/***********************************************************************//**
* @brief GSparseMatrix to GSymMatrix storage class convertor
*
* @param[in] matrix Sparse matrix (GSparseMatrix).
*
* @exception GException::matrix_not_symmetric
* Sparse matrix is not symmetric.
*
* Converts a sparse matrix into the symmetric storage class. If the input
* matrix is not symmetric, an exception is thrown.
***************************************************************************/
GSymMatrix::GSymMatrix(const GSparseMatrix& matrix)
{
// Initialise class members for clean destruction
init_members();
// Allocate matrix memory
alloc_members(matrix.rows(), matrix.cols());
// Fill matrix
for (int col = 0; col < matrix.cols(); ++col) {
for (int row = col; row < matrix.rows(); ++row) {
double value_ll = matrix(row,col);
double value_ur = matrix(col,row);
if (value_ll != value_ur) {
throw GException::matrix_not_symmetric(G_CAST_SPARSEMATRIX,
matrix.rows(),
matrix.cols());
}
(*this)(row, col) = matrix(row, col);
}
}
// Return
return;
}
/***********************************************************************//**
* @brief GSparseMatrix to GMatrix storage class convertor
*
* @param[in] matrix Sparse matrix (GSparseMatrix).
*
* This constructor converts a sparse matrix (of type GSparseMatrix) into a
* generic matrix. As the result is generic, the conversion will succeed in
* all cases.
***************************************************************************/
GMatrix::GMatrix(const GSparseMatrix& matrix) : GMatrixBase(matrix)
{
// Initialise class members for clean destruction
init_members();
// Construct matrix
alloc_members(matrix.rows(), matrix.cols());
// Fill matrix
for (int col = 0; col < matrix.cols(); ++col) {
for (int row = 0; row < matrix.rows(); ++row) {
(*this)(row, col) = matrix(row, col);
}
}
// Return
return;
}
// virtual
void GNaiveBayes::trainSparse(GSparseMatrix& features, GMatrix& labels)
{
if(features.rows() != labels.rows())
throw Ex("Expected the features and labels to have the same number of rows");
size_t featureDims = features.cols();
GUniformRelation featureRel(featureDims, 2);
beginIncrementalLearning(featureRel, labels.relation());
GVec fullRow(featureDims);
for(size_t n = 0; n < features.rows(); n++)
{
features.fullRow(fullRow, n);
for(size_t i = 0; i < featureDims; i++)
{
if(fullRow[i] < 1e-6)
fullRow[i] = 0.0;
else
fullRow[i] = 1.0;
}
trainIncremental(fullRow, labels[n]);
}
}
