Provided by: libsvm-tools_2.85.0-1_i386 bug

NAME

       svm-predict  -  make  predictions based on a trained SVM model file and
       test data

SYNOPSIS

       svm-predict  [  -b  probability_estimates  ]  [  -q  ]   model_file   [
       output_file ]

DESCRIPTION

       svm-predict  uses  a  Support Vector Machine specified by a given input
       model_file to make predictions for each of the samples in test_data
         The format of this file is identical to the training_data  file  used
       in svm_train(1) and is just a sparse vector as follows:

       <label> <index1>:<value1> <index2>:<value2> . . .

        .

        .

        .

       There  is  one sample per line.  Each sample consists of a target value
       (label or regression target) followed by a sparse representation of the
       input  vector.   All  unmentioned coordinates are assumed to be 0.  For
       classification, <label>  is  an  integer  indicating  the  class  label
       (multi-class is supported). For regression, <label> is the target value
       which can be any real number. For one-class SVM, it’s not used  so  can
       be  any number.  Except using precomputed kernels (explained in another
       section), <index>:<value> gives a feature (attribute)  value.   <index>
       is  an  integer  starting  from 1 and <value> is a real number. Indices
       must be in an ASCENDING order.  If you have label  data  available  for
       testing then you can enter these values in the test_data file.  If they
       are not available you can just enter 0 and will not know real  accuracy
       for  the  SVM  directly,  however  you can still get the results of its
       prediction for the data point.

              If output_file is given, it will be used to specify the filename
              to store the predicted results, one per line, in the same  order
              as the test_data file.

OPTIONS

       -b probability-estimates
              probability_estimates  is  a  binary value indicating whether to
              calculate probability estimates when training  the  SVC  or  SVR
              model.  Values are 0 or 1 and defaults to 0 for speed.

       -q     quiet mode; suppress messages to stdout.

FILES

       training_set_file  must  be  prepared  in  the  following simple sparse
       training vector format:

       <label> <index1>:<value1> <index2>:<value2> . . .

        .

        .

        .

       There is one sample per line.  Each sample consist of  a  target  value
       (label or regression target) followed by a sparse representation of the
       input vector.  All unmentioned coordinates are assumed to  be  0.   For
       classification,  <label>  is  an  integer  indicating  the  class label
       (multi-class is supported). For regression, <label> is the target value
       which  can  be any real number. For one-class SVM, it’s not used so can
       be any number.  Except using precomputed kernels (explained in  another
       section),  <index>:<value>  gives a feature (attribute) value.  <index>
       is an integer starting from 1 and <value> is  a  real  number.  Indices
       must be in an ASCENDING order.

ENVIRONMENT

       No environment variables.

DIAGNOSTICS

       None documented; see Vapnik et al.

BUGS

       Please report bugs to the Debian BTS.

AUTHOR

       Chih-Chung Chang, Rudi Cilibrasi  <cilibrar@cilibrar.com>  (packaging),
       and Chih-Jen Lin <cjlin@csie.ntu.edu.tw>

SEE ALSO

       svm-train(1), svm-scale(1)