20 using namespace Eigen;
32 m_tau[0]=0; m_tau[1]=1; m_tau[2]=2; m_tau[3]=3;
78 for (
int t = 0; t < N; t++)
81 EM = cor(EX,m_tau[t]);
94 for (
int t = 0; t < C.cols(); t++)
95 C.col(t) /= C.col(t).maxCoeff();
114 VectorXd
mean = x.rowwise().sum();
116 x = x.colwise() -
mean;
125 K = (L * R.transpose()) / (n-tau);
128 K = (K + K.transpose()) / 2.0;
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
std::enable_if<!std::is_same< T, complex128_t >::value, float64_t >::type mean(const Container< T > &a)
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