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| author | Edoardo Pasca <edo.paskino@gmail.com> | 2017-10-25 12:25:59 +0100 |
|---|---|---|
| committer | Edoardo Pasca <edo.paskino@gmail.com> | 2017-10-25 12:25:59 +0100 |
| commit | 7033c692bd836ca300fc6d91ce7cd733a2342cde (patch) | |
| tree | 048c488a00235674f63662b79c1c2af690551edf /src | |
| parent | c21df8581e052541a2dd39a46a4c1f50e335fd9e (diff) | |
| download | regularization-7033c692bd836ca300fc6d91ce7cd733a2342cde.tar.gz regularization-7033c692bd836ca300fc6d91ce7cd733a2342cde.tar.bz2 regularization-7033c692bd836ca300fc6d91ce7cd733a2342cde.tar.xz regularization-7033c692bd836ca300fc6d91ce7cd733a2342cde.zip | |
Added print to screen to debug faulty FGP_TV
Bug #2 came from not initializing lambda but mu.
Diffstat (limited to 'src')
| -rw-r--r-- | src/Python/fista_module.cpp | 15 |
1 files changed, 10 insertions, 5 deletions
diff --git a/src/Python/fista_module.cpp b/src/Python/fista_module.cpp index c36329e..f8fd812 100644 --- a/src/Python/fista_module.cpp +++ b/src/Python/fista_module.cpp @@ -338,7 +338,7 @@ bp::list FGP_TV(np::ndarray input, double d_mu, int iter, double d_epsil, int me A = reinterpret_cast<float *>(input.get_data()); //mu = (float)mxGetScalar(prhs[1]); /* regularization parameter */ - mu = (float)d_mu; + lambda = (float)d_mu; //iter = 35; /* default iterations number */ @@ -408,15 +408,17 @@ bp::list FGP_TV(np::ndarray input, double d_mu, int iter, double d_epsil, int me /* begin iterations */ for (ll = 0; ll<iter; ll++) { - + std::cout << "iteration " << ll << " D[0]" << D[0] << std::endl; /* computing the gradient of the objective function */ Obj_func2D(A, D, R1, R2, lambda, dimX, dimY); + std::cout << "Obj_func2D D[0] " << D[0]<< " A[0]" << A[0] << " R1[0] " << R1[0] << " R2[0] " << R2[0] << " lambda " << lambda << " dimX " << dimX << " dimY " << dimY << std::endl; /*Taking a step towards minus of the gradient*/ Grad_func2D(P1, P2, D, R1, R2, lambda, dimX, dimY); + std::cout << "Grad_func2D D[0] " << D[0]<< " P1[0]" << P1[0] << " P2[0] " << P2[0] << " R1[0] " << R1[0] << " R2[0] " << R2[0] << " lambda " << lambda << " dimX " << dimX << " dimY " << dimY << std::endl; + + - /* projection step */ - Proj_func2D(P1, P2, methTV, dimX, dimY); /*updating R and t*/ tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f; @@ -433,6 +435,7 @@ bp::list FGP_TV(np::ndarray input, double d_mu, int iter, double d_epsil, int me if (re < epsil) count++; if (count > 3) { Obj_func2D(A, D, P1, P2, lambda, dimX, dimY); + std::cout << "Obj_func2D D[0] " << D[0]<< " A[0]" << A[0] << " R1[0] " << R1[0] << " R2[0] " << R2[0] << " lambda " << lambda << " dimX " << dimX << " dimY " << dimY << std::endl; funcval = 0.0f; for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2); //funcvalA[0] = sqrt(funcval); @@ -445,6 +448,7 @@ bp::list FGP_TV(np::ndarray input, double d_mu, int iter, double d_epsil, int me if (ll > 2) { if (re > re_old) { Obj_func2D(A, D, P1, P2, lambda, dimX, dimY); + std::cout << "Obj_func2D D[0] " << D[0]<< " A[0]" << A[0] << " R1[0] " << R1[0] << " R2[0] " << R2[0] << " lambda " << lambda << " dimX " << dimX << " dimY " << dimY << std::endl; funcval = 0.0f; for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2); //funcvalA[0] = sqrt(funcval); @@ -465,6 +469,7 @@ bp::list FGP_TV(np::ndarray input, double d_mu, int iter, double d_epsil, int me /* calculating the objective function value */ if (ll == (iter - 1)) { Obj_func2D(A, D, P1, P2, lambda, dimX, dimY); + std::cout << "Obj_func2D D[0] " << D[0]<< " A[0]" << A[0] << " R1[0] " << R1[0] << " R2[0] " << R2[0] << " lambda " << lambda << " dimX " << dimX << " dimY " << dimY << std::endl; funcval = 0.0f; for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2); //funcvalA[0] = sqrt(funcval); @@ -1048,4 +1053,4 @@ BOOST_PYTHON_MODULE(cpu_regularizers) def("LLT_model", LLT_model); def("PatchBased_Regul", PatchBased_Regul); def("TGV_PD", TGV_PD); -}
\ No newline at end of file +} |