_alg2 = Greedy.Algorithm(t5.ei_ext2);
  t5.three_greedies = [[t5.ei_greedy_alg1, t5.ei_greedy_alg2], t1.greedy_alg];
  bg_descr.bg_algorithm = t5.three_greedies;
  t5.rmodel         = Test.ReducedModel(dmodel, bg_descr);
  try
    evalc('prepare(t5.three_greedies, t5.rmodel, model_data);');
    evalc('t5.dd = gen_detailed_data(t5.rmodel, model_data);');
  catch err
    ok = false;
    disp('Failed');
  end
  if ok
    disp('Success');
  end

%   disp('Test6');
%   t6.eigenerator3 = SnapshotsGenerator.SpaceOpEvals(dmodel, 'op3', t1.rbgenerator, dummyop3, true, false);
%   t6.ei_ext3      = Greedy.Plugin.EI(t6.eigenerator3);
%   t6.ei_ext       = [t2.ei_ext; t6.ei_ext3];
%   t6.init_greedy  = [ t2.greedy_alg, Greedy.Algorithm(t6.ei_ext3) ];
%   t6.ext          = Greedy.Plugin.PODEI(t1.rb_ext, t6.ei_ext, t6.init_greedy, 2);
%   t6.greedy_alg   = Greedy.Algorithm(t6.ext);
%   bg_descr.bg_algorithm = t6.greedy_alg;
%   t6.rmodel       = Test.ReducedModel(dmodel, bg_descr, true);
%   prepare(t6.greedy_alg, t6.rmodel, model_data);
%   t6.dd = gen_detailed_data(t6.rmodel, model_data);

end

function [a,b] = dummyop(a,b)
a = a * ones(4,1);
b = b * ones(4,1);
end