double BayesianClassifier::calculate_recognition_error(const math::matrix<double> & x,
const QVector<Distribution> & distributions,
const math::matrix<double> & regretMatrix)
{
double R = 0.0;
bool first = true;
for (size_t i = 0; i < regretMatrix.RowNo(); ++i)
{
double tR = 0.0;
for (int j = 0; j < distributions.size(); ++j)
tR += regretMatrix(i, j)*g(x, distributions[j]);
if (first)
{
R = tR;
first = false;
}
if (R > tR)
R = tR;
}
return -R;
}
/** Joins to matrices by multiplying the A[i,j] with B[i,j].
* Warning! Both Matrices must be squares with the same size!
*/
const math::matrix<double> Convolution::join(math::matrix<double> A, math::matrix<double> B)
{
int size = A.RowNo();
math::matrix<double> C(size, size);
for(int i = 0; i < size; i++)
for(int j = 0; j < size; j++)
{
C(i, j) = A(i, j) * B(i, j);
}
return C;
}
/** Sums all of the matrixes elements */
const double Convolution::sum(const math::matrix<double> A)
{
double sum = 0.0;
int size = A.RowNo();
for(int i = 0; i < size; i++)
for(int j = 0; j < size; j++)
{
sum += A(i, j);
}
return sum;
}
