SYNOPSIS
mlpack_nmf [h] [v] H string i string r int W string [m int] [e double] [s int] [u string] V
DESCRIPTION
This program performs nonnegative matrix factorization on the given dataset, storing the resulting decomposed matrices in the specified files. For an input dataset V, NMF decomposes V into two matrices W and H such that
V = W * H
where all elements in W and H are nonnegative. If V is of size (n x m), then W will be of size (n x r) and H will be of size (r x m), where r is the rank of the factorization (specified by rank).
Optionally, the desired update rules for each NMF iteration can be chosen from the following list:

 multdist: multiplicative distancebased update rules (Lee and Seung 1999)
 multdiv: multiplicative divergencebased update rules (Lee and Seung 1999)
 als: alternating least squares update rules (Paatero and Tapper 1994)
The maximum number of iterations is specified with max_iterations, and the minimum residue required for algorithm termination is specified with min_residue.
REQUIRED OPTIONS
 h_file (H) [string]
 File to save the calculated H matrix to.
 input_file (i) [string]
 Input dataset to perform NMF on.
 rank (r) [int]
 Rank of the factorization.
 w_file (W) [string]
 File to save the calculated W matrix to.
OPTIONS
 help (h)
 Default help info.
 info [string]
 Get help on a specific module or option. Default value ''.
 max_iterations (m) [int]
 Number of iterations before NMF terminates (0 runs until convergence. Default value 10000.
 min_residue (e) [double]
 The minimum root mean square residue allowed for each iteration, below which the program terminates. Default value 1e05.
 seed (s) [int]
 Random seed. If 0, 'std::time(NULL)' is used. Default value 0. update_rules (u) [string] Update rules for each iteration; ( multdist  multdiv  als ). Default value 'multdist'.
 verbose (v)
 Display informational messages and the full list of parameters and timers at the end of execution.
 version (V)
 Display the version of mlpack.
ADDITIONAL INFORMATION
For further information, including relevant papers, citations, and theory, consult the documentation found at http://www.mlpack.org or included with your DISTRIBUTION OF MLPACK.