Provided by: mlpack-bin_3.0.4-1_amd64 bug


       mlpack_sparse_coding - sparse coding


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


       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

       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

       $ sparse_coding --training_file data.csv --atoms  200  --lambda1  0.1  --output_model_file

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

       $  sparse_coding  --input_model_file  model.bin  --test_file  otherdata.csv   --codes_file


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

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

       --info [string]
              Get help on a specific module or option.  Default value ''.

       --initial_dictionary_file (-i) [string]
              Optional initial dictionary matrix. Default value ''.

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

       --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) [string]
              Optional matrix to be encoded by trained model.  Default value ''.

       --training_file (-t) [string]
              Matrix of training data (X). Default value ''.

       --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.


       --codes_file (-c) [string]
              Matrix to save the output sparse codes of the test matrix (--test_file) to. Default
              value ''.

       --dictionary_file (-d) [string]
              Matrix to save the output dictionary to.  Default value ''.

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


       For further information, including relevant papers, citations,  and  theory,  consult  the
       documentation found at or included with your distribution of mlpack.