NAME

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

SYNOPSIS

       svm-predict [ -b probability_estimates ] [ -q ] test_data 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,     Chih-Jen     Lin    <cjlin@csie.ntu.edu.tw>,    Chen-Tse    Tsai
       <ctse.tsai@gmail.com> (packaging)

SEE ALSO

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