From 7033c692bd836ca300fc6d91ce7cd733a2342cde Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Wed, 25 Oct 2017 12:25:59 +0100
Subject: Added print to screen to debug faulty FGP_TV

Bug #2 came from not initializing lambda but mu.
---
 src/Python/fista_module.cpp | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

(limited to 'src/Python')

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
+}
-- 
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