173 virtual const char*
get_name()
const {
return "PCA"; }
231 #endif // HAVE_EIGEN3
void set_memory_mode(EPCAMemoryMode e)
the class DimensionReductionPreprocessor, a base class for preprocessors used to lower the dimensiona...
EPCAMemoryMode m_mem_mode
SGVector< float64_t > m_mean_vector
void set_eigenvalue_zero_tolerance(float64_t eigenvalue_zero_tolerance=1e-15)
SGVector< float64_t > m_eigenvalues_vector
float64_t get_eigenvalue_zero_tolerance() const
virtual SGVector< float64_t > apply_to_feature_vector(SGVector< float64_t > vector)
CPCA(bool do_whitening=false, EPCAMode mode=FIXED_NUMBER, float64_t thresh=1e-6, EPCAMethod method=AUTO, EPCAMemoryMode mem_mode=MEM_REALLOCATE)
SGMatrix< float64_t > m_transformation_matrix
virtual EPreprocessorType get_type() const
virtual const char * get_name() const
SGVector< float64_t > get_mean()
SGVector< float64_t > get_eigenvalues()
all of classes and functions are contained in the shogun namespace
The class Features is the base class of all feature objects.
Preprocessor PCA performs principial component analysis on input feature vectors/matrices. When the init method in PCA is called with proper feature matrix X (with say N number of vectors and D feature dimension), a transformation matrix is computed and stored internally. This transformation matrix is then used to transform all D-dimensional feature vectors or feature matrices (with D feature dimensions) supplied via apply_to_feature_matrix or apply_to_feature_vector methods. This tranformation outputs the T-Dimensional approximation of all these input vectors and matrices (where T<=min(D,N)). The transformation matrix is essentially a DxT matrix, the columns of which correspond to the eigenvectors of the covariance matrix(XX') having top T eigenvalues.
EPCAMemoryMode get_memory_mode() const
virtual SGMatrix< float64_t > apply_to_feature_matrix(CFeatures *features)
float64_t m_eigenvalue_zero_tolerance
SGMatrix< float64_t > get_transformation_matrix()