moved algebra stuff to core and modified the rest

cbaselib/ImageInfo.cpp

@@ -308,6 +308,8 @@ ImageInfo::loadCategoryInfo(QDomElement *anElement)
 	label.setCategoryName(labelName);
 	label.setColor(color);
 	labels_.push_back(label);
+
+  return true;
 }
 
 //! A protected member parsing string data and returning a BoundingBox from it

classifier/fpclassifier/logisticregression/FPCSMLR.h

@@ -13,7 +13,7 @@
 #include "vislearning/classifier/classifierbase/FeaturePoolClassifier.h"
 #include "vislearning/classifier/fpclassifier/logisticregression/SLR.h"
 #include "vislearning/cbaselib/FeaturePool.h"
-#include "vislearning/math/algebra/GMSparse2.h"
+#include "core/algebra/GMSparseVectorMatrix.h"
 
 namespace OBJREC {
 

classifier/fpclassifier/logisticregression/SLR.cpp

@@ -146,9 +146,9 @@ int SLR::stepwise_regression(Examples &x, int classno)
 	int fnum = x.size();
 	
 	// create Datamatrix
-	GMSparse2 X;
+	GMSparseVectorMatrix X;
 	
-	GMSparse2 Y;
+	GMSparseVectorMatrix Y;
 	
 	vector<int> count(2, 0);
 
@@ -266,7 +266,7 @@ int SLR::stepwise_regression(Examples &x, int classno)
 	
 #undef NORMALIZATION
 #ifdef NORMALIZATION
-	GMSparse2 Xred;
+	GMSparseVectorMatrix Xred;
 	Xred.resize(X.rows(), X.cols());
 
 	for(int r = 0; r < (int)Xred.rows(); r++)
@@ -288,7 +288,7 @@ int SLR::stepwise_regression(Examples &x, int classno)
 
 	fnum = X.rows();
 	
-	GMSparse2 xY;
+	GMSparseVectorMatrix xY;
 #ifdef NORMALIZATION
 	Xred.mult(Y, xY, true);
 #else
@@ -298,7 +298,7 @@ int SLR::stepwise_regression(Examples &x, int classno)
 	weight.setDim(fdim);
 
 	// for faster Computing Xw = X*w	
-	GMSparse2 Xw;
+	GMSparseVectorMatrix Xw;
 	X.mult(weight, Xw, false, true);
 	SparseVector S(fnum);
 

classifier/fpclassifier/logisticregression/SLR.h

@@ -11,7 +11,7 @@
 
 #include "vislearning/classifier/classifierbase/FeaturePoolClassifier.h"
 #include "vislearning/cbaselib/FeaturePool.h"
-#include "vislearning/math/algebra/GMSparse2.h"
+#include "core/algebra/GMSparseVectorMatrix.h"
 
 namespace OBJREC {
 

classifier/kernelclassifier/GPLaplaceOptimizationProblem.cpp

@@ -9,8 +9,8 @@
 #include <iostream>
 
 #include "core/vector/Algorithms.h"
-#include "vislearning/math/algebra/CholeskyRobust.h"
-#include "vislearning/math/algebra/CholeskyRobustAuto.h"
+#include "core/algebra/CholeskyRobust.h"
+#include "core/algebra/CholeskyRobustAuto.h"
 
 #include "vislearning/math/kernels/OptimKernelParameterGradInterface.h"
 #include "GPLaplaceOptimizationProblem.h"

classifier/kernelclassifier/KCGPLaplaceOneVsAll.cpp

@@ -15,8 +15,8 @@
 #include "core/optimization/FirstOrderRasmussen.h"
 
 #include "vislearning/classifier/kernelclassifier/GPLaplaceOptimizationProblem.h"
-#include "vislearning/math/algebra/CholeskyRobust.h"
-#include "vislearning/math/algebra/CholeskyRobustAuto.h"
+#include "core/algebra/CholeskyRobust.h"
+#include "core/algebra/CholeskyRobustAuto.h"
 
 #include "KCGPLaplaceOneVsAll.h"
 #include "LHCumulativeGauss.h"

classifier/kernelclassifier/KCGPRegOneVsAll.cpp

@@ -19,8 +19,8 @@
 #include "core/optimization/FirstOrderRasmussen.h"
 
 #include "vislearning/regression/gpregression/GPRegressionOptimizationProblem.h"
-#include "vislearning/math/algebra/CholeskyRobust.h"
-#include "vislearning/math/algebra/CholeskyRobustAuto.h"
+#include "core/algebra/CholeskyRobust.h"
+#include "core/algebra/CholeskyRobustAuto.h"
 
 #include "KCGPRegOneVsAll.h"
 #include <limits>

classifier/kernelclassifier/KCOneVsAll.cpp

@@ -11,8 +11,8 @@
 
 #include "core/vector/Algorithms.h"
 
-#include "vislearning/math/algebra/CholeskyRobust.h"
-#include "vislearning/math/algebra/CholeskyRobustAuto.h"
+#include "core/algebra/CholeskyRobust.h"
+#include "core/algebra/CholeskyRobustAuto.h"
 #include "vislearning/regression/regressionbase/TeachWithInverseKernelMatrix.h"
 
 using namespace std;

math/algebra/AlgebraTools.cpp

@@ -1,417 +0,0 @@
-/**
-* @file AlgebraTools.cpp
-* @brief AlgebraTools Class
-* @author Michael Koch
-* @date 08/19/2008
-
- */
-#ifdef NOVISUAL
-#include <vislearning/nice_nonvis.h>
-#else
-#include <vislearning/nice.h>
-#endif
-
-#include "AlgebraTools.h"
-
-using namespace OBJREC;
-using namespace std;
-using namespace NICE;
-
-
-
-//calculate mean
-
-
-// refactor-nice.pl: check this substitution
-// old: void AlgebraTools::calculateMean(const Matrix &data, Vector &mean)
-void AlgebraTools::calculateMean(const NICE::Matrix &data, NICE::Vector &mean)
-{
-    uint features = data.rows();
-    uint dimension = data.cols();
-    mean = Vector(dimension);
-    for (uint k = 0;k < mean.size();k++)
-    {
-        mean[k] = 0.0;
-    }
-    for (uint r = 0;r < features;r++)
-    {
-        for (uint c = 0;c < dimension;c++)
-        {
-            // refactor-nice.pl: check this substitution
-            // old: if (isnan(data[r][c]) || isinf(data[r][c]))
-            if(isnan(data(r, c)) ||(isnan(data(r, c))))
-            {
-                //strange data
-            }
-            else
-            {
-                // refactor-nice.pl: check this substitution
-                // old: mean[c] = mean[c] + data[r][c];
-                mean[c] = mean[c] + data(r, c);
-            }
-        }
-    }
-    for (uint r = 0;r < mean.size();r++)
-    {
-        if (features > 0)
-        {
-            mean[r] = mean[r] / (double)features;
-        }
-    }
-    //      cout<<"mean-calc"<<mean<<endl;
-}
-
-//calculate mean
-void AlgebraTools::calculateMean(const NICE::Matrix &data, NICE::Vector &mean, NICE::Vector &sigma)
-{
-    uint features = data.rows();
-    uint dimension = data.cols();
-    mean = Vector(dimension);
-    sigma = Vector(dimension);
-
-    for (uint k = 0;k < mean.size();k++)
-    {
-        mean[k] = 0.0;
-        sigma[k] = 0.0;
-    }
-    for (uint r = 0;r < features;r++)
-    {
-        for (uint c = 0;c < dimension;c++)
-        {
-
-            // refactor-nice.pl: check this substitution
-            // old: if (isnan(data[r][c]) || isinf(data[r][c]))
-            if(isnan(data(r, c)) ||(isnan(data(r, c))))
-            {
-                //strange data
-            }
-            else
-            {
-                // refactor-nice.pl: check this substitution
-                // old: mean[c] = mean[c] + data[r][c];
-                mean[c] = mean[c] + data(r, c);
-            }
-
-        }
-    }
-    for (uint k = 0;k < mean.size();k++)
-    {
-        if (features > 0)
-        {
-            mean[k] = mean[k] / (double)features;
-        }
-    }
-    //      cout<<"mean-calc"<<mean<<endl;
-    //calc sigma
-    for (uint r = 0;r < features;r++)
-    {
-        for (uint c = 0;c < dimension;c++)
-        {
-
-            // refactor-nice.pl: check this substitution
-            // old: sigma[c] = sigma[c] + (mean[c] - data[r][c]) * (mean[c] - data[r][c]);
-            sigma[c] = sigma[c] + (mean[c] -data(r, c)) * (mean[c] -data(r, c));
-
-        }
-    }
-    for (uint k = 0;k < sigma.size();k++)
-    {
-        if (features > 0)
-        {
-            sigma[k] = sigma[k] / features;
-            sigma[k] = sqrt(sigma[k]);
-        }
-    }
-}
-
-
-// refactor-nice.pl: check this substitution
-// old: double AlgebraTools::trace(const Matrix &M)
-double AlgebraTools::trace(const NICE::Matrix &M)
-{
-    if (M.cols() != M.rows())
-    {
-        throw("non quadratic Matrix");
-    }
-    else
-    {
-        uint n = M.rows();
-        double sum = 0.0;
-        for (uint i = 0;i < n;i++)
-        {
-            // refactor-nice.pl: check this substitution
-            // old: sum += M[i][i];
-            sum +=M(i, i);
-        }
-        return sum;
-    }
-
-}
-
-
-
-
-
-double AlgebraTools::matrixRowColNorm(const NICE::Matrix &M)
-{
-    //max row norm
-    double maxr = 0.0, maxc = 0.0;
-    for (uint r = 0;r < M.rows();r++)
-    {
-        double sum = 0.0;
-        for (uint c = 0;c < M.cols();c++)
-        {
-            sum +=fabs(M(r, c));
-        }
-        if (r == 0)
-        {
-            maxr = sum;
-        }
-        else
-        {
-            if (sum > maxr)
-            {
-                maxr = sum;
-            }
-        }
-    }
-
-    //max col norm
-    for (uint c = 0;c < M.cols();c++)
-    {
-        double sum = 0.0;
-        for (uint r = 0;r < M.rows();r++)
-        {
-            sum +=fabs(M(r, c));
-        }
-        if (c == 0)
-        {
-            maxc = sum;
-        }
-        else
-        {
-            if (sum > maxc)
-            {
-                maxc = sum;
-            }
-        }
-    }
-    if (maxr > maxc)
-    {
-        return maxr;
-    }
-    else
-    {
-        return maxc;
-    }
-}
-void AlgebraTools::orthogonalize(Matrix &M, bool iterative)
-{
-#if 1
-    fprintf (stderr, "AlgebraTools::orthogonalize: FIX THIS CODE !\n");
-    exit(-1);
-#else
-
-    //iterative
-    if (iterative)
-    {
-        NICE::Vector onevec = Vector(M.rows());
-        for (uint k = 0;k < onevec.size();k++)
-        {
-            onevec[k] = 1.0;
-        }
-        NICE::Matrix One = diag(onevec);
-        NICE::Matrix Mt = M.transpose();
-        NICE::Matrix error;
-	error.multiply (M, Mt);
-	error -= One;
-        double errornorm = matrixRowColNorm(error);
-        double deltaerror = 2.0;
-        //reduceddim
-        while (deltaerror > 1.0e-03)
-        {
-            double norm = matrixRowColNorm(M);
-            if (norm > 0.0)
-            {
-                // refactor: M = (1.0 / norm) * M;
-                M *= (1.0 / norm);
-            }
-            Mt = M.transpose();
-            M = 1.5 * M - 0.5 * ((M * Mt) * M);
-            Mt = M.transpose();
-            //convergencetest
-            error = (M * (Mt) - One);
-
-            deltaerror = errornorm;
-            errornorm = matrixRowColNorm(error);
-            deltaerror = fabs(deltaerror - errornorm);
-
-        }
-    }
-    else
-    {
-        //noniteratve
-        // refactor-nice.pl: check this substitution
-        // old: Matrix D, eigenvectors;
-        NICE::Matrix D, eigenvectors;
-        // refactor-nice.pl: check this substitution
-        // old: Vector eigenvalue;
-        NICE::Vector eigenvalue;
-        // refactor-nice.pl: check this substitution
-        // old: Matrix MMt = M*!M;
-        NICE::Matrix MMt;
-	MMt.multiply ( M, M.transpose() );
-        Eigenvalue(MMt, eigenvalue, eigenvectors);
-        D = diag(eigenvalue);
-        for (int d = 0;d < D.rows();d++)
-        {
-            // refactor-nice.pl: check this substitution
-            // old: if (D[d][d] > 0.0)
-            if(D(d, d) > 0.0)
-            {
-                // refactor-nice.pl: check this substitution
-                // old: D[d][d] = 1.0 / sqrt(D[d][d]);
-                D(d, d) = 1.0 /D(d, d));
-            }
-        }
-        // refactor-nice.pl: check this substitution
-        // old: Matrix Et = !eigenvectors;
-        NICE::Matrix Et = !eigenvectors;
-        // refactor-nice.pl: check this substitution
-        // old: Matrix MMtinvsqrt = eigenvectors * D * Et;
-        NICE::Matrix MMtinvsqrt = eigenvectors * D * Et;
-        M = MMtinvsqrt * M;
-    }
-#endif
-}
-// refactor-nice.pl: check this substitution
-// old: Vector AlgebraTools::meanOfMatrix(const Matrix &M)
-NICE::Vector AlgebraTools::meanOfMatrix(const NICE::Matrix &M)
-{
-#if 1
-    fprintf (stderr, "AlgebraTools:: FIX THIS CODE !\n");
-    exit(-1);
-#else
-    // refactor-nice.pl: check this substitution
-    // old: Vector meanvec = Vector(M.cols());
-    NICE::Vector meanvec = Vector(M.cols());
-    //initialize meanvec
-    for (uint k = 0;k < meanvec.size();k++)
-    {
-        meanvec[k] = 0.0;
-    }
-    //calculate mean
-    for (int f = 0;f < M.rows();f++)
-    {
-        meanvec = M[f] + meanvec;
-    }
-    if (M.rows() > 0)
-    {
-        meanvec = 1.0 / M.rows() * meanvec;
-    }
-    return meanvec;
-#endif
-}
-// refactor-nice.pl: check this substitution
-// old: double AlgebraTools::meanOfVector(const Vector &vec)
-double AlgebraTools::meanOfVector(const NICE::Vector &vec)
-{
-#if 1
-    fprintf (stderr, "AlgebraTools:: FIX THIS CODE !\n");
-    exit(-1);
-#else
-    double mean = 0.0;
-    for (uint k = 0;k < vec.size();k++)
-    {
-        mean += vec[k];
-    }
-    if (vec.size() > 0)
-    {
-        mean /= vec.size();
-    }
-    return mean;
-#endif
-}
-
-// refactor-nice.pl: check this substitution
-// old: Matrix AlgebraTools::vectorToMatrix(const Vector &vec)
-NICE::Matrix AlgebraTools::vectorToMatrix(const NICE::Vector &vec)
-{
-    if (vec.size() > 0)
-    {
-        // refactor-nice.pl: check this substitution
-        // old: Matrix D = Matrix(vec.size(), 1, 0);
-        NICE::Matrix D = Matrix(vec.size(), 1, 0);
-        for (uint k = 0;k < vec.size();k++)
-        {
-            // refactor-nice.pl: check this substitution
-            // old: D[k][0] = vec[k];
-            D(k, 0) = vec[k];
-        }
-        return D;
-    }
-    else
-    {
-        return Matrix();
-    }
-}
-// refactor-nice.pl: check this substitution
-// old: Vector AlgebraTools::matrixToVector(const Matrix &mat)
-NICE::Vector AlgebraTools::matrixToVector(const NICE::Matrix &mat)
-{
-    if (mat.rows() < 1 && mat.cols() < 1)
-    {
-        fprintf(stderr, "Matrix has no entries!");
-    }
-    if (mat.rows() > mat.cols())
-    {
-        // refactor-nice.pl: check this substitution
-        // old: Vector tmp = Vector(mat.rows());
-        NICE::Vector tmp (mat.rows());
-	tmp.set(0.0);
-        for (uint i = 0;i < mat.rows();i++)
-        {
-            // refactor-nice.pl: check this substitution
-            // old: tmp[i] = mat[i][0];
-            tmp[i] =mat(i, 0);
-        }
-        return tmp;
-    }
-    else
-    {
-        // refactor-nice.pl: check this substitution
-        // old: Vector tmp = Vector(mat.cols());
-        NICE::Vector tmp = Vector(mat.cols());
-        for (uint i = 0;i < mat.cols();i++)
-        {
-            // refactor-nice.pl: check this substitution
-            // old: tmp[i] = mat[0][i];
-            tmp[i] =mat(0, i);
-        }
-        return tmp;
-    }
-}
-// refactor-nice.pl: check this substitution
-// old: Matrix AlgebraTools::diag(const Vector &diagonalelements)
-NICE::Matrix AlgebraTools::diag(const NICE::Vector &diagonalelements)
-{
-    if (diagonalelements.size() > 0)
-    {
-        // refactor-nice.pl: check this substitution
-        // old: Matrix D = Matrix(diagonalelements.size(), diagonalelements.size(), 0);
-        NICE::Matrix D (diagonalelements.size(), diagonalelements.size());
-	D.set(0.0);
-        for (uint k = 0;k < diagonalelements.size();k++)
-        {
-            // refactor-nice.pl: check this substitution
-            // old: D[k][k] = diagonalelements[k];
-            D(k, k) = diagonalelements[k];
-        }
-        return D;
-    }
-    else
-    {
-        return Matrix();
-    }
-
-}

math/algebra/AlgebraTools.h

@@ -1,51 +0,0 @@
-/** 
- * @file AlgebraTools.h
- * @brief Algebra Tools
- * @author Michael Koch,Erik Rodner
- * @date 05/20/2009
-
- */
-#ifndef AlgebraToolsINCLUDE
-#define AlgebraToolsINCLUDE
-
-#include <vislearning/nice_nonvis.h>
-
-namespace OBJREC 
-{
-
-class AlgebraTools
-{
-     public:
-     //FIXME: find a better place
-     template<typename T>
-		static T sign(T n);
-          
-     static void calculateMean(const NICE::Matrix &data, NICE::Vector &mean);
-     static void calculateMean(const NICE::Matrix &data,NICE::Vector &mean,NICE::Vector &sigma);
-     static void orthogonalize(NICE::Matrix &M,bool iterative=true);
-     static double trace(const NICE::Matrix &M);
-     static double matrixRowColNorm(const NICE::Matrix &M);
-     static NICE::Matrix vectorToMatrix(const NICE::Vector &vec);
-     static NICE::Vector matrixToVector(const NICE::Matrix &mat);
-      /**
-      * Converting Vector to diagonal Matrix
-      * @param diagonalelements diagonal elements
-      * @return Matrix with diagonal elements
-       */
-     static NICE::Matrix diag(const NICE::Vector &diagonalelements);
-     static double meanOfVector(const NICE::Vector &vec);
-     static NICE::Vector meanOfMatrix(const NICE::Matrix &M);
-};
-
-template<typename T>
-	T AlgebraTools::sign(T n)
-{
-     if (n < 0) return -1;
-     if (n > 0) return 1;
-     return 0;
-}
-
-}
-
-#endif
-

math/algebra/CholeskyRobust.cpp

@@ -1,85 +0,0 @@
-/** 
-* @file CholeskyRobust.cpp
-* @brief robust cholesky decomposition by adding some noise
-* @author Erik Rodner
-* @date 01/06/2010
-
-*/
-#include <iostream>
-
-#include "CholeskyRobust.h"
-#include "core/vector/Algorithms.h"
-
-#ifdef NICE_USELIB_CUDACHOLESKY
-#include "cholesky-gpu/niceinterface/CudaCholeskyNICE.h"
-#endif
-
-using namespace std;
-using namespace NICE;
-using namespace OBJREC;
-		
-		
-CholeskyRobust::CholeskyRobust ( const CholeskyRobust & src )
-	: m_verbose(src.m_verbose), m_noiseStep(src.m_noiseStep),
-	  m_minMatrixLogDeterminant(src.m_minMatrixLogDeterminant),
-	  m_useCuda(src.m_useCuda), m_logDetMatrix(src.m_logDetMatrix)
-{
-}
-
-double CholeskyRobust::robustChol ( const Matrix & A, Matrix & cholA )
-{
-	// this additional memory requirement could be skipped
-	// with a modified cholesky routine
-	Matrix ARegularized (A);
-
-	if ( m_verbose )
-		cerr << "CholeskyRobust::robustChol: Adding noise " << m_noiseStep << endl;
-	ARegularized.addIdentity ( m_noiseStep );
-
-#ifdef NICE_USELIB_CUDACHOLESKY
-	if ( m_useCuda ) 
-		CudaCholeskyNICE::choleskyDecomp ( ARegularized, cholA );
-	else
-#endif
-		choleskyDecompLargeScale ( ARegularized, cholA );
-	
-	m_logDetMatrix = 2*triangleMatrixLogDet ( cholA );
-	if ( m_verbose ) 
-			cerr << "CholeskyRobust::robustChol: Cholesky condition: " << m_logDetMatrix << endl;
-
-	if ( !finite(m_logDetMatrix) )
-	{
-		fthrow(Exception, "The matrix has infinite or not defined log determinant !" );
-	}
-
-	return m_noiseStep;
-}
-
-double CholeskyRobust::robustCholInv ( const Matrix & A, Matrix & invA )
-{
-	Matrix G;
-	double noise = robustChol ( A, G  );
-	if ( m_verbose )
-			cerr << "CholeskyRobust::robustChol: Cholesky inversion" << endl;
-
-	choleskyInvertLargeScale ( G, invA );
-
-	return noise;
-}
-
-
-CholeskyRobust::~CholeskyRobust ()
-{
-}
-
-CholeskyRobust::CholeskyRobust ( bool verbose, double noiseStep, double minMatrixLogDeterminant, bool useCuda ) :
-	m_verbose(verbose), m_noiseStep(noiseStep), m_minMatrixLogDeterminant(minMatrixLogDeterminant), m_useCuda ( useCuda )
-{
-	m_logDetMatrix = m_minMatrixLogDeterminant;
-}
-
-CholeskyRobust *CholeskyRobust::clone(void) const
-{
-	CholeskyRobust *dst = new CholeskyRobust ( *this );
-	return dst;
-}

math/algebra/CholeskyRobust.h

@@ -1,51 +0,0 @@
-/** 
-* @file CholeskyRobust.h
-* @author Erik Rodner
-* @date 01/06/2010
-
-*/
-#ifndef _NICE_OBJREC_CHOLESKYROBUSTINCLUDE
-#define _NICE_OBJREC_CHOLESKYROBUSTINCLUDE
-
-#include <limits>
-#include "core/vector/MatrixT.h"
-
-namespace OBJREC {
-  
-/** @class CholeskyRobust
- * robust cholesky decomposition by adding some noise 
- *
- * @author Erik Rodner
- */
-class CholeskyRobust
-{
-
-    protected:
-		const bool m_verbose;
-		const double m_noiseStep;
-		const double m_minMatrixLogDeterminant;
-		const bool m_useCuda;
-
-		double m_logDetMatrix;
-
-    public:
-  
-  		CholeskyRobust ( bool verbose = true, double noiseStep = 1e-8, double minMatrixLogDeterminant = - std::numeric_limits<double>::max(), bool useCuda = true );
-
-		CholeskyRobust ( const CholeskyRobust & src );
-
-		virtual ~CholeskyRobust ();
-  	
-		virtual double robustChol ( const NICE::Matrix & A, NICE::Matrix & cholA );
-
-		virtual double robustCholInv ( const NICE::Matrix & A, NICE::Matrix & invA );
-
-		double getLastLogDet () const { return m_logDetMatrix; };
-
-		virtual CholeskyRobust *clone(void) const;
-     
-};
-
-}
-
-#endif

math/algebra/CholeskyRobustAuto.cpp

@@ -1,119 +0,0 @@
-/** 
-* @file CholeskyRobustAuto.cpp
-* @brief robust cholesky decomposition by adding some noise
-* @author Erik Rodner
-* @date 01/06/2010
-
-*/
-#include <iostream>
-
-#include "CholeskyRobustAuto.h"
-#include "core/vector/Algorithms.h"
-
-#undef NICE_USELIB_CUDACHOLESKY
-#ifdef NICE_USELIB_CUDACHOLESKY
-#include "cholesky-gpu/niceinterface/CudaCholeskyNICE.h"
-#endif
-
-using namespace std;
-using namespace NICE;
-using namespace OBJREC;
-
-const int maxiterations = 30;
-const bool exponential_noise = true;
-
-CholeskyRobustAuto::CholeskyRobustAuto ( const CholeskyRobustAuto & src )
-	: CholeskyRobust ( src )
-{
-}
-
-double CholeskyRobustAuto::robustChol ( const Matrix & A, Matrix & cholA )
-{
-	// this additional memory requirement could be skipped
-	// with a modified cholesky routine
-	Matrix ARegularized (A);
-
-	if ( m_verbose ) 
-		cerr << "CholeskyRobustAuto::robustChol: A " << A.rows() << " x " << A.cols() << endl;
-
-	int i = 0;
-	double noise = 0.0;
-	double noiseStepExp = m_noiseStep;
-	bool robust = true;
-	do {
-		robust = true;
-
-		if ( m_verbose )
-			cerr << "CholeskyRobustAuto::robustChol: Cholesky decomposition" << endl;
-
-		try {
-			//choleskyDecomp ( ARegularized, cholA );
-			#ifdef NICE_USELIB_CUDACHOLESKY
-			if ( m_useCuda ) 
-				CudaCholeskyNICE::choleskyDecomp ( ARegularized, cholA );
-			else
-			#endif
-				choleskyDecompLargeScale ( ARegularized, cholA );
-#ifdef NICE_USELIB_CUDACHOLESKY
-		} catch ( CudaError & e ) {
-			fthrow(CudaError, e.what() );
-#endif
-		} catch ( Exception ) {
-			if ( m_verbose )
-				cerr << "CholeskyRobustAuto::robustChol: failed!" << endl;
-			robust = false;
-		}
-
-		if ( robust && cholA.containsNaN() ) 
-		{
-			if ( m_verbose )
-				cerr << "CholeskyRobustAuto::robustChol: cholesky matrix contains NaN values" << endl;
-			robust = false; 
-		}
-
-		m_logDetMatrix = m_minMatrixLogDeterminant;
-		if ( robust ) 
-		{
-			m_logDetMatrix = 2*triangleMatrixLogDet ( cholA );
-			if ( m_verbose ) 
-				cerr << "CholeskyRobustAuto::robustChol: Cholesky condition: " << m_logDetMatrix << endl;
-		}
-
-
-		if ( isnan(m_logDetMatrix) || (m_logDetMatrix < m_minMatrixLogDeterminant) ) {	
-			robust = false;
-		}
-
-		if ( !robust ) {	
-			robust = false;
-			if ( m_verbose ) 
-				cerr << "CholeskyRobustAuto::robustChol: Adding noise " << noiseStepExp << endl;
-			ARegularized.addIdentity ( noiseStepExp );
-			noise += noiseStepExp;
-
-			if ( exponential_noise ) 
-				noiseStepExp *= 10.0;
-		}
-
-		i++;
-	} while ( !robust && (i < maxiterations) );
-
-	if ( !robust ) 
-		fthrow( Exception, "Inverse matrix is instable (please adjust the noise step term)" );
-
-	return noise;
-}
-
-CholeskyRobustAuto::~CholeskyRobustAuto ()
-{
-}
-
-CholeskyRobustAuto::CholeskyRobustAuto ( bool verbose, double noiseStep, double minMatrixLogDeterminant, bool useCuda ) :
-	CholeskyRobust ( verbose, noiseStep, minMatrixLogDeterminant, useCuda )
-{
-}
-		
-CholeskyRobustAuto *CholeskyRobustAuto::clone(void) const
-{
-	return new CholeskyRobustAuto ( *this );
-}

math/algebra/CholeskyRobustAuto.h

@@ -1,43 +0,0 @@
-/** 
-* @file CholeskyRobustAuto.h
-* @author Erik Rodner
-* @date 01/06/2010
-
-*/
-#ifndef _NICE_OBJREC_CHOLESKYROBUSTAUTOINCLUDE
-#define _NICE_OBJREC_CHOLESKYROBUSTAUTOINCLUDE
-
-#include <limits>
-#include "core/vector/MatrixT.h"
-
-#include "CholeskyRobust.h"
-
-namespace OBJREC {
-  
-/** @class CholeskyRobustAuto
- * robust cholesky decomposition by adding some noise 
- *
- * @author Erik Rodner
- */
-class CholeskyRobustAuto : public CholeskyRobust
-{
-
-    protected:
-
-    public:
-		CholeskyRobustAuto ( const CholeskyRobustAuto & src );
-  
-  		CholeskyRobustAuto ( bool verbose = true, double noiseStep = 1e-8, double minMatrixLogDeterminant = - std::numeric_limits<double>::max(),
-			bool useCuda = true );
-
-		virtual ~CholeskyRobustAuto ();
-  	
-		virtual double robustChol ( const NICE::Matrix & A, NICE::Matrix & cholA );
-		
-		virtual CholeskyRobustAuto *clone(void) const;
-     
-};
-
-}
-
-#endif

math/algebra/EigValues.cpp

@@ -1,97 +0,0 @@
-/** 
- * @file EigValues.cpp
- * @brief EigValues Class
- * @author Michael Koch
- * @date 08/19/2008
-*/
-
-#ifdef NOVISUAL
-#include <vislearning/nice_nonvis.h>
-#else
-#include <vislearning/nice.h>
-#endif
-     
-#include <iostream>
-     
-#include "EigValues.h"
-
-using namespace OBJREC;
-#define DEBUG_ARNOLDI
-// #undef DEBUG_ARNOLDI
-     
-using namespace std;
-// refactor-nice.pl: check this substitution
-// old: using namespace ice;
-using namespace NICE;
-     
-void EVArnoldi::getEigenvalues(const GenericMatrix &data,Vector &eigenvalues,Matrix &eigenvectors,uint k)
-{
-          
-    if(data.rows()!=data.cols())
-    {
-	throw("EVArnoldi: matrix has to be quadratic");
-    }
-          
-    if(k<=0)
-    {
-       throw("EVArnoldi: please use k>0.");
-    }
-#ifdef DEBUG_ARNOLDI
-    fprintf(stderr,"Initialize Matrices\n");
-#endif
-    uint n=data.cols();
-    NICE::Matrix rmatrix=Matrix(n,k,0);//=eigenvectors
-    NICE::Matrix qmatrix=Matrix(n,k,0);//saves data =eigenvectors
-    eigenvalues.resize(k);
-    NICE::Vector q=Vector(n);
-    NICE::Vector r=Vector(n);
-   
-#ifdef DEBUG_ARNOLDI
-    fprintf(stderr,"Random Initialization\n");
-#endif
-
-    //random initialisation
-    for(uint i=0;i<k;i++)
-	for(uint j=0;j<n;j++)
-	     rmatrix(j, i)=drand48();
-   
-    //reduceddim
-    double delta=1.0;
-    uint iteration=0;
-    while (delta>mindelta &&iteration<maxiterations)
-    {
-	NICE::Vector rold (rmatrix.getColumn(k-1));
-	for(uint reduceddim=0;reduceddim<k;reduceddim++)
-	{
-	     //get Vector r_k of Matrix
-	     q=rmatrix.getColumn(reduceddim);
-	     q.normalizeL2();
-	     // q=r_k/||r_k||
-	     qmatrix.getColumnRef(reduceddim) = q;
-	     
-	     Vector currentCol = rmatrix.getColumnRef(reduceddim); // FIXME
-	     data.multiply(currentCol,q); //r_{k+1}=A*q
-	     
-	     for(uint j=0;j<reduceddim;j++)
-		  rmatrix.getColumnRef(reduceddim) -= qmatrix.getColumn(j)*(qmatrix.getColumn(j).scalarProduct(rmatrix.getColumn(reduceddim)));
-	}
-	//convergence stuff
-	NICE::Vector diff=rold-rmatrix.getColumn(k-1);
-	delta=diff.normL2();
-	iteration++;
-#ifdef DEBUG_ARNOLDI
-	fprintf (stderr, "EVArnoldi: [%d] delta=%f\n", iteration, delta );
-#endif
-    }
-    eigenvectors=rmatrix;
-  
-    for(uint i=0;i<k;i++)
-    {
-	NICE::Vector tmp;
-	eigenvectors.getColumnRef(i).normalizeL2();
-	data.multiply(tmp,eigenvectors.getColumn(i));
-	eigenvalues[i]=tmp.scalarProduct( eigenvectors.getColumn(i) );
-    }
-}
-     
-

math/algebra/EigValues.cpp.refactor

@@ -1,199 +0,0 @@
-/** 
- * @file EigValues.cpp
- * @brief EigValues Class
- * @author Michael Koch
- * @date 08/19/2008
-*/
-#ifdef NOVISUAL
-#include <nice_nonvis.h>
-#else
-#include <nice.h>
-#endif
-    
-#include <core/iceconversion/convertice.h>
-#include <image_nonvis.h>
-
-#include <iostream>
-     
-#include "EigValues.h"
-#include <time.h>
-
-#define DEBUG_ARNOLDI
-     
-using namespace std;
-// refactor-nice.pl: check this substitution
-// old: using namespace ice;
-using namespace NICE;
-     
-void EVArnoldi::getEigenvalues(const GenericMatrix &data,Vector &eigenvalues,Matrix &eigenvectors,uint k)
-{
-    if(data.rows()!=data.cols())
-    {
-	throw("EVArnoldi: matrix has to be quadratic");
-    }
-          
-    if(k<=0)
-    {
-       throw("EVArnoldi: please use k>0.");
-    }
-   
-    uint n=data.cols();
-    ice::Matrix rmatrix (k,n,0);
-    ice::Matrix qmatrix (k,n,0);//saves data =eigenvectors
-    ice::Vector q (n);
-    ice::Vector r (n);
-    
-    eigenvalues.resize(n);
-   
-    srand48(time(NULL));
-    //random initialisation
-    for(uint i=0;i<rmatrix.rows();i++)
-	for(uint j=0;j<rmatrix.cols();j++)
-	     rmatrix[i][j]=drand48();
-   
-    //reduceddim
-    double delta=1.0;
-    uint iteration=0;
-    while (delta>mindelta &&iteration<maxiterations)
-    {
-	ice::Vector rold (rmatrix[k-1]);
-	for(uint reduceddim=0;reduceddim<k;reduceddim++)
-	{
-	     //get Vector r_k of Matrix
-	     q=rmatrix[reduceddim];
-	     q.Normalize();
-	     // q=r_k/||r_k||
-	     qmatrix[reduceddim]=q;
-	     
-	     NICE::Vector tmp;
-	     data.multiply( tmp, NICE::makeEVector(q)); //r_{k+1}=A*q
-	     rmatrix[reduceddim] = NICE::makeIceVectorT(tmp);
-	    
-	     for(uint j=0;j<reduceddim;j++)
-		  rmatrix[reduceddim]=rmatrix[reduceddim]-qmatrix[j]*(qmatrix[j]*rmatrix[reduceddim]);
-	}
-	//convergence stuff
-	ice::Vector diff=rold-rmatrix[k-1];
-	delta=diff.Length();
-	iteration++;
-#ifdef DEBUG_ARNOLDI
-	fprintf (stderr, "EVArnoldi: [%d] delta=%f\n", iteration, delta );
-#endif
-    }
-    eigenvectors = NICE::makeDoubleMatrix ( rmatrix );
-    eigenvectors.transposeInplace();
-  
-    for(uint i=0;i<k;i++)
-    {
-	// refactor-nice.pl: check this substitution
-	// old: Vector tmp;
-	NICE::Vector tmp;
-	eigenvectors.getColumnRef(i).normalizeL2();
-
-	data.multiply(tmp,eigenvectors.getColumn(i));
-	eigenvalues[i] = tmp.scalarProduct (eigenvectors.getColumn(i));
-    }
-}
-     
-#ifdef USE_BROKEN_LANCZOS
-void EVLanczos::getEigenvalues(const GenericMatrix &data,Vector &eigenvalues,Matrix &eigenvectors,uint k)
-{
-    if (data.rows()!=data.cols())
-	throw("Input matrix is not quadratic.");
-     
-    uint n=data.cols();
-    uint m=data.rows();  
-    if(k<=0 ) k=n;
-    uint kint=k;
-    NICE::Matrix wmatrix(kint+1,n,0);
-    NICE::Matrix vmatrix(kint+1,n,0);//saves data =eigenvectors
-    NICE::Matrix t(kint,kint,0);
-    eigenvalues=Vector(k);
-    NICE::Matrix eigenvectorsapprox(k,n,0);
-    NICE::Vector w=Vector(n);
-        
-    // refactor-nice.pl: check this substitution
-    // old: Vector alpha=Vector(kint+1);
-    NICE::Vector alpha=Vector(kint+1);
-    // refactor-nice.pl: check this substitution
-    // old: Vector beta=Vector(kint+1);
-    NICE::Vector beta=Vector(kint+1);
-          
-    //random initialisation ?
-    vmatrix.Set(0.0);
-                    
-    if(n<=0) return;
-
-    for (uint i=0;i<vmatrix[1].size();i++)
-	// refactor-nice.pl: check this substitution
-	// old: vmatrix[1][i]=RandomD();
-	vmatrix(1, i)=RandomD();
-               
-    beta[0]=0;  
-    //iteration
-    double delta=1.0;
-    uint iteration=0;
-    while(delta>mindelta && iteration<maxiterations)
-    {
-	// refactor-nice.pl: check this substitution
-	// old: Vector check=vmatrix[kint];
-	NICE::Vector check=vmatrix[kint];
-	for(uint i=1;i<kint;i++)
-	{
-	    //iteration step
-	    NICE::Vector tmp;
-	    data.multiply(tmp,vmatrix[i]); 
-		     
-	    wmatrix[i]=tmp-(beta[i-1]*vmatrix[i-1]);//w_i=A * v[i] - beta_i * v_(i-1)
-	    alpha[i-1]=wmatrix[i]*vmatrix[i];
-	    wmatrix[i]=wmatrix[i]-(alpha[i-1]*vmatrix[i]);
-	    beta[i]=wmatrix[i].Length();
-	    vmatrix[i+1]=1.0/beta[i]*wmatrix[i];
-			       
-	    t(i-1, i-1)=alpha[i];
-	    if(i>1)
-	    {
-		t(i-1, i-2)=beta[i-1];
-		t(i-2, i-1)=beta[i-1];
-	    }
-	  
-	}
-	// refactor-nice.pl: check this substitution
-	// old: Vector diff=check-vmatrix[kint];
-	NICE::Vector diff=check-vmatrix[kint];
-	delta=diff.Length();
-	iteration++;
-    }
-    cout<<"Lanczos-Iterations:"<<iteration<<"/"<<maxiterations<<"relative Error:"<<delta<<endl;
-    // refactor-nice.pl: check this substitution
-    // old: Vector ice_eigval;
-    NICE::Vector ice_eigval;
-    // refactor-nice.pl: check this substitution
-    // old: Matrix ice_eigvect;
-    NICE::Matrix ice_eigvect;
-    Eigenvalue ( t, ice_eigval, ice_eigvect );
-
-    cerr << ice_eigval << endl;
-	   
-
-    for (uint l=0;l<k;l++)
-	eigenvectorsapprox[l]=vmatrix[l+1];
-
-    eigenvectors=eigenvectorsapprox;
-    for (uint i=0;i<(uint)eigenvectors.rows();i++)
-    {
-	// refactor-nice.pl: check this substitution
-	// old: Vector tmp;
-	NICE::Vector tmp;
-	if ( eigenvectors[i].Length() < 1e-12 )
-	{
-	    cerr << "Lanczos: numerical instability" << endl;
-	} else {
-	    eigenvectors[i].Normalize();
-	}
-	data.multiply(tmp,eigenvectors[i]);
-	eigenvalues[i]=tmp * eigenvectors[i];
-    }
-}
-#endif
-

math/algebra/EigValues.h

@@ -1,90 +0,0 @@
-/** 
-* @file EigValues.h
-* @brief Computing eigenvalues and eigenvectors
-* @author Michael Koch,Erik Rodner
-* @date 08/19/2008
-
-*/
-#ifndef EigValuesINCLUDE
-#define EigValuesINCLUDE
-
-#include <vislearning/nice_nonvis.h>
-
-#include "GenericMatrix.h"
-  
-
-namespace OBJREC {
-
-/** Computing eigenvalues and eigenvectors */
-class EigValues
-{
-
-    protected:
-    
-    public:
-	
-	/**
-         * @param data matrix interface that does allow matrix-vector multiplications
-         * @param k number of eigenvalues/eigenvectors
-         * @param eigenvectors output Eigenvectors as Matrix
-         * @param eigenvalues output Eigenvalues as Vector
-         */
-	virtual void getEigenvalues ( const GenericMatrix &data, NICE::Vector & eigenvalues, NICE::Matrix & eigenvectors, uint k ) = 0;
-
-	virtual ~EigValues() {};
-
-};
-
-#ifdef USE_BROKEN_LANCZOS
-/** BROKEN: lanczos iteration */
-class EVLanczos : public EigValues
-{
-    protected:
-	uint maxiterations;
-	double mindelta;
-
-    public:
-	EVLanczos ( uint _maxiterations = 100, double _mindelta = 0.01 ) 
-	    : maxiterations(_maxiterations), mindelta(_mindelta) {};
-
-
-	/**
-         * Lanczos Iteration, to get k first eigenvectors and eigenvalues
-         * @param data matrix interface that does allow matrix-vector multiplications
-         * @param k number of eigenvalues/eigenvectors
-         * @param eigenvectors output Eigenvectors as Matrix
-         * @param eigenvalues output Eigenvalues as Vector
-         */
-	// refactor-nice.pl: check this substitution
-	// old: void getEigenvalues ( const GenericMatrix &data, Vector & eigenvalues, Matrix & eigenvectors, uint k );	
-	void getEigenvalues ( const GenericMatrix &data, NICE::Vector & eigenvalues, NICE::Matrix & eigenvectors, uint k );	
-};
-#endif
-
-/** arnoldi iteration */
-class EVArnoldi : public EigValues
-{
-    protected:
-	uint maxiterations;
-	double mindelta;
-
-    public:
-	EVArnoldi ( uint _maxiterations = 100, double _mindelta = 0.01 ) 
-	    : maxiterations(_maxiterations), mindelta(_mindelta) {};
-
-	/**
-         * Arnoldi Iteration, to get k first eigenvectors and eigenvalues
-         * @param data matrix interface that does allow matrix-vector multiplications
-         * @param k number of eigenvalues/eigenvectors
-         * @param eigenvectors output Eigenvectors as Matrix
-         * @param eigenvalues output Eigenvalues as Vector
-         */
-	// refactor-nice.pl: check this substitution
-	// old: void getEigenvalues ( const GenericMatrix &data, Vector & eigenvalues, Matrix & eigenvectors, uint k );	
-	void getEigenvalues ( const GenericMatrix &data, NICE::Vector & eigenvalues, NICE::Matrix & eigenvectors, uint k );	
-};
-
-
-} // namespace
-
-#endif

math/algebra/GMSparse2.cpp

@@ -1,323 +0,0 @@
-#include "GMSparse2.h"
-#include <assert.h>
-
-using namespace OBJREC;
-using namespace NICE;
-using namespace std;
-
-GMSparse2::GMSparse2 ( uint _rows, uint _cols ) : m_rows(_rows), m_cols(_cols) 
-{
-	resize(_rows, _cols);
-}
-
-GMSparse2::GMSparse2 ( const NICE::Matrix & iceA, double epsilon)
-{
-	m_rows = iceA.rows();
-	m_cols = iceA.cols();
-	newvectors = true;
-	for(uint r = 0; r < m_rows; r++)
-	{
-		SparseVector *tmp = new SparseVector(m_cols);
-		for(uint c = 0; c < m_cols; c++)
-		{
-			if(fabs(iceA(r ,c)) > epsilon)
-				(*tmp)[c] = iceA(r, c);
-		}
-		A.push_back(tmp);
-	}
-}
-
-GMSparse2::~GMSparse2()
-{
-	clear();
-}
-
-SparseVector & GMSparse2::operator[](int i) const
-{
-	return *A[i];
-}
-
-void GMSparse2::addRow(const NICE::Vector &x)
-{
-	SparseVector *v = new SparseVector(x);
-	m_cols = x.size();
-	v->setDim(m_cols);
-	A.push_back(v);
-	++m_rows;
-}
-		
-void GMSparse2::addRow(SparseVector *x)
-{
-	newvectors = false;
-	A.push_back(x);
-	++m_rows;
-	m_cols = x->getDim();
-}
-		
-void GMSparse2::clear()
-{
-	if(newvectors)
-	{
-		for(uint i = 0; i < A.size(); i++)
-		{
-			delete A[i];
-		}
-	}
-	A.clear();
-}
-		
-void GMSparse2::resize( int _rows, int _cols)
-{
-	m_rows = _rows;
-	m_cols = _cols;
-	newvectors = true;
-	for(uint i = 0; i < m_rows; i++)
-	{
-		SparseVector *tmp = new SparseVector(m_cols);
-		A.push_back(tmp);
-	}
-}
-		
-void GMSparse2::mult2(GMSparse2 &y, GMSparse2 &out, bool transpx, bool transpy)
-{
-	int rowsx, colsx, rowsy, colsy;
-	if(transpx)
-	{
-		colsx = m_rows;
-		rowsx = m_cols;
-	}
-	else
-	{
-		rowsx = m_rows;
-		colsx = m_cols;
-	}
-	
-	if(transpy)
-	{
-		colsy = y.rows();
-		rowsy = y.cols();
-	}
-	else
-	{
-		rowsy = y.rows();
-		colsy = y.cols();
-	}
-	
-	if(rowsy != colsx)
-	{
-		cout << "GMSparse::mult matrix sizes missmatched" << endl;
-		exit(1);
-	}
-	
-	out.resize(rowsx,colsy);
-
-	double xval, yval, val, val2;
-	
-	for(int c = 0; c < colsy; c++)
-	{
-		for(int i = 0; i < rowsy; i++)
-		{
-			if(transpy)
-				yval = y[c].get(i);
-			else
-				yval = y[i].get(c);
-			
-			if(yval != 0.0)
-			{
-				for(int r = 0; r < rowsx; r++)
-				{
-					if(transpx)
-						xval = (*A[i]).get(r);
-					else
-						xval = (*A[r]).get(i);
-					
-					val = out[r].get(c);
-					val2 = val+xval*yval;
-					if(fabs(val2) >  10e-10)
-						out[r][c] = val2;
-					else if(val != 0.0)
-						out[r].erase(c);
-				}
-			}
-		}
-	}
-}
-		
-void GMSparse2::mult(GMSparse2 &y, GMSparse2 &out, bool transpx, bool transpy)
-{
-	int rowsx, colsx, rowsy, colsy;
-	if(transpx)
-	{
-		colsx = m_rows;
-		rowsx = m_cols;
-	}
-	else
-	{
-		rowsx = m_rows;
-		colsx = m_cols;
-	}
-	
-	if(transpy)
-	{
-		colsy = y.rows();
-		rowsy = y.cols();
-	}
-	else
-	{
-		rowsy = y.rows();
-		colsy = y.cols();
-	}
-	
-	if(rowsy != colsx)
-	{
-		cout << "GMSparse::mult matrix sizes missmatched" << endl;
-		exit(1);
-	}
-	
-	out.resize(rowsx,colsy);
-
-	double xval, yval, val;
-	for(int r = 0; r < rowsx; r++)
-	{
-	
-		for(int c = 0; c < colsy; c++)
-		{
-			val = 0.0;
-			for(int i = 0; i < rowsy; i++)
-			{
-				if(transpx)
-					xval = (*A[i]).get(r);
-				else
-					xval = (*A[r]).get(i);
-				
-				if(transpy)
-					yval = y[c].get(i);
-				else
-					yval = y[i].get(c);
-				
-				val+=xval * yval;
-			}
-
-			if(fabs(val) >  10e-7)
-				out[r][c] = val;
-			
-			if(!finite(val*val))
-			{
-				cout << "val " << val << endl;
-				for(int i = 0; i < rowsy; i++)
-				{
-					if(transpx)
-						xval = (*A[i]).get(r);
-					else
-						xval = (*A[r]).get(i);
-				
-					if(transpy)
-						yval = y[c].get(i);
-					else
-						yval = y[i].get(c);
-				
-					val+=xval * yval;
-					cout << " xval: " << xval << " yval " << yval << endl;
-				}
-				getchar();
-			}
-		}
-	}
-	
-	
-}
-
-void GMSparse2::mult(SparseVector &y, GMSparse2 &out, bool transpx, bool transpy)
-{
-	int rowsx, colsx, rowsy, colsy;
-	if(transpx)
-	{
-		colsx = m_rows;
-		rowsx = m_cols;
-	}
-	else
-	{
-		rowsx = m_rows;
-		colsx = m_cols;
-	}
-	
-	if(transpy)
-	{
-		colsy = 1;
-		rowsy = y.getDim();
-	}
-	else
-	{
-		rowsy = 1;
-		colsy = y.getDim();
-	}
-	
-	if(rowsy != colsx)
-	{
-		cout << "GMSparse::mult matrix sizes missmatched" << endl;
-		exit(1);
-	}
-	
-	out.resize(rowsx,colsy);
-
-	double xval, yval, val;
-	for(int r = 0; r < rowsx; r++)
-	{
-	
-		for(int c = 0; c < colsy; c++)
-		{
-			val = 0.0;
-			for(int i = 0; i < rowsy; i++)
-			{
-				if(transpx)
-					xval = (*A[i]).get(r);
-				else
-					xval = (*A[r]).get(i);
-				
-				if(transpy)
-					yval = y.get(i);
-				else
-					yval = y.get(c);
-				
-				val += xval * yval;
-			}
-
-			if(fabs(val) >  10e-7)
-				out[r][c] = val;
-		}
-	}
-}
-
-void GMSparse2::multiply ( NICE::Vector & y, const NICE::Vector & x ) const
-{
-	//FIXME: einbauen
-}
-
-void GMSparse2::store (ostream & os, int format) const
-{
-	os << m_rows << " " << m_cols << " " << A.size() << endl;
-	for(int i = 0; i < (int) A.size(); i++)
-	{
-		A[i]->store(os, format);
-	}
-}
-
-void GMSparse2::restore (istream & is, int format)
-{
-	A.clear();
-	is >> m_rows;
-	is >> m_cols;
-	int size;
-	is >> size;
-	for(int i = 0; i < size; i++)
-	{
-		SparseVector *s = new SparseVector(m_cols);
-		s->restore(is, format);
-		A.push_back(s);
-	}
-}
-
-void GMSparse2::setDel(bool del)
-{
-	newvectors = del;
-}

math/algebra/GMSparse2.h

@@ -1,155 +0,0 @@
-/** 
- * @file GMSparse2.h
- * @brief Sparse Matrix depends on SparseVectors
- * @author Björn Fröhlich
- * @date 05/14/2009
-
- */
-#ifndef GMSPARSE2INCLUDE
-#define GMSPARSE2INCLUDE
-
-#ifdef NOVISUAL
-#include <vislearning/nice_nonvis.h>
-#else
-#include <vislearning/nice.h>
-#endif
-
-#include "vislearning/math/algebra/GenericMatrix.h"
-  
-namespace OBJREC {
-
-class GMSparse2 : public GenericMatrix
-{
-	protected:
-		//! the sparse matrix
-		std::vector<NICE::SparseVector*> A;
-		
-		//! count of rows
-		uint m_rows;
-		
-		//! count of columns
-		uint m_cols;
-		
-		//! are the vectors new ones or came they from somewhere else
-		bool newvectors;
-		
-	public:
-		/**
-		 * simple constructor -> does nothing
-		 */
-		GMSparse2(){resize(0,0);};
-		
-		/**
-		 * constructor which set the rows and cols of the SparseMatrix
-		 * @param _rows 
-		 * @param _cols 
-		 */
-		GMSparse2 ( uint _rows, uint _cols );
-		
-		/**
-		 * converts an ICE::Matrix to a sparse matrix
-		 * @param A input ICE::Matrix
-		 * @param epsilon tolerance value
-		 */
-		GMSparse2 ( const NICE::Matrix & iceA, double epsilon = 1e-9 );
-
-		/**
-		 * simple destructor
-		 */
-		~GMSparse2();
-		
-		/**
-		 * resize the matrix
-		 * @param _rows new count of rows
-		 * @param _cols new count of cols
-		 */
-		void resize( int _rows, int _cols);
-		
-		/**
-		 * clean up
-		 */
-		void clear();
-		
-		/** get the number of rows in A */
-		uint rows() const { return m_rows; };
-
-		/** get the number of columns in A */
-		uint cols() const { return m_cols; };
-
-		/** multiply with a vector: A*x = y */
-		void multiply ( NICE::Vector & y, const NICE::Vector & x ) const;
-		
-		/**
-		 * return the i-th row
-		 * @param i 
-		 * @return SparseVector at row i
-		 */
-		NICE::SparseVector & operator[](int i) const;
-		
-		/**
-		 * restore the information of the sparse matrix
-		 * @param is inputstream
-		 * @param format 
-		 */
-		void restore (std::istream & is, int format = 0);
-		
-		/**
-		 * store the information of the sparse matrix
-		 * @param os outputstream
-		 * @param format 
-		 */
-		void store (std::ostream & os, int format = 0) const;
-		
-		
-		/**
-		 * Should the data be deleted when the destructor is called
-		 * @param del 
-		 */
-		void setDel(bool del = true);
-		
-		/**
-		 * add a new row 
-		 * @param x ICE::Vector
-		 */
-		void addRow(const NICE::Vector &x);
-		
-		/**
-		 * add a new row
-		 * @param x SparseVector
-		 */
-		void addRow(NICE::SparseVector *x);
-		
-		/**
-		 * matrixmultiplication x*y = out
-		 * @param y input
-		 * @param out output
-		 * @param transpx use the transpose of x 
-		 * @param transpy use the transpose of y
-		 * @return z
-		 */
-		void mult(GMSparse2 &y, GMSparse2 & out, bool transpx = false, bool transpy = false);
-		
-		/**
-		 * matrixmultiplication x*y = out
-		 * @param y input Sparse Vector
-		 * @param out output
-		 * @param transpx use the transpose of x 
-		 * @param transpy use the transpose of y
-		 * @return z
-		 */
-		void mult(NICE::SparseVector &y, GMSparse2 & out, bool transpx = false, bool transpy = false);
-		
-		/**
-		 * same like mult, but faster when y ist very sparse and slower if not
-		 * @param y input
-		 * @param out output
-		 * @param transpx use the transpose of x 
-		 * @param transpy use the transpose of y
-		 * @return z
-		 */
-		void mult2(GMSparse2 &y, GMSparse2 & out, bool transpx = false, bool transpy = false);
-};
-
-} // namespace
-
-#endif

math/algebra/GenericMatrix.cpp

@@ -1,81 +0,0 @@
-/** 
-* @file GenericMatrix.cpp
-* @brief matrix interface for indirect matrix multiplications
-* @author Erik Rodner
-* @date 05/14/2009
-
-*/
-
-#include "GenericMatrix.h"
-#include <assert.h>
-
-using namespace OBJREC;
-using namespace NICE;
-using namespace std;
-	
-GMSparse::GMSparse ( const NICE::Matrix & A, double epsilon )
-{
-    m_rows = A.rows();
-    m_cols = A.cols();
-    for ( u_int i = 0 ; i < m_rows ; i++ )
-	for ( u_int j = 0 ; j < m_cols ; j++ )
-	    if (fabs(A(i, j)) > epsilon )
-			this->A.insert ( this->A.begin(), NICE::triplet<int, int, double> ( i, j,A(i, j) ) );		
-}
-
-void GMSparse::multiply ( NICE::Vector & y, const NICE::Vector & x ) const
-{
-    assert ( x.size() == m_cols );
-
-    y.resize( m_rows );
-    y.set(0.0);
-    for ( vector< NICE::triplet<int, int, double> >::const_iterator k = this->A.begin();
-	    k != this->A.end(); k++ )
-    {
-	int i = k->first;
-	int j = k->second;
-	double value = k->third; // = a_ij
-	y[i] += value * x[j];
-    }
-}
-
-GMCovariance::GMCovariance ( const NICE::Matrix *data )
-{
-    this->data = data;
-    m_rows = data->rows();
-    m_cols = m_rows;
-}
-
-void GMCovariance::multiply ( NICE::Vector & y, const NICE::Vector & x ) const
-{
-    assert ( x.size() == data->rows() );
-
-	/*******************************************************************
-	 * d_k = data(:,k) vector k in the data set
-	 * f_k = d_k^T x 
-	 * fSum = \sum_k f_k
-	 * In the following we use the following relationship
-	 * S = \sum_i d_i ( d_i - \mu )^T = \sum_i (d_i - \mu) (d_i - \mu)^T
-	 * Sx = \sum_i d_i ( f_i - fSum/N )
-	 * which seems to be very efficient
-	 * *****************************************************************/
-
-    NICE::Vector f ( data->cols() );
-    f.set(0.0);
-    double fSum = 0.0;
-    for ( uint k = 0 ; k < data->cols() ; k++ )
-    {
-		for ( uint d = 0 ; d < data->rows() ; d++ )
-			f[k] += (*data)(d, k) * x[d];
-		fSum += f[k];
-    }
-    y.resize(x.size());
-    y.set(0.0);
-    int N = data->cols();
-    for ( uint k = 0 ; k < data->cols() ; k++ )
-		for ( int l = 0 ; l < (int)y.size(); l++ )
-			y[l] += ( f[k] - fSum/N ) * ( (*data)(l, k) );
-
-    for ( int i = 0 ; i < (int)y.size() ; i++ )
-		y[i] /= N;
-}

math/algebra/GenericMatrix.h

@@ -1,107 +0,0 @@
-/** 
-* @file GenericMatrix.h
-* @brief matrix interface for indirect matrix multiplications
-* @author Erik Rodner
-* @date 05/14/2009
-
-*/
-#ifndef GENERICMATRIXINCLUDE
-#define GENERICMATRIXINCLUDE
-
-#include "core/basics/triplet.h"
-#include "AlgebraTools.h"
-#include "core/vector/SparseVector.h"
-  
-
-namespace OBJREC {
-
-/** matrix interface for indirect matrix multiplications */
-class GenericMatrix
-{
-
-    protected:
-
-    public:
-
-	/** multiply with a vector: A*x = y */
-	virtual void multiply ( NICE::Vector & y, const NICE::Vector & x ) const  = 0;
-
-	/** get the number of rows in A */
-	virtual uint rows() const = 0;
-
-	/** get the number of columns in A */
-	virtual uint cols() const = 0;
-
-	/** simple destructor */
-	virtual ~GenericMatrix() {};
-     
-};
-
-class GMSlowICE : public GenericMatrix
-{
-    protected:
-	NICE::Matrix A;
-
-    public:
-	GMSlowICE ( const NICE::Matrix & _A ) : A(_A) {};
-    
-	/** get the number of rows in A */
-	uint rows() const { return A.rows(); };
-
-	/** get the number of columns in A */
-	uint cols() const { return A.cols(); };
-
-	/** multiply with a vector: A*x = y */
-	void multiply ( NICE::Vector & y, const NICE::Vector & x ) const
-	{ y.multiply ( A, x ); };
-
-};
-
-class GMSparse : public GenericMatrix
-{
-    protected:
-	std::vector< NICE::triplet<int, int, double> > A;
-	uint m_rows;
-	uint m_cols;
-
-    public:
-	GMSparse ( uint _rows, uint _cols ) : m_rows(_rows), m_cols(_cols) {}
-	GMSparse ( const NICE::Matrix & A, double epsilon = 1e-9 );
-
-	/** get the number of rows in A */
-	uint rows() const { return m_rows; };
-
-	/** get the number of columns in A */
-	uint cols() const { return m_cols; };
-
-	/** multiply with a vector: A*x = y */
-	void multiply ( NICE::Vector & y, const NICE::Vector & x ) const;
-
-};
-
-class GMCovariance : public GenericMatrix
-{
-    protected:
-	const NICE::Matrix *data;
-	uint m_rows;
-	uint m_cols;
-
-    public:
-	GMCovariance ( const NICE::Matrix *data );
-
-	/** get the number of rows in A */
-	uint rows() const { return m_rows; };
-
-	/** get the number of columns in A */
-	uint cols() const { return m_cols; };
-
-	/** multiply with a vector: A*x = y */
-	void multiply ( NICE::Vector & y, const NICE::Vector & x ) const;
-
-};
-
-
-
-} // namespace
-
-#endif

math/algebra/Makefile

@@ -1,8 +0,0 @@
-#TARGETS_FROM:=$(notdir $(patsubst %/,%,$(shell pwd)))/$(TARGETS_FROM)
-#$(info recursivly going up: $(TARGETS_FROM) ($(shell pwd)))
-
-all:
-
-%:
-	$(MAKE) TARGETS_FROM=$(notdir $(patsubst %/,%,$(shell pwd)))/$(TARGETS_FROM) -C .. $@
-

math/algebra/Makefile.inc

@@ -1,103 +0,0 @@
-# LIBRARY-DIRECTORY-MAKEFILE
-# conventions:
-# - all subdirectories containing a "Makefile.inc" are considered sublibraries
-#   exception: "progs/" and "tests/" subdirectories!
-# - all ".C", ".cpp" and ".c" files in the current directory are linked to a
-#   library
-# - the library depends on all sublibraries 
-# - the library name is created with $(LIBNAME), i.e. it will be somehow
-#   related to the directory name and with the extension .a
-#   (e.g. lib1/sublib -> lib1_sublib.a)
-# - the library will be added to the default build list ALL_LIBRARIES
-
-# --------------------------------
-# - remember the last subdirectory
-#
-# set the variable $(SUBDIR) correctly to the current subdirectory. this
-# variable can be used throughout the current makefile.inc. The many 
-# SUBDIR_before, _add, and everything are only required so that we can recover
-# the previous content of SUBDIR before exitting the makefile.inc
-
-SUBDIR_add:=$(dir $(word $(words $(MAKEFILE_LIST)),$(MAKEFILE_LIST)))
-SUBDIR_before:=$(SUBDIR)
-SUBDIR:=$(strip $(SUBDIR_add))
-SUBDIR_before_$(SUBDIR):=$(SUBDIR_before)
-ifeq "$(SUBDIR)" "./"
-SUBDIR:=
-endif
-
-# ------------------------
-# - include subdirectories
-#
-# note the variables $(SUBDIRS_OF_$(SUBDIR)) are required later on to recover
-# the dependencies automatically. if you handle dependencies on your own, you
-# can also dump the $(SUBDIRS_OF_$(SUBDIR)) variable, and include the
-# makefile.inc of the subdirectories on your own...
-
-SUBDIRS_OF_$(SUBDIR):=$(patsubst %/Makefile.inc,%,$(wildcard $(SUBDIR)*/Makefile.inc))
-include $(SUBDIRS_OF_$(SUBDIR):%=%/Makefile.inc)
-
-# ----------------------------
-# - include local dependencies
-#
-# you can specify libraries needed by the individual objects or by the whole
-# directory. the object specific additional libraries are only considered
-# when compiling the specific object files
-# TODO: update documentation...
-
--include $(SUBDIR)libdepend.inc
-
-$(foreach d,$(filter-out %progs %tests,$(SUBDIRS_OF_$(SUBDIR))),$(eval $(call PKG_DEPEND_INT,$(d))))
-
-# ---------------------------
-# - objects in this directory
-#
-# the use of the variable $(OBJS) is not mandatory. it is mandatory however
-# to update $(ALL_OBJS) in a way that it contains the path and name of
-# all objects. otherwise we can not include the appropriate .d files.
-
-OBJS:=$(patsubst %.cpp,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.cpp))) \
-      $(patsubst %.C,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.C))) \
-	  $(shell grep -ls Q_OBJECT $(SUBDIR)*.h | sed -e's@^@/@;s@.*/@$(OBJDIR)moc_@;s@\.h$$@.o@') \
-      $(patsubst %.c,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.c)))
-ALL_OBJS += $(OBJS)
-
-# ----------------------------
-# - binaries in this directory
-#
-# output of binaries in this directory. none of the variables has to be used.
-# but everything you add to $(ALL_LIBRARIES) and $(ALL_BINARIES) will be
-# compiled with `make all`. be sure again to add the files with full path.
-
-LIBRARY_BASENAME:=$(call LIBNAME,$(SUBDIR))
-ifneq "$(SUBDIR)" ""
-ALL_LIBRARIES+=$(LIBDIR)$(LIBRARY_BASENAME).$(LINK_FILE_EXTENSION)
-endif
-
-# ---------------------
-# - binary dependencies
-#
-# there is no way of determining the binary dependencies automatically, so we
-# follow conventions. the current library depends on all sublibraries.
-# all other dependencies have to be added manually by specifying, that the
-# current .pc file depends on some other .pc file. binaries depending on
-# libraries should exclusivelly use the .pc files as well.
-
-ifeq "$(SKIP_BUILD_$(OBJDIR))" "1"
-$(LIBDIR)$(LIBRARY_BASENAME).a:
-else
-$(LIBDIR)$(LIBRARY_BASENAME).a:$(OBJS) \
-	$(call PRINT_INTLIB_DEPS,$(PKGDIR)$(LIBRARY_BASENAME).a,.$(LINK_FILE_EXTENSION))
-endif
-
-$(PKGDIR)$(LIBRARY_BASENAME).pc: \
-	$(call PRINT_INTLIB_DEPS,$(PKGDIR)$(LIBRARY_BASENAME).pc,.pc)
-
-# -------------------
-# - subdir management
-#
-# as the last step, always add this line to correctly recover the subdirectory
-# of the makefile including this one!
-
-SUBDIR:=$(SUBDIR_before_$(SUBDIR))
-

math/algebra/libdepend.inc

@@ -1,7 +0,0 @@
-$(call PKG_DEPEND_EXT,ICE)
-$(call PKG_DEPEND_EXT,BSE)
-$(call PKG_DEPEND_INT,vislearning/math/mathbase)
-$(call PKG_DEPEND_INT,cholesky-gpu/niceinterface)
-
-
-

math/algebra/progs/Makefile.inc

@@ -1,88 +0,0 @@
-# BINARY-DIRECTORY-MAKEFILE
-# conventions:
-# - there are no subdirectories, they are ignored!
-# - all ".C", ".cpp" and ".c" files in the current directory are considered
-#   independent binaries, and linked as such.
-# - the binaries depend on the library of the parent directory
-# - the binary names are created with $(BINNAME), i.e. it will be more or less
-#   the name of the .o file
-# - all binaries will be added to the default build list ALL_BINARIES
-
-# --------------------------------
-# - remember the last subdirectory
-#
-# set the variable $(SUBDIR) correctly to the current subdirectory. this
-# variable can be used throughout the current makefile.inc. The many 
-# SUBDIR_before, _add, and everything are only required so that we can recover
-# the previous content of SUBDIR before exitting the makefile.inc
-
-SUBDIR_add:=$(dir $(word $(words $(MAKEFILE_LIST)),$(MAKEFILE_LIST)))
-SUBDIR_before:=$(SUBDIR)
-SUBDIR:=$(strip $(SUBDIR_add))
-SUBDIR_before_$(SUBDIR):=$(SUBDIR_before)
-
-# ------------------------
-# - include subdirectories
-#
-# note the variables $(SUBDIRS_OF_$(SUBDIR)) are required later on to recover
-# the dependencies automatically. if you handle dependencies on your own, you
-# can also dump the $(SUBDIRS_OF_$(SUBDIR)) variable, and include the
-# makefile.inc of the subdirectories on your own...
-
-#SUBDIRS_OF_$(SUBDIR):=$(patsubst %/Makefile.inc,%,$(wildcard $(SUBDIR)*/Makefile.inc))
-#include $(SUBDIRS_OF_$(SUBDIR):%=%/Makefile.inc)
-
-# ----------------------------
-# - include local dependencies
-#
-# include the libdepend.inc file, which gives additional dependencies for the
-# libraries and binaries. additionally, an automatic dependency from the library
-# of the parent directory is added (commented out in the code below).
-
--include $(SUBDIR)libdepend.inc
-
-PARENTDIR:=$(patsubst %/,%,$(dir $(patsubst %/,%,$(SUBDIR))))
-$(eval $(call PKG_DEPEND_INT,$(PARENTDIR)))
-
-# ---------------------------
-# - objects in this directory
-#
-# the use of the variable $(OBJS) is not mandatory. it is mandatory however
-# to update $(ALL_OBJS) in a way that it contains the path and name of
-# all objects. otherwise we can not include the appropriate .d files.
-
-OBJS:=$(patsubst %.cpp,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.cpp))) \
-      $(patsubst %.C,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.C))) \
-      $(shell grep -ls Q_OBJECT $(SUBDIR)*.h | sed -e's@^@/@;s@.*/@$(OBJDIR)moc_@;s@\.h$$@.o@') \
-      $(patsubst %.c,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.c)))
-ALL_OBJS += $(OBJS)
-
-# ----------------------------
-# - binaries in this directory
-#
-# output of binaries in this directory. none of the variables has to be used.
-# but everything you add to $(ALL_LIBRARIES) and $(ALL_BINARIES) will be
-# compiled with `make all`. be sure again to add the files with full path.
-
-BINARIES:=$(patsubst %.o,$(BINDIR)%,$(filter-out moc_%,$(notdir $(OBJS))))
-ALL_BINARIES+=$(BINARIES)
-
-# ---------------------
-# - binary dependencies
-#
-# there is no way of determining the binary dependencies automatically, so we
-# follow conventions. each binary depends on the corresponding .o file and
-# on the libraries specified by the INTLIBS/EXTLIBS. these dependencies can be
-# specified manually or they are automatically stored in a .bd file.
-
-$(foreach head,$(wildcard $(SUBDIR)*.h),$(eval $(shell grep -q Q_OBJECT $(head) && echo $(head) | sed -e's@^@/@;s@.*/\(.*\)\.h$$@$(BINDIR)\1:$(OBJDIR)moc_\1.o@')))
--include $(OBJS:%.o=%.bd)
-
-# -------------------
-# - subdir management
-#
-# as the last step, always add this line to correctly recover the subdirectory
-# of the makefile including this one!
-
-SUBDIR:=$(SUBDIR_before_$(SUBDIR))
-

math/algebra/progs/calcKernelMatrixProperties.cpp

@@ -1,74 +0,0 @@
-/** 
-* @file calcKernelMatrixProperties.cpp
-* @brief calc some kernel matrix properties
-* @author Erik Rodner
-* @date 01/15/2010
-
-*/
-#include "core/basics/Config.h"
-#include "core/vector/Eigen.h"
-
-using namespace std;
-using namespace NICE;
-
-
-/** 
-    
-    calc some kernel matrix properties 
-    
-*/
-int main (int argc, char **argv)
-{   
-    std::set_terminate(__gnu_cxx::__verbose_terminate_handler);
-
-    Config conf ( argc, argv );
-    
-	string in = conf.gS("main", "in");
-
-	cout << "reading kernel matrix ..." << endl;
-	ifstream ifs ( in.c_str(), ios::in );
-
-	if ( !ifs.good() )
-		fthrow(IOException, "Unable to read " << in << "." );
-
-	Matrix kernelMatrixOrig;
-	ifs >> kernelMatrixOrig;
-
-	ifs.close();
-
-	int norig = kernelMatrixOrig.rows();
-	int morig = kernelMatrixOrig.cols();
-
-	if ( norig != morig )
-		fthrow(Exception, "Kernel matrix has to be quadratic.");
-
-	int size = conf.gI("main", "size", 100 );
-	if ( size > norig ) size = norig;
-	
-	Matrix kernelMatrix ( size, size );
-	for ( int i = 0 ; i < kernelMatrix.rows(); i++ )
-		for ( int j = 0 ; j < kernelMatrix.cols(); j++ )
-			kernelMatrix(i,j) = kernelMatrixOrig(i,j);
-			
-	int n = kernelMatrix.rows();
-	int m = kernelMatrix.cols();
-
-	cout << "size: " << n << " x " << m << " (orig: " << norig << " x " << norig << " )" << endl;
-	cout << "performing eigenvalue decomposition ..." << endl;
-    Vector eigvals (n);
-	eigenvalues ( kernelMatrix, &eigvals );
-
-	double min_lambda = eigvals.Min();
-	double max_lambda = eigvals.Max();
-
-	cout << "smallest eigenvalue: " << min_lambda << endl;
-	cout << "largest eigenvalue: " << max_lambda << endl;
-   
-    double sigma = conf.gD("main", "sigma", 0.01);
-    double upper_bound = 0.5 * sigma * sigma / ( pow(min_lambda,3) );
-	double lower_bound = 0.5 * sigma * sigma / ( pow(min_lambda,3) + sigma*sigma );
-    cout << "inverse approximation bounds (cf. GP Multi-Task Notes): " << lower_bound << " <= |R|_2 <= " <<  upper_bound << endl;
-    
-    
-    return 0;
-}

math/algebra/progs/libdepend.inc

@@ -1 +0,0 @@
-$(call PKG_DEPEND_INT,vislearning/baselib/)

math/algebra/tests/Makefile.inc

@@ -1,89 +0,0 @@
-# BINARY-DIRECTORY-MAKEFILE
-# conventions:
-# - there are no subdirectories, they are ignored!
-# - all ".C", ".cpp" and ".c" files in the current directory are considered
-#   independent binaries, and linked as such.
-# - the binaries depend on the library of the parent directory
-# - the binary names are created with $(BINNAME), i.e. it will be more or less
-#   the name of the .o file
-# - all binaries will be added to the default build list ALL_BINARIES
-
-# --------------------------------
-# - remember the last subdirectory
-#
-# set the variable $(SUBDIR) correctly to the current subdirectory. this
-# variable can be used throughout the current makefile.inc. The many 
-# SUBDIR_before, _add, and everything are only required so that we can recover
-# the previous content of SUBDIR before exitting the makefile.inc
-
-SUBDIR_add:=$(dir $(word $(words $(MAKEFILE_LIST)),$(MAKEFILE_LIST)))
-SUBDIR_before:=$(SUBDIR)
-SUBDIR:=$(strip $(SUBDIR_add))
-SUBDIR_before_$(SUBDIR):=$(SUBDIR_before)
-
-# ------------------------
-# - include subdirectories
-#
-# note the variables $(SUBDIRS_OF_$(SUBDIR)) are required later on to recover
-# the dependencies automatically. if you handle dependencies on your own, you
-# can also dump the $(SUBDIRS_OF_$(SUBDIR)) variable, and include the
-# makefile.inc of the subdirectories on your own...
-
-#SUBDIRS_OF_$(SUBDIR):=$(patsubst %/Makefile.inc,%,$(wildcard $(SUBDIR)*/Makefile.inc))
-#include $(SUBDIRS_OF_$(SUBDIR):%=%/Makefile.inc)
-
-# ----------------------------
-# - include local dependencies
-#
-# include the libdepend.inc file, which gives additional dependencies for the
-# libraries and binaries. additionally, an automatic dependency from the library
-# of the parent directory is added (commented out in the code below).
-
--include $(SUBDIR)libdepend.inc
-
-PARENTDIR:=$(patsubst %/,%,$(dir $(patsubst %/,%,$(SUBDIR))))
-$(call PKG_DEPEND_INT,$(PARENTDIR))
-$(call PKG_DEPEND_EXT,CPPUNIT)
-
-# ---------------------------
-# - objects in this directory
-#
-# the use of the variable $(OBJS) is not mandatory. it is mandatory however
-# to update $(ALL_OBJS) in a way that it contains the path and name of
-# all objects. otherwise we can not include the appropriate .d files.
-
-OBJS:=$(patsubst %.cpp,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.cpp))) \
-      $(patsubst %.C,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.C))) \
-      $(shell grep -ls Q_OBJECT $(SUBDIR)*.h | sed -e's@^@/@;s@.*/@$(OBJDIR)moc_@;s@\.h$$@.o@') \
-      $(patsubst %.c,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.c)))
-ALL_OBJS += $(OBJS)
-
-# ----------------------------
-# - binaries in this directory
-#
-# output of binaries in this directory. none of the variables has to be used.
-# but everything you add to $(ALL_LIBRARIES) and $(ALL_BINARIES) will be
-# compiled with `make all`. be sure again to add the files with full path.
-
-CHECKS:=$(BINDIR)$(call LIBNAME,$(SUBDIR))
-ALL_CHECKS+=$(CHECKS)
-
-# ---------------------
-# - binary dependencies
-#
-# there is no way of determining the binary dependencies automatically, so we
-# follow conventions. each binary depends on the corresponding .o file and
-# on the libraries specified by the INTLIBS/EXTLIBS. these dependencies can be
-# specified manually or they are automatically stored in a .bd file.
-
-$(foreach head,$(wildcard $(SUBDIR)*.h),$(eval $(shell grep -q Q_OBJECT $(head) && echo $(head) | sed -e's@^@/@;s@.*/\(.*\)\.h$$@$(BINDIR)\1:$(OBJDIR)moc_\1.o@')))
-$(eval $(foreach c,$(CHECKS),$(c):$(BUILDDIR)$(CPPUNIT_MAIN_OBJ) $(OBJS) $(call PRINT_INTLIB_DEPS,$(c),.a)))
-
-# -------------------
-# - subdir management
-#
-# as the last step, always add this line to correctly recover the subdirectory
-# of the makefile including this one!
-
-SUBDIR:=$(SUBDIR_before_$(SUBDIR))
-

math/algebra/tests/TestEigenValue.cpp

@@ -1,134 +0,0 @@
-//
-// C++ Implementation: TestEigenValue
-//
-// Description:
-//
-//
-// Author: Michael Koch <Koch.Michael@uni-jena.de>, (C) 2009
-//
-// Copyright: See COPYING file that comes with this distribution
-//
-/**
- * @file TestEigenValue.cpp
- * @brief TestEigenValue
- * @author Michael Koch
- * @date Di Aug 4 2009
- */
-
-#include "TestEigenValue.h"
-#include <string>
-
-#include "core/basics/cppunitex.h"
-#include "core/basics/numerictools.h"
-#include "core/vector/Distance.h"
-
-#include "vislearning/math/algebra/EigValues.h"
-#include "vislearning/math/algebra/AlgebraTools.h"
-#include "vislearning/math/algebra/GenericMatrix.h"
-
-#include "vislearning/nice_nonvis.h"
-
-using namespace std;
-using namespace NICE;
-using namespace OBJREC;
-
-CPPUNIT_TEST_SUITE_REGISTRATION(TestEigenValue);
-
-void TestEigenValue::setUp()
-{
-}
-
-void TestEigenValue::tearDown()
-{
-}
-
-void TestEigenValue::TestEigenValueComputation()
-{
-    uint rows = 3;
-    uint cols = rows;
-    uint k = rows;
-    uint maxiterations =  200;
-    double mindelta = 1e-8;
-    double sparse_prob = 0.0;
-    int trlan_magnitude = 1;
-    bool init_random = true ;
-
-    // refactor-nice.pl: check this substitution
-    // old: Matrix T (rows, cols);
-    NICE::Matrix T(rows, cols);
-    T.set(0.0);
-    if (init_random)
-        srand48(time(NULL));
-
-    // generate random symmetric matrix
-    for (uint i = 0 ; i < rows ; i++)
-        for (uint j = i ; j < cols ; j++)
-        {
-            if (sparse_prob != 0.0)
-                if (drand48() < sparse_prob)
-                    continue;
-            T(i, j) = drand48();
-            T(j, i) = T(i, j);
-        }
-
-    // refactor-nice.pl: check this substitution
-    // old: Vector ice_eigvalues;
-
-
-    EigValues *eig;
-    for (int trlan = 0;trlan <= 1;trlan++) //this is creepy but funny
-    {
-        if (trlan) //this is creepy but saves lot of code
-        {
-#ifdef NICE_USELIB_TRLAN
-            eig = new EigValuesTRLAN(trlan_magnitude);
-#else
-            break;
-#endif
-        }
-        else
-        {
-            eig = new EVArnoldi(maxiterations, mindelta);
-        }
-
-        NICE::Vector eigvalues_dense;
-        NICE::Matrix eigvect_dense;
-        NICE::Vector eigvalues_sparse;
-        NICE::Matrix eigvect_sparse;
-
-        GMSlowICE Tg(T);
-        eig->getEigenvalues(Tg, eigvalues_dense, eigvect_dense, k);
-
-        GMSparse Ts(T);
-        eig->getEigenvalues(Ts, eigvalues_sparse, eigvect_sparse, k);
-
-        // test property
-        NICE::EuclidianDistance<double> eucliddist;
-        for (uint i = 0 ; i < k ; i++)
-        {
-            NICE::Vector v_dense = eigvect_dense.getColumn(i);
-            double lambda_dense = eigvalues_dense[i];
-            NICE::Vector Tv_dense;
-            Tv_dense.multiply(T, v_dense);
-            NICE::Vector lv_dense = v_dense;
-            lv_dense *= lambda_dense;
-            double err_dense = eucliddist(Tv_dense, lv_dense);
-
-            NICE::Vector v_sparse = eigvect_sparse.getColumn(i);
-            double lambda_sparse = eigvalues_sparse[i];
-            NICE::Vector Tv_sparse;
-            Tv_sparse.multiply(T, v_sparse);
-            NICE::Vector lv_sparse = v_sparse;
-            lv_sparse *= lambda_sparse;
-            double err_sparse = eucliddist(Tv_sparse, lv_sparse);
-
-//             cerr << "eigenvalue " << i << endl;
-//             cerr << "lambda (dense) = " << lambda_dense << endl;
-//             cerr << "lambda (sparse) = " << lambda_sparse << endl;
-//             cerr << "||Av - lambda v|| (dense) = " << err_dense << endl;
-//             cerr << "||Av - lambda v|| (sparse) = " << err_sparse << endl;
-            CPPUNIT_ASSERT_DOUBLES_EQUAL_NOT_NAN(0.0,err_dense,1e-4);
-            CPPUNIT_ASSERT_DOUBLES_EQUAL_NOT_NAN(0.0,err_sparse,1e-4);
-        }
-    }
-}

math/algebra/tests/TestEigenValue.h

@@ -1,48 +0,0 @@
-//
-// C++ Interface: TestEigenValue
-//
-// Description: 
-//
-//
-// Author: Michael Koch <Koch.Michael@uni-jena.de>, (C) 2009
-//
-// Copyright: See COPYING file that comes with this distribution
-//
-/** 
- * @file TestEigenValue.h
- * @brief TestEigenValue
- * @author Michael Koch
- * @date Di Aug 4 2009
- */
- 
-
-#ifndef OBJREC_TestEigenValue_H
-#define OBJREC_TestEigenValue_H
-
-
-#include <cppunit/extensions/HelperMacros.h>
-
-/**
- * CppUnit-Testcase. 
- * Tests for EigenValue Operations
- */
-class TestEigenValue : public CppUnit::TestFixture
-{
-     CPPUNIT_TEST_SUITE( TestEigenValue );
-
-    
-     CPPUNIT_TEST( TestEigenValueComputation );
-
-     CPPUNIT_TEST_SUITE_END();
-
-     private:
-
-     public:
-          void setUp();
-          void tearDown();
-          void TestEigenValueComputation();
-       
-};
-
-#endif // _TestEigenValue_H_
-

math/algebra_trlan/EigValuesTRLAN.cpp

@@ -1,111 +0,0 @@
-#include "EigValuesTRLAN.h"
-
-#include <iostream>
-#include <assert.h>
-#define DEBUG_TRLAN
-// #undef DEBUG_TRLAN
-using namespace OBJREC;
-
-using namespace std;
-
-static const GenericMatrix* mat = NULL;
-
-static void op(int* nrow, int* ncol, double* xin, int* ldx, double* xout, int* ldy)
-{
-    NICE::Vector xin_v(xin, *ldy, NICE::VectorBase::external);
-    NICE::Vector xout_v(xout, *ldy, NICE::VectorBase::external);
-    mat->multiply(xout_v, xin_v);
-}
-
-typedef void (*OpFunc)(int* nrow, int* ncol, double* xin, int* ldx, double* xout, int* ldy);
-extern "C" void trlan77_(OpFunc op, int ipar[32], int* nrowl, int* mev, double* eval,
-                             double* evec, int* nrow2, double* wrk, int* lwrk);
-
-EigValuesTRLAN::EigValuesTRLAN(int magnitude,
-                               int restarting_scheme,
-                               double tolerance,
-                               int max_mult,
-                               int mult_flops)
-{
-    this->magnitude = magnitude;
-    this->restarting_scheme = restarting_scheme;
-    this->tolerance = tolerance;
-    this->max_mult = max_mult;
-    this->mult_flops = mult_flops;
-}
-
-// refactor-nice.pl: check this substitution
-// old: void EigValuesTRLAN::getEigenvalues ( const GenericMatrix &data, Vector & eigenvalues, Matrix & eigenvectors, uint k )
-void EigValuesTRLAN::getEigenvalues(const GenericMatrix &data, NICE::Vector & eigenvalues, NICE::Matrix & eigenvectors, uint k)
-{
-#ifdef DEBUG_TRLAN
-    fprintf(stderr, "Starting eigenvalue computation with Lanczos Iteration ...\n");
-#endif
-    assert(data.rows() == data.cols());
-    mat = &data;
-    int mat_size = (int)data.rows();
-
-    if (max_mult <= 0) max_mult = 10 * mat_size;
-    if (mult_flops <= 0) mult_flops = 10 * mat_size;
-    
-    int computed_eigenpairs = k;
-    int ipar[32] = {0, magnitude, computed_eigenpairs, 0, 0, restarting_scheme, max_mult, 0, 1, 99, -1, 98, mult_flops};
-    double* evalbuff = new double[k];
-    memset(evalbuff, 0, k*sizeof(double));
-    double* evecbuff = new double[k*mat_size];
-    memset(evecbuff, 1, k*mat_size*sizeof(double));
-    int ritz_size = (computed_eigenpairs < 3 ? 2 * computed_eigenpairs : computed_eigenpairs + 6);
-    int workspace_size = ritz_size * (ritz_size + 10);
-    double* workspace = new double[workspace_size];
-    workspace[0] = tolerance;
-#ifdef DEBUG_TRLAN
-    fprintf(stderr, "Initialization ready ... Starting TRLAN\n");
-#endif
-    // call lanczos iteration of trlan
-    int kint = k;
-    trlan77_(op, ipar, &mat_size, &kint, evalbuff, evecbuff, &mat_size, workspace, &workspace_size);
-
-    if(ipar[0]!=0)
-    {
-    	fprintf(stderr,"EigValuesTRLAN: Error occured by calling TRLAN Eigenvalue Computation.\n");
-    	fprintf(stderr,"EigValuesTRLAN: %d converged eigenvectors \n",ipar[3]);
-
-    }
-#ifdef DEBUG_TRLAN
-    fprintf(stderr, "TRLAN ready\n");
-#endif
-
-    //copy eigenvectors in evecbuff to stl vector
-    eigenvectors.resize(mat_size, k);
-    eigenvalues.resize(k);
-#ifdef DEBUG_TRLAN
-    fprintf(stderr, "Store results\n");
-#endif
-    if (magnitude < 0)
-    {
-        for (uint i = 0;i < k;i++)
-            for (uint j = 0;j < (uint)mat_size;j++)
-                eigenvectors(j, i) = evecbuff[i*mat_size+j];
-        for (uint i = 0;i < k;i++)
-            eigenvalues[i] = evalbuff[i];
-    }
-    else
-    {
-        for (uint i = 0;i < k;i++)
-            for (uint j = 0;j < (uint)mat_size;j++)
-                eigenvectors(j, k - i - 1) = evecbuff[i*mat_size+j];
-        for (uint i = 0;i < k;i++)
-            eigenvalues[i] = evalbuff[k-i-1];
-
-    }
-#ifdef DEBUG_TRLAN
-    fprintf(stderr, "Clean up\n");
-#endif
-
-    delete[] workspace;
-    delete[] evalbuff;
-    delete[] evecbuff;
-#ifdef DEBUG_TRLAN
-    fprintf(stderr, "Clean up Done\n");
-#endif
-}

math/algebra_trlan/EigValuesTRLAN.h

@@ -1,58 +0,0 @@
-#ifndef EigenproblemINCLUDE
-#define EigenproblemINCLUDE
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <math.h>
-#include <vector>
-#include <string.h>
-
-
-#include "vislearning/math/algebra/EigValues.h"
-
-
-namespace OBJREC {
-
-/** Real Symmetric Eigenproblem - no parallel mode,
-    the eigenpairs returned by the method solve are ordered increasingly */
-class EigValuesTRLAN : public EigValues {
-      
-private:
-	int magnitude;
-	int restarting_scheme; 
-	double tolerance; 
-	int max_mult;
-	int mult_flops;
-
-public:
-
-	/** Init the TRLAN lanczos routine with the following parameters:
-           magnitude       -   Specifies the nature of the EigValuesTRLAN: 
-                               find eigenpair with respect to the k largest (magnitude>0) 
-                               or smallest (magnitude<0) eigenvalues
-           restarting_...  -   restarting scheme of TRLAN
-           max_mult        -   maximum number of matrix-vector multiplications to perform
-           tolerance       -   stop criterion (abs. resudual |A*x-lambda*x|?)
-           mult_flops       -  number of flops per matrix multiplication (used for statistics calculation)
-	*/
-	EigValuesTRLAN( int magnitude=1,
-                      int restarting_scheme=1,
-		      double tolerance=1e-8, 
-		      int max_mult=-1,
-                      int mult_flops=-1 );
-
-	~EigValuesTRLAN(){};
-
-	/**
-         * @param data matrix interface that does allow matrix-vector multiplications
-         * @param k number of eigenvalues/eigenvectors
-         * @param eigenvectors output Eigenvectors as Matrix
-         * @param eigenvalues output Eigenvalues as Vector
-         */
-	virtual void getEigenvalues ( const GenericMatrix &data, NICE::Vector & eigenvalues, NICE::Matrix & eigenvectors, uint k );
-};
-
-
-} // namespace
-
-#endif

math/algebra_trlan/Eigenproblem.cpp

@@ -1,69 +0,0 @@
-#include "Eigenproblem.h"
-
-#include <iostream>
-
-using namespace OBJREC;
-
-using namespace std;
-
-	static Eigenproblem* eigen=NULL;
-
-	static void op(int* nrow, int* ncol, double* xin, int* ldx, double* xout, int* ldy){
-		eigen->sparseMult(xin,xout,*ldy);
-	}
-
-	void Eigenproblem::sparseMult(double* xin, double* xout,int ldy){
-		memset(xout,0,ldy*sizeof(double));
-		for(uint i=0;i<mat.size();i++){
-			xout[mat[i].x]+=mat[i].val * xin[mat[i].y];
-		}
-	}
-
-
-	typedef void (*OpFunc) (int* nrow,int* ncol, double* xin,int* ldx, double* xout, int* ldy);
-	extern "C" void trlan77_(OpFunc op, int ipar[32], int* nrowl, int* mev, double* eval, 
-                             double* evec, int* nrow2, double* wrk, int* lwrk);	
-
-	void Eigenproblem::solve(vector<element> mat, int mat_size, vector<double> &evals, 
-                             vector< vector<double> > &evecs, int k, int magnitude,
-                             int restarting_scheme, double tolerance, int max_mult,
-                             int mult_flops){
-	
-		this->mat=mat;
-
-		if(max_mult<=0) max_mult=10*mat_size;
-		if(mult_flops<=0) mult_flops=10*mat_size;
-
-		eigen=this;
-
-		int computed_eigenpairs=k;
-		int ipar[32]={0,magnitude,computed_eigenpairs,0,0,restarting_scheme,max_mult,0,1,99,-1,98,mult_flops};
-		double* evalbuff=new double[k];
-		memset(evalbuff,0,k*sizeof(double));
-		double* evecbuff=new double[k*mat_size];
-		memset(evecbuff,1,k*mat_size*sizeof(double));
-		int ritz_size=(computed_eigenpairs<3 ? 2*computed_eigenpairs : computed_eigenpairs+6);
-		int workspace_size=ritz_size*(ritz_size+10);
-		double* workspace=new double[workspace_size];
-		workspace[0]=tolerance;
-		trlan77_(op,ipar,&mat_size,&k,evalbuff,evecbuff,&mat_size,workspace,&workspace_size);
-		if(ipar[0]!=0)
-		    {
-		    	fprintf(stderr,"EigValuesTRLAN: Error occured by calling TRLAN Eigenvalue Computation.\n");
-		    	fprintf(stderr,"EigValuesTRLAN: %d converged eigenvectors \n",ipar[3]);
-
-		    }
-		//copy eigenvectors in evecbuff to stl vector
-    		for(int i=0;i<k;i++){
-			vector<double> vec;
-			for(int j=0;j<mat_size;j++){
-				vec.push_back(evecbuff[i*mat_size+j]);
-			}
-			evecs.push_back(vec);
-		}
-		for(int i=0;i<k;i++){
-			evals.push_back(evalbuff[i]);
-		}
-		delete[] evalbuff;
-		delete[] evecbuff;           
-	}

math/algebra_trlan/Eigenproblem.h

@@ -1,59 +0,0 @@
-#ifndef EigenproblemINCLUDE
-#define EigenproblemINCLUDE
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <math.h>
-#include <vector>
-#include <string.h>
-
-
-typedef struct{
-	uint 	x;
-	uint 	y;
-	double 	val;
-} element;
-
-
-namespace OBJREC {
-
-/** depreceated version: Real Symmetric Eigenproblem - no parallel mode
- the eigenpairs returned by the method solve are ordered increasingly */
-class Eigenproblem{
-      
-private:
-
-public:
-
-	///performs matrix-vector multiplication with sparse matrix this->mat
-	void sparseMult(double* xin, double* yout, int ldy);
-
-	std::vector<element> mat;
-
-	Eigenproblem(){};
-	~Eigenproblem(){};
-
-	/** Solves the user defined eigenproblem with the following parameters:
-           mat             -   Sparse matrix
-           mat_size        -   matrix size, i.e. dim(mat)=mat_size x mat_size
-           evals           -   std::vector of eigenvalues
-           evecs           -   std::vector of k eigenvectors
-           k               -   number of eigenvalues/eigenvectors to compute
-           magnitude       -   Specifies the nature of the eigenproblem: 
-                               find eigenpair with respect to the k largest (magnitude>0) 
-                               or smallest (magnitude<0) eigenvalues
-           restarting_...  -   restarting scheme of TRLAN
-           max_mult        -   maximum number of matrix-vector multiplications to perform
-           tolerance       -   stop criterion (abs. resudual |A*x-lambda*x|?)
-           mult_flops       -  number of flops per matrix multiplication (used for statistics calculation)
-	*/
-	void solve(	std::vector<element> mat, int mat_size, std::vector<double> &evals, 
-                        std::vector< std::vector<double> > &evecs, int k=1, int magnitude=1,
-                        int restarting_scheme=1,double tolerance=1e-8, int max_mult=-1,
-                        int mult_flops=-1);
-};
-
-
-} // namespace
-
-#endif

math/algebra_trlan/Makefile

@@ -1,8 +0,0 @@
-#TARGETS_FROM:=$(notdir $(patsubst %/,%,$(shell pwd)))/$(TARGETS_FROM)
-#$(info recursivly going up: $(TARGETS_FROM) ($(shell pwd)))
-
-all:
-
-%:
-	$(MAKE) TARGETS_FROM=$(notdir $(patsubst %/,%,$(shell pwd)))/$(TARGETS_FROM) -C .. $@
-

math/algebra_trlan/Makefile.inc

@@ -1,103 +0,0 @@
-# LIBRARY-DIRECTORY-MAKEFILE
-# conventions:
-# - all subdirectories containing a "Makefile.inc" are considered sublibraries
-#   exception: "progs/" and "tests/" subdirectories!
-# - all ".C", ".cpp" and ".c" files in the current directory are linked to a
-#   library
-# - the library depends on all sublibraries 
-# - the library name is created with $(LIBNAME), i.e. it will be somehow
-#   related to the directory name and with the extension .a
-#   (e.g. lib1/sublib -> lib1_sublib.a)
-# - the library will be added to the default build list ALL_LIBRARIES
-
-# --------------------------------
-# - remember the last subdirectory
-#
-# set the variable $(SUBDIR) correctly to the current subdirectory. this
-# variable can be used throughout the current makefile.inc. The many 
-# SUBDIR_before, _add, and everything are only required so that we can recover
-# the previous content of SUBDIR before exitting the makefile.inc
-
-SUBDIR_add:=$(dir $(word $(words $(MAKEFILE_LIST)),$(MAKEFILE_LIST)))
-SUBDIR_before:=$(SUBDIR)
-SUBDIR:=$(strip $(SUBDIR_add))
-SUBDIR_before_$(SUBDIR):=$(SUBDIR_before)
-ifeq "$(SUBDIR)" "./"
-SUBDIR:=
-endif
-
-# ------------------------
-# - include subdirectories
-#
-# note the variables $(SUBDIRS_OF_$(SUBDIR)) are required later on to recover
-# the dependencies automatically. if you handle dependencies on your own, you
-# can also dump the $(SUBDIRS_OF_$(SUBDIR)) variable, and include the
-# makefile.inc of the subdirectories on your own...
-
-SUBDIRS_OF_$(SUBDIR):=$(patsubst %/Makefile.inc,%,$(wildcard $(SUBDIR)*/Makefile.inc))
-include $(SUBDIRS_OF_$(SUBDIR):%=%/Makefile.inc)
-
-# ----------------------------
-# - include local dependencies
-#
-# you can specify libraries needed by the individual objects or by the whole
-# directory. the object specific additional libraries are only considered
-# when compiling the specific object files
-# TODO: update documentation...
-
--include $(SUBDIR)libdepend.inc
-
-$(foreach d,$(filter-out %progs %tests,$(SUBDIRS_OF_$(SUBDIR))),$(eval $(call PKG_DEPEND_INT,$(d))))
-
-# ---------------------------
-# - objects in this directory
-#
-# the use of the variable $(OBJS) is not mandatory. it is mandatory however
-# to update $(ALL_OBJS) in a way that it contains the path and name of
-# all objects. otherwise we can not include the appropriate .d files.
-
-OBJS:=$(patsubst %.cpp,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.cpp))) \
-      $(patsubst %.C,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.C))) \
-	  $(shell grep -ls Q_OBJECT $(SUBDIR)*.h | sed -e's@^@/@;s@.*/@$(OBJDIR)moc_@;s@\.h$$@.o@') \
-      $(patsubst %.c,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.c)))
-ALL_OBJS += $(OBJS)
-
-# ----------------------------
-# - binaries in this directory
-#
-# output of binaries in this directory. none of the variables has to be used.
-# but everything you add to $(ALL_LIBRARIES) and $(ALL_BINARIES) will be
-# compiled with `make all`. be sure again to add the files with full path.
-
-LIBRARY_BASENAME:=$(call LIBNAME,$(SUBDIR))
-ifneq "$(SUBDIR)" ""
-ALL_LIBRARIES+=$(LIBDIR)$(LIBRARY_BASENAME).$(LINK_FILE_EXTENSION)
-endif
-
-# ---------------------
-# - binary dependencies
-#
-# there is no way of determining the binary dependencies automatically, so we
-# follow conventions. the current library depends on all sublibraries.
-# all other dependencies have to be added manually by specifying, that the
-# current .pc file depends on some other .pc file. binaries depending on
-# libraries should exclusivelly use the .pc files as well.
-
-ifeq "$(SKIP_BUILD_$(OBJDIR))" "1"
-$(LIBDIR)$(LIBRARY_BASENAME).a:
-else
-$(LIBDIR)$(LIBRARY_BASENAME).a:$(OBJS) \
-	$(call PRINT_INTLIB_DEPS,$(PKGDIR)$(LIBRARY_BASENAME).a,.$(LINK_FILE_EXTENSION))
-endif
-
-$(PKGDIR)$(LIBRARY_BASENAME).pc: \
-	$(call PRINT_INTLIB_DEPS,$(PKGDIR)$(LIBRARY_BASENAME).pc,.pc)
-
-# -------------------
-# - subdir management
-#
-# as the last step, always add this line to correctly recover the subdirectory
-# of the makefile including this one!
-
-SUBDIR:=$(SUBDIR_before_$(SUBDIR))
-

math/algebra_trlan/libdepend.inc

@@ -1,2 +0,0 @@
-$(call PKG_DEPEND_EXT_ESSENTIAL,TRLAN)
-$(call PKG_DEPEND_INT,vislearning/math/algebra)

math/algebra_trlan/progs/Makefile.inc

@@ -1,88 +0,0 @@
-# BINARY-DIRECTORY-MAKEFILE
-# conventions:
-# - there are no subdirectories, they are ignored!
-# - all ".C", ".cpp" and ".c" files in the current directory are considered
-#   independent binaries, and linked as such.
-# - the binaries depend on the library of the parent directory
-# - the binary names are created with $(BINNAME), i.e. it will be more or less
-#   the name of the .o file
-# - all binaries will be added to the default build list ALL_BINARIES
-
-# --------------------------------
-# - remember the last subdirectory
-#
-# set the variable $(SUBDIR) correctly to the current subdirectory. this
-# variable can be used throughout the current makefile.inc. The many 
-# SUBDIR_before, _add, and everything are only required so that we can recover
-# the previous content of SUBDIR before exitting the makefile.inc
-
-SUBDIR_add:=$(dir $(word $(words $(MAKEFILE_LIST)),$(MAKEFILE_LIST)))
-SUBDIR_before:=$(SUBDIR)
-SUBDIR:=$(strip $(SUBDIR_add))
-SUBDIR_before_$(SUBDIR):=$(SUBDIR_before)
-
-# ------------------------
-# - include subdirectories
-#
-# note the variables $(SUBDIRS_OF_$(SUBDIR)) are required later on to recover
-# the dependencies automatically. if you handle dependencies on your own, you
-# can also dump the $(SUBDIRS_OF_$(SUBDIR)) variable, and include the
-# makefile.inc of the subdirectories on your own...
-
-#SUBDIRS_OF_$(SUBDIR):=$(patsubst %/Makefile.inc,%,$(wildcard $(SUBDIR)*/Makefile.inc))
-#include $(SUBDIRS_OF_$(SUBDIR):%=%/Makefile.inc)
-
-# ----------------------------
-# - include local dependencies
-#
-# include the libdepend.inc file, which gives additional dependencies for the
-# libraries and binaries. additionally, an automatic dependency from the library
-# of the parent directory is added (commented out in the code below).
-
--include $(SUBDIR)libdepend.inc
-
-PARENTDIR:=$(patsubst %/,%,$(dir $(patsubst %/,%,$(SUBDIR))))
-$(eval $(call PKG_DEPEND_INT,$(PARENTDIR)))
-
-# ---------------------------
-# - objects in this directory
-#
-# the use of the variable $(OBJS) is not mandatory. it is mandatory however
-# to update $(ALL_OBJS) in a way that it contains the path and name of
-# all objects. otherwise we can not include the appropriate .d files.
-
-OBJS:=$(patsubst %.cpp,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.cpp))) \
-      $(patsubst %.C,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.C))) \
-      $(shell grep -ls Q_OBJECT $(SUBDIR)*.h | sed -e's@^@/@;s@.*/@$(OBJDIR)moc_@;s@\.h$$@.o@') \
-      $(patsubst %.c,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.c)))
-ALL_OBJS += $(OBJS)
-
-# ----------------------------
-# - binaries in this directory
-#
-# output of binaries in this directory. none of the variables has to be used.
-# but everything you add to $(ALL_LIBRARIES) and $(ALL_BINARIES) will be
-# compiled with `make all`. be sure again to add the files with full path.
-
-BINARIES:=$(patsubst %.o,$(BINDIR)%,$(filter-out moc_%,$(notdir $(OBJS))))
-ALL_BINARIES+=$(BINARIES)
-
-# ---------------------
-# - binary dependencies
-#
-# there is no way of determining the binary dependencies automatically, so we
-# follow conventions. each binary depends on the corresponding .o file and
-# on the libraries specified by the INTLIBS/EXTLIBS. these dependencies can be
-# specified manually or they are automatically stored in a .bd file.
-
-$(foreach head,$(wildcard $(SUBDIR)*.h),$(eval $(shell grep -q Q_OBJECT $(head) && echo $(head) | sed -e's@^@/@;s@.*/\(.*\)\.h$$@$(BINDIR)\1:$(OBJDIR)moc_\1.o@')))
--include $(OBJS:%.o=%.bd)
-
-# -------------------
-# - subdir management
-#
-# as the last step, always add this line to correctly recover the subdirectory
-# of the makefile including this one!
-
-SUBDIR:=$(SUBDIR_before_$(SUBDIR))
-

math/algebra_trlan/progs/testTRLAN.cpp

@@ -1,51 +0,0 @@
-/** 
-2009.05.25.
-by M.Kemmler
-*/
-
-#include "vislearning/math/algebra_trlan/Eigenproblem.h"
-#include <iostream>
-#include <fstream>
-
-using namespace OBJREC;
-
-int main (int argc, char **argv)
-{   
-#ifdef NICE_USELIB_TRLAN
-	int N=10;
-	int problem_size=N*N;
-	int eval_nr=N;
-	element e;
-	std::vector<element> sparse_mat;
-	std::vector<double> evals;
-	std::vector< std::vector<double> > evecs;
-	//build sparse matrix
-	for(int i=0;i<N;i++){
-		for(int j=0;j<N;j++){
-			for(int k=i-5;k<i+5;k++){
-				for(int l=j-5;l<j+5;l++){
-					if(k<0||l<0||k>=N||l>=N) continue;
-					e.x=i*N+j;
-					e.y=k*N+l;
-					e.val=1.0/(1.0+i*j+k*l);
-					sparse_mat.push_back(e);
-					e.y=i*N+j;
-					e.x=k*N+l;
-					sparse_mat.push_back(e);
-				}
-			}
-		}
-	}
-
-	Eigenproblem eig;
-	eig.solve(sparse_mat,problem_size,evals,evecs,eval_nr,+1);
-
-	for(int i=0;i<eval_nr;i++){
-		std::cerr << "eigenvalue "<<i+1<<": \n\t"<<evals[i]<<std::endl;
-		std::cerr << "corresponding eigenvector:"<<i+1<<": \n";
-		for(int j=0;j<problem_size;j++) std::cerr << evecs[i][j] << std::endl;
-
-	}
-#endif
-
-}

math/ftransform/FTransform.cpp

@@ -4,21 +4,15 @@
  * @author Michael Koch
  * @date 08/19/2008
      
-      */
-     /** \example testPCA.cpp
+*/
+/** \example testPCA.cpp
  * This is an example of how to use an instance of FTransform the Principal Component Analysis (PCA) class.
       */
-// #ifdef NOVISUAL
-// #include <vislearning/nice_nonvis.h>
-// #else
-// #include <vislearning/nice.h>
-// #endif
      
      
 #include "FTransform.h"
-// #include "vislearning/fourier/FourierLibrary.h"
      
-     // basic file operations
+// basic file operations
 #include <sstream>
 #include <iostream>
 #include <fstream>

math/ftransform/FTransform.h

@@ -1,6 +1,6 @@
 /** 
 * @file FTransform.h
- * @brief FTransform Class Definition
+* @brief FTransform Class Definition
 * @author Michael Koch
 * @date 08/19/2008
 
@@ -8,17 +8,13 @@
 #ifndef FTRANSFORMINCLUDE
 #define FTRANSFORMINCLUDE
 
-#ifdef NOVISUAL
-#include <vislearning/nice_nonvis.h>
-#else
-#include <vislearning/nice.h>
-#endif
-  
 #include "core/basics/Persistent.h"
+#include "core/vector/VectorT.h"
+#include "core/vector/MatrixT.h"
 
 namespace OBJREC {
 
-/** __DESC__ */
+/** Abstract class for feature transformations */
 class FTransform: public NICE::Persistent
 {
 

math/ftransform/PCA.cpp

@@ -12,20 +12,15 @@
 // #define DEBUG
 #undef DEBUG
 
-#include <vislearning/nice_nonvis.h>
-
 #include <iostream>
 #include <math.h>
 #include <vector>
 #include <map>
 
-#include "vislearning/math/algebra/AlgebraTools.h"
-#include "vislearning/math/algebra/GenericMatrix.h"
-#include "vislearning/math/algebra/EigValues.h"
+#include "core/algebra/GenericMatrix.h"
+#include "core/algebra/EigValues.h"
+#include "core/algebra/EigValuesTRLAN.h"
 
-#ifdef NICE_USELIB_TRLAN
-#include "vislearning/math/algebra_trlan/EigValuesTRLAN.h"
-#endif
 // #include "vislearning/fourier/FourierLibrary.h"
 #include "PCA.h"
 #include "vislearning/baselib/Gnuplot.h"
@@ -71,186 +66,61 @@ void PCA::restore(istream & is, int format)
 	is >> mean;
 	is >> targetDimension;
 }
+
 void PCA::store(ostream & os, int format) const
 {
 	os << basis << normalization << mean << targetDimension;
 }
+
 void PCA::clear()
 {
 
 }
 
 void PCA::calculateBasis(const NICE::Matrix &features,
-		const uint targetDimension, const uint mode)
+		const uint targetDimension)
 {
-	calculateBasis(features, targetDimension, mode, false);
+	calculateBasis(features, targetDimension, false);
 }
+
+void PCA::calculateMean ( const NICE::Matrix &features, NICE::Vector & mean )
+{
+  // data vectors are put row-wise in the matrix
+  mean.resize(features.cols());
+  for ( uint i = 0 ; i < features.rows(); i++ )
+    mean = mean + features.getRow(i);
+}
+
 void PCA::calculateBasis(const NICE::Matrix &features,
-		const uint targetDimension, const uint mode, const bool adaptive,
+		const uint targetDimension, const bool adaptive,
 		const double targetRatio)
 {
 	this->targetDimension = targetDimension;
+  // dimension of the feature vectors
 	uint srcFeatureSize = features.cols();
 	uint mindimension = std::min(this->targetDimension, srcFeatureSize);
 
-#ifdef DEBUG
-	cout << "calculateBasis ... stay a while and listen ..." << endl;
-#endif
-
 	NICE::Matrix eigenvectors;
 
 	NICE::Vector eigenvalue;
-#ifdef DEBUG
-	cout << "EigenValue Decomposition Working..." << endl;
-#endif
-	//switch mode: svd, lanczos, broken lanczos, arnoldi
-	//switch eigenvalue decomposition
-	switch (mode)
-	{
-	case 0:
-	{
-#ifndef NICE_USELIB_ICE
-		fthrow(Exception, "PCA: ICE needed for mode 0");
-#else
-#ifdef DEBUG
-		fprintf(stderr, "PCA: warning covariance computation in progress, memory and time consuming ...!\n");
-#endif
-		ice::Matrix sigma;
-		ice::Matrix eigenvectors_ice;
-		ice::Vector eigenvalue_ice;
-		ice::Matrix features_ice(NICE::makeICEMatrix(features));
-
-		Statistics st(srcFeatureSize);
-#ifdef DEBUG
-		fprintf(stderr, "Transform Features ... %d %d \n", features_ice.rows(), features_ice.cols());
-#endif
-		Put(st, features_ice);
-
-		ice::Vector mean_ice = Mean(st);
-		mean = makeEVector(mean_ice);
-#ifdef DEBUG
-		cout << "MEAN: " << mean_ice << endl;
-#endif
-		sigma = Covariance(st);
-#ifdef DEBUG
-		fprintf(stderr, "Covariance Ready \n");
-		fprintf(stderr, "Eigenvalue Computation ... \n");
-#endif
-		Eigenvalue(sigma, eigenvalue_ice, eigenvectors_ice);
-		NICE::Matrix eigenvectors_tmp;
-		eigenvectors_tmp = makeDoubleMatrix(eigenvectors_ice); //bad error
-		eigenvectors.resize(eigenvectors_tmp.rows(), eigenvectors_tmp.cols());
-		eigenvectors = eigenvectors_tmp;
-		eigenvalue = makeEVector(eigenvalue_ice);
-
-#endif
-		break;
-	}
-	case 1:
-	{
-
-#ifdef DEBUG
-		fprintf(stderr, "Calc mean \n");
-#endif
-		AlgebraTools::calculateMean(features, mean);
-#ifdef DEBUG
-		fprintf(stderr, "fastest available method \n");
-		//                cerr<<"mean:"<<mean<<endl;
-#endif
+  
+  calculateMean(features, mean);
 #ifdef NICE_USELIB_TRLAN
-		EigValues *eig = new EigValuesTRLAN();//fast lanczos TRLAN
+  EigValues *eig = new EigValuesTRLAN();//fast lanczos TRLAN
 #else
-		EigValues *eig = new EVArnoldi();//Arnoldi for (srcFeatureSize<n)
-#endif
-		NICE::Matrix features_transpose = features.transpose();
-		GMCovariance C(&features_transpose);
-#ifdef DEBUG
-		fprintf(stderr, "Perform PCA\n");
-		fprintf(stderr, "Matrix size %d %d %d %d \n", features.rows(), features.cols(), C.rows(), C.cols());
+  EigValues *eig = new EVArnoldi();//Arnoldi for (srcFeatureSize<n)
 #endif
-		if (adaptive)
-		{
-			eig->getEigenvalues(C, eigenvalue, eigenvectors, srcFeatureSize);
-		}
-		else
-		{
-			eig->getEigenvalues(C, eigenvalue, eigenvectors, mindimension);
-		}
-
-#ifdef DEBUG
-		fprintf(stderr, "PCA: Computation ready \n");
-		//                cerr<<eigenvectors<<endl;
-#endif
-
-#ifdef DEBUG
-		fprintf(stderr, "PCA: Transpose ready \n");
-		//                cerr<<eigenvectors<<endl;
-
-#endif
-		break;
-	}
-#ifdef USE_BROKEN_LANCZOS
-		case 2:
-		{
-			//Experimental TODO
-#ifdef DEBUG
-			fprintf(stderr, "Calc mean \n");
-#endif
-			AlgebraTools::calculateMean(features, mean);
-#ifdef DEBUG
-			fprintf(stderr, "Lanczos \n");
-#endif
-			EigValues *eig = new EVLanczos(100, 1e-4);
-			GMCovariance C(&features.transpose());
-			if(adaptive)
-			{
-				eig->getEigenvalues(C, eigenvalue, eigenvectors, srcFeatureSize);
-			}
-			else
-			{
-				eig->getEigenvalues(C, eigenvalue, eigenvectors, mindimension); //fast lanczos
-			}
-#ifdef DEBUG
-			fprintf(stderr, "PCA: Computation ready \n");
-#endif
-			break;
-		}
-#endif
-	case 3:
-	{
+  NICE::Matrix features_transpose = features.transpose();
+  GMCovariance C(&features_transpose);
+  if (adaptive)
+  {
+    eig->getEigenvalues(C, eigenvalue, eigenvectors, srcFeatureSize);
+  }
+  else
+  {
+    eig->getEigenvalues(C, eigenvalue, eigenvectors, mindimension);
+  }
 
-#ifdef DEBUG
-		fprintf(stderr, "Calc mean \n");
-#endif
-		AlgebraTools::calculateMean(features, mean);
-#ifdef DEBUG
-		fprintf(stderr, "Arnoldi \n");
-#endif
-		EigValues *eig = new EVArnoldi(100, 1e-4);
-		NICE::Matrix tmppointer = features.transpose();
-		GMCovariance C(&tmppointer);
-		if (adaptive)
-		{
-			eig->getEigenvalues(C, eigenvalue, eigenvectors, srcFeatureSize);
-		}
-		else
-		{
-			eig->getEigenvalues(C, eigenvalue, eigenvectors, mindimension); //fast arnoldi
-		}
-#ifdef DEBUG
-		fprintf(stderr, "PCA: Computation ready \n");
-#endif
-
-#ifdef DEBUG
-		fprintf(stderr, "PCA: Transpose ready \n");
-#endif
-		break;
-	}
-	default:
-		fprintf(stderr, "PCA: Wrong Computation mode: %d!\n", mode);
-		break;
-
-	}
 #ifdef DEBUG
 	fprintf(stderr, "Eigenvalue Decomposition ready \n");
 	cerr << eigenvectors << endl;
@@ -322,15 +192,8 @@ void PCA::calculateBasis(const NICE::Matrix &features,
 	}
 
 #ifdef DEBUG
-	cerr << basis << endl;
-	//     if (mode == 0)
-	//     {
-	//         cout << eigenvectors << endl;
-	//         cout << basis << endl;
-	//         cout << mean << endl;
 	cout << "Eigenvalue-absolute:" << eigenvalue << endl;
 	cout << "Eigenvalue-ratio:" << ratio << endl;
-	//     }
 #endif
 
 }

math/ftransform/PCA.h

@@ -8,12 +8,6 @@
 #ifndef PCAINCLUDE
 #define PCAINCLUDE
 
-#ifdef NOVISUAL
-#include <vislearning/nice_nonvis.h>
-#else
-#include <vislearning/nice.h>
-#endif
-
 #include "vislearning/math/ftransform/FTransform.h"
 
 
@@ -27,12 +21,14 @@ class PCA: public FTransform
           NICE::Vector mean;
 
           NICE::Matrix basis;
-		  NICE::Matrix normbasis;
+		      NICE::Matrix normbasis;
 		  
           NICE::Matrix normalization;
           uint targetDimension; 
           uint maxiteration;
           double mindelta; 
+
+          void calculateMean ( const NICE::Matrix &features, NICE::Vector & mean );
  
      public:
        
@@ -50,11 +46,10 @@ class PCA: public FTransform
            * get Basis Vectors of PCA
            * @param features Input Features
            * @param dimension Dimension size / number of principal components 
-           * @param mode Algorithm mode 0=SVD 1=Arnoldi
           * @return Basis Vectors 
           */
-          void calculateBasis(const NICE::Matrix &features,const uint targetDimension,const uint mode=0);
-          void calculateBasis(const NICE::Matrix &features,const uint targetDimension,const uint mode,const bool adaptive,const double targetRatio=1.0);
+          void calculateBasis(const NICE::Matrix &features,const uint targetDimension);
+          void calculateBasis(const NICE::Matrix &features,const uint targetDimension,const bool adaptive,const double targetRatio=1.0);
 		  
 		  /**
 		   * returns the dimension of the transformed features

math/ftransform/libdepend.inc

@@ -1,4 +1 @@
-$(call PKG_DEPEND_EXT,TRLAN)
-$(call PKG_DEPEND_EXT,ICE)
-$(call PKG_DEPEND_INT,vislearning/math/algebra)
-$(call PKG_DEPEND_INT,vislearning/math/algebra_trlan)
+$(call PKG_DEPEND_INT,core/algebra)

math/kernels/KernelData.cpp

@@ -8,8 +8,8 @@
 #include <iostream>
 
 #include "KernelData.h"
-#include "vislearning/math/algebra/CholeskyRobustAuto.h"
-#include "vislearning/math/algebra/CholeskyRobust.h"
+#include "core/algebra/CholeskyRobustAuto.h"
+#include "core/algebra/CholeskyRobust.h"
 #include "core/vector/Algorithms.h"
 
 #ifdef NICE_USELIB_OPENMP
@@ -79,7 +79,7 @@ void KernelData::initFromConfig ( const Config *conf, const string & section )
 		if ( verbose )
 			std::cerr << "KernelData: using the cholesky method with static regularization" << std::endl;
 
-		cr = new CholeskyRobust ( verbose, noiseStep, minimumLogDet, useCuda );
+		cr = new CholeskyRobust ( verbose, noiseStep, useCuda );
 	}
 }
 
@@ -629,9 +629,9 @@ void KernelData::perform_Rank_2k_Update(const std::vector<int> & rowIndices, con
 		if (verbose)
 		{
 			// we have to consider the entries which are added twice
-			for (int x = 0; x < rowIndices.size(); x++)
+			for (int x = 0; x < (int)rowIndices.size(); x++)
 			{
-				for (int y = x; y < rowIndices.size(); y++)
+				for (int y = x; y < (int)rowIndices.size(); y++)
 				{
 					UV(rowIndices[x] , rowIndices[y]) /= 2.0;
 					if (x!=y)

math/kernels/KernelData.h

@@ -8,7 +8,7 @@
 #define _NICE_OBJREC_KERNELDATAINCLUDE
 
 #include "core/basics/Config.h"
-#include "vislearning/math/algebra/CholeskyRobust.h"
+#include "core/algebra/CholeskyRobust.h"
 #include "core/vector/MatrixT.h"
 
 namespace OBJREC {
@@ -28,7 +28,7 @@ class KernelData
 	protected:
 		bool verbose;
 
-		CholeskyRobust *cr;
+		NICE::CholeskyRobust *cr;
 
     protected:
 		NICE::Matrix kernelMatrix;

math/kernels/libdepend.inc

@@ -1,3 +1,3 @@
 $(call PKG_DEPEND_INT,vislearning/math/mathbase)
 $(call PKG_DEPEND_INT,vislearning/math/distances)
-$(call PKG_DEPEND_INT,vislearning/math/algebra)
+$(call PKG_DEPEND_INT,core/algebra)

math/pdf/PDFGaussian.cpp

@@ -15,7 +15,7 @@
 #include <sys/time.h>
 
 #include "vislearning/math/pdf/PDFGaussian.h"
-#include "vislearning/math/algebra/CholeskyRobustAuto.h"
+#include "core/algebra/CholeskyRobustAuto.h"
 
 #include "core/vector/Algorithms.h"
 #include "core/basics/numerictools.h"

math/pdf/libdepend.inc

@@ -1,2 +1,2 @@
 $(call PKG_DEPEND_INT,vislearning/math/mathbase)
-$(call PKG_DEPEND_INT,vislearning/math/algebra)
+$(call PKG_DEPEND_INT,core/algebra)

math/progs/testEigenvalues.cpp

@@ -1,192 +0,0 @@
-/** 
-* @file testEigenvalues.cpp
-* @brief test eigenvalues
-* @author Erik Rodner
-* @date 05/21/2008
-
-*/
-
-
-#include <vislearning/nice.h>
-
-#ifdef NICE_USELIB_ICE
-
-#include <image_nonvis.h>
-#include <core/iceconversion/convertice.h>
-#include <core/vector/Distance.h>
-
-#include "vislearning/math/algebra/EigValues.h"
-#ifdef NICE_USELIB_TRLAN
-#include "vislearning/math/algebra_trlan/EigValuesTRLAN.h"
-#endif
-#include "core/basics/Config.h"
-
-using namespace OBJREC;
-
-using namespace NICE;
-using namespace std;
-
-
-int main (int argc, char **argv)
-{   
-    std::set_terminate(__gnu_cxx::__verbose_terminate_handler);
-
-    Config conf ( argc, argv );
-    uint rows = conf.gI("main", "n", 2);
-    uint cols = rows;
-    uint k = conf.gI("main", "k", rows);
-    uint maxiterations = conf.gI("main", "maxiterations", 100);
-    double mindelta = conf.gD("main", "mindelta", 1e-4 );
-    double sparse_prob = conf.gD("main", "sparseprob", 0.0 );
-    bool trlan = conf.gB("main", "trlan", false);
-    int trlan_magnitude = conf.gI("main", "trlan_magnitude", 1);
-    bool init_random = conf.gB("main", "init_random", true );
-
-    // refactor-nice.pl: check this substitution
-    // old: Matrix T (rows, cols);
-    NICE::Matrix T (rows, cols);
-    T.set(0.0);
-    if ( init_random )
-	srand48(time(NULL));
-
-    // generate random symmetric matrix
-    for ( uint i = 0 ; i < rows ; i++ )
-	for ( uint j = i ; j < cols ; j++ )
-	{
-	    if ( sparse_prob != 0.0 )
-		if ( drand48() < sparse_prob )
-		    continue;
-	    T(i, j) = drand48();
-	    T(j, i) = T(i, j);
-	}
-
-    // refactor-nice.pl: check this substitution
-    // old: Vector ice_eigvalues;
-    ice::Vector ice_eigvalues;
-    // refactor-nice.pl: check this substitution
-    // old: Matrix ice_eigvect;
-    ice::Matrix ice_eigvect;
-    Eigenvalue ( NICE::makeICEMatrix(T), ice_eigvalues, ice_eigvect );
-
-    EigValues *eig; 
-    
-    if ( ! trlan )
-    {
-		fprintf (stderr, "Eigenvalue method: Arnoldi iteration\n");
-		eig = new EVArnoldi( maxiterations, mindelta );
-    } else {
-#ifdef NICE_USELIB_TRLAN
-		fprintf (stderr, "Eigenvalue method: Lanczos (TRLAN) magn=%d\n", trlan_magnitude);
-		eig = new EigValuesTRLAN(trlan_magnitude);
-#else 
-		fprintf (stderr, "Eigenvalue method: Lanczos (TRLAN) not supported !\n");
-		exit(-1);
-#endif
-    }
-    
-    NICE::Vector eigvalues_dense;
-    NICE::Matrix eigvect_dense;
-    NICE::Vector eigvalues_sparse;
-    NICE::Matrix eigvect_sparse;
-
-    GMSlowICE Tg (T);
-    double start_dense = ice::TimeD();
-    eig->getEigenvalues ( Tg, eigvalues_dense, eigvect_dense, k );
-    double time_dense = ice::TimeD() - start_dense;
-
-    GMSparse Ts (T);
-    double start_sparse = ice::TimeD();
-    eig->getEigenvalues ( Ts, eigvalues_sparse, eigvect_sparse, k );
-    double time_sparse = ice::TimeD() - start_sparse;
-
-    fprintf (stderr, "time: sparse %fs, dense %fs\n", time_sparse, time_dense );
-
-    // test property
-    NICE::EuclidianDistance<double> eucliddist;
-    for ( uint i = 0 ; i < k ; i++ )
-    {
-		NICE::Vector v_dense = eigvect_dense.getColumn(i);
-		double lambda_dense = eigvalues_dense[i];
-		NICE::Vector Tv_dense;
-		Tv_dense.multiply ( T, v_dense );
-		NICE::Vector lv_dense = v_dense;
-		lv_dense *= lambda_dense;
-		double err_dense = eucliddist(Tv_dense, lv_dense);
-		
-		NICE::Vector v_sparse = eigvect_sparse.getColumn(i);
-		double lambda_sparse = eigvalues_sparse[i];
-			NICE::Vector Tv_sparse;
-		Tv_sparse.multiply ( T, v_sparse );
-		NICE::Vector lv_sparse = v_sparse;
-		lv_sparse *= lambda_sparse;
-		double err_sparse = eucliddist(Tv_sparse, lv_sparse);
-
-		ice::Matrix ice_T = makeICEMatrix(T);
-		cerr << T << endl;
-		cerr << ice_T << endl;
-		double err_ice = (ice_T*(!ice_eigvect)[i] - ice_eigvalues[i]*(!ice_eigvect)[i]).Length();
-		cerr << "eigenvalue " << i << endl;
-		cerr << "lambda (dense) = " << lambda_dense << endl;
-		cerr << "lambda (sparse) = " << lambda_sparse << endl;
-		cerr << "lambda (ICE) = " << ice_eigvalues[i] << endl;
-		cerr << "||Av - lambda v|| (dense) = " << err_dense << endl;
-		cerr << "||Av - lambda v|| (sparse) = " << err_sparse << endl;
-		cerr << "||Av - lambda v|| (ICE) = " << err_ice << endl;
-    }
-
-    // test covariance part
-
-    cerr << "------- Covariance Test -------" << endl;
-
-    int n = 2*rows; // number of data elements
-    NICE::Matrix data(rows, n);
-    ice::Statistics stat (rows);
-    for (int i = 0 ; i < n ; i++ )
-    {
-		for ( int j = 0 ; j < (int)rows ; j++ )
-			data(j, i) = drand48() + 100.0;
-
-		Put ( stat, NICE::makeIceVectorT( data.getColumn(i) ) );
-    }
-	cerr << "Mean: " << Mean(stat) << endl;
-    NICE::Matrix cov = NICE::makeDoubleMatrix ( Covariance(stat) );
-
-    GMSlowICE cov_slow ( cov );
-    GMCovariance cov_cov ( &data );
-    NICE::Vector x1t_slow;
-    cov_slow.multiply ( x1t_slow, data.getColumn(0) );
-    cerr << x1t_slow << endl;
-
-    NICE::Vector x1t_cov;
-    cov_cov.multiply ( x1t_cov, data.getColumn(0) );
-    cerr << x1t_cov << endl;
-    
-    cerr << "Difference (simple vector multiplication): " << eucliddist( x1t_slow, x1t_cov ) << endl;
-
-    double start_std = ice::TimeD();
-    eig->getEigenvalues ( cov_slow, eigvalues_dense, eigvect_dense, k );
-    double time_std = ice::TimeD() - start_std;
-
-    NICE::Vector eigvalues_cov;
-    NICE::Matrix eigvect_cov;
-    
-    double start_cov = ice::TimeD();
-    eig->getEigenvalues ( cov_cov, eigvalues_cov, eigvect_cov, k );
-    double time_cov = ice::TimeD() - start_cov;
-
-    cerr << "Eigenvalues (std) = " << eigvalues_dense << endl;
-    cerr << "Eigenvalues (covariance) = " << eigvalues_cov << endl;
-
-    fprintf (stderr, "time: cov %fs, std %fs\n", time_std, time_cov );
-    
-    
-    return 0;
-}
-
-#else
-int main (int argc, char **argv)
-{   
-    throw("no ice installed\n");
-    return 0;
-}
-#endif

optimization/quadprog/QuadProg++.cpp

@@ -35,9 +35,8 @@
 #include <sstream>
 #include <stdexcept>
 #include "vislearning/optimization/quadprog/QuadProg++.h"
-#include "vislearning/math/algebra/CholeskyRobustAuto.h"
-#include "vislearning/math/algebra/CholeskyRobust.h"
-#include <vislearning/nice.h>
+#include "core/algebra/CholeskyRobustAuto.h"
+#include "core/algebra/CholeskyRobust.h"
 
 //#define TRACE_SOLVER
 namespace QuadProgPP{
@@ -724,7 +723,7 @@ void cholesky_decomposition(Matrix<double>& A)
 	}
 	NICE::Matrix L;
 	L.resize(n,n);
-	OBJREC::CholeskyRobust *cra = new OBJREC::CholeskyRobustAuto(true);
+	NICE::CholeskyRobust *cra = new NICE::CholeskyRobustAuto(true);
 	cra->robustChol ( M, L );
 	//copy back
 	for (i = 0; i < n; i++)

optimization/quadprog/libdepend.inc

@@ -1,3 +1,3 @@
 $(call PKG_DEPEND_INT,core/image)
 $(call PKG_DEPEND_INT,core/imagedisplay)
-$(call PKG_DEPEND_INT,vislearning/math/algebra)
+$(call PKG_DEPEND_INT,core/algebra)

regression/gpregression/GPRegressionOptimizationProblem.cpp

@@ -9,8 +9,8 @@
 #include <iostream>
 
 #include "core/vector/Algorithms.h"
-#include "vislearning/math/algebra/CholeskyRobust.h"
-#include "vislearning/math/algebra/CholeskyRobustAuto.h"
+#include "core/algebra/CholeskyRobust.h"
+#include "core/algebra/CholeskyRobustAuto.h"
 
 #include "vislearning/math/kernels/OptimKernelParameterGradInterface.h"
 #include "GPRegressionOptimizationProblem.h"

regression/gpregression/RegGaussianProcess.cpp

@@ -13,8 +13,8 @@
 #include "core/optimization/FirstOrderRasmussen.h"
 
 #include "core/vector/Algorithms.h"
-#include "vislearning/math/algebra/CholeskyRobust.h"
-#include "vislearning/math/algebra/CholeskyRobustAuto.h"
+#include "core/algebra/CholeskyRobust.h"
+#include "core/algebra/CholeskyRobustAuto.h"
 
 #include "RegGaussianProcess.h"
 #include "GPRegressionOptimizationProblem.h"

regression/gpregression/modelselcrit/GPMSCLooEstimates.cpp

@@ -10,7 +10,7 @@
 #include "GPMSCLooEstimates.h"
 #include "vislearning/cbaselib/ClassificationResults.h"
 #include "vislearning/classifier/kernelclassifier/LikelihoodFunction.h"
-#include "vislearning/math/algebra/CholeskyRobustAuto.h"
+#include "core/algebra/CholeskyRobustAuto.h"
 #include "core/optimization/FirstOrderTrustRegion.h"
 
 #include "core/vector/Algorithms.h"