21 using namespace Eigen;
33 m_tau[0]=0; m_tau[1]=1; m_tau[2]=2; m_tau[3]=3;
61 REQUIRE(features,
"features is null");
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
T * get_matrix(index_t matIdx) const
SGNDArray< float64_t > get_covs() 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
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)
virtual CFeatures * apply(CFeatures *features)