vnl_matrix <double> Normalize_Feature_Matrix(vnl_matrix<double> feats)
{
mbl_stats_nd stats;
for(int i = 0; i<feats.rows() ; ++i)
{
vnl_vector<double> temp_row = feats.get_row(i);
stats.obs(temp_row);
}
vnl_vector<double> std_vec = stats.sd();
vnl_vector<double> mean_vec = stats.mean();
for(int i = 0; i<feats.columns()-3 ; ++i)
{
vnl_vector<double> temp_col = feats.get_column(i);
if(std_vec(i) > 0)
{
for(int j =0; j<temp_col.size() ; ++j)
temp_col[j] = (temp_col[j] - mean_vec(i))/std_vec(i) ;
}
feats.set_column(i,temp_col);
}
return feats;
}
vnl_matrix <double> MCLR_SM::Normalize_Feature_Matrix(vnl_matrix<double> feats)
{
mbl_stats_nd stats;
for(int i = 0; i<feats.rows() ; ++i)
{
vnl_vector<double> temp_row = feats.get_row(i);
stats.obs(temp_row);
}
std_vec = stats.sd();
mean_vec = stats.mean();
//The last column is the training column
for(int i = 0; i<feats.columns() ; ++i)
{
vnl_vector<double> temp_col = feats.get_column(i);
if(std_vec(i) > 0)
{
for(int j =0; j<temp_col.size() ; ++j)
temp_col[j] = (temp_col[j] - mean_vec(i))/std_vec(i) ;
}
feats.set_column(i,temp_col);
}
return feats;
}
//std::vector<double> Vectorize(const vnl_matrix<double> &M)
vnl_vector<double> Vectorize(const vnl_matrix<double> &M)
{
//std::vector<double> V;
vnl_vector<double> V(M.rows() * M.columns());
for (unsigned j = 0; j < M.rows(); j++)
{
for (unsigned i = 0; i < M.columns(); i++)
{
//V.push_back(M(i,j));
V[M.columns() * j + i] = M(i,j);
}
}
return V;
}
std::vector<vnl_vector<double> > EigenVectors(const vnl_matrix<double> &M)
{
vnl_symmetric_eigensystem<double> Eigs(M);
std::vector<vnl_vector<double> > EVecs;
for(unsigned int i = 0; i < M.columns(); i++)
EVecs.push_back(Eigs.get_eigenvector(i));
return EVecs;
}
void LocalGeometryRef::Initialize(vnl_matrix<double> data)
{
this->num_row = data.rows();
this->num_col = data.columns();
this->data_matrix.set_size(this->num_row, this->num_col);
for(int row = 0; row < this->num_row; row++)
{
for(int col = 0; col < this->num_col; col++)
{
this->data_matrix(row, col) = data(row, col);
}
}
}
bool CloseEnough(const vnl_matrix<double> &M1, const vnl_matrix<double> &M2, const double eps)
{
unsigned int NumRows = M1.rows();
unsigned int NumCols = M1.columns();
if((M2.rows() != NumRows) || (M2.columns() != NumCols))
{
std::cout << "Dimensions do not match!" << std::endl;
return false;
}
for(unsigned int r = 0; r < NumRows; r++)
{
for(unsigned int c = 0; c < NumCols; c++)
{
if(fabs(M1(r,c) - M2(r,c)) > eps)
{
std::cout << "Failed comparison: " << "M1: " << M1(r,c) << " M2: " << M2(r,c) << " diff: " << fabs(M1(r,c) - M2(r,c)) << std::endl;
return false;
}
}
}
return true;
}
void WriteMatrixImage(const vnl_matrix<double> &M, const std::string &Filename)
{
vil_image_view<vxl_byte> Image(M.rows(), M.columns(), 1, 1); //(ni, nj, n_planes, n_interleaved_planes)
for (unsigned j = 0; j < Image.nj(); j++)
{
for (unsigned i = 0; i < Image.ni(); i++)
{
Image(i,j) = static_cast<vxl_byte>(255 * M(i,j));
}
}
vil_save(Image, Filename.c_str());
}
void WriteMatrixImageScaled(const vnl_matrix<double> &M, const std::string &Filename)
{
vil_image_view<vxl_byte> Image(M.rows(), M.columns(), 1, 1); //(ni, nj, n_planes, n_interleaved_planes)
//double Max = Tools::VectorMax(Vectorize(M));
double Max = M.max_value();
for (unsigned j = 0; j < Image.nj(); j++)
{
for (unsigned i = 0; i < Image.ni(); i++)
{
Image(i,j) = static_cast<vxl_byte>(255 * M(i,j)/Max);
//cout << "M: " << M(i,j) << endl;
//cout << "Image: " << Image(i,j) << endl;
}
}
vil_save(Image, Filename.c_str());
}
vnl_matrix <double> MCLR_SM::Normalize_Feature_Matrix_1(vnl_matrix<double> feats, vnl_vector<double> vector_1, vnl_vector<double> vector_2)
{
std_vec = vector_1;
mean_vec = vector_2;
//The last column is the training column
for(int i = 0; i<feats.columns() ; ++i)
{
vnl_vector<double> temp_col = feats.get_column(i);
if(std_vec(i) > 0)
{
for(int j =0; j<temp_col.size() ; ++j)
temp_col[j] = (temp_col[j] - mean_vec(i))/std_vec(i) ;
}
feats.set_column(i,temp_col);
}
return feats;
}
