21 using namespace Eigen;
33 m_tau[0]=0; m_tau[1]=1; m_tau[2]=2; m_tau[3]=3;
79 for (
int t = 0; t < N; t++)
82 EM = cor(EX,m_tau[t]);
95 for (
int t = 0; t < C.cols(); t++)
96 C.col(t) /= C.col(t).maxCoeff();
115 VectorXd mean = x.rowwise().sum();
117 x = x.colwise() - mean;
126 K = (L * R.transpose()) / (n-tau);
129 K = (K + K.transpose()) / 2.0;
135 #endif // HAVE_EIGEN3
virtual CFeatures * apply(CFeatures *features)
static SGMatrix< float64_t > diagonalize(SGNDArray< float64_t > C, SGMatrix< float64_t > V0=SGMatrix< float64_t >(NULL, 0, 0, false), double eps=CMath::MACHINE_EPSILON, int itermax=200)
SGNDArray< float64_t > get_covs() const
T * get_matrix(index_t matIdx) const
SGVector< float64_t > get_tau() const
void set_tau(SGVector< float64_t > tau)
static void inverse(SGMatrix< float64_t > matrix)
inverses square matrix in-place
all of classes and functions are contained in the shogun namespace
class ICAConverter Base class for ICA algorithms
The class Features is the base class of all feature objects.
SGMatrix< float64_t > m_mixing_matrix