svm-predict(1)

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)