Represents a NonNegative Matrix Factorization (NMF) algorithm that uses additive update rules and the (KullbackLeibler) divergence metric.
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#include <NMFadditiveDivergence.h>

void  DetermineQualityImprovement (bool calculateWH) 

Represents a NonNegative Matrix Factorization (NMF) algorithm that uses additive update rules and the (KullbackLeibler) divergence metric.
Definition at line 34 of file NMFadditiveDivergence.h.
Constructs a Nonnegative Matrix Factorization (NMF) algorithm object that uses additive update rules and the (KullbackLeibler) divergence metric. Given a nonnegative matrix V (n x m), the NMF algorithm will find nonnegative matrix factors W (n x r) and H (r x m) such that V is approximately equal to WH.
 Parameters

v  n x m matrix (V) containing a set of multivariate ndimensional data vectors. m is the number of examples in the dataset. 
r  Determines the size of matrices W (n x r) and H (r x m). 
 Attention
 Matrix V must be in columnmajor order
Definition at line 46 of file NMFadditiveDivergence.h.
Constructs a Nonnegative Matrix Factorization (NMF) algorithm object that uses additive update rules and the (KullbackLeibler) divergence metric. Given a nonnegative matrix V (n x m), the NMF algorithm will find nonnegative matrix factors W (n x r) and H (r x m) such that V is approximately equal to WH.
 Parameters

v  n x m matrix (V) containing a set of multivariate ndimensional data vectors. m is the number of examples in the dataset. 
w  n x r matrix. 
h  r x m matrix. 
 Attention
 Matrix V must be in columnmajor order
Definition at line 55 of file NMFadditiveDivergence.h.
Gets the H matrix
 Returns
 the H matrix
Definition at line 126 of file BaseNMF.h.
Gets the W matrix
 Returns
 the W matrix
Definition at line 120 of file BaseNMF.h.
Gets the approximation, given by WH, to the matrix V
 Returns
 the approximation, given by WH, to the matrix V
Definition at line 132 of file BaseNMF.h.
The documentation for this class was generated from the following files: