NAME

       mlpack_sparse_coding - sparse coding

SYNOPSIS

        mlpack_sparse_coding [-k int] [-i unknown] [-m unknown] [-l double] [-L double] [-n int] [-w double] [-N bool] [-o double] [-s int] [-T unknown] [-t unknown] [-V bool] [-c unknown] [-d unknown] [-M unknown] [-h -v]

DESCRIPTION

       An  implementation  of Sparse Coding with Dictionary Learning, which achieves sparsity via
       an l1-norm regularizer on the codes (LASSO) or an (l1+l2)-norm regularizer  on  the  codes
       (the  Elastic  Net).  Given  a  dense data matrix X with d dimensions and n points, sparse
       coding seeks to find a dense dictionary matrix D with k  atoms  in  d  dimensions,  and  a
       sparse coding matrix Z with n points in k dimensions.

       The  original  data  matrix  X can then be reconstructed as Z * D. Therefore, this program
       finds a representation of each point in X as a sparse linear combination of atoms  in  the
       dictionary D.

       The  sparse  coding is found with an algorithm which alternates between a dictionary step,
       which updates the dictionary D, and a sparse coding step, which updates the sparse  coding
       matrix.

       Once  a  dictionary  D  is  found,  the  sparse  coding  model may be used to encode other
       matrices, and saved for future usage.

       To run this program, either an input matrix or an already-saved sparse coding  model  must
       be  specified.  An  input  matrix may be specified with the ’--training_file (-t)' option,
       along with the number of atoms in  the  dictionary  (specified  with  the  '--atoms  (-k)'
       parameter).  It  is  also  possible to specify an initial dictionary for the optimization,
       with the ’--initial_dictionary_file (-i)' parameter. An input model may be specified  with
       the '--input_model_file (-m)' parameter.

       As  an  example,  to build a sparse coding model on the dataset 'data.csv' using 200 atoms
       and an l1-regularization parameter of 0.1, saving the model into ’model.bin', use

       $   mlpack_sparse_coding   --training_file   data.csv   --atoms    200    --lambda1    0.1
       --output_model_file model.bin

       Then,  this  model  could  be  used  to encode a new matrix, 'otherdata.csv', and save the
       output codes to 'codes.csv':

       $ mlpack_sparse_coding --input_model_file model.bin --test_file otherdata.csv --codes_file
       codes.csv

OPTIONAL INPUT OPTIONS

       --atoms (-k) [int]
              Number of atoms in the dictionary. Default value 15.

       --help (-h) [bool]
              Default help info.

       --info [string]
              Print help on a specific option. Default value ''.

       --initial_dictionary_file (-i) [unknown]
              Optional initial dictionary matrix.

       --input_model_file (-m) [unknown]
              File containing input sparse coding model.

       --lambda1 (-l) [double]
              Sparse coding l1-norm regularization parameter.  Default value 0.

       --lambda2 (-L) [double]
              Sparse coding l2-norm regularization parameter.  Default value 0.

       --max_iterations (-n) [int]
              Maximum  number  of  iterations  for  sparse coding (0 indicates no limit). Default
              value 0.

       --newton_tolerance (-w) [double]
              Tolerance for convergence of Newton method.  Default value 1e-06.

       --normalize (-N) [bool]
              If set, the input data matrix will be normalized before coding.

       --objective_tolerance (-o) [double]
              Tolerance for convergence of the objective function. Default value 0.01.

       --seed (-s) [int]
              Random seed. If 0, 'std::time(NULL)' is used.  Default value 0.

       --test_file (-T) [unknown]
              Optional matrix to be encoded by trained model.

       --training_file (-t) [unknown]
              Matrix of training data (X).

       --verbose (-v) [bool]
              Display informational messages and the full list of parameters and  timers  at  the
              end of execution.

       --version (-V) [bool]
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

       --codes_file (-c) [unknown]
              Matrix to save the output sparse codes of the test matrix (--test_file) to.

       --dictionary_file (-d) [unknown]
              Matrix to save the output dictionary to.

       --output_model_file (-M) [unknown]
              File to save trained sparse coding model to.

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.