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)