svm-predict − make predictions based on a trained SVM model file and test data
svm-predict [ -b probability_estimates ] [ -q ] test_data model_file [ output_file ]
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> . . .
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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.
-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. |
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.
No environment variables.
None documented; see Vapnik et al.
Please report bugs to the Debian BTS.
Chih-Chung Chang, Chih-Jen Lin <cjlin AT csie DOT ntu DOT edu DOT tw>, Chen-Tse Tsai <ctse DOT tsai AT gmail DOT com> (packaging)