diff --git a/Code/Hyperspectral/otbMDMDNMFImageFilter.h b/Code/Hyperspectral/otbMDMDNMFImageFilter.h
index 3005a6de4b990e3ba94ab47353c2cf15f935244b..401a2afdbbd08d68023d58e54dc3d915c7a1c042 100644
--- a/Code/Hyperspectral/otbMDMDNMFImageFilter.h
+++ b/Code/Hyperspectral/otbMDMDNMFImageFilter.h
@@ -34,46 +34,46 @@ namespace otb
* works:
* K. S. F.J. Theis and T. Tanaka, First results on uniqueness of
sparse non-negative matrix factorisation.
- * M. G. A. Huck and J. Blanc-Talon, IEEE TGRS, vol. 48, no. 6,pp. 2590-2602, 2010.
+ * M. G. A. Huck and J. Blanc-Talon, IEEE TGRS, vol. 48, no. 6, pp. 2590-2602, 2010.
* A. Huck and M. Guillaume, in WHISPERS, 2010, Grenoble.
- *
+ *
* Let $\matR$ be the matrix of the hyperspectral data, whose $I$ columns are the
* spectral pixels and the $L$ rows are the vectorial spectral band
* images. The linear mixing model can be written as follow :
- * \begin{equation}
+ * \begin{equation}
* \matR=\matA \matS + \matN= \matX + \matN
- * \end{equation}
+ * \end{equation}
* The $I$ columns of $\matR$ contain the spectral pixels
* and the $I$ columns of $\matS$ hold their respective sets of abundance
* fractions. The $J$ rows of $\matS$ are the abundance maps
* corresponding to the respective end-members. The $J$ columns of
* $\matA$ are the end members spectra, and $\matX$ is the signal
* matrix. Both $\matA$ and $\matS$ are unknown.
- *
+ *
* The basic NMF formulation is to find two matrices $\hat{\matA}$ and
- * $\hat{ \matS}$ such as:
- * \begin{equation}
- * \matX\simeq \hat{\matA} \hat{\matS}
- * \end{equation}
+ * $\hat{ \matS}$ such as:
+ * \begin{equation}
+ * \matX\simeq \hat{\matA} \hat{\matS}
+ * \end{equation}
* NMF based algorithms consider the
- * properties of the dual spaces $span^+(\matA')$ and $span^+(\matS)$,in
+ * properties of the dual spaces $span^+(\matA')$ and $span^+(\matS)$, in
* which $span^+(\mathbf m^1 ...\mathbf m^d)=\{\mathbf v=\sum_i \mathbf
* m^i\mathbf a_i|\mathbf a\in \matR _+^d\}$. The
* positiveness is then a fundamental assumption and is exploited to
* restrict the admissible solutions set.
- *
+ *
* A common used solution is to minimize the reconstruction quadratic
- * error $RQE({\matA},{\matS})=\|\matR-{\matA} {\matS}\|^2_F$. In order to
+ * error $RQE({\matA}, {\matS})=\|\matR-{\matA} {\matS}\|^2_F$. In order to
* satisfy the sum-to-one constraint for hyperspectral data, a
* regularization term $STU(\matS)$ can be added to the objective
* function.
- *
+ *
* A generic expression for the optimized function is $$
* f(\matA,\matS)=\|\matA \matS-\matR\|_{norm}+\sum_i \lambda_i
* D_i(\matA) + \sum_j \lambda_j D_j(\matS)$$ in which $\|\matA
* \matS-\matR\|_{norm}$ is the connection-to-the-data term, and
- * $\lambda_{\{i,j\}}$ are regularization parameters for end members and
- * abundances constraints $D_{\{i,j\}}$.
+ * $\lambda_{\{i, j\}}$ are regularization parameters for end members and
+ * abundances constraints $D_{\{i, j\}}$.
* In \cite{Huck2010a}, they
* propose an other regularization term,
* $D_A(\matA)=Tr(\matA^T\matA)-\frac{1}{L}Tr\left ( \matA^T \1_{LL}\matA
@@ -89,7 +89,7 @@ namespace otb
* minimizes the following function $ f(\matA,\matS) =RQE(\matA,
* \matS)+\delta.STU(\matS)+\lambda_A D_A(\matA)-\lambda_S D_S(\matS)$,
* $STU$ the sum-to-one constraint.
- *
+ *
* In the literature, NMF based optimization algorithms are mainly based
* on multiplicative rules, or else alternate gradient descent
* iterations, or else on alternate least square methods. In MDMD-NMF, the update rules
@@ -110,7 +110,7 @@ namespace otb
* \delta\cdot\1_{1I}\end{array}\right],\enspace \bar\matA=\left[
* \begin{array}{c} \matA \\
* \delta\cdot\1_{1J}\end{array}\right]\enspace$.
- *
+ *
* \ingroup ImageFilters
*/
template <class TInputImage, class TOutputImage>
@@ -198,7 +198,7 @@ private:
MDMDNMFImageFilter(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
- static void AddOneRowOfOnes(const MatrixType & m,MatrixType & M);
+ static void AddOneRowOfOnes(const MatrixType & m, MatrixType & M);
static double Criterion(const MatrixType & X,
const MatrixType & A,
diff --git a/Code/Hyperspectral/otbMDMDNMFImageFilter.txx b/Code/Hyperspectral/otbMDMDNMFImageFilter.txx
index 1445246df1187536a8b134c567c93695610d4aaf..3bbd4f0b4282b38156b980e3669bb0c15396854e 100644
--- a/Code/Hyperspectral/otbMDMDNMFImageFilter.txx
+++ b/Code/Hyperspectral/otbMDMDNMFImageFilter.txx
@@ -56,7 +56,7 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
{
this->GetOutput()->SetNumberOfComponentsPerPixel(m_Endmembers.columns());
}
- else
+ else
{
throw itk::ExceptionObject(__FILE__, __LINE__,
"Endmembers matrix columns size required to know the output size",
@@ -119,7 +119,7 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
E1 = Xsu - Asu*S;
// Bloc 2
- E2 = S - 1./ ((double) nbEndmembers);// * ones - S;
+ E2 = S - 1./ ((double) nbEndmembers); // * ones - S;
// Computing trace(transpose(A)*A)
double trAtA = 0;
@@ -147,7 +147,7 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
{
for (int j2=0; j2<nbEndmembers; ++j2)
{
- E3(j1,j2) = sumColsOfA(j1)*sumColsOfA(j2);
+ E3(j1, j2) = sumColsOfA(j1)*sumColsOfA(j2);
}
}
*/
@@ -168,7 +168,7 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
const double &m_LambdS,
MatrixType & gradS)
{
- // Calculus of: gradS = 2 * Asu' * (Asu*S-Xsu) - lambd * 2 * (S - 1/J*ones(J,I));
+ // Calculus of: gradS = 2 * Asu' * (Asu*S-Xsu) - lambd * 2 * (S - 1/J*ones(J, I));
MatrixType Xsu, Asu, ones;
Xsu.set_size(X.rows()+1, X.cols());
@@ -192,8 +192,8 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
MatrixType &gradA)
{
// Compute gradA
- // = (A*S-X) * (transpose(S)) + m_LambdD*(A-1/nbBands*ones(L,L)*A)
- // = (A*S-X) * (transpose(S)) + m_LambdD*A- m_LambdD*/nbBands*ones(L,L)*A)
+ // = (A*S-X) * (transpose(S)) + m_LambdD*(A-1/nbBands*ones(L, L)*A)
+ // = (A*S-X) * (transpose(S)) + m_LambdD*A- m_LambdD*/nbBands*ones(L, L)*A)
MatrixType onesA;
VectorType sumColulmnsOfA;
@@ -310,8 +310,8 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
{
for (unsigned int j=0; j<M.cols(); ++j)
{
- if (M(i,j)<0)
- M(i,j) = 0;
+ if (M(i, j)<0)
+ M(i, j) = 0;
}
}
}
@@ -328,7 +328,7 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
{
for (unsigned int j=0; j<M.cols(); ++j)
{
- M(i,j) = M1(i,j) * M2(i,j);
+ M(i, j) = M1(i, j) * M2(i, j);
}
}
return M;
@@ -435,7 +435,7 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
MatrixType A(this->m_Endmembers);
MatrixType S;
- S.set_size(nbEndmembers,nbPixels);
+ S.set_size(nbEndmembers, nbPixels);
S.fill(1.);
//std::cout << "S " << S.cols() << std::endl;
//----------- Declaration of useful variables -----------//
@@ -477,19 +477,19 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
std::cout << "Iteration = " << counter << std::endl;
std::cout << "Criterion = " << Criterion(X, A, S, m_Delt, m_LambdS, m_LambdD) << std::endl;
std::cout << "statGradS = " << gradS.fro_norm() << std::endl;
- std::cout << "gradS(0,0) = " << gradS(0,0) << std::endl;
+ std::cout << "gradS(0, 0) = " << gradS(0, 0) << std::endl;
std::cout << "alphS = " << alphS << std::endl;
std::cout << "normS = " << S.fro_norm() << std::endl;
- std::cout << "S(0,0) = " << S(0,0) << std::endl;
+ std::cout << "S(0, 0) = " << S(0, 0) << std::endl;
}
- ProjGradOneStep(X, A, gradS, sig, bet, m_Delt,m_LambdS, m_LambdD, S, alphS, false);
+ ProjGradOneStep(X, A, gradS, sig, bet, m_Delt, m_LambdS, m_LambdD, S, alphS, false);
if (counter%divisorParam == 0)
{
std::cout << "alphS = " << alphS << std::endl;
std::cout << "normS = " << S.fro_norm() << std::endl;
- std::cout << "S(0,0) = " << S(0,0) << std::endl;
+ std::cout << "S(0, 0) = " << S(0, 0) << std::endl;
}
//---------------- Update A -----------------//
@@ -499,7 +499,7 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
if (counter%divisorParam == 0)
{
- std::cout << "gradA(0,0) = " << gradA(0,0) << std::endl;
+ std::cout << "gradA(0, 0) = " << gradA(0, 0) << std::endl;
}
@@ -507,7 +507,7 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
{
std::cout << "alphA = " << alphA << std::endl;
std::cout << "normA = " << A.fro_norm() << std::endl;
- std::cout << "A(0,0) = " << A(0,0) << std::endl;
+ std::cout << "A(0, 0) = " << A(0, 0) << std::endl;
}
ProjGradOneStep(X, S, gradA, sig, bet, m_Delt, m_LambdS, m_LambdD, A, alphA, true);
@@ -515,7 +515,7 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
{
std::cout << "alphA = " << alphA << std::endl;
std::cout << "normA = " << A.fro_norm() << std::endl;
- std::cout << "A(0,0) = " << A(0,0) << std::endl;
+ std::cout << "A(0, 0) = " << A(0, 0) << std::endl;
}
//------------ crit evaluation --------------//
@@ -523,7 +523,7 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
critA = Adiff.absolute_value_max();
Sdiff = Sold - S;
critS = Sdiff.absolute_value_max();
- crit = std::max(critA,critS);
+ crit = std::max(critA, critS);
if (counter%divisorParam == 0)
{
@@ -538,7 +538,7 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
}
//--- Putting the rows of in the bands of the output vector image ---//
- //TODO
+ //TODO
// Could be impoved choosing an imageList for the abundance maps
// and a vector list for the endmember spectra (columns of A).
@@ -553,7 +553,7 @@ MDMDNMFImageFilter<TInputImage, TOutputImage>
{
for (unsigned int j=0; j<nbEndmembers; ++j)
{
- vectorPixel.SetElement(j, S(j,i));
+ vectorPixel.SetElement(j, S(j, i));
}
outputIt.Set(vectorPixel);
++i;
diff --git a/Code/Simulation/otbReduceSpectralResponse.h b/Code/Simulation/otbReduceSpectralResponse.h
index b50bd1583b0f027945af820e137e5251956b20c5..1922668fae208b760fddad9ebb97887da33f228e 100644
--- a/Code/Simulation/otbReduceSpectralResponse.h
+++ b/Code/Simulation/otbReduceSpectralResponse.h
@@ -75,20 +75,20 @@ public:
typedef typename std::vector<ValuePrecisionType> ReduceSpectralResponseVectorType;
/** Standard macros */
itkNewMacro(Self)
- ; itkTypeMacro(ReduceSpectralResponse, DataObject)
- ;
+; itkTypeMacro(ReduceSpectralResponse, DataObject)
+;
itkGetConstObjectMacro(InputSatRSR, InputRSRType)
- ; itkSetObjectMacro(InputSatRSR, InputRSRType)
- ;
+; itkSetObjectMacro(InputSatRSR, InputRSRType)
+;
itkGetConstObjectMacro(InputSpectralResponse, InputSpectralResponseType)
- ; itkSetObjectMacro(InputSpectralResponse, InputSpectralResponseType)
- ;
+; itkSetObjectMacro(InputSpectralResponse, InputSpectralResponseType)
+;
/** The GetReduceResponse method gives the output. The first value in the pair is the central wavelength of the band (see SpectralResponse). */
itkGetObjectMacro(ReduceResponse, InputSpectralResponseType)
- ;
+;
/** Clear the vector data */
virtual bool Clear();
@@ -119,7 +119,7 @@ protected:
virtual ~ReduceSpectralResponse()
{
}
- ;
+;
/** PrintSelf method */
//void PrintSelf(std::ostream& os, itk::Indent indent) const;
diff --git a/Code/Simulation/otbReduceSpectralResponse.txx b/Code/Simulation/otbReduceSpectralResponse.txx
index 9c7676f8c89d12f516de91d1428671cd73bbe290..7058060d0b877694b7a2c704623ba8ba134688ea 100644
--- a/Code/Simulation/otbReduceSpectralResponse.txx
+++ b/Code/Simulation/otbReduceSpectralResponse.txx
@@ -64,7 +64,6 @@ ReduceSpectralResponse<TSpectralResponse , TRSR>
PrecisionType lambda2;
-
typename VectorPairType::const_iterator it;
VectorPairType pairs = (m_InputSatRSR->GetRSR())[numBand]->GetResponse();
it = pairs.begin();
diff --git a/Code/Simulation/otbSatelliteRSR.h b/Code/Simulation/otbSatelliteRSR.h
index d3dfbb17ce3ce412e17c3e6f82220d5657d61d1c..a0c01707b4392a30e71c5390ec8806226b3a59c6 100644
--- a/Code/Simulation/otbSatelliteRSR.h
+++ b/Code/Simulation/otbSatelliteRSR.h
@@ -54,17 +54,17 @@ public:
/** Standard macros */
itkNewMacro(Self)
- ; itkTypeMacro(SatelliteRSR, DataObject)
- ;
+; itkTypeMacro(SatelliteRSR, DataObject)
+;
/** Set the number of band of the satellite from an ASCII file
* Need to parse first all the file to determine the number of columns */
itkGetConstMacro(NbBands, unsigned int)
- ; itkSetMacro(NbBands, unsigned int)
- ;
+; itkSetMacro(NbBands, unsigned int)
+;
itkSetMacro(SortBands, bool)
- ;
+;
/** Template parameters typedef */
typedef TPrecision PrecisionType;
@@ -141,7 +141,7 @@ protected:
virtual ~SatelliteRSR()
{
}
- ;
+;
bool m_SortBands;
diff --git a/Code/Simulation/otbSpectralResponse.h b/Code/Simulation/otbSpectralResponse.h
index 500a4f89e2c271311a88eef8ca7ffd6689f62c26..64856b52be112364e5be86cae9829e5ef5b878fd 100644
--- a/Code/Simulation/otbSpectralResponse.h
+++ b/Code/Simulation/otbSpectralResponse.h
@@ -119,7 +119,6 @@ public:
inline ValuePrecisionType operator()(const PrecisionType & lambda);
-
/** Operator for comparing Pair Lambda/Response
* Pairs are ordered by wavelength
*/
@@ -162,7 +161,7 @@ protected:
virtual ~SpectralResponse()
{
}
- ;
+ ;
/** PrintSelf method */
//void PrintSelf(std::ostream& os, itk::Indent indent) const;
diff --git a/Code/Simulation/otbSpectralResponse.txx b/Code/Simulation/otbSpectralResponse.txx
index 1b8c1d01dcbb78c86d76bf69b0ba1e41025ee29b..9307dde59739234338b064f9fc696da749378000 100644
--- a/Code/Simulation/otbSpectralResponse.txx
+++ b/Code/Simulation/otbSpectralResponse.txx
@@ -88,7 +88,6 @@ unsigned int SpectralResponse<TPrecision, TValuePrecision>::Size() const
}
-
template<class TPrecision, class TValuePrecision>
void SpectralResponse<TPrecision, TValuePrecision>::SetPosGuessMin(const PrecisionType & lambda)
{
@@ -114,7 +113,6 @@ void SpectralResponse<TPrecision, TValuePrecision>::SetPosGuessMin(const Precisi
}
-
template<class TPrecision, class TValuePrecision>
inline typename SpectralResponse<TPrecision, TValuePrecision>::ValuePrecisionType SpectralResponse<TPrecision,
TValuePrecision>::operator()(const PrecisionType & lambda)
diff --git a/Testing/Code/Hyperspectral/otbMDMDNMFImageFilter.cxx b/Testing/Code/Hyperspectral/otbMDMDNMFImageFilter.cxx
index c33ff78c4e9074c70510b289aad66455d1c3b55d..f5a7efeedd70cb91b7f6808296a174836f1575bf 100644
--- a/Testing/Code/Hyperspectral/otbMDMDNMFImageFilter.cxx
+++ b/Testing/Code/Hyperspectral/otbMDMDNMFImageFilter.cxx
@@ -56,10 +56,10 @@ int otbMDMDNMFImageFilterTest(int argc, char * argv[])
MDMDNMFImageFilterType::MatrixType A;
A.set_size(readerImage->GetOutput()->GetNumberOfComponentsPerPixel(), 5);
A.fill(100.);
- A.set_column(1,200.);
- A.set_column(2,300.);
- A.set_column(3,400.);
- A.set_column(4,500.);
+ A.set_column(1, 200.);
+ A.set_column(2, 300.);
+ A.set_column(3, 400.);
+ A.set_column(4, 500.);
unmixer->SetEndmembersMatrix(A);
unmixer->SetMaxIter(maxIter);