20 using namespace Eigen;
32 m_tau[0]=0; m_tau[1]=1; m_tau[2]=2; m_tau[3]=3;
60 REQUIRE(features,
"features is null");
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;
T * get_matrix(index_t matIdx) const
void set_tau(SGVector< float64_t > tau)
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=1e-12, int itermax=200)
all of classes and functions are contained in the shogun namespace
SGNDArray< float64_t > get_covs() const
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 void inverse(SGMatrix< float64_t > matrix)
inverses square matrix in-place
SGVector< float64_t > get_tau() const